mirror of https://github.com/vladmandic/human
8047 lines
1.5 MiB
8047 lines
1.5 MiB
/*
|
|
Human
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
|
|
var Human=(()=>{var a2=Object.defineProperty;var kE=(e,t,n)=>t in e?a2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var SE=e=>a2(e,"__esModule",{value:!0});var Ia=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof require!="undefined"?require:t)[n]}):e)(function(e){if(typeof require!="undefined")return require.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')});var Yc=(e,t)=>{SE(e);for(var n in t)a2(e,n,{get:t[n],enumerable:!0})};var he=(e,t,n)=>(kE(e,typeof t!="symbol"?t+"":t,n),n),F5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var Jc=(e,t,n)=>(F5(e,t,"read from private field"),n?n.call(e):t.get(e)),Qc=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},ed=(e,t,n,s)=>(F5(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);var u1e={};Yc(u1e,{Human:()=>IN,default:()=>IN,defaults:()=>Ca,env:()=>de});function Z(...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)}function We(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var ie=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function o2(e,t,n="config",s=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")o2(e[r],t[r],r,s);else{let a=e&&typeof e[r]!="undefined";a||s.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let o=e&&typeof e[r]==typeof t[r];a&&!o&&s.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&s.length>0&&Z("invalid configuration",s),s}function _n(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,s)=>(Object.keys(s||{}).forEach(r=>{let a=n[r],o=s[r];Array.isArray(a)&&Array.isArray(o)?n[r]=a.concat(...o):t(a)&&t(o)?n[r]=_n(a,o):n[r]=o}),n),{})}var Ca={backend:"",modelBasePath:"",wasmPath:"",debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,cropFactor:1.6,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",detector:{modelPath:""},maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var Ml={};Yc(Ml,{Abs:()=>fi,Acos:()=>iu,Acosh:()=>lu,AdadeltaOptimizer:()=>Ff,AdagradOptimizer:()=>Of,AdamOptimizer:()=>Mf,AdamaxOptimizer:()=>zf,Add:()=>Kr,AddN:()=>Ra,All:()=>uu,Any:()=>cu,ArgMax:()=>$a,ArgMin:()=>du,Asin:()=>pu,Asinh:()=>hu,Atan:()=>fu,Atan2:()=>gu,Atanh:()=>mu,AvgPool:()=>_a,AvgPool3D:()=>rd,AvgPool3DGrad:()=>kh,AvgPoolGrad:()=>wh,BackendWasm:()=>Q6,BatchMatMul:()=>Da,BatchToSpaceND:()=>mi,Bincount:()=>Sh,BroadcastArgs:()=>Ih,BroadcastTo:()=>Y5,Callback:()=>l7,CallbackList:()=>Zw,Cast:()=>Pa,Ceil:()=>Fa,ClipByValue:()=>Zr,Complex:()=>ad,ComplexAbs:()=>od,Concat:()=>gi,Conv2D:()=>Oa,Conv2DBackpropFilter:()=>Ch,Conv2DBackpropInput:()=>Ma,Conv3D:()=>id,Conv3DBackpropFilterV2:()=>Th,Conv3DBackpropInputV2:()=>Nh,Cos:()=>za,Cosh:()=>La,CropAndResize:()=>yi,Cumsum:()=>Ai,CustomCallback:()=>Jw,DataStorage:()=>td,DenseBincount:()=>Eh,DepthToSpace:()=>xi,DepthwiseConv2dNative:()=>Ba,DepthwiseConv2dNativeBackpropFilter:()=>Rh,DepthwiseConv2dNativeBackpropInput:()=>$h,Diag:()=>_h,Dilation2D:()=>ld,Dilation2DBackpropFilter:()=>Ph,Dilation2DBackpropInput:()=>Dh,ENV:()=>Rr,EarlyStopping:()=>c7,Einsum:()=>ud,Elu:()=>Va,EluGrad:()=>Fh,Environment:()=>K5,Equal:()=>bi,Erf:()=>Au,Exp:()=>Ua,ExpandDims:()=>vi,Expm1:()=>wi,FFT:()=>Oh,Fill:()=>yu,FlipLeftRight:()=>ki,Floor:()=>Ga,FloorDiv:()=>Ha,FromPixels:()=>yd,FusedBatchNorm:()=>ja,FusedConv2D:()=>ko,FusedDepthwiseConv2D:()=>So,GPGPUContext:()=>Vm,GatherNd:()=>Ii,GatherV2:()=>Si,GraphModel:()=>V7,Greater:()=>Ci,GreaterEqual:()=>qa,History:()=>Yw,IFFT:()=>Mh,Identity:()=>Xa,Imag:()=>cd,InputSpec:()=>Yt,IsFinite:()=>xu,IsInf:()=>bu,IsNan:()=>vu,KernelBackend:()=>ru,LRN:()=>pd,LRNGrad:()=>Lh,LayerVariable:()=>Hw,LayersModel:()=>aa,LeakyRelu:()=>Ti,Less:()=>Ni,LessEqual:()=>Ei,LinSpace:()=>zh,Log:()=>Ka,Log1p:()=>wu,LogSoftmax:()=>J5,LogicalAnd:()=>Ri,LogicalNot:()=>ku,LogicalOr:()=>dd,MathBackendCPU:()=>Vy,MathBackendWebGL:()=>yp,Max:()=>Za,MaxPool:()=>Ja,MaxPool3D:()=>hd,MaxPool3DGrad:()=>Wh,MaxPoolGrad:()=>Bh,MaxPoolWithArgmax:()=>Vh,Maximum:()=>Ya,Mean:()=>Qa,Min:()=>eo,Minimum:()=>to,MirrorPad:()=>no,Mod:()=>Su,MomentumOptimizer:()=>Lf,Multinomial:()=>Uh,Multiply:()=>so,Neg:()=>$i,NonMaxSuppressionV3:()=>Di,NonMaxSuppressionV4:()=>Iu,NonMaxSuppressionV5:()=>Pi,NotEqual:()=>_i,OP_SCOPE_SUFFIX:()=>h3,OneHot:()=>Oi,OnesLike:()=>Fi,Optimizer:()=>na,OptimizerConstructors:()=>Oo,Pack:()=>Mi,PadV2:()=>ro,Pool:()=>p9,Pow:()=>ao,Prelu:()=>oo,Prod:()=>zi,RMSPropOptimizer:()=>Bf,RNN:()=>oa,Range:()=>Cu,Rank:()=>A2,Real:()=>fd,RealDiv:()=>Wa,Reciprocal:()=>Tu,Reduction:()=>Gn,Relu:()=>io,Relu6:()=>uo,Reshape:()=>Li,ResizeBilinear:()=>lo,ResizeBilinearGrad:()=>Hh,ResizeNearestNeighbor:()=>Nu,ResizeNearestNeighborGrad:()=>Gh,Reverse:()=>Bi,RotateWithOffset:()=>el,Round:()=>Wi,Rsqrt:()=>co,SGDOptimizer:()=>Vd,ScatterNd:()=>Vi,Select:()=>Ui,Selu:()=>Eu,Sequential:()=>dm,Sigmoid:()=>ho,Sign:()=>Ru,Sin:()=>po,Sinh:()=>Hi,Slice:()=>Gi,Softmax:()=>go,Softplus:()=>$u,SpaceToBatchND:()=>ji,SparseFillEmptyRows:()=>jh,SparseReshape:()=>qh,SparseSegmentMean:()=>Xh,SparseSegmentSum:()=>Kh,SparseToDense:()=>md,SplitV:()=>qi,Sqrt:()=>fo,Square:()=>_u,SquaredDifference:()=>Ao,Step:()=>vo,StridedSlice:()=>Xi,StringNGrams:()=>gd,StringSplit:()=>Zh,StringToHashBucketFast:()=>Yh,Sub:()=>yo,Sum:()=>mo,SymbolicTensor:()=>xr,Tan:()=>Ki,Tanh:()=>xo,Tensor:()=>Qe,TensorBuffer:()=>nn,Tile:()=>Yr,TopK:()=>Zi,Transform:()=>Yi,Transpose:()=>bo,Unique:()=>Jh,Unpack:()=>Ji,UnsortedSegmentSum:()=>Ad,Variable:()=>Cd,ZerosLike:()=>Qi,_FusedMatMul:()=>wo,abs:()=>sn,acos:()=>Q3,acosh:()=>ev,add:()=>ue,addN:()=>df,all:()=>Z2,any:()=>pf,argMax:()=>Zs,argMin:()=>tv,asin:()=>nv,asinh:()=>sv,atan:()=>rv,atan2:()=>av,atanh:()=>ov,avgPool:()=>ff,avgPool3d:()=>Q2,backend:()=>Dr,backend_util:()=>E,basicLSTMCell:()=>Q$,batchNorm:()=>Bu,batchNorm2d:()=>cv,batchNorm3d:()=>dv,batchNorm4d:()=>pv,batchToSpaceND:()=>mf,bincount:()=>e1,booleanMaskAsync:()=>dF,broadcastArgs:()=>hv,broadcastTo:()=>_d,broadcast_util:()=>ol,browser:()=>Ks,buffer:()=>ze,callbacks:()=>CU,cast:()=>me,ceil:()=>fv,clipByValue:()=>gs,clone:()=>Vn,complex:()=>To,concat:()=>kt,concat1d:()=>mv,concat2d:()=>Wu,concat3d:()=>gv,concat4d:()=>Av,constraints:()=>Sw,conv1d:()=>t1,conv2d:()=>_o,conv2dTranspose:()=>s1,conv3d:()=>r1,conv3dTranspose:()=>xv,copyRegisteredKernels:()=>g9,cos:()=>gf,cosh:()=>a1,cosineWindow:()=>R1,cumsum:()=>o1,customGrad:()=>Fr,data:()=>U7,denseBincount:()=>bv,deprecationWarn:()=>X2,depthToSpace:()=>vv,depthwiseConv2d:()=>Dd,deregisterOp:()=>EU,device_util:()=>Fu,diag:()=>E_,dilation2d:()=>wv,disableDeprecationWarnings:()=>m$,dispose:()=>ne,disposeVariables:()=>g$,div:()=>ge,divNoNan:()=>kv,dot:()=>O_,dropout:()=>Jv,einsum:()=>Sv,elu:()=>Pd,enableDebugMode:()=>f$,enableProdMode:()=>Y3,enclosingPowerOfTwo:()=>Qv,engine:()=>as,env:()=>Y,equal:()=>$s,erf:()=>Iv,exp:()=>_s,expandDims:()=>Zt,expm1:()=>Cv,eye:()=>i1,fft:()=>Tf,fill:()=>Vu,findBackend:()=>K2,findBackendFactory:()=>b$,floor:()=>Fd,floorDiv:()=>cf,forceHalfFloat:()=>x4,fused:()=>Fo,gather:()=>Uu,gatherND:()=>Yv,gather_util:()=>B2,getBackend:()=>Rs,getGradient:()=>h2,getKernel:()=>Qh,getKernelsForBackend:()=>Jr,getThreadsCount:()=>n2e,gpgpu_util:()=>jI,grad:()=>iD,grads:()=>lD,greater:()=>As,greaterEqual:()=>dl,ifft:()=>Ld,imag:()=>Af,image:()=>Ce,inTopKAsync:()=>wF,initializers:()=>$w,input:()=>vk,io:()=>rs,irfft:()=>S1,isFinite:()=>Y_,isInf:()=>Q_,isNaN:()=>Tv,keep:()=>An,kernel_impls:()=>Qs,layers:()=>Vw,leakyRelu:()=>yf,less:()=>l1,lessEqual:()=>pl,linalg:()=>uw,linspace:()=>Nv,loadGraphModel:()=>Be,loadLayersModel:()=>MW,localResponseNormalization:()=>Ev,log:()=>Ds,log1p:()=>xf,logSigmoid:()=>fD,logSoftmax:()=>u1,logSumExp:()=>Pv,logicalAnd:()=>hr,logicalNot:()=>vf,logicalOr:()=>p1,logicalXor:()=>CD,losses:()=>oM,matMul:()=>He,math:()=>$3,max:()=>yn,maxPool:()=>wf,maxPool3d:()=>h1,maxPoolWithArgmax:()=>Fv,maximum:()=>ea,mean:()=>Vt,memory:()=>lf,meshgrid:()=>_D,metrics:()=>a7,min:()=>Do,minimum:()=>Od,mirrorPad:()=>Ov,mod:()=>Md,model:()=>FW,models:()=>o7,moments:()=>kf,movingAverage:()=>fF,mul:()=>L,multiRNNCell:()=>BD,multinomial:()=>Mv,neg:()=>Mt,nextFrame:()=>cw,norm:()=>N1,notEqual:()=>Hu,oneHot:()=>Rd,ones:()=>ys,onesLike:()=>Ps,op:()=>V,outerProduct:()=>HD,pad:()=>Js,pad1d:()=>XD,pad2d:()=>ZD,pad3d:()=>JD,pad4d:()=>eP,pool:()=>aP,pow:()=>Po,prelu:()=>If,print:()=>I3,prod:()=>f1,profile:()=>A$,rand:()=>cP,randomGamma:()=>fP,randomNormal:()=>zv,randomUniform:()=>ju,range:()=>qu,ready:()=>uf,real:()=>zd,reciprocal:()=>Lv,registerBackend:()=>ul,registerCallbackConstructor:()=>zW,registerGradient:()=>Q5,registerKernel:()=>cr,registerOp:()=>NU,regularizers:()=>i7,relu:()=>Or,relu6:()=>A1,removeBackend:()=>x$,reshape:()=>H,reverse:()=>Fs,reverse1d:()=>kP,reverse2d:()=>IP,reverse3d:()=>TP,reverse4d:()=>EP,rfft:()=>Nf,round:()=>y1,rsqrt:()=>x1,scalar:()=>Ie,scatterND:()=>Zv,scatter_util:()=>W2,selu:()=>b1,separableConv2d:()=>Bv,sequential:()=>OW,serialization:()=>ce,setBackend:()=>J3,setPlatform:()=>v$,setThreadsCount:()=>t2e,setWasmPath:()=>e2e,setWasmPaths:()=>t8,setWebGLContext:()=>Dm,setdiff1dAsync:()=>Wv,shared:()=>Tm,sigmoid:()=>ms,sign:()=>Vv,signal:()=>aM,sin:()=>v1,sinh:()=>w1,slice:()=>Pe,slice1d:()=>Cf,slice2d:()=>k1,slice3d:()=>fl,slice4d:()=>ml,slice_util:()=>Ot,softmax:()=>Xu,softplus:()=>Gu,spaceToBatchND:()=>Sf,sparse:()=>Wd,sparseToDense:()=>E1,spectral:()=>rM,split:()=>rn,sqrt:()=>Pn,square:()=>yt,squaredDifference:()=>I1,squeeze:()=>it,stack:()=>xn,step:()=>Bd,stridedSlice:()=>Uv,string:()=>Pf,sub:()=>fe,sum:()=>ke,sumOutType:()=>Td,tan:()=>Gv,tanh:()=>Lu,tensor:()=>ct,tensor1d:()=>Ut,tensor2d:()=>fr,tensor3d:()=>D3,tensor4d:()=>tF,tensor5d:()=>nF,tensor6d:()=>sF,tensor_util:()=>dr,test_util:()=>X3,tidy:()=>X,tile:()=>Ys,time:()=>y$,topk:()=>Hv,train:()=>gl,transpose:()=>et,truncatedNormal:()=>Ef,unique:()=>C1,unregisterGradient:()=>m9,unregisterKernel:()=>f9,unsortedSegmentSum:()=>jv,unstack:()=>os,upcastType:()=>Wn,util:()=>v,valueAndGrad:()=>uD,valueAndGrads:()=>cD,variable:()=>qv,variableGrads:()=>Rv,version:()=>s8,version_converter:()=>_G,version_core:()=>_p,version_cpu:()=>yj,version_layers:()=>pA,version_wasm:()=>s2e,version_webgl:()=>HQ,webgl:()=>jQ,webgl_util:()=>gI,webgpu:()=>t6,where:()=>Un,whereAsync:()=>T1,zeros:()=>jt,zerosLike:()=>tt});var su=(e=>typeof Ia!="undefined"?Ia:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof Ia!="undefined"?Ia:t)[n]}):e)(function(e){if(typeof Ia!="undefined")return Ia.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),IE=Object.create,gh=Object.defineProperty,CE=Object.getOwnPropertyDescriptor,TE=Object.getOwnPropertyNames,NE=Object.getPrototypeOf,EE=Object.prototype.hasOwnProperty,O5=e=>gh(e,"__esModule",{value:!0}),js=(e=>typeof su!="undefined"?su:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof su!="undefined"?su:t)[n]}):e)(function(e){if(typeof su!="undefined")return su.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),ss=(e,t)=>function(){return t||(0,e[Object.keys(e)[0]])((t={exports:{}}).exports,t),t.exports},Oe=(e,t)=>{O5(e);for(var n in t)gh(e,n,{get:t[n],enumerable:!0})},RE=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of TE(t))!EE.call(e,s)&&s!=="default"&&gh(e,s,{get:()=>t[s],enumerable:!(n=CE(t,s))||n.enumerable});return e},di=e=>RE(O5(gh(e!=null?IE(NE(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),$E=ss({"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(e,t){t.exports=s;var n=null;try{n=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(P){}function s(P,T,F){this.low=P|0,this.high=T|0,this.unsigned=!!F}s.prototype.__isLong__,Object.defineProperty(s.prototype,"__isLong__",{value:!0});function r(P){return(P&&P.__isLong__)===!0}s.isLong=r;var a={},o={};function i(P,T){var F,U,q;return T?(P>>>=0,(q=0<=P&&P<256)&&(U=o[P],U)?U:(F=c(P,(P|0)<0?-1:0,!0),q&&(o[P]=F),F)):(P|=0,(q=-128<=P&&P<128)&&(U=a[P],U)?U:(F=c(P,P<0?-1:0,!1),q&&(a[P]=F),F))}s.fromInt=i;function l(P,T){if(isNaN(P))return T?b:y;if(T){if(P<0)return b;if(P>=g)return R}else{if(P<=-A)return O;if(P+1>=A)return N}return P<0?l(-P,T).neg():c(P%m|0,P/m|0,T)}s.fromNumber=l;function c(P,T,F){return new s(P,T,F)}s.fromBits=c;var u=Math.pow;function d(P,T,F){if(P.length===0)throw Error("empty string");if(P==="NaN"||P==="Infinity"||P==="+Infinity"||P==="-Infinity")return y;if(typeof T=="number"?(F=T,T=!1):T=!!T,F=F||10,F<2||36<F)throw RangeError("radix");var U;if((U=P.indexOf("-"))>0)throw Error("interior hyphen");if(U===0)return d(P.substring(1),T,F).neg();for(var q=l(u(F,8)),z=y,K=0;K<P.length;K+=8){var J=Math.min(8,P.length-K),Q=parseInt(P.substring(K,K+J),F);if(J<8){var te=l(u(F,J));z=z.mul(te).add(l(Q))}else z=z.mul(q),z=z.add(l(Q))}return z.unsigned=T,z}s.fromString=d;function p(P,T){return typeof P=="number"?l(P,T):typeof P=="string"?d(P,T):c(P.low,P.high,typeof T=="boolean"?T:P.unsigned)}s.fromValue=p;var h=1<<16,f=1<<24,m=h*h,g=m*m,A=g/2,x=i(f),y=i(0);s.ZERO=y;var b=i(0,!0);s.UZERO=b;var w=i(1);s.ONE=w;var k=i(1,!0);s.UONE=k;var I=i(-1);s.NEG_ONE=I;var N=c(4294967295|0,2147483647|0,!1);s.MAX_VALUE=N;var R=c(4294967295|0,4294967295|0,!0);s.MAX_UNSIGNED_VALUE=R;var O=c(0,2147483648|0,!1);s.MIN_VALUE=O;var $=s.prototype;$.toInt=function(){return this.unsigned?this.low>>>0:this.low},$.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},$.toString=function(T){if(T=T||10,T<2||36<T)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(O)){var F=l(T),U=this.div(F),q=U.mul(F).sub(this);return U.toString(T)+q.toInt().toString(T)}else return"-"+this.neg().toString(T);for(var z=l(u(T,6),this.unsigned),K=this,J="";;){var Q=K.div(z),te=K.sub(Q.mul(z)).toInt()>>>0,re=te.toString(T);if(K=Q,K.isZero())return re+J;for(;re.length<6;)re="0"+re;J=""+re+J}},$.getHighBits=function(){return this.high},$.getHighBitsUnsigned=function(){return this.high>>>0},$.getLowBits=function(){return this.low},$.getLowBitsUnsigned=function(){return this.low>>>0},$.getNumBitsAbs=function(){if(this.isNegative())return this.eq(O)?64:this.neg().getNumBitsAbs();for(var T=this.high!=0?this.high:this.low,F=31;F>0&&(T&1<<F)==0;F--);return this.high!=0?F+33:F+1},$.isZero=function(){return this.high===0&&this.low===0},$.eqz=$.isZero,$.isNegative=function(){return!this.unsigned&&this.high<0},$.isPositive=function(){return this.unsigned||this.high>=0},$.isOdd=function(){return(this.low&1)==1},$.isEven=function(){return(this.low&1)==0},$.equals=function(T){return r(T)||(T=p(T)),this.unsigned!==T.unsigned&&this.high>>>31==1&&T.high>>>31==1?!1:this.high===T.high&&this.low===T.low},$.eq=$.equals,$.notEquals=function(T){return!this.eq(T)},$.neq=$.notEquals,$.ne=$.notEquals,$.lessThan=function(T){return this.comp(T)<0},$.lt=$.lessThan,$.lessThanOrEqual=function(T){return this.comp(T)<=0},$.lte=$.lessThanOrEqual,$.le=$.lessThanOrEqual,$.greaterThan=function(T){return this.comp(T)>0},$.gt=$.greaterThan,$.greaterThanOrEqual=function(T){return this.comp(T)>=0},$.gte=$.greaterThanOrEqual,$.ge=$.greaterThanOrEqual,$.compare=function(T){if(r(T)||(T=p(T)),this.eq(T))return 0;var F=this.isNegative(),U=T.isNegative();return F&&!U?-1:!F&&U?1:this.unsigned?T.high>>>0>this.high>>>0||T.high===this.high&&T.low>>>0>this.low>>>0?-1:1:this.sub(T).isNegative()?-1:1},$.comp=$.compare,$.negate=function(){return!this.unsigned&&this.eq(O)?O:this.not().add(w)},$.neg=$.negate,$.add=function(T){r(T)||(T=p(T));var F=this.high>>>16,U=this.high&65535,q=this.low>>>16,z=this.low&65535,K=T.high>>>16,J=T.high&65535,Q=T.low>>>16,te=T.low&65535,re=0,G=0,se=0,oe=0;return oe+=z+te,se+=oe>>>16,oe&=65535,se+=q+Q,G+=se>>>16,se&=65535,G+=U+J,re+=G>>>16,G&=65535,re+=F+K,re&=65535,c(se<<16|oe,re<<16|G,this.unsigned)},$.subtract=function(T){return r(T)||(T=p(T)),this.add(T.neg())},$.sub=$.subtract,$.multiply=function(T){if(this.isZero())return y;if(r(T)||(T=p(T)),n){var F=n.mul(this.low,this.high,T.low,T.high);return c(F,n.get_high(),this.unsigned)}if(T.isZero())return y;if(this.eq(O))return T.isOdd()?O:y;if(T.eq(O))return this.isOdd()?O:y;if(this.isNegative())return T.isNegative()?this.neg().mul(T.neg()):this.neg().mul(T).neg();if(T.isNegative())return this.mul(T.neg()).neg();if(this.lt(x)&&T.lt(x))return l(this.toNumber()*T.toNumber(),this.unsigned);var U=this.high>>>16,q=this.high&65535,z=this.low>>>16,K=this.low&65535,J=T.high>>>16,Q=T.high&65535,te=T.low>>>16,re=T.low&65535,G=0,se=0,oe=0,pe=0;return pe+=K*re,oe+=pe>>>16,pe&=65535,oe+=z*re,se+=oe>>>16,oe&=65535,oe+=K*te,se+=oe>>>16,oe&=65535,se+=q*re,G+=se>>>16,se&=65535,se+=z*te,G+=se>>>16,se&=65535,se+=K*Q,G+=se>>>16,se&=65535,G+=U*re+q*te+z*Q+K*J,G&=65535,c(oe<<16|pe,G<<16|se,this.unsigned)},$.mul=$.multiply,$.divide=function(T){if(r(T)||(T=p(T)),T.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&T.low===-1&&T.high===-1)return this;var F=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,T.low,T.high);return c(F,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:y;var U,q,z;if(this.unsigned){if(T.unsigned||(T=T.toUnsigned()),T.gt(this))return b;if(T.gt(this.shru(1)))return k;z=b}else{if(this.eq(O)){if(T.eq(w)||T.eq(I))return O;if(T.eq(O))return w;var K=this.shr(1);return U=K.div(T).shl(1),U.eq(y)?T.isNegative()?w:I:(q=this.sub(T.mul(U)),z=U.add(q.div(T)),z)}else if(T.eq(O))return this.unsigned?b:y;if(this.isNegative())return T.isNegative()?this.neg().div(T.neg()):this.neg().div(T).neg();if(T.isNegative())return this.div(T.neg()).neg();z=y}for(q=this;q.gte(T);){U=Math.max(1,Math.floor(q.toNumber()/T.toNumber()));for(var J=Math.ceil(Math.log(U)/Math.LN2),Q=J<=48?1:u(2,J-48),te=l(U),re=te.mul(T);re.isNegative()||re.gt(q);)U-=Q,te=l(U,this.unsigned),re=te.mul(T);te.isZero()&&(te=w),z=z.add(te),q=q.sub(re)}return z},$.div=$.divide,$.modulo=function(T){if(r(T)||(T=p(T)),n){var F=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,T.low,T.high);return c(F,n.get_high(),this.unsigned)}return this.sub(this.div(T).mul(T))},$.mod=$.modulo,$.rem=$.modulo,$.not=function(){return c(~this.low,~this.high,this.unsigned)},$.and=function(T){return r(T)||(T=p(T)),c(this.low&T.low,this.high&T.high,this.unsigned)},$.or=function(T){return r(T)||(T=p(T)),c(this.low|T.low,this.high|T.high,this.unsigned)},$.xor=function(T){return r(T)||(T=p(T)),c(this.low^T.low,this.high^T.high,this.unsigned)},$.shiftLeft=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?c(this.low<<T,this.high<<T|this.low>>>32-T,this.unsigned):c(0,this.low<<T-32,this.unsigned)},$.shl=$.shiftLeft,$.shiftRight=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?c(this.low>>>T|this.high<<32-T,this.high>>T,this.unsigned):c(this.high>>T-32,this.high>=0?0:-1,this.unsigned)},$.shr=$.shiftRight,$.shiftRightUnsigned=function(T){if(r(T)&&(T=T.toInt()),T&=63,T===0)return this;var F=this.high;if(T<32){var U=this.low;return c(U>>>T|F<<32-T,F>>>T,this.unsigned)}else return T===32?c(F,0,this.unsigned):c(F>>>T-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.toSigned=function(){return this.unsigned?c(this.low,this.high,!1):this},$.toUnsigned=function(){return this.unsigned?this:c(this.low,this.high,!0)},$.toBytes=function(T){return T?this.toBytesLE():this.toBytesBE()},$.toBytesLE=function(){var T=this.high,F=this.low;return[F&255,F>>>8&255,F>>>16&255,F>>>24,T&255,T>>>8&255,T>>>16&255,T>>>24]},$.toBytesBE=function(){var T=this.high,F=this.low;return[T>>>24,T>>>16&255,T>>>8&255,T&255,F>>>24,F>>>16&255,F>>>8&255,F&255]},s.fromBytes=function(T,F,U){return U?s.fromBytesLE(T,F):s.fromBytesBE(T,F)},s.fromBytesLE=function(T,F){return new s(T[0]|T[1]<<8|T[2]<<16|T[3]<<24,T[4]|T[5]<<8|T[6]<<16|T[7]<<24,F)},s.fromBytesBE=function(T,F){return new s(T[4]<<24|T[5]<<16|T[6]<<8|T[7],T[0]<<24|T[1]<<16|T[2]<<8|T[3],F)}}}),_E=ss({"(disabled):node-fetch"(){}}),DE=ss({"(disabled):util"(){}}),PE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(c){var u=this,d=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=d(" "),u.s1=d(" "),u.s2=d(" "),u.s0-=d(c),u.s0<0&&(u.s0+=1),u.s1-=d(c),u.s1<0&&(u.s1+=1),u.s2-=d(c),u.s2<0&&(u.s2+=1),d=null}function o(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function i(c,u){var d=new a(c),p=u&&u.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,p&&(typeof p=="object"&&o(p,d),h.state=function(){return o(d,{})}),h}function l(){var c=4022871197,u=function(d){d=String(d);for(var p=0;p<d.length;p++){c+=d.charCodeAt(p);var h=.02519603282416938*c;c=h>>>0,h-=c,h*=c,c=h>>>0,h-=c,c+=h*4294967296}return(c>>>0)*23283064365386963e-26};return u}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),FE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),OE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=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^(p^p<<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 d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,d==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function o(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 i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),ME=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.x,p=c.i,h,f,m;return h=d[p],h^=h>>>7,f=h^h<<24,h=d[p+1&7],f^=h^h>>>10,h=d[p+3&7],f^=h^h>>>3,h=d[p+4&7],f^=h^h<<7,h=d[p+7&7],h=h^h<<13,f^=h^h<<9,d[p]=f,c.i=p+1&7,f};function u(d,p){var h,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,h=0;h<p.length;++h)m[h&7]=m[h&7]<<15^p.charCodeAt(h)+m[h+1&7]<<13;for(;m.length<8;)m.push(0);for(h=0;h<8&&m[h]===0;++h);for(h==8?f=m[7]=-1:f=m[h],d.x=m,d.i=0,h=256;h>0;--h)d.next()}u(c,l)}function o(l,c){return c.x=l.x.slice(),c.i=l.i,c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.x&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),zE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.w,p=c.X,h=c.i,f,m;return c.w=d=d+1640531527|0,m=p[h+34&127],f=p[h=h+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[h]=m^f,c.i=h,m+(d^d>>>16)|0};function u(d,p){var h,f,m,g,A,x=[],y=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,y=Math.max(y,p.length)),m=0,g=-32;g<y;++g)p&&(f^=p.charCodeAt((g+32)%p.length)),g===0&&(A=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(A=A+1640531527|0,h=x[g&127]^=f+A,m=h==0?m+1:0);for(m>=128&&(x[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=x[m+34&127],h=x[m=m+1&127],f^=f<<13,h^=h<<17,f^=f>>>15,h^=h>>>12,x[m]=f^h;d.w=A,d.X=x,d.i=m}u(c,l)}function o(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.X&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),LE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=c.b,h=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^h,h=h-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^h,c.c=h=h-f|0,c.d=f<<16^h>>>16^m,c.a=m-p|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 d=0;d<u.length+20;d++)c.b^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),BE=ss({"(disabled):crypto"(){}}),WE=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s,r){var a=256,o=6,i=52,l="random",c=r.pow(a,o),u=r.pow(2,i),d=u*2,p=a-1,h;function f(w,k,I){var N=[];k=k==!0?{entropy:!0}:k||{};var R=x(A(k.entropy?[w,b(s)]:w==null?y():w,3),N),O=new m(N),$=function(){for(var P=O.g(o),T=c,F=0;P<u;)P=(P+F)*a,T*=a,F=O.g(1);for(;P>=d;)P/=2,T/=2,F>>>=1;return(P+F)/T};return $.int32=function(){return O.g(4)|0},$.quick=function(){return O.g(4)/4294967296},$.double=$,x(b(O.S),s),(k.pass||I||function(P,T,F,U){return U&&(U.S&&g(U,O),P.state=function(){return g(O,{})}),F?(r[l]=P,T):P})($,R,"global"in k?k.global:this==r,k.state)}function m(w){var k,I=w.length,N=this,R=0,O=N.i=N.j=0,$=N.S=[];for(I||(w=[I++]);R<a;)$[R]=R++;for(R=0;R<a;R++)$[R]=$[O=p&O+w[R%I]+(k=$[R])],$[O]=k;(N.g=function(P){for(var T,F=0,U=N.i,q=N.j,z=N.S;P--;)T=z[U=p&U+1],F=F*a+z[p&(z[U]=z[q=p&q+T])+(z[q]=T)];return N.i=U,N.j=q,F})(a)}function g(w,k){return k.i=w.i,k.j=w.j,k.S=w.S.slice(),k}function A(w,k){var I=[],N=typeof w,R;if(k&&N=="object")for(R in w)try{I.push(A(w[R],k-1))}catch(O){}return I.length?I:N=="string"?w:w+"\0"}function x(w,k){for(var I=w+"",N,R=0;R<I.length;)k[p&R]=p&(N^=k[p&R]*19)+I.charCodeAt(R++);return b(k)}function y(){try{var w;return h&&(w=h.randomBytes)?w=w(a):(w=new Uint8Array(a),(n.crypto||n.msCrypto).getRandomValues(w)),b(w)}catch(N){var k=n.navigator,I=k&&k.plugins;return[+new Date,n,I,n.screen,b(s)]}}function b(w){return String.fromCharCode.apply(0,w)}if(x(r.random(),s),typeof t=="object"&&t.exports){t.exports=f;try{h=BE()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):r["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),Ah=ss({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=PE(),s=FE(),r=OE(),a=ME(),o=zE(),i=LE(),l=WE();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),M5=ss({"(disabled):src/node_modules/string_decoder/index.js"(){}}),VE=ss({"src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};function a(){return se.buffer!=Et&&In(se.buffer),Pt}function o(){return se.buffer!=Et&&In(se.buffer),Ts}function i(){return se.buffer!=Et&&In(se.buffer),Mn}function l(){return se.buffer!=Et&&In(se.buffer),hs}function c(){return se.buffer!=Et&&In(se.buffer),Ns}var u=typeof r!="undefined"?r:{},d,p;u.ready=new Promise(function(C,_){d=C,p=_});var h={},f;for(f in u)u.hasOwnProperty(f)&&(h[f]=u[f]);var m=[],g="./this.program",A=function(C,_){throw _},x=!1,y=!1,b=!1,w=!1;x=typeof window=="object",y=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!x&&!b&&!y;var k=u.ENVIRONMENT_IS_PTHREAD||!1;k&&(Et=u.buffer);var I="";function N(C){return u.locateFile?u.locateFile(C,I):I+C}var R,O,$,P,T,F;if(b){y?I=js("path").dirname(I)+"/":I=__dirname+"/",R=function(_,B){return T||(T=js("fs")),F||(F=js("path")),_=F.normalize(_),T.readFileSync(_,B?null:"utf8")},$=function(_){var B=R(_,!0);return B.buffer||(B=new Uint8Array(B)),we(B.buffer),B},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(C){if(!(C instanceof Zc))throw C}),process.on("unhandledRejection",jr),A=function(C){process.exit(C)},u.inspect=function(){return"[Emscripten Module object]"};var U;try{U=js("worker_threads")}catch(C){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),C}global.Worker=U.Worker}else w?(typeof read!="undefined"&&(R=function(_){return read(_)}),$=function(_){var B;return typeof readbuffer=="function"?new Uint8Array(readbuffer(_)):(B=read(_,"binary"),we(typeof B=="object"),B)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(A=function(C){quit(C)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(x||y)&&(y?I=self.location.href:typeof document!="undefined"&&document.currentScript&&(I=document.currentScript.src),typeof s!="undefined"&&s&&(I=s),I.indexOf("blob:")!==0?I=I.substr(0,I.lastIndexOf("/")+1):I="",b?(R=function(_,B){return T||(T=js("fs")),F||(F=js("path")),_=F.normalize(_),T.readFileSync(_,B?null:"utf8")},$=function(_){var B=R(_,!0);return B.buffer||(B=new Uint8Array(B)),we(B.buffer),B}):(R=function(C){var _=new XMLHttpRequest;return _.open("GET",C,!1),_.send(null),_.responseText},y&&($=function(C){var _=new XMLHttpRequest;return _.open("GET",C,!1),_.responseType="arraybuffer",_.send(null),new Uint8Array(_.response)}),O=function(C,_,B){var ee=new XMLHttpRequest;ee.open("GET",C,!0),ee.responseType="arraybuffer",ee.onload=function(){if(ee.status==200||ee.status==0&&ee.response){_(ee.response);return}B()},ee.onerror=B,ee.send(null)}),P=function(C){document.title=C});b&&typeof performance=="undefined"&&(global.performance=js("perf_hooks").performance);var q=u.print||console.log.bind(console),z=u.printErr||console.warn.bind(console);for(f in h)h.hasOwnProperty(f)&&(u[f]=h[f]);h=null,u.arguments&&(m=u.arguments),u.thisProgram&&(g=u.thisProgram),u.quit&&(A=u.quit);function K(C){K.shown||(K.shown={}),K.shown[C]||(K.shown[C]=1,z(C))}var J=Atomics.load,Q=Atomics.store,te=Atomics.compareExchange,re;u.wasmBinary&&(re=u.wasmBinary);var G=u.noExitRuntime||!0;typeof WebAssembly!="object"&&jr("no native wasm support detected");var se,oe,pe=!1,ye;function we(C,_){C||jr("Assertion failed: "+_)}function Ne(C){var _=u["_"+C];return we(_,"Cannot call unknown function "+C+", make sure it is exported"),_}function Me(C,_,B,ee,be){var Ae={string:function(zn){var nu=0;if(zn!=null&&zn!==0){var P5=(zn.length<<2)+1;nu=Ql(P5),ht(zn,nu,P5)}return nu},array:function(zn){var nu=Ql(zn.length);return St(zn,nu),nu}};function xe(zn){return _==="string"?Ke(zn):_==="boolean"?Boolean(zn):zn}var Ee=Ne(C),dt=[],fn=0;if(ee)for(var tn=0;tn<ee.length;tn++){var Sa=Ae[B[tn]];Sa?(fn===0&&(fn=Kc()),dt[tn]=Sa(ee[tn])):dt[tn]=ee[tn]}var tu=Ee.apply(null,dt);return tu=xe(tu),fn!==0&&Jl(fn),tu}function Ue(C,_,B,ee){B=B||[];var be=B.every(function(xe){return xe==="number"}),Ae=_!=="string";return Ae&&be&&!ee?Ne(C):function(){return Me(C,_,B,arguments,ee)}}function qe(C,_,B){for(var ee=_+B,be="";!(_>=ee);){var Ae=C[_++];if(!Ae)return be;if(!(Ae&128)){be+=String.fromCharCode(Ae);continue}var xe=C[_++]&63;if((Ae&224)==192){be+=String.fromCharCode((Ae&31)<<6|xe);continue}var Ee=C[_++]&63;if((Ae&240)==224?Ae=(Ae&15)<<12|xe<<6|Ee:Ae=(Ae&7)<<18|xe<<12|Ee<<6|C[_++]&63,Ae<65536)be+=String.fromCharCode(Ae);else{var dt=Ae-65536;be+=String.fromCharCode(55296|dt>>10,56320|dt&1023)}}return be}function Ke(C,_){return C?qe(o(),C,_):""}function pt(C,_,B,ee){if(!(ee>0))return 0;for(var be=B,Ae=B+ee-1,xe=0;xe<C.length;++xe){var Ee=C.charCodeAt(xe);if(Ee>=55296&&Ee<=57343){var dt=C.charCodeAt(++xe);Ee=65536+((Ee&1023)<<10)|dt&1023}if(Ee<=127){if(B>=Ae)break;_[B++]=Ee}else if(Ee<=2047){if(B+1>=Ae)break;_[B++]=192|Ee>>6,_[B++]=128|Ee&63}else if(Ee<=65535){if(B+2>=Ae)break;_[B++]=224|Ee>>12,_[B++]=128|Ee>>6&63,_[B++]=128|Ee&63}else{if(B+3>=Ae)break;_[B++]=240|Ee>>18,_[B++]=128|Ee>>12&63,_[B++]=128|Ee>>6&63,_[B++]=128|Ee&63}}return _[B]=0,B-be}function ht(C,_,B){return pt(C,o(),_,B)}function at(C){for(var _=0,B=0;B<C.length;++B){var ee=C.charCodeAt(B);ee>=55296&&ee<=57343&&(ee=65536+((ee&1023)<<10)|C.charCodeAt(++B)&1023),ee<=127?++_:ee<=2047?_+=2:ee<=65535?_+=3:_+=4}return _}function St(C,_){a().set(C,_)}function gt(C,_){return C%_>0&&(C+=_-C%_),C}var Et,Pt,Ts,Sn,lr,Mn,hs,Gs,Ns;function In(C){Et=C,u.HEAP8=Pt=new Int8Array(C),u.HEAP16=Sn=new Int16Array(C),u.HEAP32=Mn=new Int32Array(C),u.HEAPU8=Ts=new Uint8Array(C),u.HEAPU16=lr=new Uint16Array(C),u.HEAPU32=hs=new Uint32Array(C),u.HEAPF32=Gs=new Float32Array(C),u.HEAPF64=Ns=new Float64Array(C)}var Tr=u.INITIAL_MEMORY||16777216;if(k)se=u.wasmMemory,Et=u.buffer;else if(u.wasmMemory)se=u.wasmMemory;else if(se=new WebAssembly.Memory({initial:Tr/65536,maximum:2147483648/65536,shared:!0}),!(se.buffer instanceof SharedArrayBuffer))throw z("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");se&&(Et=se.buffer),Tr=Et.byteLength,In(Et);var $n,Nr=[],Er=[],ya=[],Lc=[],ur=[],Hp=!1,M0=!1;k||Er.push({func:function(){uh()}});function jp(){if(!k){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)z0(u.preRun.shift());Kl(Nr)}}function qp(){Hp=!0,!k&&Kl(Er)}function Xp(){k||Kl(ya)}function ts(){k||(M0=!0)}function Kp(){if(!k){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)L0(u.postRun.shift());Kl(ur)}}function z0(C){Nr.unshift(C)}function L0(C){ur.unshift(C)}var Hs=0,Bc=null,ii=null;function B0(C){we(!k,"addRunDependency cannot be used in a pthread worker"),Hs++,u.monitorRunDependencies&&u.monitorRunDependencies(Hs)}function W0(C){if(Hs--,u.monitorRunDependencies&&u.monitorRunDependencies(Hs),Hs==0&&(Bc!==null&&(clearInterval(Bc),Bc=null),ii)){var _=ii;ii=null,_()}}u.preloadedImages={},u.preloadedAudios={};function jr(C){u.onAbort&&u.onAbort(C),k&&console.error("Pthread aborting at "+new Error().stack),C+="",z(C),pe=!0,ye=1,C="abort("+C+"). Build with -s ASSERTIONS=1 for more info.";var _=new WebAssembly.RuntimeError(C);throw p(_),_}function li(C,_){return String.prototype.startsWith?C.startsWith(_):C.indexOf(_)===0}var V0="data:application/octet-stream;base64,";function Zp(C){return li(C,V0)}var U0="file://";function Yp(C){return li(C,U0)}var ns="tfjs-backend-wasm-threaded-simd.wasm";Zp(ns)||(ns=N(ns));function G0(C){try{if(C==ns&&re)return new Uint8Array(re);if($)return $(C);throw"both async and sync fetching of the wasm failed"}catch(_){jr(_)}}function Jp(){if(!re&&(x||y)){if(typeof fetch=="function"&&!Yp(ns))return fetch(ns,{credentials:"same-origin"}).then(function(C){if(!C.ok)throw"failed to load wasm binary file at '"+ns+"'";return C.arrayBuffer()}).catch(function(){return G0(ns)});if(O)return new Promise(function(C,_){O(ns,function(B){C(new Uint8Array(B))},_)})}return Promise.resolve().then(function(){return G0(ns)})}function H0(){var C={a:Mg};function _(xe,Ee){var dt=xe.exports;if(u.asm=dt,$n=u.asm.kb,oe=Ee,!k){var fn=$e.unusedWorkers.length;$e.unusedWorkers.forEach(function(tn){$e.loadWasmModuleToWorker(tn,function(){--fn||W0("wasm-instantiate")})})}}k||B0("wasm-instantiate");function B(xe){_(xe.instance,xe.module)}function ee(xe){return Jp().then(function(Ee){return WebAssembly.instantiate(Ee,C)}).then(xe,function(Ee){z("failed to asynchronously prepare wasm: "+Ee),jr(Ee)})}function be(){return!re&&typeof WebAssembly.instantiateStreaming=="function"&&!Zp(ns)&&!Yp(ns)&&typeof fetch=="function"?fetch(ns,{credentials:"same-origin"}).then(function(xe){var Ee=WebAssembly.instantiateStreaming(xe,C);return Ee.then(B,function(dt){return z("wasm streaming compile failed: "+dt),z("falling back to ArrayBuffer instantiation"),ee(B)})}):ee(B)}if(u.instantiateWasm)try{var Ae=u.instantiateWasm(C,_);return Ae}catch(xe){return z("Module.instantiateWasm callback failed with error: "+xe),!1}return be().catch(p),{}}var Qp={10072:function(){throw"Canceled!"},10090:function(C,_){setTimeout(function(){N5(C,_)},0)}};function j0(){$e.initRuntime()}function Kl(C){for(;C.length>0;){var _=C.shift();if(typeof _=="function"){_(u);continue}var B=_.func;typeof B=="number"?_.arg===void 0?$n.get(B)():$n.get(B)(_.arg):B(_.arg===void 0?null:_.arg)}}var xa={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};function Wc(C,_){if(C<=0||C>a().length||C&!0||_<0)return-28;if(_==0)return 0;_>=2147483647&&(_=1/0);var B=Atomics.load(i(),eu>>2),ee=0;if(B==C){var be=Atomics.compareExchange(i(),eu>>2,B,0);if(be==B&&(--_,ee=1,_<=0))return 1}var Ae=Atomics.notify(i(),C>>2,_);if(Ae>=0)return Ae+ee;throw"Atomics.notify returned an unexpected value "+Ae}u._emscripten_futex_wake=Wc;function q0(C){if(k)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in killThread!";i()[C+12>>2]=0;var _=$e.pthreads[C];_.worker.terminate(),$e.freeThreadData(_),$e.runningWorkers.splice($e.runningWorkers.indexOf(_.worker),1),_.worker.pthread=void 0}function X0(C){if(k)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cancelThread!";var _=$e.pthreads[C];_.worker.postMessage({cmd:"cancel"})}function eh(C){if(k)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cleanupThread!";var _=$e.pthreads[C];if(_){i()[C+12>>2]=0;var B=_.worker;$e.returnWorkerToPool(B)}}var $e={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var C=8,_=0;_<C;++_)$e.allocateUnusedWorker()},initRuntime:function(){for(var C=ci(228),_=0;_<228/4;++_)l()[C/4+_]=0;i()[C+12>>2]=C;var B=C+152;i()[B>>2]=B;for(var ee=ci(512),_=0;_<128;++_)l()[ee/4+_]=0;Atomics.store(l(),C+100>>2,ee),Atomics.store(l(),C+40>>2,C),s2(C,!y,1),C5(C)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;$e.threadExitHandlers.length>0;)$e.threadExitHandlers.pop()();k&&ka()&&I5()},runExitHandlersAndDeinitThread:function(C,_){Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),$e.runExitHandlers(),Atomics.store(l(),C+4>>2,_),Atomics.store(l(),C+0>>2,1),Wc(C+0,2147483647),s2(0,0,0)},threadExit:function(C){var _=ka();_&&($e.runExitHandlersAndDeinitThread(_,C),k&&postMessage({cmd:"exit"}))},threadCancel:function(){$e.runExitHandlersAndDeinitThread(ka(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var C in $e.pthreads){var _=$e.pthreads[C];_&&_.worker&&$e.returnWorkerToPool(_.worker)}$e.pthreads={};for(var B=0;B<$e.unusedWorkers.length;++B){var ee=$e.unusedWorkers[B];ee.terminate()}$e.unusedWorkers=[];for(var B=0;B<$e.runningWorkers.length;++B){var ee=$e.runningWorkers[B],_=ee.pthread;$e.freeThreadData(_),ee.terminate()}$e.runningWorkers=[]},freeThreadData:function(C){if(!!C){if(C.threadInfoStruct){var _=i()[C.threadInfoStruct+100>>2];i()[C.threadInfoStruct+100>>2]=0,Xc(_),Xc(C.threadInfoStruct)}C.threadInfoStruct=0,C.allocatedOwnStack&&C.stackBase&&Xc(C.stackBase),C.stackBase=0,C.worker&&(C.worker.pthread=null)}},returnWorkerToPool:function(C){$e.runWithoutMainThreadQueuedCalls(function(){delete $e.pthreads[C.pthread.threadInfoStruct],$e.unusedWorkers.push(C),$e.runningWorkers.splice($e.runningWorkers.indexOf(C),1),$e.freeThreadData(C.pthread),C.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(C){i()[D5>>2]=0;try{C()}finally{i()[D5>>2]=1}},receiveObjectTransfer:function(C){},loadWasmModuleToWorker:function(C,_){C.onmessage=function(B){var ee=B.data,be=ee.cmd;if(C.pthread&&($e.currentProxiedOperationCallerThread=C.pthread.threadInfoStruct),ee.targetThread&&ee.targetThread!=ka()){var Ae=$e.pthreads[ee.targetThread];Ae?Ae.worker.postMessage(B.data,ee.transferList):console.error('Internal error! Worker sent a message "'+be+'" to target pthread '+ee.targetThread+", but that thread no longer exists!"),$e.currentProxiedOperationCallerThread=void 0;return}if(be==="processQueuedMainThreadWork")fh();else if(be==="spawnThread")ih(B.data);else if(be==="cleanupThread")eh(ee.thread);else if(be==="killThread")q0(ee.thread);else if(be==="cancelThread")X0(ee.thread);else if(be==="loaded")C.loaded=!0,_&&_(C),C.runPthread&&(C.runPthread(),delete C.runPthread);else if(be==="print")q("Thread "+ee.threadId+": "+ee.text);else if(be==="printErr")z("Thread "+ee.threadId+": "+ee.text);else if(be==="alert")alert("Thread "+ee.threadId+": "+ee.text);else if(be==="exit"){var xe=C.pthread&&Atomics.load(l(),C.pthread.threadInfoStruct+64>>2);xe&&$e.returnWorkerToPool(C)}else if(be==="exitProcess")try{wE(ee.returnCode)}catch(Ee){if(Ee instanceof Zc)return;throw Ee}else be==="cancelDone"?$e.returnWorkerToPool(C):be==="objectTransfer"?$e.receiveObjectTransfer(B.data):B.data.target==="setimmediate"?C.postMessage(B.data):z("worker sent an unknown command "+be);$e.currentProxiedOperationCallerThread=void 0},C.onerror=function(B){z("pthread sent an error! "+B.filename+":"+B.lineno+": "+B.message)},b&&(C.on("message",function(B){C.onmessage({data:B})}),C.on("error",function(B){C.onerror(B)}),C.on("exit",function(B){})),C.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||s,wasmMemory:se,wasmModule:oe})},allocateUnusedWorker:function(){var C=N("tfjs-backend-wasm-threaded-simd.worker.js");$e.unusedWorkers.push(new Worker(C))},getNewWorker:function(){return $e.unusedWorkers.length==0&&($e.allocateUnusedWorker(),$e.loadWasmModuleToWorker($e.unusedWorkers[0])),$e.unusedWorkers.length>0?$e.unusedWorkers.pop():null},busySpinWait:function(C){for(var _=performance.now()+C;performance.now()<_;);}};function K0(C,_){$5(C,_),Jl(C)}u.establishStackSpace=K0;function Z0(){return G}u.getNoExitRuntime=Z0;function Y0(C,_){return $n.get(C)(_)}u.invokeEntryPoint=Y0;function J0(C,_,B,ee){jr("Assertion failed: "+Ke(C)+", at: "+[_?Ke(_):"unknown filename",B,ee?Ke(ee):"unknown function"])}function Q0(C,_){var B=_main(C,_)}var ui;b?ui=function(){var C=process.hrtime();return C[0]*1e3+C[1]/1e6}:k?ui=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?ui=dateNow:ui=function(){return performance.now()};function eg(C){return i()[k5()>>2]=C,C}function tg(C,_){if(k)return ba(1,1,C,_)}function ng(C,_){if(C==_)postMessage({cmd:"processQueuedMainThreadWork"});else if(k)postMessage({targetThread:C,cmd:"processThreadQueue"});else{var B=$e.pthreads[C],ee=B&&B.worker;if(!ee)return;ee.postMessage({cmd:"processThreadQueue"})}return 1}function sg(){jr()}function rg(C,_,B){var ee=lg(_,B);return Qp[C].apply(null,ee)}function ag(C,_){}function th(C,_,B){if(C<=0||C>a().length||C&!0)return-28;if(x){if(Atomics.load(i(),C>>2)!=_)return-6;for(var be=performance.now(),Ae=be+B,xe=Atomics.exchange(i(),eu>>2,C);;){if(be=performance.now(),be>Ae)return xe=Atomics.exchange(i(),eu>>2,0),-73;if(xe=Atomics.exchange(i(),eu>>2,0),xe==0)break;if(fh(),Atomics.load(i(),C>>2)!=_)return-6;xe=Atomics.exchange(i(),eu>>2,C)}return 0}else{var ee=Atomics.wait(i(),C>>2,_,B);if(ee==="timed-out")return-73;if(ee==="not-equal")return-6;if(ee==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ee}}function og(C,_,B){o().copyWithin(C,_,_+B)}function ig(){return b?js("os").cpus().length:navigator.hardwareConcurrency}function ba(C,_){for(var B=arguments.length-2,ee=Kc(),be=B,Ae=Ql(be*8),xe=Ae>>3,Ee=0;Ee<B;Ee++){var dt=arguments[2+Ee];c()[xe+Ee]=dt}var fn=R5(C,be,Ae,_);return Jl(ee),fn}var Vc=[],Uc=[];function lg(C,_){Uc.length=0;var B;for(_>>=2;B=o()[C++];){var ee=B<105;ee&&_&1&&_++,Uc.push(ee?c()[_++>>1]:i()[_]),++_}return Uc}function ug(C,_,B){Vc.length=_;for(var ee=B>>3,be=0;be<_;be++)Vc[be]=c()[ee+be];var Ae=C<0,xe=Ae?Qp[-C-1]:Og[C];return xe.apply(null,Vc)}function cg(){return o().length}function dg(C){try{return se.grow(C-Et.byteLength+65535>>>16),In(se.buffer),1}catch(_){}}function pg(C){var _=cg();if(C<=_)return!1;var B=2147483648;if(C>B)return!1;for(var ee=1;ee<=4;ee*=2){var be=_*(1+.2/ee);be=Math.min(be,C+100663296);var Ae=Math.min(B,gt(Math.max(C,be),65536)),xe=dg(Ae);if(xe)return!0}return!1}var Ge={inEventHandler:0,removeAllEventListeners:function(){for(var C=Ge.eventHandlers.length-1;C>=0;--C)Ge._removeHandler(C);Ge.eventHandlers=[],Ge.deferredCalls=[]},registerRemoveEventListeners:function(){Ge.removeEventListenersRegistered||(Lc.push(Ge.removeAllEventListeners),Ge.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(C,_,B){function ee(xe,Ee){if(xe.length!=Ee.length)return!1;for(var dt in xe)if(xe[dt]!=Ee[dt])return!1;return!0}for(var be in Ge.deferredCalls){var Ae=Ge.deferredCalls[be];if(Ae.targetFunction==C&&ee(Ae.argsList,B))return}Ge.deferredCalls.push({targetFunction:C,precedence:_,argsList:B}),Ge.deferredCalls.sort(function(xe,Ee){return xe.precedence<Ee.precedence})},removeDeferredCalls:function(C){for(var _=0;_<Ge.deferredCalls.length;++_)Ge.deferredCalls[_].targetFunction==C&&(Ge.deferredCalls.splice(_,1),--_)},canPerformEventHandlerRequests:function(){return Ge.inEventHandler&&Ge.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!Ge.canPerformEventHandlerRequests())for(var C=0;C<Ge.deferredCalls.length;++C){var _=Ge.deferredCalls[C];Ge.deferredCalls.splice(C,1),--C,_.targetFunction.apply(null,_.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(C,_){for(var B=0;B<Ge.eventHandlers.length;++B)Ge.eventHandlers[B].target==C&&(!_||_==Ge.eventHandlers[B].eventTypeString)&&Ge._removeHandler(B--)},_removeHandler:function(C){var _=Ge.eventHandlers[C];_.target.removeEventListener(_.eventTypeString,_.eventListenerFunc,_.useCapture),Ge.eventHandlers.splice(C,1)},registerOrRemoveHandler:function(C){var _=function(be){++Ge.inEventHandler,Ge.currentEventHandler=C,Ge.runDeferredCalls(),C.handlerFunc(be),Ge.runDeferredCalls(),--Ge.inEventHandler};if(C.callbackfunc)C.eventListenerFunc=_,C.target.addEventListener(C.eventTypeString,_,C.useCapture),Ge.eventHandlers.push(C),Ge.registerRemoveEventListeners();else for(var B=0;B<Ge.eventHandlers.length;++B)Ge.eventHandlers[B].target==C.target&&Ge.eventHandlers[B].eventTypeString==C.eventTypeString&&Ge._removeHandler(B--)},queueEventHandlerOnThread_iiii:function(C,_,B,ee,be){var Ae=Kc(),xe=Ql(12);i()[xe>>2]=B,i()[xe+4>>2]=ee,i()[xe+8>>2]=be,n2(0,C,637534208,_,ee,xe),Jl(Ae)},getTargetThreadForEventCallback:function(C){switch(C){case 1:return 0;case 2:return $e.currentProxiedOperationCallerThread;default:return C}},getNodeNameForTarget:function(C){return C?C==window?"#window":C==screen?"#screen":C&&C.nodeName?C.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function hg(C){var _=at(C)+1,B=ci(_);return ht(C,B,_),B}function fg(C,_,B,ee){var be=Kc(),Ae=Ql(12),xe=0;_&&(xe=hg(_)),i()[Ae>>2]=xe,i()[Ae+4>>2]=B,i()[Ae+8>>2]=ee,n2(0,C,657457152,0,xe,Ae),Jl(be)}function mg(C,_,B,ee){_=_?Ke(_):"",fg(C,_,B,ee)}function gg(C){return C>2?Ke(C):C}var Ag=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function yg(C){C=gg(C);var _=Ag[C]||(typeof document!="undefined"?document.querySelector(C):void 0);return _}function Gc(C){return yg(C)}function nh(C,_,B){var ee=Gc(C);if(!ee)return-4;if(ee.canvasSharedPtr&&(i()[ee.canvasSharedPtr>>2]=_,i()[ee.canvasSharedPtr+4>>2]=B),ee.offscreenCanvas||!ee.controlTransferredOffscreen){ee.offscreenCanvas&&(ee=ee.offscreenCanvas);var be=!1;if(ee.GLctxObject&&ee.GLctxObject.GLctx){var Ae=ee.GLctxObject.GLctx.getParameter(2978);be=Ae[0]===0&&Ae[1]===0&&Ae[2]===ee.width&&Ae[3]===ee.height}ee.width=_,ee.height=B,be&&ee.GLctxObject.GLctx.viewport(0,0,_,B)}else if(ee.canvasSharedPtr){var xe=i()[ee.canvasSharedPtr+8>>2];return mg(xe,C,_,B),1}else return-4;return 0}function sh(C,_,B){return k?ba(2,1,C,_,B):nh(C,_,B)}function xg(C,_,B){var ee=Gc(C);return ee?nh(C,_,B):sh(C,_,B)}function bg(C){}function vg(C,_){}function wg(C){var _=C.getExtension("ANGLE_instanced_arrays");if(_)return C.vertexAttribDivisor=function(B,ee){_.vertexAttribDivisorANGLE(B,ee)},C.drawArraysInstanced=function(B,ee,be,Ae){_.drawArraysInstancedANGLE(B,ee,be,Ae)},C.drawElementsInstanced=function(B,ee,be,Ae,xe){_.drawElementsInstancedANGLE(B,ee,be,Ae,xe)},1}function kg(C){var _=C.getExtension("OES_vertex_array_object");if(_)return C.createVertexArray=function(){return _.createVertexArrayOES()},C.deleteVertexArray=function(B){_.deleteVertexArrayOES(B)},C.bindVertexArray=function(B){_.bindVertexArrayOES(B)},C.isVertexArray=function(B){return _.isVertexArrayOES(B)},1}function Sg(C){var _=C.getExtension("WEBGL_draw_buffers");if(_)return C.drawBuffers=function(B,ee){_.drawBuffersWEBGL(B,ee)},1}function Ig(C){return!!(C.multiDrawWebgl=C.getExtension("WEBGL_multi_draw"))}var ut={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(_){ut.lastError||(ut.lastError=_)},getNewId:function(C){for(var _=ut.counter++,B=C.length;B<_;B++)C[B]=null;return _},getSource:function(C,_,B,ee){for(var be="",Ae=0;Ae<_;++Ae){var xe=ee?i()[ee+Ae*4>>2]:-1;be+=Ke(i()[B+Ae*4>>2],xe<0?void 0:xe)}return be},createContext:function(C,_){var B=C.getContext("webgl",_);if(!B)return 0;var ee=ut.registerContext(B,_);return ee},registerContext:function(C,_){var B=ci(8);i()[B+4>>2]=ka();var ee={handle:B,attributes:_,version:_.majorVersion,GLctx:C};return C.canvas&&(C.canvas.GLctxObject=ee),ut.contexts[B]=ee,(typeof _.enableExtensionsByDefault=="undefined"||_.enableExtensionsByDefault)&&ut.initExtensions(ee),B},makeContextCurrent:function(C){return ut.currentContext=ut.contexts[C],u.ctx=va=ut.currentContext&&ut.currentContext.GLctx,!(C&&!va)},getContext:function(C){return ut.contexts[C]},deleteContext:function(C){ut.currentContext===ut.contexts[C]&&(ut.currentContext=null),typeof Ge=="object"&&Ge.removeAllHandlersOnTarget(ut.contexts[C].GLctx.canvas),ut.contexts[C]&&ut.contexts[C].GLctx.canvas&&(ut.contexts[C].GLctx.canvas.GLctxObject=void 0),Xc(ut.contexts[C].handle),ut.contexts[C]=null},initExtensions:function(C){if(C||(C=ut.currentContext),!C.initExtensionsDone){C.initExtensionsDone=!0;var _=C.GLctx;wg(_),kg(_),Sg(_),_.disjointTimerQueryExt=_.getExtension("EXT_disjoint_timer_query"),Ig(_);var B=_.getSupportedExtensions()||[];B.forEach(function(ee){ee.indexOf("lose_context")<0&&ee.indexOf("debug")<0&&_.getExtension(ee)})}},populateUniformTable:function(C){for(var _=ut.programs[C],B=ut.programInfos[C]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},ee=B.uniforms,be=va.getProgramParameter(_,35718),Ae=0;Ae<be;++Ae){var xe=va.getActiveUniform(_,Ae),Ee=xe.name;B.maxUniformLength=Math.max(B.maxUniformLength,Ee.length+1),Ee.slice(-1)=="]"&&(Ee=Ee.slice(0,Ee.lastIndexOf("[")));var dt=va.getUniformLocation(_,Ee);if(dt){var fn=ut.getNewId(ut.uniforms);ee[Ee]=[xe.size,fn],ut.uniforms[fn]=dt;for(var tn=1;tn<xe.size;++tn){var Sa=Ee+"["+tn+"]";dt=va.getUniformLocation(_,Sa),fn=ut.getNewId(ut.uniforms),ut.uniforms[fn]=dt}}}}},Cg=["default","low-power","high-performance"];function Tg(C,_){var B=_>>2,ee=i()[B+(24>>2)],be={alpha:!!i()[B+(0>>2)],depth:!!i()[B+(4>>2)],stencil:!!i()[B+(8>>2)],antialias:!!i()[B+(12>>2)],premultipliedAlpha:!!i()[B+(16>>2)],preserveDrawingBuffer:!!i()[B+(20>>2)],powerPreference:Cg[ee],failIfMajorPerformanceCaveat:!!i()[B+(28>>2)],majorVersion:i()[B+(32>>2)],minorVersion:i()[B+(36>>2)],enableExtensionsByDefault:i()[B+(40>>2)],explicitSwapControl:i()[B+(44>>2)],proxyContextToMainThread:i()[B+(48>>2)],renderViaOffscreenBackBuffer:i()[B+(52>>2)]},Ae=Gc(C);if(!Ae||be.explicitSwapControl)return 0;var xe=ut.createContext(Ae,be);return xe}function Ng(C,_){return Tg(C,_)}var Zl={mappings:{},buffers:[null,[],[]],printChar:function(C,_){var B=Zl.buffers[C];_===0||_===10?((C===1?q:z)(qe(B,0)),B.length=0):B.push(_)},varargs:void 0,get:function(){Zl.varargs+=4;var C=i()[Zl.varargs-4>>2];return C},getStr:function(C){var _=Ke(C);return _},get64:function(C,_){return C}};function rh(C){return k?ba(3,1,C):0}function ah(C,_,B,ee,be){if(k)return ba(4,1,C,_,B,ee,be)}function oh(C,_,B,ee){if(k)return ba(5,1,C,_,B,ee);for(var be=0,Ae=0;Ae<B;Ae++){for(var xe=i()[_+Ae*8>>2],Ee=i()[_+(Ae*8+4)>>2],dt=0;dt<Ee;dt++)Zl.printChar(C,o()[xe+dt]);be+=Ee}return i()[ee>>2]=be,0}function Eg(C){var _=$e.threadExitHandlers.pop();C&&_()}function Rg(C,_){$e.threadExitHandlers.push(function(){$n.get(C)(_)})}function ih(C){if(k)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var _=$e.getNewWorker();if(_.pthread!==void 0)throw"Internal error!";if(!C.pthread_ptr)throw"Internal error, no pthread ptr!";$e.runningWorkers.push(_);for(var B=ci(128*4),ee=0;ee<128;++ee)i()[B+ee*4>>2]=0;var be=C.stackBase+C.stackSize,Ae=$e.pthreads[C.pthread_ptr]={worker:_,stackBase:C.stackBase,stackSize:C.stackSize,allocatedOwnStack:C.allocatedOwnStack,threadInfoStruct:C.pthread_ptr},xe=Ae.threadInfoStruct>>2;Atomics.store(l(),xe+(64>>2),C.detached),Atomics.store(l(),xe+(100>>2),B),Atomics.store(l(),xe+(40>>2),Ae.threadInfoStruct),Atomics.store(l(),xe+(80>>2),C.stackSize),Atomics.store(l(),xe+(76>>2),be),Atomics.store(l(),xe+(104>>2),C.stackSize),Atomics.store(l(),xe+(104+8>>2),be),Atomics.store(l(),xe+(104+12>>2),C.detached);var Ee=S5(),dt=Ee+40;Atomics.store(l(),xe+(172>>2),dt),_.pthread=Ae;var fn={cmd:"run",start_routine:C.startRoutine,arg:C.arg,threadInfoStruct:C.pthread_ptr,stackBase:C.stackBase,stackSize:C.stackSize};_.runPthread=function(){fn.time=performance.now(),_.postMessage(fn,C.transferList)},_.loaded&&(_.runPthread(),delete _.runPthread)}function $g(C,_,B,ee){if(typeof SharedArrayBuffer=="undefined")return z("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!C)return z("pthread_create called with a null thread pointer!"),28;var be=[],Ae=0;if(k&&(be.length===0||Ae))return E5(687865856,C,_,B,ee);if(Ae)return Ae;var xe=0,Ee=0,dt=0;_&&_!=-1?(xe=i()[_>>2],xe+=81920,Ee=i()[_+8>>2],dt=i()[_+12>>2]!==0):xe=2097152;var fn=Ee==0;fn?Ee=_5(16,xe):(Ee-=xe,we(Ee>0));for(var tn=ci(228),Sa=0;Sa<228>>2;++Sa)l()[(tn>>2)+Sa]=0;i()[C>>2]=tn,i()[tn+12>>2]=tn;var tu=tn+152;i()[tu>>2]=tu;var zn={stackBase:Ee,stackSize:xe,allocatedOwnStack:fn,detached:dt,startRoutine:B,pthread_ptr:tn,arg:ee,transferList:be};return k?(zn.cmd="spawnThread",postMessage(zn,be)):ih(zn),0}function _g(){if(!!k){var C=ka();if(!!C){var _=Atomics.load(l(),C+56>>2);if(!_){var B=Atomics.load(l(),C+0>>2);if(B==2)throw"Canceled!"}}}}function Dg(){b||y||K("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread")}function Pg(C,_,B){if(!C)return z("pthread_join attempted on a null thread pointer!"),xa.ESRCH;if(k&&ka()==C)return z("PThread "+C+" is attempting to join to itself!"),xa.EDEADLK;if(!k&&T5()==C)return z("Main thread "+C+" is attempting to join to itself!"),xa.EDEADLK;var ee=i()[C+12>>2];if(ee!==C)return z("pthread_join attempted on thread "+C+", which does not point to a valid thread, or does not exist anymore!"),xa.ESRCH;var be=Atomics.load(l(),C+64>>2);if(be)return z("Attempted to join thread "+C+", which was already detached!"),xa.EINVAL;for(B&&Dg();;){var Ae=Atomics.load(l(),C+0>>2);if(Ae==1){var xe=Atomics.load(l(),C+4>>2);return _&&(i()[_>>2]=xe),Atomics.store(l(),C+64>>2,1),k?postMessage({cmd:"cleanupThread",thread:C}):eh(C),0}if(!B)return xa.EBUSY;_g(),k||fh(),th(C+0,Ae,k?100:1)}}function Fg(C,_){return Pg(C,_,!0)}function lh(C){if(k)return ba(6,1,C);switch(C){case 30:return 16384;case 85:var _=2147483648;return _/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 eg(28),-1}k||$e.initMainThreadBlock();var va,Og=[null,tg,sh,rh,ah,oh,lh],Mg={e:J0,r:Q0,x:ng,b:sg,y:rg,j:ag,d:th,c:Wc,f:ui,p:og,A:ig,u:ug,q:pg,v:xg,i:bg,s:vg,w:Ng,l:rh,n:ah,g:oh,o:j0,a:se||u.wasmMemory,z:Eg,k:Rg,h:$g,m:Fg,t:lh},w5=H0(),uh=u.___wasm_call_ctors=function(){return(uh=u.___wasm_call_ctors=u.asm.B).apply(null,arguments)},zg=u._init=function(){return(zg=u._init=u.asm.C).apply(null,arguments)},Lg=u._init_with_threads_count=function(){return(Lg=u._init_with_threads_count=u.asm.D).apply(null,arguments)},Bg=u._get_threads_count=function(){return(Bg=u._get_threads_count=u.asm.E).apply(null,arguments)},Wg=u._register_tensor=function(){return(Wg=u._register_tensor=u.asm.F).apply(null,arguments)},Vg=u._dispose_data=function(){return(Vg=u._dispose_data=u.asm.G).apply(null,arguments)},Ug=u._dispose=function(){return(Ug=u._dispose=u.asm.H).apply(null,arguments)},Gg=u._Abs=function(){return(Gg=u._Abs=u.asm.I).apply(null,arguments)},Hg=u._Add=function(){return(Hg=u._Add=u.asm.J).apply(null,arguments)},jg=u._AddN=function(){return(jg=u._AddN=u.asm.K).apply(null,arguments)},qg=u._All=function(){return(qg=u._All=u.asm.L).apply(null,arguments)},Xg=u._Any=function(){return(Xg=u._Any=u.asm.M).apply(null,arguments)},Kg=u._ArgMax=function(){return(Kg=u._ArgMax=u.asm.N).apply(null,arguments)},Zg=u._AvgPool=function(){return(Zg=u._AvgPool=u.asm.O).apply(null,arguments)},Yg=u._BatchMatMul=function(){return(Yg=u._BatchMatMul=u.asm.P).apply(null,arguments)},Jg=u._Ceil=function(){return(Jg=u._Ceil=u.asm.Q).apply(null,arguments)},Qg=u._ClipByValue=function(){return(Qg=u._ClipByValue=u.asm.R).apply(null,arguments)},e2=u._Conv2D=function(){return(e2=u._Conv2D=u.asm.S).apply(null,arguments)},ch=u._Conv2DBackpropInput=function(){return(ch=u._Conv2DBackpropInput=u.asm.T).apply(null,arguments)},dh=u._Cos=function(){return(dh=u._Cos=u.asm.U).apply(null,arguments)},Hc=u._Cosh=function(){return(Hc=u._Cosh=u.asm.V).apply(null,arguments)},Yl=u._CropAndResize=function(){return(Yl=u._CropAndResize=u.asm.W).apply(null,arguments)},t2=u._Cumsum=function(){return(t2=u._Cumsum=u.asm.X).apply(null,arguments)},jc=u._DepthToSpace=function(){return(jc=u._DepthToSpace=u.asm.Y).apply(null,arguments)},ae=u._DepthwiseConv2dNative=function(){return(ae=u._DepthwiseConv2dNative=u.asm.Z).apply(null,arguments)},le=u._Elu=function(){return(le=u._Elu=u.asm._).apply(null,arguments)},Se=u._Equal=function(){return(Se=u._Equal=u.asm.$).apply(null,arguments)},ot=u._Exp=function(){return(ot=u._Exp=u.asm.aa).apply(null,arguments)},Bt=u._FlipLeftRight=function(){return(Bt=u._FlipLeftRight=u.asm.ba).apply(null,arguments)},Rt=u._Floor=function(){return(Rt=u._Floor=u.asm.ca).apply(null,arguments)},Ye=u._FloorDiv=function(){return(Ye=u._FloorDiv=u.asm.da).apply(null,arguments)},Je=u._FusedBatchNorm=function(){return(Je=u._FusedBatchNorm=u.asm.ea).apply(null,arguments)},Cn=u._FusedConv2D=function(){return(Cn=u._FusedConv2D=u.asm.fa).apply(null,arguments)},qr=u._FusedDepthwiseConv2D=function(){return(qr=u._FusedDepthwiseConv2D=u.asm.ga).apply(null,arguments)},Xr=u._Gather=function(){return(Xr=u._Gather=u.asm.ha).apply(null,arguments)},ph=u._GatherNd=function(){return(ph=u._GatherNd=u.asm.ia).apply(null,arguments)},qc=u._Greater=function(){return(qc=u._Greater=u.asm.ja).apply(null,arguments)},fs=u._GreaterEqual=function(){return(fs=u._GreaterEqual=u.asm.ka).apply(null,arguments)},wa=u._LeakyRelu=function(){return(wa=u._LeakyRelu=u.asm.la).apply(null,arguments)},hh=u._Less=function(){return(hh=u._Less=u.asm.ma).apply(null,arguments)},CN=u._LessEqual=function(){return(CN=u._LessEqual=u.asm.na).apply(null,arguments)},TN=u._Log=function(){return(TN=u._Log=u.asm.oa).apply(null,arguments)},NN=u._LogicalAnd=function(){return(NN=u._LogicalAnd=u.asm.pa).apply(null,arguments)},EN=u._Max=function(){return(EN=u._Max=u.asm.qa).apply(null,arguments)},RN=u._MaxPool=function(){return(RN=u._MaxPool=u.asm.ra).apply(null,arguments)},$N=u._Maximum=function(){return($N=u._Maximum=u.asm.sa).apply(null,arguments)},_N=u._Mean=function(){return(_N=u._Mean=u.asm.ta).apply(null,arguments)},DN=u._Min=function(){return(DN=u._Min=u.asm.ua).apply(null,arguments)},PN=u._Minimum=function(){return(PN=u._Minimum=u.asm.va).apply(null,arguments)},FN=u._MirrorPad=function(){return(FN=u._MirrorPad=u.asm.wa).apply(null,arguments)},ON=u._Multiply=function(){return(ON=u._Multiply=u.asm.xa).apply(null,arguments)},MN=u._Neg=function(){return(MN=u._Neg=u.asm.ya).apply(null,arguments)},zN=u._NonMaxSuppressionV3=function(){return(zN=u._NonMaxSuppressionV3=u.asm.za).apply(null,arguments)},LN=u._NonMaxSuppressionV4=function(){return(LN=u._NonMaxSuppressionV4=u.asm.Aa).apply(null,arguments)},BN=u._NonMaxSuppressionV5=function(){return(BN=u._NonMaxSuppressionV5=u.asm.Ba).apply(null,arguments)},WN=u._NotEqual=function(){return(WN=u._NotEqual=u.asm.Ca).apply(null,arguments)},VN=u._OneHot=function(){return(VN=u._OneHot=u.asm.Da).apply(null,arguments)},UN=u._PadV2=function(){return(UN=u._PadV2=u.asm.Ea).apply(null,arguments)},GN=u._Pow=function(){return(GN=u._Pow=u.asm.Fa).apply(null,arguments)},HN=u._Prelu=function(){return(HN=u._Prelu=u.asm.Ga).apply(null,arguments)},jN=u._Prod=function(){return(jN=u._Prod=u.asm.Ha).apply(null,arguments)},qN=u._RealDiv=function(){return(qN=u._RealDiv=u.asm.Ia).apply(null,arguments)},XN=u._Relu=function(){return(XN=u._Relu=u.asm.Ja).apply(null,arguments)},KN=u._Relu6=function(){return(KN=u._Relu6=u.asm.Ka).apply(null,arguments)},ZN=u._ResizeBilinear=function(){return(ZN=u._ResizeBilinear=u.asm.La).apply(null,arguments)},YN=u._Reverse=function(){return(YN=u._Reverse=u.asm.Ma).apply(null,arguments)},JN=u._RotateWithOffset=function(){return(JN=u._RotateWithOffset=u.asm.Na).apply(null,arguments)},QN=u._Round=function(){return(QN=u._Round=u.asm.Oa).apply(null,arguments)},eE=u._Rsqrt=function(){return(eE=u._Rsqrt=u.asm.Pa).apply(null,arguments)},tE=u._ScatterNd=function(){return(tE=u._ScatterNd=u.asm.Qa).apply(null,arguments)},nE=u._SelectV2=function(){return(nE=u._SelectV2=u.asm.Ra).apply(null,arguments)},sE=u._Sigmoid=function(){return(sE=u._Sigmoid=u.asm.Sa).apply(null,arguments)},rE=u._Sin=function(){return(rE=u._Sin=u.asm.Ta).apply(null,arguments)},aE=u._Softmax=function(){return(aE=u._Softmax=u.asm.Ua).apply(null,arguments)},oE=u._Sqrt=function(){return(oE=u._Sqrt=u.asm.Va).apply(null,arguments)},iE=u._Square=function(){return(iE=u._Square=u.asm.Wa).apply(null,arguments)},lE=u._SquaredDifference=function(){return(lE=u._SquaredDifference=u.asm.Xa).apply(null,arguments)},uE=u._Step=function(){return(uE=u._Step=u.asm.Ya).apply(null,arguments)},cE=u._StridedSlice=function(){return(cE=u._StridedSlice=u.asm.Za).apply(null,arguments)},dE=u._Sub=function(){return(dE=u._Sub=u.asm._a).apply(null,arguments)},pE=u._Sum=function(){return(pE=u._Sum=u.asm.$a).apply(null,arguments)},hE=u._Tan=function(){return(hE=u._Tan=u.asm.ab).apply(null,arguments)},fE=u._Tanh=function(){return(fE=u._Tanh=u.asm.bb).apply(null,arguments)},mE=u._Tile=function(){return(mE=u._Tile=u.asm.cb).apply(null,arguments)},gE=u._TopK=function(){return(gE=u._TopK=u.asm.db).apply(null,arguments)},AE=u._Transform=function(){return(AE=u._Transform=u.asm.eb).apply(null,arguments)},yE=u._Transpose=function(){return(yE=u._Transpose=u.asm.fb).apply(null,arguments)},xE=u.__FusedMatMul=function(){return(xE=u.__FusedMatMul=u.asm.gb).apply(null,arguments)},ci=u._malloc=function(){return(ci=u._malloc=u.asm.hb).apply(null,arguments)},Xc=u._free=function(){return(Xc=u._free=u.asm.ib).apply(null,arguments)},k5=u.___errno_location=function(){return(k5=u.___errno_location=u.asm.jb).apply(null,arguments)},S5=u._emscripten_get_global_libc=function(){return(S5=u._emscripten_get_global_libc=u.asm.lb).apply(null,arguments)},ka=u._pthread_self=function(){return(ka=u._pthread_self=u.asm.mb).apply(null,arguments)},I5=u.___pthread_tsd_run_dtors=function(){return(I5=u.___pthread_tsd_run_dtors=u.asm.nb).apply(null,arguments)},fh=u._emscripten_main_thread_process_queued_calls=function(){return(fh=u._emscripten_main_thread_process_queued_calls=u.asm.ob).apply(null,arguments)},bE=u._emscripten_current_thread_process_queued_calls=function(){return(bE=u._emscripten_current_thread_process_queued_calls=u.asm.pb).apply(null,arguments)},C5=u._emscripten_register_main_browser_thread_id=function(){return(C5=u._emscripten_register_main_browser_thread_id=u.asm.qb).apply(null,arguments)},T5=u._emscripten_main_browser_thread_id=function(){return(T5=u._emscripten_main_browser_thread_id=u.asm.rb).apply(null,arguments)},N5=u.__emscripten_do_dispatch_to_thread=function(){return(N5=u.__emscripten_do_dispatch_to_thread=u.asm.sb).apply(null,arguments)},E5=u._emscripten_sync_run_in_main_thread_4=function(){return(E5=u._emscripten_sync_run_in_main_thread_4=u.asm.tb).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.ub).apply(null,arguments)},n2=u.__emscripten_call_on_thread=function(){return(n2=u.__emscripten_call_on_thread=u.asm.vb).apply(null,arguments)},vE=u._emscripten_tls_init=function(){return(vE=u._emscripten_tls_init=u.asm.wb).apply(null,arguments)},s2=u.__emscripten_thread_init=function(){return(s2=u.__emscripten_thread_init=u.asm.xb).apply(null,arguments)},Kc=u.stackSave=function(){return(Kc=u.stackSave=u.asm.yb).apply(null,arguments)},Jl=u.stackRestore=function(){return(Jl=u.stackRestore=u.asm.zb).apply(null,arguments)},Ql=u.stackAlloc=function(){return(Ql=u.stackAlloc=u.asm.Ab).apply(null,arguments)},$5=u._emscripten_stack_set_limits=function(){return($5=u._emscripten_stack_set_limits=u.asm.Bb).apply(null,arguments)},_5=u._memalign=function(){return(_5=u._memalign=u.asm.Cb).apply(null,arguments)},D5=u.__emscripten_allow_main_runtime_queued_calls=10064,eu=u.__emscripten_main_thread_futex=10268;u.cwrap=Ue,u.PThread=$e,u.PThread=$e,u.wasmMemory=se,u.ExitStatus=Zc;var mh;function Zc(C){this.name="ExitStatus",this.message="Program terminated with exit("+C+")",this.status=C}ii=function C(){mh||r2(),mh||(ii=C)};function r2(C){if(C=C||m,Hs>0)return;if(k){d(u),qp(),postMessage({cmd:"loaded"});return}if(jp(),Hs>0)return;function _(){mh||(mh=!0,u.calledRun=!0,!pe&&(qp(),Xp(),d(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),Kp()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),_()},1)):_()}u.run=r2;function wE(C,_){if(!(_&&G&&C===0)){if(!_&&k)throw postMessage({cmd:"exitProcess",returnCode:C}),new Zc(C);G||($e.terminateAllThreads(),ye=C,ts(),u.onExit&&u.onExit(C),pe=!0),A(C,new Zc(C))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return k&&(G=!1,$e.initWorker()),r2(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}}),UE=ss({"src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};var a=typeof r!="undefined"?r:{},o,i;a.ready=new Promise(function(ae,le){o=ae,i=le});var l={},c;for(c in a)a.hasOwnProperty(c)&&(l[c]=a[c]);var u=[],d="./this.program",p=function(ae,le){throw le},h=!1,f=!1,m=!1,g=!1;h=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g=!h&&!m&&!f;var A="";function x(ae){return a.locateFile?a.locateFile(ae,A):A+ae}var y,b,w,k,I,N;m?(f?A=js("path").dirname(A)+"/":A=__dirname+"/",y=function(le,Se){return I||(I=js("fs")),N||(N=js("path")),le=N.normalize(le),I.readFileSync(le,Se?null:"utf8")},w=function(le){var Se=y(le,!0);return Se.buffer||(Se=new Uint8Array(Se)),q(Se.buffer),Se},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(ae){if(!(ae instanceof t2))throw ae}),process.on("unhandledRejection",ur),p=function(ae){process.exit(ae)},a.inspect=function(){return"[Emscripten Module object]"}):g?(typeof read!="undefined"&&(y=function(le){return read(le)}),w=function(le){var Se;return typeof readbuffer=="function"?new Uint8Array(readbuffer(le)):(Se=read(le,"binary"),q(typeof Se=="object"),Se)},typeof scriptArgs!="undefined"?u=scriptArgs:typeof arguments!="undefined"&&(u=arguments),typeof quit=="function"&&(p=function(ae){quit(ae)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(h||f)&&(f?A=self.location.href:typeof document!="undefined"&&document.currentScript&&(A=document.currentScript.src),s&&(A=s),A.indexOf("blob:")!==0?A=A.substr(0,A.lastIndexOf("/")+1):A="",y=function(ae){var le=new XMLHttpRequest;return le.open("GET",ae,!1),le.send(null),le.responseText},f&&(w=function(ae){var le=new XMLHttpRequest;return le.open("GET",ae,!1),le.responseType="arraybuffer",le.send(null),new Uint8Array(le.response)}),b=function(ae,le,Se){var ot=new XMLHttpRequest;ot.open("GET",ae,!0),ot.responseType="arraybuffer",ot.onload=function(){if(ot.status==200||ot.status==0&&ot.response){le(ot.response);return}Se()},ot.onerror=Se,ot.send(null)},k=function(ae){document.title=ae});var R=a.print||console.log.bind(console),O=a.printErr||console.warn.bind(console);for(c in l)l.hasOwnProperty(c)&&(a[c]=l[c]);l=null,a.arguments&&(u=a.arguments),a.thisProgram&&(d=a.thisProgram),a.quit&&(p=a.quit);var $;a.wasmBinary&&($=a.wasmBinary);var P=a.noExitRuntime||!0;typeof WebAssembly!="object"&&ur("no native wasm support detected");var T,F=!1,U;function q(ae,le){ae||ur("Assertion failed: "+le)}function z(ae){var le=a["_"+ae];return q(le,"Cannot call unknown function "+ae+", make sure it is exported"),le}function K(ae,le,Se,ot,Bt){var Rt={string:function(fs){var wa=0;if(fs!=null&&fs!==0){var hh=(fs.length<<2)+1;wa=Hc(hh),se(fs,wa,hh)}return wa},array:function(fs){var wa=Hc(fs.length);return oe(fs,wa),wa}};function Ye(fs){return le==="string"?re(fs):le==="boolean"?Boolean(fs):fs}var Je=z(ae),Cn=[],qr=0;if(ot)for(var Xr=0;Xr<ot.length;Xr++){var ph=Rt[Se[Xr]];ph?(qr===0&&(qr=ch()),Cn[Xr]=ph(ot[Xr])):Cn[Xr]=ot[Xr]}var qc=Je.apply(null,Cn);return qc=Ye(qc),qr!==0&&dh(qr),qc}function J(ae,le,Se,ot){Se=Se||[];var Bt=Se.every(function(Ye){return Ye==="number"}),Rt=le!=="string";return Rt&&Bt&&!ot?z(ae):function(){return K(ae,le,Se,arguments,ot)}}var Q=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(ae,le,Se){for(var ot=le+Se,Bt=le;ae[Bt]&&!(Bt>=ot);)++Bt;if(Bt-le>16&&ae.subarray&&Q)return Q.decode(ae.subarray(le,Bt));for(var Rt="";le<Bt;){var Ye=ae[le++];if(!(Ye&128)){Rt+=String.fromCharCode(Ye);continue}var Je=ae[le++]&63;if((Ye&224)==192){Rt+=String.fromCharCode((Ye&31)<<6|Je);continue}var Cn=ae[le++]&63;if((Ye&240)==224?Ye=(Ye&15)<<12|Je<<6|Cn:Ye=(Ye&7)<<18|Je<<12|Cn<<6|ae[le++]&63,Ye<65536)Rt+=String.fromCharCode(Ye);else{var qr=Ye-65536;Rt+=String.fromCharCode(55296|qr>>10,56320|qr&1023)}}return Rt}function re(ae,le){return ae?te(Ne,ae,le):""}function G(ae,le,Se,ot){if(!(ot>0))return 0;for(var Bt=Se,Rt=Se+ot-1,Ye=0;Ye<ae.length;++Ye){var Je=ae.charCodeAt(Ye);if(Je>=55296&&Je<=57343){var Cn=ae.charCodeAt(++Ye);Je=65536+((Je&1023)<<10)|Cn&1023}if(Je<=127){if(Se>=Rt)break;le[Se++]=Je}else if(Je<=2047){if(Se+1>=Rt)break;le[Se++]=192|Je>>6,le[Se++]=128|Je&63}else if(Je<=65535){if(Se+2>=Rt)break;le[Se++]=224|Je>>12,le[Se++]=128|Je>>6&63,le[Se++]=128|Je&63}else{if(Se+3>=Rt)break;le[Se++]=240|Je>>18,le[Se++]=128|Je>>12&63,le[Se++]=128|Je>>6&63,le[Se++]=128|Je&63}}return le[Se]=0,Se-Bt}function se(ae,le,Se){return G(ae,Ne,le,Se)}function oe(ae,le){we.set(ae,le)}function pe(ae,le){return ae%le>0&&(ae+=le-ae%le),ae}var ye,we,Ne,Me,Ue,qe,Ke,pt,ht;function at(ae){ye=ae,a.HEAP8=we=new Int8Array(ae),a.HEAP16=Me=new Int16Array(ae),a.HEAP32=qe=new Int32Array(ae),a.HEAPU8=Ne=new Uint8Array(ae),a.HEAPU16=Ue=new Uint16Array(ae),a.HEAPU32=Ke=new Uint32Array(ae),a.HEAPF32=pt=new Float32Array(ae),a.HEAPF64=ht=new Float64Array(ae)}var St=a.INITIAL_MEMORY||16777216,gt,Et=[],Pt=[],Ts=[],Sn=[],lr=!1;Pt.push({func:function(){Jp()}});function Mn(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)In(a.preRun.shift());Hs(Et)}function hs(){lr=!0,Hs(Pt)}function Gs(){Hs(Ts)}function Ns(){if(a.postRun)for(typeof a.postRun=="function"&&(a.postRun=[a.postRun]);a.postRun.length;)Tr(a.postRun.shift());Hs(Sn)}function In(ae){Et.unshift(ae)}function Tr(ae){Sn.unshift(ae)}var $n=0,Nr=null,Er=null;function ya(ae){$n++,a.monitorRunDependencies&&a.monitorRunDependencies($n)}function Lc(ae){if($n--,a.monitorRunDependencies&&a.monitorRunDependencies($n),$n==0&&(Nr!==null&&(clearInterval(Nr),Nr=null),Er)){var le=Er;Er=null,le()}}a.preloadedImages={},a.preloadedAudios={};function ur(ae){a.onAbort&&a.onAbort(ae),ae+="",O(ae),F=!0,U=1,ae="abort("+ae+"). Build with -s ASSERTIONS=1 for more info.";var le=new WebAssembly.RuntimeError(ae);throw i(le),le}function Hp(ae,le){return String.prototype.startsWith?ae.startsWith(le):ae.indexOf(le)===0}var M0="data:application/octet-stream;base64,";function jp(ae){return Hp(ae,M0)}var qp="file://";function Xp(ae){return Hp(ae,qp)}var ts="tfjs-backend-wasm.wasm";jp(ts)||(ts=x(ts));function Kp(ae){try{if(ae==ts&&$)return new Uint8Array($);if(w)return w(ae);throw"both async and sync fetching of the wasm failed"}catch(le){ur(le)}}function z0(){if(!$&&(h||f)){if(typeof fetch=="function"&&!Xp(ts))return fetch(ts,{credentials:"same-origin"}).then(function(ae){if(!ae.ok)throw"failed to load wasm binary file at '"+ts+"'";return ae.arrayBuffer()}).catch(function(){return Kp(ts)});if(b)return new Promise(function(ae,le){b(ts,function(Se){ae(new Uint8Array(Se))},le)})}return Promise.resolve().then(function(){return Kp(ts)})}function L0(){var ae={a:ns};function le(Ye,Je){var Cn=Ye.exports;a.asm=Cn,T=a.asm.h,at(T.buffer),gt=a.asm.Sa,Lc("wasm-instantiate")}ya("wasm-instantiate");function Se(Ye){le(Ye.instance)}function ot(Ye){return z0().then(function(Je){return WebAssembly.instantiate(Je,ae)}).then(Ye,function(Je){O("failed to asynchronously prepare wasm: "+Je),ur(Je)})}function Bt(){return!$&&typeof WebAssembly.instantiateStreaming=="function"&&!jp(ts)&&!Xp(ts)&&typeof fetch=="function"?fetch(ts,{credentials:"same-origin"}).then(function(Ye){var Je=WebAssembly.instantiateStreaming(Ye,ae);return Je.then(Se,function(Cn){return O("wasm streaming compile failed: "+Cn),O("falling back to ArrayBuffer instantiation"),ot(Se)})}):ot(Se)}if(a.instantiateWasm)try{var Rt=a.instantiateWasm(ae,le);return Rt}catch(Ye){return O("Module.instantiateWasm callback failed with error: "+Ye),!1}return Bt().catch(i),{}}function Hs(ae){for(;ae.length>0;){var le=ae.shift();if(typeof le=="function"){le(a);continue}var Se=le.func;typeof Se=="number"?le.arg===void 0?gt.get(Se)():gt.get(Se)(le.arg):Se(le.arg===void 0?null:le.arg)}}function Bc(){ur()}function ii(ae,le,Se){Ne.copyWithin(ae,le,le+Se)}function B0(){return Ne.length}function W0(ae){try{return T.grow(ae-ye.byteLength+65535>>>16),at(T.buffer),1}catch(le){}}function jr(ae){var le=B0(),Se=2147483648;if(ae>Se)return!1;for(var ot=1;ot<=4;ot*=2){var Bt=le*(1+.2/ot);Bt=Math.min(Bt,ae+100663296);var Rt=Math.min(Se,pe(Math.max(ae,Bt),65536)),Ye=W0(Rt);if(Ye)return!0}return!1}var li={mappings:{},buffers:[null,[],[]],printChar:function(ae,le){var Se=li.buffers[ae];le===0||le===10?((ae===1?R:O)(te(Se,0)),Se.length=0):Se.push(le)},varargs:void 0,get:function(){li.varargs+=4;var ae=qe[li.varargs-4>>2];return ae},getStr:function(ae){var le=re(ae);return le},get64:function(ae,le){return ae}};function V0(ae){return 0}function Zp(ae,le,Se,ot,Bt){}function U0(ae,le,Se,ot){for(var Bt=0,Rt=0;Rt<Se;Rt++){for(var Ye=qe[le+Rt*8>>2],Je=qe[le+(Rt*8+4)>>2],Cn=0;Cn<Je;Cn++)li.printChar(ae,Ne[Ye+Cn]);Bt+=Je}return qe[ot>>2]=Bt,0}function Yp(){return 28}var ns={a:Bc,d:ii,e:jr,f:V0,c:Zp,b:U0,g:Yp},G0=L0(),Jp=a.___wasm_call_ctors=function(){return(Jp=a.___wasm_call_ctors=a.asm.i).apply(null,arguments)},H0=a._init=function(){return(H0=a._init=a.asm.j).apply(null,arguments)},Qp=a._init_with_threads_count=function(){return(Qp=a._init_with_threads_count=a.asm.k).apply(null,arguments)},j0=a._get_threads_count=function(){return(j0=a._get_threads_count=a.asm.l).apply(null,arguments)},Kl=a._register_tensor=function(){return(Kl=a._register_tensor=a.asm.m).apply(null,arguments)},xa=a._dispose_data=function(){return(xa=a._dispose_data=a.asm.n).apply(null,arguments)},Wc=a._dispose=function(){return(Wc=a._dispose=a.asm.o).apply(null,arguments)},q0=a._Abs=function(){return(q0=a._Abs=a.asm.p).apply(null,arguments)},X0=a._Add=function(){return(X0=a._Add=a.asm.q).apply(null,arguments)},eh=a._AddN=function(){return(eh=a._AddN=a.asm.r).apply(null,arguments)},$e=a._All=function(){return($e=a._All=a.asm.s).apply(null,arguments)},K0=a._Any=function(){return(K0=a._Any=a.asm.t).apply(null,arguments)},Z0=a._ArgMax=function(){return(Z0=a._ArgMax=a.asm.u).apply(null,arguments)},Y0=a._AvgPool=function(){return(Y0=a._AvgPool=a.asm.v).apply(null,arguments)},J0=a._BatchMatMul=function(){return(J0=a._BatchMatMul=a.asm.w).apply(null,arguments)},Q0=a._Ceil=function(){return(Q0=a._Ceil=a.asm.x).apply(null,arguments)},ui=a._ClipByValue=function(){return(ui=a._ClipByValue=a.asm.y).apply(null,arguments)},eg=a._Conv2D=function(){return(eg=a._Conv2D=a.asm.z).apply(null,arguments)},tg=a._Conv2DBackpropInput=function(){return(tg=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},ng=a._Cos=function(){return(ng=a._Cos=a.asm.B).apply(null,arguments)},sg=a._Cosh=function(){return(sg=a._Cosh=a.asm.C).apply(null,arguments)},rg=a._CropAndResize=function(){return(rg=a._CropAndResize=a.asm.D).apply(null,arguments)},ag=a._Cumsum=function(){return(ag=a._Cumsum=a.asm.E).apply(null,arguments)},th=a._DepthToSpace=function(){return(th=a._DepthToSpace=a.asm.F).apply(null,arguments)},og=a._DepthwiseConv2dNative=function(){return(og=a._DepthwiseConv2dNative=a.asm.G).apply(null,arguments)},ig=a._Elu=function(){return(ig=a._Elu=a.asm.H).apply(null,arguments)},ba=a._Equal=function(){return(ba=a._Equal=a.asm.I).apply(null,arguments)},Vc=a._Exp=function(){return(Vc=a._Exp=a.asm.J).apply(null,arguments)},Uc=a._FlipLeftRight=function(){return(Uc=a._FlipLeftRight=a.asm.K).apply(null,arguments)},lg=a._Floor=function(){return(lg=a._Floor=a.asm.L).apply(null,arguments)},ug=a._FloorDiv=function(){return(ug=a._FloorDiv=a.asm.M).apply(null,arguments)},cg=a._FusedBatchNorm=function(){return(cg=a._FusedBatchNorm=a.asm.N).apply(null,arguments)},dg=a._FusedConv2D=function(){return(dg=a._FusedConv2D=a.asm.O).apply(null,arguments)},pg=a._FusedDepthwiseConv2D=function(){return(pg=a._FusedDepthwiseConv2D=a.asm.P).apply(null,arguments)},Ge=a._Gather=function(){return(Ge=a._Gather=a.asm.Q).apply(null,arguments)},hg=a._GatherNd=function(){return(hg=a._GatherNd=a.asm.R).apply(null,arguments)},fg=a._Greater=function(){return(fg=a._Greater=a.asm.S).apply(null,arguments)},mg=a._GreaterEqual=function(){return(mg=a._GreaterEqual=a.asm.T).apply(null,arguments)},gg=a._LeakyRelu=function(){return(gg=a._LeakyRelu=a.asm.U).apply(null,arguments)},Ag=a._Less=function(){return(Ag=a._Less=a.asm.V).apply(null,arguments)},yg=a._LessEqual=function(){return(yg=a._LessEqual=a.asm.W).apply(null,arguments)},Gc=a._Log=function(){return(Gc=a._Log=a.asm.X).apply(null,arguments)},nh=a._LogicalAnd=function(){return(nh=a._LogicalAnd=a.asm.Y).apply(null,arguments)},sh=a._Max=function(){return(sh=a._Max=a.asm.Z).apply(null,arguments)},xg=a._MaxPool=function(){return(xg=a._MaxPool=a.asm._).apply(null,arguments)},bg=a._Maximum=function(){return(bg=a._Maximum=a.asm.$).apply(null,arguments)},vg=a._Mean=function(){return(vg=a._Mean=a.asm.aa).apply(null,arguments)},wg=a._Min=function(){return(wg=a._Min=a.asm.ba).apply(null,arguments)},kg=a._Minimum=function(){return(kg=a._Minimum=a.asm.ca).apply(null,arguments)},Sg=a._MirrorPad=function(){return(Sg=a._MirrorPad=a.asm.da).apply(null,arguments)},Ig=a._Multiply=function(){return(Ig=a._Multiply=a.asm.ea).apply(null,arguments)},ut=a._Neg=function(){return(ut=a._Neg=a.asm.fa).apply(null,arguments)},Cg=a._NonMaxSuppressionV3=function(){return(Cg=a._NonMaxSuppressionV3=a.asm.ga).apply(null,arguments)},Tg=a._NonMaxSuppressionV4=function(){return(Tg=a._NonMaxSuppressionV4=a.asm.ha).apply(null,arguments)},Ng=a._NonMaxSuppressionV5=function(){return(Ng=a._NonMaxSuppressionV5=a.asm.ia).apply(null,arguments)},Zl=a._NotEqual=function(){return(Zl=a._NotEqual=a.asm.ja).apply(null,arguments)},rh=a._OneHot=function(){return(rh=a._OneHot=a.asm.ka).apply(null,arguments)},ah=a._PadV2=function(){return(ah=a._PadV2=a.asm.la).apply(null,arguments)},oh=a._Pow=function(){return(oh=a._Pow=a.asm.ma).apply(null,arguments)},Eg=a._Prelu=function(){return(Eg=a._Prelu=a.asm.na).apply(null,arguments)},Rg=a._Prod=function(){return(Rg=a._Prod=a.asm.oa).apply(null,arguments)},ih=a._RealDiv=function(){return(ih=a._RealDiv=a.asm.pa).apply(null,arguments)},$g=a._Relu=function(){return($g=a._Relu=a.asm.qa).apply(null,arguments)},_g=a._Relu6=function(){return(_g=a._Relu6=a.asm.ra).apply(null,arguments)},Dg=a._ResizeBilinear=function(){return(Dg=a._ResizeBilinear=a.asm.sa).apply(null,arguments)},Pg=a._Reverse=function(){return(Pg=a._Reverse=a.asm.ta).apply(null,arguments)},Fg=a._RotateWithOffset=function(){return(Fg=a._RotateWithOffset=a.asm.ua).apply(null,arguments)},lh=a._Round=function(){return(lh=a._Round=a.asm.va).apply(null,arguments)},va=a._Rsqrt=function(){return(va=a._Rsqrt=a.asm.wa).apply(null,arguments)},Og=a._ScatterNd=function(){return(Og=a._ScatterNd=a.asm.xa).apply(null,arguments)},Mg=a._SelectV2=function(){return(Mg=a._SelectV2=a.asm.ya).apply(null,arguments)},w5=a._Sigmoid=function(){return(w5=a._Sigmoid=a.asm.za).apply(null,arguments)},uh=a._Sin=function(){return(uh=a._Sin=a.asm.Aa).apply(null,arguments)},zg=a._Softmax=function(){return(zg=a._Softmax=a.asm.Ba).apply(null,arguments)},Lg=a._Sqrt=function(){return(Lg=a._Sqrt=a.asm.Ca).apply(null,arguments)},Bg=a._Square=function(){return(Bg=a._Square=a.asm.Da).apply(null,arguments)},Wg=a._SquaredDifference=function(){return(Wg=a._SquaredDifference=a.asm.Ea).apply(null,arguments)},Vg=a._Step=function(){return(Vg=a._Step=a.asm.Fa).apply(null,arguments)},Ug=a._StridedSlice=function(){return(Ug=a._StridedSlice=a.asm.Ga).apply(null,arguments)},Gg=a._Sub=function(){return(Gg=a._Sub=a.asm.Ha).apply(null,arguments)},Hg=a._Sum=function(){return(Hg=a._Sum=a.asm.Ia).apply(null,arguments)},jg=a._Tan=function(){return(jg=a._Tan=a.asm.Ja).apply(null,arguments)},qg=a._Tanh=function(){return(qg=a._Tanh=a.asm.Ka).apply(null,arguments)},Xg=a._Tile=function(){return(Xg=a._Tile=a.asm.La).apply(null,arguments)},Kg=a._TopK=function(){return(Kg=a._TopK=a.asm.Ma).apply(null,arguments)},Zg=a._Transform=function(){return(Zg=a._Transform=a.asm.Na).apply(null,arguments)},Yg=a._Transpose=function(){return(Yg=a._Transpose=a.asm.Oa).apply(null,arguments)},Jg=a.__FusedMatMul=function(){return(Jg=a.__FusedMatMul=a.asm.Pa).apply(null,arguments)},Qg=a._malloc=function(){return(Qg=a._malloc=a.asm.Qa).apply(null,arguments)},e2=a._free=function(){return(e2=a._free=a.asm.Ra).apply(null,arguments)},ch=a.stackSave=function(){return(ch=a.stackSave=a.asm.Ta).apply(null,arguments)},dh=a.stackRestore=function(){return(dh=a.stackRestore=a.asm.Ua).apply(null,arguments)},Hc=a.stackAlloc=function(){return(Hc=a.stackAlloc=a.asm.Va).apply(null,arguments)};a.cwrap=J;var Yl;function t2(ae){this.name="ExitStatus",this.message="Program terminated with exit("+ae+")",this.status=ae}Er=function ae(){Yl||jc(),Yl||(Er=ae)};function jc(ae){if(ae=ae||u,$n>0||(Mn(),$n>0))return;function le(){Yl||(Yl=!0,a.calledRun=!0,!F&&(hs(),Gs(),o(a),a.onRuntimeInitialized&&a.onRuntimeInitialized(),Ns()))}a.setStatus?(a.setStatus("Running..."),setTimeout(function(){setTimeout(function(){a.setStatus("")},1),le()},1)):le()}if(a.run=jc,a.preInit)for(typeof a.preInit=="function"&&(a.preInit=[a.preInit]);a.preInit.length>0;)a.preInit.pop()();return jc(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}}),GE=1e-7,HE=1e-4,td=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},ru=class{refCount(e){return qs("refCount")}incRef(e){return qs("incRef")}timerAvailable(){return!0}time(e){return qs("time")}read(e){return qs("read")}readSync(e){return qs("readSync")}numDataIds(){return qs("numDataIds")}disposeData(e,t){return qs("disposeData")}write(e,t,n){return qs("write")}move(e,t,n,s,r){return qs("move")}memory(){return qs("memory")}floatPrecision(){return qs("floatPrecision")}epsilon(){return this.floatPrecision()===32?GE:HE}dispose(){return qs("dispose")}};function qs(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function z5(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,yh(e,t,n)}function jE(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,s=0;for(;n>0;)s=Math.random()*n|0,n--,yh(e,n,s),yh(t,n,s)}function nd(e,t,n){return Math.max(e,Math.min(t,n))}function qE(e){return e%2==0?e:e+1}function yh(e,t,n){let s=e[t];e[t]=e[n],e[n]=s}function XE(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function KE(e,t){let n=Math.random();return t*n+(1-n)*e}function ZE(e,t){let n=0;for(let s=0;s<e.length;s++){let r=Number(e[s])-Number(t[s]);n+=r*r}return n}function M(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function Ln(e,t,n=""){M(Ta(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function pi(e){M(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function hi(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||Dn(e)&&!n)for(let s=0;s<e.length;++s)hi(e[s],t,n);else t.push(e);return t}function Ht(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 YE(e){return e.length===0}function Ta(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 mn(e){return e%1==0}function JE(e){if(Math.tanh!=null)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function QE(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function e9(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return z5(t),t}function sd(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function t9(e,t=s=>0,n){return new Promise((s,r)=>{let a=0,o=()=>{if(e()){s();return}a++;let i=t(a);if(n!=null&&a>=n){r();return}setTimeout(o,i)};o()})}function n9(e,t){let n=1,s=-1;for(let a=0;a<e.length;++a)if(e[a]>=0)n*=e[a];else if(e[a]===-1){if(s!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`);s=a}else if(e[a]<0)throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);if(s===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let r=e.slice();return r[s]=t/n,r}function Xs(e,t){let n=t.length;return e=e==null?t.map((s,r)=>r):[].concat(e),M(e.every(s=>s>=-n&&s<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),M(e.every(s=>mn(s)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(s=>s<0?n+s:s)}function L5(e,t){let n=[],s=[],r=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||r?null:Xs(t,e).sort(),o=0;for(let i=0;i<e.length;++i){if(a!=null){if(a[o]===i&&e[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);(a[o]==null||a[o]>i)&&e[i]===1&&(n.push(e[i]),s.push(i)),a[o]<=i&&o++}e[i]!==1&&(n.push(e[i]),s.push(i))}return{newShape:n,keptDims:s}}function B5(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 W5(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 V5(e,t){for(let n=0;n<e.length;n++){let s=e[n];if(isNaN(s)||!isFinite(s))throw Error(`A tensor of type ${t} being uploaded contains ${s}.`)}}function U5(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function s9(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function Dn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray}function i2(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function G5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Na(e){return typeof e=="string"||e instanceof String}function H5(e){return typeof e=="boolean"}function j5(e){return typeof e=="number"}function xh(e){return Array.isArray(e)?xh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray?"int32":j5(e)?"float32":Na(e)?"string":H5(e)?"bool":"float32"}function Ea(e){return!!(e&&e.constructor&&e.call&&e.apply)}function bh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function au(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let s=t-3;s>=0;--s)n[s]=n[s+1]*e[s+1];return n}function q5(e,t,n,s=!1){let r=new Array;if(t.length===1){let a=t[0]*(s?2:1);for(let o=0;o<a;o++)r[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,c)=>l*c)*(s?2:1);for(let l=0;l<a;l++)r[l]=q5(e+l*i,o,n,s)}return r}function ou(e,t,n=!1){if(e.length===0)return t[0];let s=e.reduce((r,a)=>r*a)*(n?2:1);if(s===0)return[];if(s!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return q5(0,e,t,n)}function l2(e,t){let n=vh(e,t);for(let s=0;s<n.length;s++)n[s]=1;return n}function vh(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 r9(e,t){let n=e.reduce((s,r)=>s*r,1);if(t==null||t==="float32")return ou(e,new Float32Array(n));if(t==="int32")return ou(e,new Int32Array(n));if(t==="bool")return ou(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function u2(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function a9(e,t,n){if(t===0)return 0;if(t===1)return e[0];let s=e[e.length-1];for(let r=0;r<e.length-1;++r)s+=n[r]*e[r];return s}function o9(e,t,n){if(t===0)return[];if(t===1)return[e];let s=new Array(t);for(let r=0;r<s.length-1;++r)s[r]=Math.floor(e/n[r]),e-=s[r]*n[r];return s[s.length-1]=e,s}function c2(e){return e&&e.then&&typeof e.then=="function"}var X5="tfjsflags",K5=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=i9,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&(Y().getBool("IS_TEST")||Y().getBool("PROD")||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 s=this.urlFlags[e];Y().getBool("IS_TEST")||Y().getBool("PROD")||console.warn(`Setting feature override from URL ${e}: ${s}.`),this.set(e,s)}}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(c2(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);X5 in e&&e[X5].split(",").forEach(n=>{let[s,r]=n.split(":");this.urlFlags[s]=u9(s,r)})}};function i9(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...s)=>(l9(t,s[0],s[1]),s.join("="))),t}function l9(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function u9(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 ${e}.`)}function Y(){return Rr}var Rr=null;function c9(e){Rr=e}var d2;function Z5(){if(d2==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");d2=e}return d2}function d9(){let e=Z5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function p2(e,t){let n=d9();if(n.has(e))return n.get(e);{let s=t();return n.set(e,s),n.get(e)}}var fi="Abs",iu="Acos",lu="Acosh",Kr="Add",Ra="AddN",uu="All",cu="Any",$a="ArgMax",du="ArgMin",pu="Asin",hu="Asinh",fu="Atan",mu="Atanh",gu="Atan2",_a="AvgPool",wh="AvgPoolGrad",rd="AvgPool3D",kh="AvgPool3DGrad",Da="BatchMatMul",mi="BatchToSpaceND",Sh="Bincount",Y5="BroadcastTo",Ih="BroadcastArgs",Pa="Cast",Fa="Ceil",Zr="ClipByValue",ad="Complex",od="ComplexAbs",gi="Concat",Oa="Conv2D",Ch="Conv2DBackpropFilter",Ma="Conv2DBackpropInput",id="Conv3D",Th="Conv3DBackpropFilterV2",Nh="Conv3DBackpropInputV2",za="Cos",La="Cosh",Ai="Cumsum",yi="CropAndResize",Eh="DenseBincount",xi="DepthToSpace",Ba="DepthwiseConv2dNative",Rh="DepthwiseConv2dNativeBackpropFilter",$h="DepthwiseConv2dNativeBackpropInput",_h="Diag",ld="Dilation2D",Dh="Dilation2DBackpropInput",Ph="Dilation2DBackpropFilter",Wa="RealDiv",ud="Einsum",Va="Elu",Fh="EluGrad",Au="Erf",bi="Equal",Ua="Exp",vi="ExpandDims",wi="Expm1",Oh="FFT",yu="Fill",ki="FlipLeftRight",Ga="Floor",Ha="FloorDiv",ja="FusedBatchNorm",Si="GatherV2",Ii="GatherNd",Ci="Greater",qa="GreaterEqual",Xa="Identity",Mh="IFFT",cd="Imag",xu="IsFinite",bu="IsInf",vu="IsNan",Ti="LeakyRelu",Ni="Less",Ei="LessEqual",zh="LinSpace",Ka="Log",wu="Log1p",Ri="LogicalAnd",ku="LogicalNot",dd="LogicalOr",J5="LogSoftmax",pd="LRN",Lh="LRNGrad",Za="Max",Ya="Maximum",Ja="MaxPool",Bh="MaxPoolGrad",hd="MaxPool3D",Wh="MaxPool3DGrad",Vh="MaxPoolWithArgmax",Qa="Mean",eo="Min",to="Minimum",no="MirrorPad",Su="Mod",Uh="Multinomial",so="Multiply",$i="Neg",_i="NotEqual",Di="NonMaxSuppressionV3",Iu="NonMaxSuppressionV4",Pi="NonMaxSuppressionV5",Fi="OnesLike",Oi="OneHot",Mi="Pack",ro="PadV2",p9="Pool",ao="Pow",oo="Prelu",zi="Prod",Cu="Range",fd="Real",Tu="Reciprocal",io="Relu",Li="Reshape",Nu="ResizeNearestNeighbor",Gh="ResizeNearestNeighborGrad",lo="ResizeBilinear",Hh="ResizeBilinearGrad",uo="Relu6",Bi="Reverse",Wi="Round",co="Rsqrt",Vi="ScatterNd",Ui="Select",Eu="Selu",Gi="Slice",po="Sin",Hi="Sinh",Ru="Sign",ho="Sigmoid",$u="Softplus",fo="Sqrt",mo="Sum",ji="SpaceToBatchND",qi="SplitV",go="Softmax",jh="SparseFillEmptyRows",qh="SparseReshape",Xh="SparseSegmentMean",Kh="SparseSegmentSum",md="SparseToDense",Ao="SquaredDifference",_u="Square",Xi="StridedSlice",gd="StringNGrams",Zh="StringSplit",Yh="StringToHashBucketFast",yo="Sub",Ki="Tan",xo="Tanh",Yr="Tile",Zi="TopK",Yi="Transform",bo="Transpose",Jh="Unique",Ji="Unpack",Ad="UnsortedSegmentSum",Qi="ZerosLike",vo="Step",yd="FromPixels",el="RotateWithOffset",wo="_FusedMatMul",ko="FusedConv2D",So="FusedDepthwiseConv2D";function Io(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.warn(...e)}function h9(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.log(...e)}var Du=p2("kernelRegistry",()=>new Map),xd=p2("gradRegistry",()=>new Map);function Qh(e,t){let n=f2(e,t);return Du.get(n)}function h2(e){return xd.get(e)}function Jr(e){let t=Du.entries(),n=[];for(;;){let{done:s,value:r}=t.next();if(s)break;let[a,o]=r,[i]=a.split("_");i===e&&n.push(o)}return n}function cr(e){let{kernelName:t,backendName:n}=e,s=f2(t,n);Du.has(s)&&Io(`The kernel '${t}' for backend '${n}' is already registered`),Du.set(s,e)}function Q5(e){let{kernelName:t}=e;xd.has(t)&&Y().getBool("DEBUG")&&Io(`Overriding the gradient for '${t}'`),xd.set(t,e)}function f9(e,t){let n=f2(e,t);if(!Du.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Du.delete(n)}function m9(e){if(!xd.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);xd.delete(e)}function g9(e,t){Jr(e).forEach(s=>{let r=Object.assign({},s,{backendName:t});cr(r)})}function f2(e,t){return`${t}_${e}`}var v={};Oe(v,{arraysEqual:()=>Ta,assert:()=>M,assertNonNegativeIntegerDimensions:()=>u2,assertNonNull:()=>pi,assertShapesMatch:()=>Ln,bytesFromStringArray:()=>G5,bytesPerElement:()=>i2,checkConversionForErrors:()=>V5,clamp:()=>nd,computeStrides:()=>au,createScalarValue:()=>w9,createShuffledIndices:()=>e9,decodeString:()=>nf,distSquared:()=>ZE,encodeString:()=>wd,fetch:()=>S9,fingerPrint64:()=>v9,flatten:()=>hi,getArrayFromDType:()=>W5,getTypedArrayFromDType:()=>B5,hasEncodingLoss:()=>s9,hexToLong:()=>bd,indexToLoc:()=>o9,inferDtype:()=>xh,inferFromImplicitShape:()=>n9,isBoolean:()=>H5,isFunction:()=>Ea,isInt:()=>mn,isNumber:()=>j5,isPromise:()=>c2,isScalarShape:()=>YE,isString:()=>Na,isTypedArray:()=>Dn,isValidDtype:()=>U5,locToIndex:()=>a9,makeOnesTypedArray:()=>l2,makeZerosNestedTypedArray:()=>r9,makeZerosTypedArray:()=>vh,nearestDivisor:()=>bh,nearestLargerEven:()=>qE,now:()=>vd,parseAxisParam:()=>Xs,randUniform:()=>KE,repeatedTry:()=>t9,rightPad:()=>sd,shuffle:()=>z5,shuffleCombo:()=>jE,sizeFromShape:()=>Ht,sizeToSquarishShape:()=>QE,squeezeShape:()=>L5,sum:()=>XE,swap:()=>yh,tanh:()=>JE,toNestedArray:()=>ou,toTypedArray:()=>tf});var e3=di($E()),tl=e3.default||e3;function bd(e){return tl.fromString(e,!0,16)}var t3=bd("c3a5c85c97cb3127"),nl=bd("b492b66fbe98f273"),Bn=bd("9ae16a3b2f90404f");function m2(e){return e.xor(e.shru(47))}function n3(e,t,n){let s=e.slice(t,t+n);return tl.fromBytes(Array.from(s),!0,!0)}function wt(e,t){return n3(e,t,8)}function s3(e,t){return n3(e,t,4)}function gn(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Co(e,t,n=bd("9ddfea08eb382d69")){let s=e.xor(t).mul(n);s=s.xor(s.shru(47));let r=t.xor(s).mul(n);return r=r.xor(r.shru(47)),r=r.mul(n),r}function A9(e,t,n,s,r,a){r=r.add(e),a=gn(a.add(r).add(s),21);let o=r;return r=r.add(t),r=r.add(n),a=a.add(gn(r,44)),[r.add(s),a.add(o)]}function ef(e,t,n,s){return A9(wt(e,t),wt(e,t+8),wt(e,t+16),wt(e,t+24),n,s)}function y9(e,t=e.length){if(t>=8){let n=Bn.add(t*2),s=wt(e,0).add(Bn),r=wt(e,t-8),a=gn(r,37).mul(n).add(s),o=gn(s,25).add(r).mul(n);return Co(a,o,n)}if(t>=4){let n=Bn.add(t*2),s=s3(e,0);return Co(s.shl(3).add(t),s3(e,t-4),n)}if(t>0){let n=e[0],s=e[t>>1],r=e[t-1],a=n+(s<<8),o=t+(r<<2);return m2(Bn.mul(a).xor(t3.mul(o))).mul(Bn)}return Bn}function x9(e,t=e.length){let n=Bn.add(t*2),s=wt(e,0).mul(nl),r=wt(e,8),a=wt(e,t-8).mul(n),o=wt(e,t-16).mul(Bn);return Co(gn(s.add(r),43).add(gn(a,30)).add(o),s.add(gn(r.add(Bn),18)).add(a),n)}function b9(e,t=e.length){let n=Bn.add(t*2),s=wt(e,0).mul(Bn),r=wt(e,8),a=wt(e,t-8).mul(n),o=wt(e,t-16).mul(Bn),i=gn(s.add(r),43).add(gn(a,30)).add(o),l=Co(i,s.add(gn(r.add(Bn),18)).add(a),n),c=wt(e,16).mul(n),u=wt(e,24),d=i.add(wt(e,t-32)).mul(n),p=l.add(wt(e,t-24)).mul(n);return Co(gn(c.add(u),43).add(gn(d,30)).add(p),c.add(gn(u.add(s),18)).add(d),n)}function v9(e,t=e.length){let n=tl.fromNumber(81,!0);if(t<=32)return t<=16?y9(e,t):x9(e,t);if(t<=64)return b9(e,t);let s=n,r=n.mul(nl).add(113),a=m2(r.mul(Bn).add(113)).mul(Bn),o=[tl.UZERO,tl.UZERO],i=[tl.UZERO,tl.UZERO];s=s.mul(Bn).add(wt(e,0));let l=0,c=(t-1>>6)*64,u=c+(t-1&63)-63;do s=gn(s.add(r).add(o[0]).add(wt(e,l+8)),37).mul(nl),r=gn(r.add(o[1]).add(wt(e,l+48)),42).mul(nl),s=s.xor(i[1]),r=r.add(o[0]).add(wt(e,l+40)),a=gn(a.add(i[0]),33).mul(nl),o=ef(e,l,o[1].mul(nl),s.add(i[0])),i=ef(e,l+32,a.add(i[1]),r.add(wt(e,l+16))),[a,s]=[s,a],l+=64;while(l!==c);let d=nl.add(a.and(255).shl(1));return l=u,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),s=gn(s.add(r).add(o[0]).add(wt(e,l+8)),37).mul(d),r=gn(r.add(o[1]).add(wt(e,l+48)),42).mul(d),s=s.xor(i[1].mul(9)),r=r.add(o[0].mul(9).add(wt(e,l+40))),a=gn(a.add(i[0]),33).mul(d),o=ef(e,l,o[1].mul(d),s.add(i[0])),i=ef(e,l+32,a.add(i[1]),r.add(wt(e,l+16))),[a,s]=[s,a],Co(Co(o[0],i[0],d).add(m2(r).mul(t3)).add(a),Co(o[1],i[1],d).add(s),d)}function w9(e,t){return t==="string"?wd(e):tf([e],t)}function k9(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function tf(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=hi(e)),Y().getBool("DEBUG")&&V5(e,t),k9(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 s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function vd(){return Y().platform.now()}function S9(e,t){return Y().platform.fetch(e,t)}function wd(e,t="utf-8"){return t=t||"utf-8",Y().platform.encode(e,t)}function nf(e,t="utf-8"){return t=t||"utf-8",Y().platform.decode(e,t)}var I9=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new T9)}profileKernel(e,t,n){let s,r=()=>{s=n()},a,o=vd();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(r);else{r();for(let l of s)l.dataSync();a=Promise.resolve({kernelMs:vd()-o})}if(Y().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<s.length;l++){let c=s[l];c.data().then(u=>{C9(u,c.dtype,e)})}return{kernelName:e,outputs:s,inputs:t,timeMs:a.then(l=>l.kernelMs),extraInfo:a.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:s,inputs:r,extraInfo:a}=e;n.forEach(o=>{Promise.all([o.data(),s,a]).then(i=>{this.logger.logKernelProfile(t,o,i[0],i[1],r,i[2])})})}};function C9(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){let r=e[s];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var T9=class{logKernelProfile(e,t,n,s,r,a){let o=typeof s=="number"?sd(`${s}ms`,9):s.error,i=sd(e,25),l=t.rank,c=t.size,u=sd(t.shape.toString(),14),d="";for(let p in r){let h=r[p];if(h!=null){let f=h.shape||t.shape,m=f.length;d+=`${p}: ${m}D ${m>0?f:""} `}}console.log(`%c${i} %c${o} %c${l}D ${u} %c${c} %c${d} %c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function N9(e,t,n){let s={},r={};for(let l=0;l<t.length;l++)s[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let d in u){let p=u[d],h=!1;for(let f=0;f<t.length;f++)if(s[p.id]){c.outputs.forEach(m=>s[m.id]=!0),h=!0,r[c.id]=!0;break}if(h)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let d=0;d<c.outputs.length;d++)if(a[c.outputs[d].id]){for(let p in u)a[u[p].id]=!0,o[c.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let c=e[l];if(r[c.id]&&o[c.id]){let u={};for(let p in c.inputs){let h=c.inputs[p];s[h.id]&&(u[p]=h)}let d=Object.assign({},c);d.inputs=u,d.outputs=c.outputs,i.push(d)}}return i}function E9(e,t,n,s){for(let r=t.length-1;r>=0;r--){let a=t[r],o=[];if(a.outputs.forEach(l=>{let c=e[l.id];c!=null?o.push(c):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let c=n(()=>i[l]());if(c.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${c.dtype}'`);let u=a.inputs[l];if(!Ta(c.shape,u.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${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 d=e[u.id];e[u.id]=s(d,c),d.dispose()}}}}var r3=20,kd=3,g2=7;function R9(e,t,n,s){let r=au(t),a=$9(e,t,n,r),o=t.length,i=sf(e,t,n,r,a),l=["Tensor"];return s&&(l.push(` dtype: ${n}`),l.push(` rank: ${o}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(i.map(c=>" "+c).join(`
|
|
`)),l.join(`
|
|
`)}function $9(e,t,n,s){let r=Ht(t),a=s[s.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?Id(e):e;if(i>1)for(let c=0;c<r/a;c++){let u=c*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],Sd(l[u+d],0,n).length)}return o}function Sd(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(g2))} + ${parseFloat(e[1].toFixed(g2))}j`:Na(e)?s=`'${e}'`:n==="bool"?s=a3(e):s=parseFloat(e.toFixed(g2)).toString(),sd(s,t)}function a3(e){return e===0?"false":"true"}function sf(e,t,n,s,r,a=!0){let o=n==="complex64"?2:1,i=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=Id(e);return[Sd(m[0],0,n)]}return n==="bool"?[a3(e[0])]:[e[0].toString()]}if(l===1){if(i>r3){let g=kd*o,A=Array.from(e.slice(0,g)),x=Array.from(e.slice((i-kd)*o,i*o));return n==="complex64"&&(A=Id(A),x=Id(x)),["["+A.map((y,b)=>Sd(y,r[b],n)).join(", ")+", ..., "+x.map((y,b)=>Sd(y,r[i-kd+b],n)).join(", ")+"]"]}let m=n==="complex64"?Id(e):Array.from(e);return["["+m.map((g,A)=>Sd(g,r[A],n)).join(", ")+"]"]}let c=t.slice(1),u=s.slice(1),d=s[0]*o,p=[];if(i>r3){for(let m=0;m<kd;m++){let g=m*d,A=g+d;p.push(...sf(e.slice(g,A),c,n,u,r,!1))}p.push("...");for(let m=i-kd;m<i;m++){let g=m*d,A=g+d;p.push(...sf(e.slice(g,A),c,n,u,r,m===i-1))}}else for(let m=0;m<i;m++){let g=m*d,A=g+d;p.push(...sf(e.slice(g,A),c,n,u,r,m===i-1))}let h=l===2?",":"";p[0]="["+p[0]+h;for(let m=1;m<p.length-1;m++)p[m]=" "+p[m]+h;let f=`,
|
|
`;for(let m=2;m<l;m++)f+=`
|
|
`;return p[p.length-1]=" "+p[p.length-1]+"]"+(a?"":f),p}function Id(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var nn=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Ht(e),n!=null){let s=n.length;M(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${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||W5(t,this.size),this.strides=au(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 s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return $r().makeTensor(this.values,this.shape,this.dtype)}},$r=null,Pu=null,_9=null;function D9(e){$r=e}function P9(e){Pu=e}function F9(e){_9=e}var Qe=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ht(e),this.strides=au(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Pu.buffer(this.shape,this.dtype,e)}bufferSync(){return Pu.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ou(this.shape,e,this.dtype==="complex64")}arraySync(){return ou(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=$r().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>nf(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=$r().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>nf(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await $r().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||($r().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Pu.print(this,e)}clone(){return this.throwIfDisposed(),Pu.clone(this)}toString(e=!1){let t=this.dataSync();return R9(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Pu.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),$r().makeVariable(this,e,t,n)}};Object.defineProperty(Qe,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function O9(){return p2("Tensor",()=>Qe)}O9();var Cd=class extends Qe{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Ta(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);$r().disposeTensor(this),this.dataId=e.dataId,$r().incRef(this,null)}dispose(){$r().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Cd,Symbol.hasInstance,{value:e=>e instanceof Qe&&e.assign!=null&&e.assign instanceof Function});var dr={};Oe(dr,{assertTypesMatch:()=>o3,getTensorsInContainer:()=>w2,isTensorInList:()=>z9,makeTypesMatch:()=>Ft});var A2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(A2||(A2={}));var y2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(y2||(y2={}));var x2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(x2||(x2={}));var b2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(b2||(b2={}));var v2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(v2||(v2={}));var M9={float32:b2,int32:y2,bool:x2,complex64:v2};function Wn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return M9[e][t]}function Td(e){return Wn(e,"int32")}function Ft(e,t){if(e.dtype===t.dtype)return[e,t];let n=Wn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function o3(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function z9(e,t){return t.some(n=>n.id===e.id)}function w2(e){let t=[],n=new Set;return i3(e,t,n),t}function i3(e,t,n){if(e==null)return;if(e instanceof Qe){t.push(e);return}if(!L9(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),i3(a,t,n))}}function L9(e){return Array.isArray(e)||typeof e=="object"}function k2(e){return e.kernelName!=null}var l3=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},S2=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new l3}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Io(`${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 I9(this.backendInstance),!0}setupRegisteredKernels(){Jr(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jr(e).forEach(n=>{n.disposeFunc!=null&&n.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 ru)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,Io(`Initialization of backend ${e} failed`),Io(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Io(`Initialization of backend ${e} failed`),Io(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),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 s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return S2.nextTensorId++}nextVariableId(){return S2.nextVariableId++}clone(e){let t=W.runKernel(Xa,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return W.runKernel(Pa,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Qh(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 s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=k2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(k2(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Qh(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,x);let y=x.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:k,dtype:I}=b;return this.makeTensorFromDataId(w,k,I)});if(s){let b=this.getTensorsForGradient(h,f,y);n=this.saveTensorsForBackwardMode(b)}return y}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:c,attrs:u}=e,d=k2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=h2(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,c)=>a[c]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&Na(e[0])&&(r=e.map(i=>wd(i)));let a=s.write(r,t,n),o=new Qe(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=G5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Qe(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new Cd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*i2(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 Cd||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*i2(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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=h2(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=vh(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return c}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}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=w2(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!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 r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(r instanceof Qe,()=>"The result y returned by f() must be a tensor.");let a=N9(this.state.activeTape,t,r);if(!s&&a.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 o={};o[r.id]=n==null?B9(r.shape):n,E9(o,a,l=>this.tidy(l),W9);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return M(Ea(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(o=>o instanceof Qe),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((o,i)=>{s[i]=o});let r=(o,i)=>(n=e(...t,i),M(n.value instanceof Qe,()=>"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),a=(o,i)=>{let l=n.gradFunc(o,i),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(d=>d instanceof Qe),()=>"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((d,p)=>{u[p]=()=>d}),u};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}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=vd(),n=await this.backend.time(e);return n.wallMs=vd()-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 l3;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}},I2=S2;I2.nextTensorId=0;I2.nextVariableId=0;function B9(e){let t=l2(Ht(e),"float32");return W.makeTensor(t,e,"float32")}function u3(){let e=Z5();if(e._tfengine==null){let t=new K5(e);e._tfengine=new I2(t)}return c9(e._tfengine.ENV),D9(()=>e._tfengine),e._tfengine}var W=u3();function W9(e,t){let n={a:e,b:t};return W.runKernel(Kr,n)}var Fu={};Oe(Fu,{isBrowser:()=>c3,isMobile:()=>G9,mockIsMobile:()=>U9});function V9(){return typeof navigator!="undefined"&&navigator!=null}var C2;function U9(e){C2=e}function G9(e){if(C2!==void 0)return C2;if(e||V9()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return n.userAgentData&&n.userAgentData.mobile}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 c3(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var pr=Y();pr.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.")});pr.registerFlag("IS_BROWSER",()=>c3());pr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");pr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));pr.registerFlag("PROD",()=>!1);pr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>pr.getBool("DEBUG"));pr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);pr.registerFlag("IS_TEST",()=>!1);pr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);pr.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function _r(e,t){let n=e;if(Dn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||Dn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&Y().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&d3(e,s,[]),s}function d3(e,t,n){if(n=n||[],!Array.isArray(e)&&!Dn(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 ${e.length} elements`);let s=t.slice(1);for(let r=0;r<e.length;++r)d3(e[r],s,n.concat(r))}function p3(e,t,n,s){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}}function D(e,t,n,s="numeric"){if(e instanceof Qe)return p3(s,e.dtype,t,n),e;let r=xh(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),p3(s,r,t,n),e==null||!Dn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=_r(e,r);!Dn(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?tf(e,r):hi(e,[],!0);return W.makeTensor(i,a,r)}function Nd(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>D(a,`${t}[${o}]`,n,s))}var h3="__op";function V(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],s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+h3;let r=(...a)=>{W.startScope(n);try{let o=s(...a);return c2(o)&&console.error("Cannot return a Promise inside of tidy."),W.endScope(o),o}catch(o){throw W.endScope(null),o}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function H9(e,t){let n=D(e,"real","complex"),s=D(t,"imag","complex");Ln(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);let r={real:n,imag:s};return W.runKernel(ad,r)}var To=V({complex_:H9});function No(e,t,n,s){if(s==null&&(s=xh(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!Dn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){u2(t);let r=Ht(t),a=Ht(n);M(r===a,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==Ht(t.slice(o)):!0;M(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!Dn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?tf(e,s):hi(e,[],!0),W.makeTensor(e,t,s)}function ct(e,t,n){let s=_r(e,n);return No(e,t,s,n)}var T2={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},rf=4;async function j9(e,t){let n=[],s=[],r=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<r.length;++o){let i=r[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let c={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async d=>{let p=await l.bytes(),h=p.reduce((g,A)=>g+A.length,0)+rf*p.length,f=new Uint8Array(h),m=0;for(let g=0;g<p.length;g++){let A=p[g],x=new Uint8Array(new Uint32Array([A.length]).buffer);f.set(x,m),m+=rf,f.set(A,m),m+=A.length}d(f)});s.push(u)}else s.push(l.data());t!=null&&(c.group=t),n.push(c)}let a=await Promise.all(s);return{data:q9(a),specs:n}}function f3(e,t){let n={},s,r=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,c=Ht(l),u;if("quantization"in a){let d=a.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${a.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(i!=="float32")throw new Error(`Weight ${a.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${i}.`)}else throw new Error(`Weight ${a.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let p=T2[d.dtype],h=e.slice(r,r+c*p),f=d.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){u=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];u[m]=g*d.scale+d.min}}else if(d.dtype==="float16")s===void 0&&(s=Q9()),u=s(f);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(i==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];u[m]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=c*p}else if(i==="string"){let d=Ht(a.shape);u=[];for(let p=0;p<d;p++){let h=new Uint32Array(e.slice(r,r+rf))[0];r+=rf;let f=new Uint8Array(e.slice(r,r+h));u.push(f),r+=h}}else{let d=T2[i],p=e.slice(r,r+c*d);if(i==="float32")u=new Float32Array(p);else if(i==="int32")u=new Int32Array(p);else if(i==="bool")u=new Uint8Array(p);else if(i==="complex64"){u=new Float32Array(p);let h=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let A=0;A<h.length;A++)h[A]=u[A*2],f[A]=u[A*2+1];let m=ct(h,l,"float32"),g=ct(f,l,"float32");n[o]=To(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=c*d}i!=="complex64"&&(n[o]=ct(u,l,i))}return n}function q9(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(a=>{if(t+=a.byteLength,n.push(a.byteLength===a.buffer.byteLength?a:new a.constructor(a)),!(a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`)});let s=new Uint8Array(t),r=0;return n.forEach(a=>{s.set(new Uint8Array(a.buffer),r),r+=a.byteLength}),s.buffer}var N2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function m3(e){return N2?Buffer.byteLength(e):new Blob([e]).size}function X9(e){if(N2)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let s=0,r=t.length;s<r;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function K9(e){if(N2){let s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function E2(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),s=0;return e.forEach(r=>{n.set(new Uint8Array(r),s),s+=r.byteLength}),n.buffer}function g3(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 A3(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:t};return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),n}async function R2(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};if(e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),e.weightsManifest!=null){let[s,r]=await t(e.weightsManifest);n.weightSpecs=s,n.weightData=r}return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),n}function Ed(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:m3(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:m3(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function Z9(){let e=n=>{let s=n<<13,r=0;for(;(s&8388608)==0;)r-=8388608,s<<=1;return s&=~8388608,r+=947912704,s|r},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function Y9(){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 J9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function Q9(){let e=Z9(),t=Y9(),n=J9();return s=>{let r=new ArrayBuffer(4*s.length),a=new Uint32Array(r);for(let o=0;o<s.length;o++){let i=s[o],l=e[n[i>>10]+(i&1023)]+t[i>>10];a[o]=l}return new Float32Array(r)}}var Wt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Wt.instance==null&&(Wt.instance=new Wt),Wt.instance}static registerSaveRouter(e){Wt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Wt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Wt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Wt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let s=[];return(t==="load"?Wt.getInstance().loadRouters:Wt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&s.push(o)}),s}},eR=e=>Wt.registerSaveRouter(e),tR=e=>Wt.registerLoadRouter(e),nR=e=>Wt.getSaveHandlers(e),sR=(e,t)=>Wt.getLoadHandlers(e,t),$2="tensorflowjs",_2=1,sl="models_store",Eo="model_info_store";function y3(){if(!Y().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 D2(e){let t=e.result;t.createObjectStore(sl,{keyPath:"modelPath"}),t.createObjectStore(Eo,{keyPath:"modelPath"})}var rl=class{constructor(e){if(this.indexedDB=y3(),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,s)=>{let r=this.indexedDB.open($2,_2);r.onupgradeneeded=()=>D2(r),r.onsuccess=()=>{let a=r.result;if(t==null){let o=a.transaction(sl,"readonly"),l=o.objectStore(sl).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=c=>(a.close(),s(l.error)),o.oncomplete=()=>a.close()}else{let o=Ed(t),i=a.transaction(Eo,"readwrite"),l=i.objectStore(Eo),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),u;c.onsuccess=()=>{u=a.transaction(sl,"readwrite");let p=u.objectStore(sl).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});p.onsuccess=()=>n({modelArtifactsInfo:o}),p.onerror=h=>{l=i.objectStore(Eo);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),s(p.error)),f.onerror=m=>(a.close(),s(p.error))}},c.onerror=d=>(a.close(),s(c.error)),i.oncomplete=()=>{u==null?a.close():u.oncomplete=()=>a.close()}}},r.onerror=a=>s(r.error)})}};rl.URL_SCHEME="indexeddb://";var x3=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(rl.URL_SCHEME)?rR(e.slice(rl.URL_SCHEME.length)):null;Wt.registerSaveRouter(x3);Wt.registerLoadRouter(x3);function rR(e){return new rl(e)}function aR(e){return e.startsWith(rl.URL_SCHEME)?e.slice(rl.URL_SCHEME.length):e}var oR=class{constructor(){this.indexedDB=y3()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open($2,_2);n.onupgradeneeded=()=>D2(n),n.onsuccess=()=>{let s=n.result,r=s.transaction(Eo,"readonly"),o=r.objectStore(Eo).getAll();o.onsuccess=()=>{let i={};for(let l of o.result)i[l.modelPath]=l.modelArtifactsInfo;e(i)},o.onerror=i=>(s.close(),t(o.error)),r.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=aR(e),new Promise((t,n)=>{let s=this.indexedDB.open($2,_2);s.onupgradeneeded=()=>D2(s),s.onsuccess=()=>{let r=s.result,a=r.transaction(Eo,"readwrite"),o=a.objectStore(Eo),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=o.delete(e),u=()=>{l=r.transaction(sl,"readwrite");let p=l.objectStore(sl).delete(e);p.onsuccess=()=>t(i.result.modelArtifactsInfo),p.onerror=h=>n(i.error)};c.onsuccess=u,c.onerror=d=>(u(),r.close(),n(i.error))}},i.onerror=c=>(r.close(),n(i.error)),a.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},s.onerror=r=>n(s.error)})}},Qr="/",Ou="tensorflowjs_models",b3="info",iR="model_topology",lR="weight_specs",uR="weight_data",cR="model_metadata";function v3(e){return{info:[Ou,e,b3].join(Qr),topology:[Ou,e,iR].join(Qr),weightSpecs:[Ou,e,lR].join(Qr),weightData:[Ou,e,uR].join(Qr),modelMetadata:[Ou,e,cR].join(Qr)}}function w3(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function dR(e){let t=e.split(Qr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Qr)}function pR(e){return e.startsWith(al.URL_SCHEME)?e.slice(al.URL_SCHEME.length):e}var al=class{constructor(e){if(!Y().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=v3(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Ed(e);try{this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,X9(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:s}}catch(r){throw w3(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let o=JSON.parse(r);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer),o.trainingConfig!=null&&(t.trainingConfig=o.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=K9(a),t}};al.URL_SCHEME="localstorage://";var k3=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(al.URL_SCHEME)?hR(e.slice(al.URL_SCHEME.length)):null;Wt.registerSaveRouter(k3);Wt.registerLoadRouter(k3);function hR(e){return new al(e)}var fR=class{constructor(){M(Y().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Ou+Qr,n=Qr+b3;for(let s=0;s<this.LS.length;++s){let r=this.LS.key(s);if(r.startsWith(t)&&r.endsWith(n)){let a=dR(r);e[a]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=pR(e);let t=v3(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return w3(t),n}},Mu="://",Es=class{constructor(){this.managers={}}static getInstance(){return Es.instance==null&&(Es.instance=new Es),Es.instance}static registerManager(e,t){M(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Mu)&&(e=e.slice(0,e.indexOf(Mu))),M(e.length>0,()=>"scheme must not be an empty string.");let n=Es.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 af(e){if(e.indexOf(Mu)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Es.getSchemes().join(",")}`);return{scheme:e.split(Mu)[0],path:e.split(Mu)[1]}}async function S3(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let s=Wt.getLoadHandlers(e);M(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),M(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);let r=s[0],a=Wt.getSaveHandlers(t);M(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),M(a.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);let o=a[0],i=af(e).scheme,l=af(e).path,c=i===af(e).scheme,u=await r.load();n&&c&&await Es.getManager(i).removeModel(l);let d=await o.save(u);return n&&!c&&await Es.getManager(i).removeModel(l),d.modelArtifactsInfo}async function mR(){let e=Es.getSchemes(),t={};for(let n of e){let s=await Es.getManager(n).listModels();for(let r in s){let a=n+Mu+r;t[a]=s[r]}}return t}async function gR(e){let t=af(e);return Es.getManager(t.scheme).removeModel(t.path)}async function AR(e,t){return S3(e,t,!1)}async function yR(e,t){return S3(e,t,!0)}var xR=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(Y().get("IS_BROWSER")){Y().setPlatform("browser",new xR);try{Es.registerManager(al.URL_SCHEME,new fR)}catch(e){}try{Es.registerManager(rl.URL_SCHEME,new oR)}catch(e){}}var bR={importFetch:()=>_E()},P2,vR=class{constructor(){this.util=DE(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Y().global.fetch!=null?Y().global.fetch(e,t):(P2==null&&(P2=bR.importFetch()),P2(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)}};Y().get("IS_NODE")&&Y().setPlatform("node",new vR);function ze(e,t="float32",n){return t=t||"float32",u2(e),new nn(e,t,n)}function wR(e,t){let n=D(e,"x","cast");if(!U5(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let s={x:n},r={dtype:t};return W.runKernel(Pa,s,r)}var me=V({cast_:wR});function kR(e){let n={x:D(e,"x","clone","string_or_numeric")};return W.runKernel(Xa,n)}var Vn=V({clone_:kR});function I3(e,t=!1){console.log(e.toString(t))}u3();var SR={buffer:ze,cast:me,clone:Vn,print:I3};P9(SR);var rs={};Oe(rs,{browserFiles:()=>$R,browserHTTPRequest:()=>OR,concatenateArrayBuffers:()=>E2,copyModel:()=>AR,decodeWeights:()=>f3,encodeWeights:()=>j9,fromMemory:()=>zR,getLoadHandlers:()=>sR,getModelArtifactsForJSON:()=>R2,getModelArtifactsInfoForJSON:()=>Ed,getSaveHandlers:()=>nR,http:()=>z2,isHTTPScheme:()=>M2,listModels:()=>mR,loadWeights:()=>_R,moveModel:()=>yR,registerLoadRouter:()=>tR,registerSaveRouter:()=>eR,removeModel:()=>gR,weightsLoaderFactory:()=>E3,withSaveHandler:()=>LR});var IR="model",CR=".json",TR=".weights.bin";function C3(e){return new Promise(t=>setTimeout(t)).then(e)}var F2=class{constructor(e){if(!Y().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(F2.URL_SCHEME)&&(e=e.slice(F2.URL_SCHEME.length)),(e==null||e.length===0)&&(e=IR),this.modelJsonFileName=e+CR,this.weightDataFileName=e+TR}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}],s=A3(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=r,await C3(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let o=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;o.download=this.weightDataFileName,o.href=t,await C3(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ed(e)}}}},of=F2;of.URL_SCHEME="downloads://";var NR=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.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let n=new FileReader;n.onload=s=>{let r=JSON.parse(s.target.result),a=r.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(r.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let i=R2(r,l=>this.loadWeights(l));e(i)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(e){let t=[],n=[];for(let a of e)t.push(...a.weights),n.push(...a.paths);let s=this.checkManifestAndWeightFiles(e),r=n.map(a=>this.loadWeightsFile(a,s[a]));return Promise.all(r).then(a=>[t,E2(a)])}loadWeightsFile(e,t){return new Promise((n,s)=>{let r=new FileReader;r.onload=a=>{let o=a.target.result;n(o)},r.onerror=a=>s(`Failed to weights data from file of path '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>g3(r.name)),s={};for(let r of e)r.paths.forEach(a=>{let o=g3(a);if(t.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(t.push(o),n.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);s[a]=this.weightsFiles[n.indexOf(o)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return s}},ER=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(of.URL_SCHEME)?RR(e.slice(of.URL_SCHEME.length)):null;Wt.registerSaveRouter(ER);function RR(e="model"){return new of(e)}function $R(e){return new NR(e)}function T3(e,t,n,s){o(e),n=n==null?0:n,s=s==null?1:s,i(n,s);let r=0,a=l=>(l.then(c=>{let u=n+ ++r/e.length*(s-n);return t(u),c}),l);function o(l){M(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(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(a))}async function N3(e,t){t==null&&(t={});let n=t.fetchFunc==null?Y().platform.fetch:t.fetchFunc,s=e.map(d=>n(d,t.requestInit,{isBinary:!0})),r=0,a=.5,i=(t.onProgress==null?await Promise.all(s):await T3(s,t.onProgress,r,a)).map(d=>d.arrayBuffer()),l=.5,c=1;return t.onProgress==null?await Promise.all(i):await T3(i,t.onProgress,l,c)}async function _R(e,t="",n,s){return E3(o=>N3(o,{requestInit:s}))(e,t,n)}function E3(e){return async(t,n="",s)=>{let r=t.map(()=>!1),a={},o=s!=null?s.map(()=>!1):[],i=[];if(t.forEach((h,f)=>{let m=0;h.weights.forEach(g=>{let A="quantization"in g?g.quantization.dtype:g.dtype,x=T2[A]*Ht(g.shape),y=()=>{r[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:g,groupOffset:m,sizeBytes:x})};s!=null?s.forEach((b,w)=>{b===g.name&&(y(),o[w]=!0)}):y(),i.push(g.name),m+=x})}),!o.every(h=>h)){let h=s.filter((f,m)=>!o[m]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
|
|
Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=r.reduce((h,f,m)=>(f&&h.push(m),h),[]),c=[];l.forEach(h=>{t[h].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;c.push(m)})});let u=await e(c),d={},p=0;return l.forEach(h=>{let f=t[h].paths.length,m=0;for(let b=0;b<f;b++)m+=u[p+b].byteLength;let g=new ArrayBuffer(m),A=new Uint8Array(g),x=0;for(let b=0;b<f;b++){let w=new Uint8Array(u[p+b]);A.set(w,x),x+=w.byteLength}a[h].forEach(b=>{let w=g.slice(b.groupOffset,b.groupOffset+b.sizeBytes),k=f3(w,[b.manifestEntry]);for(let I in k)d[I]=k[I]}),p+=f}),d}}var DR="application/octet-stream",PR="application/json",O2=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=Y().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}],s=A3(e,n);t.body.append("model.json",new Blob([JSON.stringify(s)],{type:PR}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:DR}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Ed(e),responses:[r]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(r){let a=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?a+=" 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.":a+=" Please make sure the server is serving valid JSON for this request.",new Error(a)}let n=t.modelTopology,s=t.weightsManifest;if(n==null&&s==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return R2(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,s]=FR(t),r=this.weightPathPrefix||n,a=[];for(let c of e)a.push(...c.weights);let o=[],i=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(u)):o.push(r+u+s);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await N3(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,E2(l)]}};O2.URL_SCHEME_REGEX=/^https?:\/\//;function FR(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),s=e.substring(0,t),r=n>t?e.substring(n):"";return[s+"/",r]}function M2(e){return e.match(O2.URL_SCHEME_REGEX)!=null}var R3=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(s=>M2(s)):n=M2(e),n)return z2(e,t)}return null};Wt.registerSaveRouter(R3);Wt.registerLoadRouter(R3);function z2(e,t){return new O2(e,t)}function OR(e,t){return z2(e,t)}var L2=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},MR=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function zR(e,t,n,s){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new L2(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 L2({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 L2({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:s}))}function LR(e){return new MR(e)}var $3={};Oe($3,{confusionMatrix:()=>GR});function BR(e,t,n=!1,s=!1){let r=D(e,"a","matMul"),a=D(t,"b","matMul");[r,a]=Ft(r,a);let o={a:r,b:a},i={transposeA:n,transposeB:s};return W.runKernel(Da,o,i)}var He=V({matMul_:BR});function WR(e,t,n=1,s=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:D(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:s};return W.runKernel(Oi,a,o)}var Rd=V({oneHot_:WR});function VR(e,t){let n=D(e,"x","transpose");if(t==null&&(t=n.shape.map((a,o)=>o).reverse()),M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(a=>{M(a>=0&&a<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let s={x:n},r={perm:t};return W.runKernel(bo,s,r)}var et=V({transpose_:VR});function UR(e,t,n){let s=D(e,"labels","confusionMatrix"),r=D(t,"predictions","confusionMatrix");M(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),M(s.rank===1,()=>`Expected the rank of labels to be 1, but got ${s.rank}`),M(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),M(s.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${s.shape[0]} vs. ${r.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 a=Rd(me(s,"int32"),n),o=Rd(me(r,"int32"),n),i=et(a),l=He(i,o);return me(l,"int32")}var GR=V({confusionMatrix_:UR}),ol={};Oe(ol,{assertAndGetBroadcastShape:()=>At,getBroadcastDims:()=>_3,getReductionAxes:()=>Kt});function _3(e,t){let n=e.length,s=[];for(let r=0;r<n;r++){let a=n-1-r,o=e[a]||1;(t[t.length-1-r]||1)>1&&o===1&&s.unshift(a)}return s}function Kt(e,t){let n=[];for(let s=0;s<t.length;s++){let r=e[e.length-s-1],a=t.length-s-1,o=t[a];(r==null||r===1&&o>1)&&n.unshift(a)}return n}function At(e,t){let n=[],s=Math.max(e.length,t.length);for(let r=0;r<s;r++){let a=e[e.length-r-1];a==null&&(a=1);let o=t[t.length-r-1];if(o==null&&(o=1),a===1)n.unshift(o);else if(o===1)n.unshift(a);else if(a!==o){let i=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(i)}else n.unshift(a)}return n}var Ks={};Oe(Ks,{fromPixels:()=>YR,fromPixelsAsync:()=>KR,toPixels:()=>ZR});function D3(e,t,n){if(pi(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let s=_r(e,n);if(s.length!==3&&s.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return No(e,t,s,n)}var il;function P3(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,s=!1,r=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)s=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)a=!0;else if(e.getContext!=null)o=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)i=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(r){let f=2;if(r&&e.readyState<f)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(Qh(yd,W.backendName)!=null){let f={pixels:e},m={numChannels:t};return W.runKernel(yd,f,m)}let[c,u]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;if(o)d=e.getContext("2d").getImageData(0,0,c,u).data;else if(s||n)d=e.data;else if(a||r||i){if(il==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")il=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else il=document.createElement("canvas").getContext("2d");il.canvas.width=c,il.canvas.height=u,il.drawImage(e,0,0,c,u),d=il.getImageData(0,0,c,u).data}let p;if(t===4)p=new Int32Array(d);else{let f=c*u;p=new Int32Array(f*t);for(let m=0;m<f;m++)for(let g=0;g<t;++g)p[m*t+g]=d[m*4+g]}return D3(p,[u,c,t],"int32")}function HR(e){return e!=null&&e.data instanceof Uint8Array}function jR(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function qR(e){return e!=null&&e.width!==0&&e.height!==0}function XR(e){return jR()&&!(e instanceof ImageBitmap)&&qR(e)&&!HR(e)}async function KR(e,t=3){let n=null;if(Y().getBool("WRAP_TO_IMAGEBITMAP")&&XR(e)){let s;try{s=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){s=null}s!=null&&s.width===e.width&&s.height===e.height?n=s:n=e}else n=e;return P3(n,t)}async function ZR(e,t){let n=D(e,"img","toPixels");if(!(e instanceof Qe)){let c=n;n=me(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[s,r]=n.shape.slice(0,2),a=n.rank===2?1:n.shape[2];if(a>4||a===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let o=await n.data(),i=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*s*4);for(let c=0;c<s*r;++c){let u=[0,0,0,255];for(let p=0;p<a;p++){let h=o[c*a+p];if(n.dtype==="float32"){if(h<0||h>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`)}else if(n.dtype==="int32"&&(h<0||h>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);a===1?(u[0]=h*i,u[1]=h*i,u[2]=h*i):u[p]=h*i}let d=c*4;l[d+0]=Math.round(u[0]),l[d+1]=Math.round(u[1]),l[d+2]=Math.round(u[2]),l[d+3]=Math.round(u[3])}if(t!=null){t.width=r,t.height=s;let c=t.getContext("2d"),u=new ImageData(l,r,s);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var YR=V({fromPixels_:P3}),B2={};Oe(B2,{prepareAndValidate:()=>F3});function F3(e,t){let n=e.shape.length,s=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(s<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${s}.`);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[s-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[s-1]} vs. ${n}`);if(Ht(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,a=r[r.length-1],o=1;for(let d=0;d<r.length-1;++d)o*=r[d];let i=e.shape,l=r.slice();l.pop();let c=1;for(let d=a;d<n;++d)c*=i[d],l.push(i[d]);let u=[...au(e.shape).map(d=>d/c),1].slice(0,a);return[l,o,c,u]}var W2={};Oe(W2,{calculateShapes:()=>O3,validateInput:()=>U2,validateUpdateShape:()=>V2});function V2(e,t,n){let s=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(a+` update.rank < ${r}. `);if(e.length<s+(n.rank-r))throw new Error(a+` Output shape length < ${s+(n.rank-r)}`);if(n.rank!==r+e.length-s)throw new Error(a+` update.rank != ${r+e.length-s}`);for(let o=0;o<r;++o)if(n.shape[o]!==t.shape[o])throw new Error(a+` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);for(let o=0;o<n.rank-r;++o)if(n.shape[o+r]!==e[o+s])throw new Error(a+` updates.shape[${o+r}] (${n.shape[o+r]}) != shape[${o+r}] (${e[o+r]})`)}function U2(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: ${e.shape}`)}V2(n,t,e)}function O3(e,t,n){let s=t.shape.length,r=s>1?t.shape[s-1]:1,a=n.length,o=1;for(let d=r;d<a;++d)o*=n[d];let i=r<1?1:r,l=Ht(t.shape)/i,c=[...au(n.slice(0,r)),1],u=Ht(n);return{sliceRank:r,numUpdates:l,sliceSize:o,strides:c,outputSize:u}}var Ot={};Oe(Ot,{assertParamsValid:()=>QR,computeFlatOffset:()=>r$,computeOutShape:()=>t$,getNormalizedAxes:()=>n$,isSliceContinous:()=>s$,maskToAxes:()=>e$,parseSliceParams:()=>H3,sliceInfo:()=>a$,startForAxis:()=>U3,startIndicesWithElidedDims:()=>B3,stopForAxis:()=>G3,stopIndicesWithElidedDims:()=>W3,stridesForAxis:()=>V3,stridesWithElidedDims:()=>M3});var G2=-2,JR=-1;function QR(e,t,n){let s=e.shape.length;M(s===t.length,()=>`Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`),M(s===n.length,()=>`Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);for(let r=0;r<s;++r)M(t[r]+n[r]<=e.shape[r],()=>`Error in slice${s}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function e$(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function t$(e,t,n){let s=[];for(let r=0;r<e.length;r++)s[r]=Math.ceil((t[r]-e[r])/n[r]);return s}function M3(e,t,n,s){let r=[...e];for(let a=r.length;a<s.length;a++)r.push(1);for(let a=0;a<n;a++)a===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function z3(e,t,n){return n<=e?n:n-(t-1)}function L3(e,t){let n=[];for(let s=0;s<e;s++)n.push(t+s);return n}function n$(e,t,n,s,r,a,o,i,l){let c=e.length,u=new Array(c),d=new Array(c),p=new Array(c);if(t.length&&n>0){let h=t[0],f=n+1;u=B3(o,h,f,s,e),d=W3(i,h,f,r,e),p=M3(a,h,f,e)}else for(let h=0;h<c;h++)u[h]=U3(o,s,a,e,h,l),d[h]=G3(i,r,a,e,h,l),p[h]=V3(a,h,l);return{begin:u,end:d,strides:p}}function B3(e,t,n,s,r){let a=[...r],o=L3(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=z3(t,n,i),c=s[l];e&1<<l&&(c=0),a[i]=c}return a}function W3(e,t,n,s,r){let a=[...r],o=L3(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=z3(t,n,i),c=s[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),a[i]=c}for(let i=0;i<a.length;i++){let l=r[i];a[i]<0&&(a[i]+=l),a[i]=nd(0,a[i],r[i])}return a}function V3(e,t,n){let s=e[t];return(n&1<<t||s==null)&&(s=1),s}function U3(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MIN_SAFE_INTEGER:o=Number.MAX_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),o=nd(0,o,l-1),o}function G3(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MAX_SAFE_INTEGER:o=Number.MIN_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),i>0?o=nd(0,o,l):o=nd(-1,o,l-1),o}function s$(e,t,n){let s=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){s=r;break}for(let r=s+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function r$(e,t){let n=e.length>0?e[e.length-1]:1;for(let s=0;s<e.length-1;s++)n+=e[s]*t[s];return n}function H3(e,t,n){let s,r=e.shape.length;typeof t=="number"?s=[t,...new Array(r-1).fill(0)]:t.length<r?s=t.concat(new Array(r-t.length).fill(0)):s=t.slice(),s.forEach(o=>{M(o!==-1,()=>"slice() does not support negative begin indexing.")});let a;return n==null?a=new Array(r).fill(-1):typeof n=="number"?a=[n,...new Array(r-1).fill(-1)]:n.length<r?a=n.concat(new Array(r-n.length).fill(-1)):a=n,a=a.map((o,i)=>o>=0?o:(M(o===-1,()=>`Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`),e.shape[i]-s[i])),[s,a]}function a$(e,t,n,s,r,a,o,i,l){let c;if(s==null?(c=new Array(t.length),c.fill(1)):c=s,o!=null&&(o&o-1)!=0)throw new Error("Multiple ellipses in slice is not allowed.");let u=!1,d={dims:c.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:n.slice(),strides:c.slice(),beginMask:r,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};for(let y=0;y<d.dims;y++)u&&(1<<y&i)!=0&&d.numAddAxisAfterEllipsis++,1<<y&o&&(u=!0);u||(d.ellipsisMask|=1<<d.dims,d.dims++);let p={dims:e.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};o$(d,p);let h=!0,f=!0,m=!0,g=[],A=[];for(let y=0;y<e.length;++y){if(p.strides[y]===0)throw Error(`strides[${y}] must be non-zero`);let b=!!(p.shrinkAxisMask&1<<y),w=e[y];if(w===-1){g.push(b?1:-1);continue}let k=[p.beginMask&1<<y,p.endMask&1<<y],I=[p.strides[y]>0?0:-1,p.strides[y]>0?w:w-1];if(b&&p.strides[y]<=0)throw Error("only stride 1 allowed on non-range indexing.");m=m&&p.strides[y]===1;let N=!!(p.beginMask&1<<y&&p.endMask&1<<y);if(p.beginValid&&p.endValid){if(b){let P=p.begin[y]<0?w+p.begin[y]:p.begin[y];if(p.begin[y]=P,p.end[y]=p.begin[y]+1,P<0||P>=w)throw Error(`slice index ${p.begin[y]} of dimension ${y} out of bounds.`)}else p.begin[y]=j3(p.begin[y],0,p.strides[y],w,k,I),p.end[y]=j3(p.end[y],1,p.strides[y],w,k,I);let $=p.strides[y]===1&&p.begin[y]===0&&p.end[y]===w;h=h&&$,f=f&&(y===0&&p.strides[y]===1||$)}else h=h&&p.strides[y]===1&&N,f=f&&(y===0&&p.strides[y]===1||N);let R,O=!1;if(p.beginValid&&p.endValid?(R=p.end[y]-p.begin[y],O=!0):b?(R=1,O=!0):N&&w>=0&&(p.strides[y]<0?R=-w:R=w,O=!0),O){let $;R===0||R<0!=p.strides[y]<0?$=0:$=Math.trunc(R/p.strides[y])+(R%p.strides[y]!=0?1:0),g.push($)}else g.push(-1)}for(let y=0;y<p.finalShapeGatherIndices.length;++y){let b=p.finalShapeGatherIndices[y];b>=0?A.push(g[b]):b===G2&&A.push(1)}return{finalShapeSparse:A.filter((y,b)=>p.finalShapeGatherIndices[b]!==G2),finalShape:A,isIdentity:h,sliceDim0:f,isSimpleSlice:m,begin:p.begin,end:p.end,strides:p.strides}}function o$(e,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let n=0;t.beginValid=e.begin!=null,t.endValid=e.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let s=0;s<e.dims;s++)if(1<<s&e.ellipsisMask){let r=Math.min(t.dims-(e.dims-s)+1+e.numAddAxisAfterEllipsis,t.dims);for(;n<r;n++)t.begin[n]=0,t.end[n]=0,t.strides[n]=1,t.beginMask|=1<<n,t.endMask|=1<<n,t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[n]=s}else if(1<<s&e.newAxisMask)t.finalShapeGatherIndices.push(G2),t.finalShapeGatherIndicesSparse.push(-1);else{if(n===t.begin.length)throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);e.begin!=null&&(t.begin[n]=e.begin[s]),e.end!=null&&(t.end[n]=e.end[s]),t.strides[n]=e.strides[s],e.beginMask&1<<s&&(t.beginMask|=1<<n),e.endMask&1<<s&&(t.endMask|=1<<n),e.shrinkAxisMask&1<<s?(t.finalShapeGatherIndices.push(JR),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<n):(t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(s)),t.inputShapeGatherIndicesSparse[n]=s,n++}}function j3(e,t,n,s,r,a){if(r[t])return n>0?a[t]:a[t+1&1];{let o=e<0?s+e:e;return o<a[0]?a[0]:o>a[1]?a[1]:o}}var ce={};Oe(ce,{Serializable:()=>q3,SerializationMap:()=>ll,registerClass:()=>Ro});var q3=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ll=class{constructor(){this.classNameMap={}}static getMap(){return ll.instance==null&&(ll.instance=new ll),ll.instance}static register(e){ll.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Ro(e){M(e.className!=null,()=>"Class being registered does not have the static className property defined."),M(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),M(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ll.register(e)}var X3={};Oe(X3,{TEST_EPSILON_FLOAT16:()=>K3,encodeStrings:()=>Z3,expectArrayBuffersEqual:()=>h$,expectArraysClose:()=>l$,expectArraysEqual:()=>c$,expectNumbersClose:()=>d$,expectPromiseToFail:()=>u$,expectValuesInRange:()=>p$,testEpsilon:()=>H2});var i$=.001,K3=.1;function l$(e,t,n){return n==null&&(n=H2()),j2(e,t,(s,r)=>q2(s,r,n))}function H2(){return W.backend.floatPrecision()===32?i$:K3}function j2(e,t,n){let s=!0;if((Dn(e)||Dn(t))&&(s=!1),Dn(e)&&Dn(t)&&(s=!0),s){let o=e.constructor.name,i=t.constructor.name;if(o!==i)throw new Error(`Arrays are of different type. Actual: ${o}. Expected: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=_r(e),i=_r(t);if(!Ta(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let r=Dn(e)?e:hi(e),a=Dn(t)?t:hi(t);if(r.length!==a.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
|
|
Actual: ${r}.
|
|
Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=r[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
|
|
Actual: ${r}.
|
|
Expected: ${a}.`)}}function u$(e,t){e().then(()=>t.fail(),()=>t())}function c$(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Na(e)||Na(e[0])||Na(t)||Na(t[0])?j2(e,n,(s,r)=>s==r):j2(e,t,(s,r)=>q2(s,r,0))}function d$(e,t,n){if(n==null&&(n=H2()),!q2(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function q2(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function p$(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function h$(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function Z3(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?Z3(n):e[t]=wd(n)}return e}function Y3(){Y().set("PROD",!0)}function f$(){Y().set("DEBUG",!0)}function m$(){Y().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function X2(e){Y().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}F9(X2);function g$(){W.disposeVariables()}function as(){return W}function lf(){return W.memory()}function A$(e){return W.profile(e)}function X(e,t){return W.tidy(e,t)}function ne(e){w2(e).forEach(n=>n.dispose())}function An(e){return W.keep(e)}function y$(e){return W.time(e)}function J3(e){return W.setBackend(e)}function uf(){return W.ready()}function Rs(){return W.backendName}function x$(e){W.removeBackend(e)}function K2(e){return W.findBackend(e)}function b$(e){return W.findBackendFactory(e)}function ul(e,t,n=1){return W.registerBackend(e,t,n)}function Dr(){return W.backend}function v$(e,t){Y().setPlatform(e,t)}function w$(e,t){let n=D(e,"a","add"),s=D(t,"b","add");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(Kr,r)}var ue=V({add_:w$});function k$(e,t){let n=D(e,"a","floorDiv"),s=D(t,"b","floorDiv");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(Ha,r)}var cf=V({floorDiv_:k$});function S$(e,t){let n=D(e,"a","div"),s=D(t,"b","div");if([n,s]=Ft(n,s),n.dtype==="int32"&&s.dtype==="int32")return cf(n,s);let r={a:n,b:s},a={};return W.runKernel(Wa,r,a)}var ge=V({div_:S$});function I$(e,t){let n=D(e,"a","mul"),s=D(t,"b","mul");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(so,r)}var L=V({mul_:I$});function C$(e){let t=D(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return W.runKernel(od,n)}else{let n={x:t};return W.runKernel(fi,n)}}var sn=V({abs_:C$});function T$(e){let n={x:D(e,"x","acos")};return W.runKernel(iu,n)}var Q3=V({acos_:T$});function N$(e){let n={x:D(e,"x","acosh")};return W.runKernel(lu,n)}var ev=V({acosh_:N$});function E$(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 ${e.length}`);let t=e.map((r,a)=>D(r,`tensors${a}`,"addN")),n=t[0];t.forEach(r=>{if(r.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(r=>{if(!Ta(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let s=t;return W.runKernel(Ra,s)}var df=V({addN_:E$});function R$(e,t=null,n=!1){let r={x:D(e,"x","all","bool")},a={axis:t,keepDims:n};return W.runKernel(uu,r,a)}var Z2=V({all_:R$});function $$(e,t=null,n=!1){let r={x:D(e,"x","any","bool")},a={axis:t,keepDims:n};return W.runKernel(cu,r,a)}var pf=V({any_:$$});function _$(e,t=0){let s={x:D(e,"x","argMax")},r={axis:t};return W.runKernel($a,s,r)}var Zs=V({argMax_:_$});function D$(e,t=0){let s={x:D(e,"x","argMin")},r={axis:t};return W.runKernel(du,s,r)}var tv=V({argMin_:D$});function P$(e){let n={x:D(e,"x","asin")};return W.runKernel(pu,n)}var nv=V({asin_:P$});function F$(e){let n={x:D(e,"x","asinh")};return W.runKernel(hu,n)}var sv=V({asinh_:F$});function O$(e){let n={x:D(e,"x","atan")};return W.runKernel(fu,n)}var rv=V({atan_:O$});function M$(e,t){let n=D(e,"a","atan2"),s=D(t,"b","atan2");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(gu,r)}var av=V({atan2_:M$});function z$(e){let n={x:D(e,"x","atanh")};return W.runKernel(mu,n)}var ov=V({atanh_:z$});function L$(e,t,n,s,r="NHWC",a){let o=e[3],i=[...t,o],l=uv(r);return $d(e,i,n,a,s,null,null,l)}function iv(e,t,n,s,r,a,o="channelsLast"){let[i,l]=hf(t),c;if(o==="channelsLast")c=[i,l,e[3],e[3]];else if(o==="channelsFirst")c=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return $d(e,c,n,s,r,a,!1,o)}function B$(e,t,n,s,r,a,o="NDHWC"){let[i,l,c]=J2(t),u,d;if(o==="NDHWC")d="channelsLast",u=[i,l,c,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",u=[i,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return lv(e,u,n,s,r,!1,d,a)}function $d(e,t,n,s,r,a,o=!1,i="channelsLast"){let[l,c,u,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,d]=e;else if(i==="channelsFirst")[l,d,c,u]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,h,,f]=t,[m,g]=hf(n),[A,x]=hf(s),y=zu(p,A),b=zu(h,x),{padInfo:w,outHeight:k,outWidth:I}=U$(r,c,u,m,g,y,b,a,i),N=o?f*d:f,R;return i==="channelsFirst"?R=[l,N,k,I]:i==="channelsLast"&&(R=[l,k,I,N]),{batchSize:l,dataFormat:i,inHeight:c,inWidth:u,inChannels:d,outHeight:k,outWidth:I,outChannels:N,padInfo:w,strideHeight:m,strideWidth:g,filterHeight:p,filterWidth:h,effectiveFilterHeight:y,effectiveFilterWidth:b,dilationHeight:A,dilationWidth:x,inShape:e,outShape:R,filterShape:t}}function lv(e,t,n,s,r,a=!1,o="channelsLast",i){let[l,c,u,d,p]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,d,p]=e;else if(o==="channelsFirst")[l,p,c,u,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[h,f,m,,g]=t,[A,x,y]=J2(n),[b,w,k]=J2(s),I=zu(h,b),N=zu(f,w),R=zu(m,k),{padInfo:O,outDepth:$,outHeight:P,outWidth:T}=G$(r,c,u,d,A,x,y,I,N,R,i),F=a?g*p:g,U;return o==="channelsFirst"?U=[l,F,$,P,T]:o==="channelsLast"&&(U=[l,$,P,T,F]),{batchSize:l,dataFormat:o,inDepth:c,inHeight:u,inWidth:d,inChannels:p,outDepth:$,outHeight:P,outWidth:T,outChannels:F,padInfo:O,strideDepth:A,strideHeight:x,strideWidth:y,filterDepth:h,filterHeight:f,filterWidth:m,effectiveFilterDepth:I,effectiveFilterHeight:N,effectiveFilterWidth:R,dilationDepth:b,dilationHeight:w,dilationWidth:k,inShape:e,outShape:U,filterShape:t}}function W$(e,t,n,s,r){s==null&&(s=Y2(e,t,n));let a=e[0],o=e[1],i=cl((a-t+2*s)/n+1,r),l=cl((o-t+2*s)/n+1,r);return[i,l]}function V$(e,t,n,s,r,a){r==null&&(r=Y2(e,t,s));let o=e[0],i=e[1],l=e[2],c=cl((o-t+2*r)/s+1,a),u=cl((i-t+2*r)/s+1,a),d=cl((l-t+2*r)/s+1,a);return[c,u,d,n]}function Y2(e,t,n,s=1){let r=zu(t,s);return Math.floor((e[0]*(n-1)-n+r)/2)}function hf(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function J2(e){return typeof e=="number"?[e,e,e]:e}function zu(e,t){return t<=1?e:e+(e-1)*(t-1)}function U$(e,t,n,s,r,a,o,i,l){let c,u,d;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let h=W$([t,n],a,s,e,i);u=h[0],d=h[1]}else if(e==="same"){u=Math.ceil(t/s),d=Math.ceil(n/r);let p=Math.max(0,(u-1)*s+a-t),h=Math.max(0,(d-1)*r+o-n),f=Math.floor(p/2),m=p-f,g=Math.floor(h/2),A=h-g;c={top:f,bottom:m,left:g,right:A,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=Math.ceil((t-a+1)/s),d=Math.ceil((n-o+1)/r);else if(typeof e=="object"){let p=l==="channelsLast"?e[1][0]:e[2][0],h=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:p,bottom:h,left:f,right:m,type:p===0&&h===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=cl((t-a+p+h)/s+1,i),d=cl((n-o+f+m)/r+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:d}}function G$(e,t,n,s,r,a,o,i,l,c,u){let d,p,h,f;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let g=V$([t,n,s,1],i,1,r,e,u);p=g[0],h=g[1],f=g[2]}else if(e==="same"){p=Math.ceil(t/r),h=Math.ceil(n/a),f=Math.ceil(s/o);let m=(p-1)*r+i-t,g=(h-1)*a+l-n,A=(f-1)*o+c-s,x=Math.floor(m/2),y=m-x,b=Math.floor(g/2),w=g-b,k=Math.floor(A/2),I=A-k;d={top:b,bottom:w,left:k,right:I,front:x,back:y,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-i+1)/r),h=Math.ceil((n-l+1)/a),f=Math.ceil((s-c+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:p,outHeight:h,outWidth:f}}function cl(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 $o(e){let[t,n,s]=hf(e);return t===1&&n===1&&s===1}function Pr(e,t){return $o(e)||$o(t)}function uv(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function H$(e,t){let s={x:D(e,"x","reshape","string_or_numeric")},r={shape:t};return W.runKernel(Li,s,r)}var H=V({reshape_:H$});function j$(e,t,n,s,r){let a=D(e,"x","avgPool","float32"),o=1;M(Pr(n,o),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);let i=a,l=!1;a.rank===3&&(l=!0,i=H(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),r!=null&&M(mn(s),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=W.runKernel(_a,c,u);return d=me(d,a.dtype),l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var ff=V({avgPool_:j$});function q$(e,t,n,s,r,a="NDHWC"){let o=D(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),M(a==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&M(mn(s),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=W.runKernel(rd,c,u);return d=me(d,i.dtype),l?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Q2=V({avgPool3d_:q$});function X$(e,t=0){M(e.length>=1,()=>"Pass at least one tensor to concat");let n=Nd(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(a=>{if(a.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
|
|
with dtype ${a.dtype}. `)}),n.length===1)return Vn(n[0]);let s=n,r={axis:t};return W.runKernel(gi,s,r)}var kt=V({concat_:X$});function K$(e){let n={x:D(e,"x","sigmoid","float32")};return W.runKernel(ho,n)}var ms=V({sigmoid_:K$});function Z$(e,t,n){let s=D(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return W.runKernel(Gi,r,a)}var Pe=V({slice_:Z$});function Y$(e){let n={x:D(e,"x","tanh","float32")};return W.runKernel(xo,n)}var Lu=V({tanh_:Y$});function J$(e,t,n,s,r,a){let o=D(e,"forgetBias","basicLSTMCell"),i=D(t,"lstmKernel","basicLSTMCell"),l=D(n,"lstmBias","basicLSTMCell"),c=D(s,"data","basicLSTMCell"),u=D(r,"c","basicLSTMCell"),d=D(a,"h","basicLSTMCell"),p=kt([c,d],1),h=He(p,i),f=ue(h,l),m=f.shape[0],g=f.shape[1]/4,A=[m,g],x=Pe(f,[0,0],A),y=Pe(f,[0,g],A),b=Pe(f,[0,g*2],A),w=Pe(f,[0,g*3],A),k=ue(L(ms(x),Lu(y)),L(u,ms(ue(o,b)))),I=L(Lu(k),ms(w));return[k,I]}var Q$=V({basicLSTMCell_:J$});function e_(e,t,n){let s=D(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);M(s.rank>=1+t.length,()=>`input rank is ${s.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(s.shape[0]%r==0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return W.runKernel(mi,a,o)}var mf=V({batchToSpaceND_:e_});function t_(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 n_(e,t,n,s,r,a){a==null&&(a=.001);let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;s!=null&&(u=D(s,"offset","batchNorm")),M(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),M(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),M(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:t_(o),scale:c,offset:u,mean:i,variance:l},h={varianceEpsilon:a},f=W.runKernel(ja,p,h);return H(f,o.shape)}var Bu=V({batchNorm_:n_});function s_(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;return s!=null&&(u=D(s,"offset","batchNorm")),M(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),M(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.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}.`),Bu(o,i,l,u,c,a)}var cv=V({batchNorm2d_:s_});function r_(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;return s!=null&&(u=D(s,"offset","batchNorm")),M(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),M(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.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}.`),Bu(o,i,l,u,c,a)}var dv=V({batchNorm3d_:r_});function a_(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;return s!=null&&(u=D(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.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}.`),Bu(o,i,l,u,c,a)}var pv=V({batchNorm4d_:a_});function o_(e,t,n){let s=D(e,"x","bincount"),r=D(t,"weights","bincount");M(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return W.runKernel(Sh,a,o)}var e1=V({bincount_:o_});function i_(e,t){let n=D(e,"s0","broadcastArgs","int32"),s=D(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return W.runKernel(Ih,r)}var hv=V({broadcastArgs_:i_});function l_(e,t){let n=D(e,"broadcastTo","x"),s=n.shape;if(t.some(c=>!(c>0)||c%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let c=n.shape.slice();for(;c.length<t.length;)c.unshift(1);n=H(n,c)}let r=n.shape,a=Array.from(t);for(let c=t.length-1;c>=0;c--)if(r[c]===t[c])a[c]=1;else if(n.shape[c]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((c,u)=>c>1?u:-1).filter(c=>c>=0).length===0)return Vn(n);let i={x:n},l={reps:a};return W.runKernel(Yr,i,l)}var _d=V({broadcastTo_:l_});function u_(e){let n={x:D(e,"x","ceil","float32")};return W.runKernel(Fa,n)}var fv=V({ceil_:u_});function c_(e,t,n){let s=D(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return W.runKernel(Zr,r,a)}var gs=V({clipByValue_:c_});function d_(e){return kt(e,0)}var mv=V({concat1d_:d_});function p_(e,t){return kt(e,t)}var Wu=V({concat2d_:p_});function h_(e,t){return kt(e,t)}var gv=V({concat3d_:h_});function f_(e,t){return kt(e,t)}var Av=V({concat4d_:f_});function m_(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","conv2d","float32"),l=D(t,"filter","conv2d","float32"),c=i,u=!1;i.rank===3&&(u=!0,c=H(i,[1,i.shape[0],i.shape[1],i.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}.`),o!=null&&M(mn(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d=r==="NHWC"?c.shape[3]:c.shape[1];M(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),M(Pr(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let p={x:c,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=W.runKernel(Oa,p,h);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var _o=V({conv2d_:m_});function g_(e,t,n,s,r="NWC",a=1,o){let i=D(e,"x","conv1d"),l=D(t,"filter","conv1d"),c=i,u=!1;i.rank===2&&(u=!0,c=H(i,[1,i.shape[0],i.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}.`),o!=null&&M(mn(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(Pr(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),M(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),g=_o(p,d,[1,n],s,"NHWC",[1,a],o);return u?H(g,[g.shape[2],g.shape[3]]):H(g,[g.shape[0],g.shape[2],g.shape[3]])}var t1=V({conv1d_:g_});function A_(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),M(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.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 ${n.rank}`);let u=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&M(mn(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=W.runKernel(Ma,p,h);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var n1=V({conv2DBackpropInput_:A_});function y_(e,t,n,s,r,a){let o=D(e,"x","conv2dTranspose"),i=D(t,"filter","conv2dTranspose");return n1(n,o,i,s,r,"NHWC",a)}var s1=V({conv2dTranspose_:y_});function x_(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=D(e,"x","conv3d"),i=D(t,"filter","conv3d"),l=o,c=!1;o.rank===4&&(c=!0,l=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),M(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),M(Pr(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=W.runKernel(id,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var r1=V({conv3d_:x_});function b_(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],c=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.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:o,filter:n},d={pad:r,strides:s,inputShape:a},p=W.runKernel(Nh,u,d);return i?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var yv=V({conv3DBackpropInput_:b_});function v_(e,t,n,s,r){let a=D(e,"x","conv3dTranspose"),o=D(t,"filter","conv3dTranspose");return yv(n,a,o,s,r)}var xv=V({conv3dTranspose_:v_});function w_(e){let n={x:D(e,"x","cos","float32")};return W.runKernel(za,n)}var gf=V({cos_:w_});function k_(e){let n={x:D(e,"x","cosh","float32")};return W.runKernel(La,n)}var a1=V({cosh_:k_});function S_(e,t=0,n=!1,s=!1){let a={x:D(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return W.runKernel(Ai,a,o)}var o1=V({cumsum_:S_});function I_(e,t,n,s=!1){let r=D(e,"x","denseBincount"),a=D(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return W.runKernel(Eh,o,i)}var bv=V({denseBincount_:I_});function C_(e,t,n="NHWC"){let s=D(e,"x","depthToSpace","float32"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${r} and ${t} for depthToSpace with input shape
|
|
${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${a} and ${t} for depthToSpace with input shape
|
|
${s.shape}`),M(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return W.runKernel(xi,i,l)}var vv=V({depthToSpace_:C_});function T_(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","depthwiseConv2d","float32"),l=D(t,"filter","depthwiseConv2d","float32"),c=i,u=!1;i.rank===3&&(u=!0,c=H(i,[1,i.shape[0],i.shape[1],i.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]}.`),o!=null&&M(mn(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=W.runKernel(Ba,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Dd=V({depthwiseConv2d_:T_});function N_(e){let n={x:D(e,"x","diag")};return W.runKernel(_h,n)}var E_=V({diag_:N_});function R_(e,t,n,s,r=[1,1],a="NHWC"){let o=D(e,"x","dilation2d"),i=D(t,"filter","dilation2d");M(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),M(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),M(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,c=!1;o.rank===3&&(l=H(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0);let u={x:l,filter:i},d={strides:n,pad:s,dilations:r},p=W.runKernel(ld,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var wv=V({dilation2d_:R_});function $_(e,t){let n=D(e,"a","equal","string_or_numeric"),s=D(t,"b","equal","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(bi,r)}var $s=V({equal_:$_});function __(e,t,n){let s=D(t,"a","where"),r=D(n,"b","where"),a=D(e,"condition","where","bool"),o=At(At(a.shape,s.shape),r.shape),i=_d(a,o),l=_d(s,o),c=_d(r,o),u={condition:i,t:l,e:c};return W.runKernel(Ui,u)}var Un=V({where_:__});function D_(e){let n={x:D(e,"x","zerosLike")};return W.runKernel(Qi,n)}var tt=V({zerosLike_:D_});function P_(e,t){let n=D(e,"a","div"),s=D(t,"b","div");[n,s]=Ft(n,s);let r=ge(n,s),a=tt(r),o=$s(s,a);return Un(o,a,r)}var kv=V({divNoNan_:P_});function F_(e,t){let n=D(e,"t1","dot"),s=D(t,"t2","dot");M((n.rank===1||n.rank===2)&&(s.rank===1||s.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);let r=n.rank===1?n.size:n.shape[1],a=s.rank===1?s.size:s.shape[0];if(M(r===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`),n.rank===1&&s.rank===1){let o=H(n,[1,-1]),i=H(s,[-1,1]),l=He(o,i);return H(l,[])}else if(n.rank===1&&s.rank===2){let o=H(n,[1,-1]),i=H(s,[s.shape[0],s.shape[1]]),l=He(o,i);return H(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=H(s,[-1,1]),i=He(n,o);return H(i,[i.size])}else{let o=H(s,[s.shape[0],s.shape[1]]);return He(n,o)}}var O_=V({dot_:F_});function M_(e,...t){let n=t.map((r,a)=>D(r,`tensors${a}`,"einsum")),s={equation:e};return W.runKernel(ud,n,s)}var Sv=V({einsum_:M_});function z_(e){let n={x:D(e,"x","elu","float32")};return W.runKernel(Va,n)}var Pd=V({elu_:z_});function L_(e){let t=D(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return W.runKernel(Au,n)}var Iv=V({erf_:L_});function B_(e){let n={x:D(e,"x","exp")};return W.runKernel(Ua,n)}var _s=V({exp_:B_});function W_(e,t=0){let n=D(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return W.runKernel(vi,s,r)}var Zt=V({expandDims_:W_});function V_(e){let n={x:D(e,"x","expm1")};return W.runKernel(wi,n)}var Cv=V({expm1_:V_});function U_(e,t){let n=D(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 s={x:n},r={reps:t};return W.runKernel(Yr,s,r)}var Ys=V({tile_:U_});function G_(e,t,n,s="float32"){t==null&&(t=e);let r=ze([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=H(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Ys(Zt(o,0),[n[0],1,1]);if(n.length===2)return Ys(Zt(Zt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Ys(Zt(Zt(Zt(o,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 i1=V({eye_:G_});function Vu(e,t,n){let s={shape:e,value:t,dtype:n};return W.runKernel(yu,{},s)}function H_(e){let n={x:D(e,"x","floor","float32")};return W.runKernel(Ga,n)}var Fd=V({floor_:H_});function j_(e,t,n=0,s=0){let r=D(e,"x","gather"),a=D(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return W.runKernel(Si,o,i)}var Uu=V({gather_:j_});function q_(e,t){let n=D(e,"a","greater","string_or_numeric"),s=D(t,"b","greater","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ci,r)}var As=V({greater_:q_});function X_(e,t){let n=D(e,"a","greaterEqual","string_or_numeric"),s=D(t,"b","greaterEqual","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(qa,r)}var dl=V({greaterEqual_:X_});function K_(e){let n={input:D(e,"input","imag")};return W.runKernel(cd,n)}var Af=V({imag_:K_});function Z_(e){let n={x:D(e,"x","isFinite")};return W.runKernel(xu,n)}var Y_=V({isFinite_:Z_});function J_(e){let n={x:D(e,"x","isInf")};return W.runKernel(bu,n)}var Q_=V({isInf_:J_});function eD(e){let n={x:D(e,"x","isNaN")};return W.runKernel(vu,n)}var Tv=V({isNaN_:eD});function tD(e,t=.2){let s={x:D(e,"x","leakyRelu")},r={alpha:t};return W.runKernel(Ti,s,r)}var yf=V({leakyRelu_:tD});function nD(e,t){let n=D(e,"a","less","string_or_numeric"),s=D(t,"b","less","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ni,r)}var l1=V({less_:nD});function sD(e,t){let n=D(e,"a","lessEqual","string_or_numeric"),s=D(t,"b","lessEqual","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ei,r)}var pl=V({lessEqual_:sD});function Nv(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let s={start:e,stop:t,num:n};return W.runKernel(zh,{},s)}function rD(e,t=5,n=1,s=1,r=.5){let a=D(e,"x","localResponseNormalization");M(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
|
|
rank ${a.rank}.`),M(mn(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},c={depthRadius:t,bias:n,alpha:s,beta:r},u=W.runKernel(pd,l,c);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Ev=V({localResponseNormalization_:rD});function aD(e){let n={x:D(e,"x","log","float32")};return W.runKernel(Ka,n)}var Ds=V({log_:aD});function oD(e){let n={x:D(e,"x","log1p")};return W.runKernel(wu,n)}var xf=V({log1p_:oD});function iD(e){return M(Ea(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=D(t,"x","tf.grad","string_or_numeric"),r=n!=null?D(n,"dy","tf.grad"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(s),[s],r);return r!=null&&Ln(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),bf(o),o[0]})}}function lD(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 s=Nd(t,"args","tf.grads","string_or_numeric"),r=n!=null?D(n,"dy","tf.grads"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(...s),s,r);return r!=null&&Ln(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),bf(o),o})}}function uD(e){return M(Ea(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Qe,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Qe,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=W.gradients(()=>e(t),[t],n);return bf(s),{grad:s[0],value:r}}}function cD(e){return M(Ea(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof Qe),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Qe,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=W.gradients(()=>e(...t),t,n);return n!=null&&Ln(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),bf(s.grads),s}}function Rv(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 Cd),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in W.registeredVariables)t.push(W.registeredVariables[c])}let s=n?t.filter(c=>!c.trainable):null,r=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 ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=W.gradients(e,t,null,a);M(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((c,u)=>{i[u]!=null&&(l[c.name]=i[u])}),s!=null&&s.forEach(c=>l[c.name]=null),{value:o,grads:l}}function Fr(e){return W.customGrad(e)}function bf(e){if(e.filter(n=>n==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 dD(e){let n={x:D(e,"x","neg")};return W.runKernel($i,n)}var Mt=V({neg_:dD});function pD(e){let n={x:D(e,"x","softplus")};return W.runKernel($u,n)}var Gu=V({softplus_:pD});function hD(e){let t=D(e,"x","logSigmoid");return Fr(s=>({value:Mt(Gu(Mt(s))),gradFunc:o=>L(o,ms(Mt(s)))}))(t)}var fD=V({logSigmoid_:hD});function mD(e,t=null,n=!1){let r={x:D(e,"x","max")},a={reductionIndices:t,keepDims:n};return W.runKernel(Za,r,a)}var yn=V({max_:mD});function gD(e,t){let n=D(e,"a","sub"),s=D(t,"b","sub");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(yo,r)}var fe=V({sub_:gD});function AD(e,t=null,n=!1){let s=D(e,"x","sum");s.dtype==="bool"&&(s=me(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(mo,r,a)}var ke=V({sum_:AD});function yD(e,t=-1){let n=D(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 Fr((r,a)=>{let o=!0,i=yn(r,t,!0),l=fe(r,i),c=fe(me(l,"float32"),Ds(ke(_s(l),t,o)));return a([c]),{value:c,gradFunc:(d,p)=>{let[h]=p,f=!0,m=_s(h);return fe(d,L(ke(d,t,f),m))}}})(n)}var u1=V({logSoftmax_:yD});function c1(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function $v(e,t,n){let s=e.length+t.length,r=[],a=0,o=0;for(let i=0;i<s;i++)n.indexOf(i)===-1?r.push(e[a++]):r.push(t[o++]);return r}function _v(e,t){let n=[],s=e.length;for(let a=0;a<s;a++)t.indexOf(a)===-1&&n.push(e[a]);let r=t.map(a=>e[a]);return[n,r]}function hl(e,t){let n=t.map(s=>1);return $v(e,n,t)}function xD(e,t,n){M(c1(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Dv(e,t){if(c1(e,t))return null;let n=[];for(let s=0;s<t;++s)e.indexOf(s)===-1&&n.push(s);return e.forEach(s=>n.push(s)),n}function d1(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function bD(e,t){let n=[];for(let s=t-e;s<t;++s)n.push(s);return n}function vD(e,t=null,n=!1){let s=D(e,"x","logSumExp"),r=Xs(t,s.shape),a=yn(s,r,!0),o=fe(s,a),i=_s(o),l=ke(i,r),c=Ds(l),u=ue(H(a,c.shape),c);if(n){let d=hl(u.shape,r);return H(u,d)}return u}var Pv=V({logSumExp_:vD});function wD(e,t){let n=D(e,"a","logicalAnd","bool"),s=D(t,"b","logicalAnd","bool");At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ri,r)}var hr=V({logicalAnd_:wD});function kD(e){let n={x:D(e,"x","logicalNot","bool")};return W.runKernel(ku,n)}var vf=V({logicalNot_:kD});function SD(e,t){let n=D(e,"a","logicalOr","bool"),s=D(t,"b","logicalOr","bool");At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(dd,r)}var p1=V({logicalOr_:SD});function ID(e,t){let n=D(e,"a","logicalXor","bool"),s=D(t,"b","logicalXor","bool");return At(n.shape,s.shape),hr(p1(e,t),vf(hr(e,t)))}var CD=V({logicalXor_:ID});function TD(e,t,n,s,r){let a=D(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=H(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),M(Pr(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),r!=null&&M(mn(s),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=W.runKernel(Ja,c,u);return l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var wf=V({maxPool_:TD});function ND(e,t=[1,1,1],n,s,r,a="NDHWC"){let o=D(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),M(a==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&M(mn(s),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=W.runKernel(hd,c,u);return l?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var h1=V({maxPool3d_:ND});function ED(e,t,n,s,r=!1){let o={x:D(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:s,includeBatchInIndex:r},l=W.runKernel(Vh,o,i);return{result:l[0],indexes:l[1]}}var Fv=V({maxPoolWithArgmax_:ED});function RD(e,t){let n=D(e,"a","maximum"),s=D(t,"b","maximum");[n,s]=Ft(n,s),n.dtype==="bool"&&(n=me(n,"int32"),s=me(s,"int32")),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ya,r)}var ea=V({maximum_:RD});function $D(e,t=null,n=!1){let r={x:D(e,"x","mean")},a={axis:t,keepDims:n};return W.runKernel(Qa,r,a)}var Vt=V({mean_:$D});function jt(e,t="float32"){if(t==="complex64"){let s=jt(e,"float32"),r=jt(e,"float32");return To(s,r)}let n=vh(Ht(e),t);return W.makeTensor(n,e,t)}function ys(e,t="float32"){if(t==="complex64"){let s=ys(e,"float32"),r=jt(e,"float32");return To(s,r)}let n=l2(Ht(e),t);return W.makeTensor(n,e,t)}function _D(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 s=D(e,"x","meshgrid",e instanceof Qe?e.dtype:"float32");if(t===void 0)return[s];let r=D(t,"y","meshgrid",t instanceof Qe?t.dtype:"float32"),a=Ht(s.shape),o=Ht(r.shape);return n==="xy"?(s=H(s,[1,-1]),r=H(r,[-1,1]),[He(ys([o,1],s.dtype),s),He(r,ys([1,a],r.dtype))]):(s=H(s,[-1,1]),r=H(r,[1,-1]),[He(s,ys([1,o],s.dtype)),He(ys([a,1],r.dtype),r)])}function DD(e,t=null,n=!1){let r={x:D(e,"x","min")},a={axis:t,keepDims:n};return W.runKernel(eo,r,a)}var Do=V({min_:DD});function PD(e,t){let n=D(e,"a","minimum"),s=D(t,"b","minimum");[n,s]=Ft(n,s),n.dtype==="bool"&&(n=me(n,"int32"),s=me(s,"int32")),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(to,r)}var Od=V({minimum_:PD});function FD(e,t,n){M(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let s=D(e,"x","mirrorPad");if(s.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");M(t.length===s.rank,()=>`Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let i=0;i<s.rank;i++)M(t[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),M(t[i][0]>=0&&t[i][0]<=s.shape[i]-r&&t[i][1]>=0&&t[i][1]<=s.shape[i]-r,()=>`Padding in dimension ${i} cannot be greater than or equal to ${s.shape[i]-r} or less than 0 for input of shape ${s.shape}`);let a={paddings:t,mode:n},o={x:s};return W.runKernel(no,o,a)}var Ov=V({mirrorPad_:FD});function OD(e,t){let n=D(e,"a","mod"),s=D(t,"b","mod");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(Su,r)}var Md=V({mod_:OD});function MD(e){let t=D(e,"x","square"),n={};return W.runKernel("Square",{x:t},n)}var yt=V({square_:MD});function zD(e,t=null,n=!1){e=D(e,"x","moments");let s=Xs(t,e.shape),r=Vt(e,s,n),a=r.shape;n||(a=hl(r.shape,s));let o=yt(fe(me(e,"float32"),H(r,a))),i=Vt(o,s,n);return{mean:r,variance:i}}var kf=V({moments_:zD});function LD(e,t,n,s){let r=D(t,"data","multiRNNCell"),a=Nd(n,"c","multiRNNCell"),o=Nd(s,"h","multiRNNCell"),i=r,l=[];for(let d=0;d<e.length;d++){let p=e[d](i,a[d],o[d]);l.push(p[0]),l.push(p[1]),i=p[1]}let c=[],u=[];for(let d=0;d<l.length;d+=2)c.push(l[d]),u.push(l[d+1]);return[c,u]}var BD=V({multiRNNCell_:LD});function WD(e,t,n,s=!1){let r=D(e,"logits","multinomial"),a=r.size,o=r.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();let l={logits:o===1?H(r,[1,-1]):r},c={numSamples:t,seed:n,normalized:s},u=W.runKernel(Uh,l,c);return o===1?H(u,[u.size]):u}var Mv=V({multinomial_:WD});function VD(e,t){let n=D(e,"a","notEqual","string_or_numeric"),s=D(t,"b","notEqual","string_or_numeric");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(_i,r)}var Hu=V({notEqual_:VD});function UD(e){let n={x:D(e,"x","onesLike")};return W.runKernel(Fi,n)}var Ps=V({onesLike_:UD});function GD(e,t){let n=D(e,"v1","outerProduct"),s=D(t,"v2","outerProduct");M(n.rank===1&&s.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);let r=H(n,[-1,1]),a=H(s,[1,-1]);return He(r,a)}var HD=V({outerProduct_:GD});function jD(e,t,n=0){let s=D(e,"x","pad");if(s.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},a={x:s};return W.runKernel(ro,a,r)}var Js=V({pad_:jD});function qD(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Js(e,[t],n)}var XD=V({pad1d_:qD});function KD(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."),Js(e,t,n)}var ZD=V({pad2d_:KD});function YD(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."),Js(e,t,n)}var JD=V({pad3d_:YD});function QD(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."),Js(e,t,n)}var eP=V({pad4d_:QD});function tP(e,t,n){let s=D(e,"x","spaceToBatchND");M(s.rank>=1+t.length,()=>`input rank ${s.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(s.shape.reduce((o,i,l)=>l>0&&l<=t.length?o&&(i+n[l-1][0]+n[l-1][1])%t[l-1]==0:o,!0),()=>`input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:s},a={blockShape:t,paddings:n};return W.runKernel(ji,r,a)}var Sf=V({spaceToBatchND_:tP});function nP(e,t,n,s,r,a){r==null&&(r=[1,1]),a==null&&(a=1),s===0&&(s="valid");let o=D(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(Pr(a,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);let c=iv(i.shape,t,a,r,s),u=[c.dilationHeight,c.dilationWidth],d;s==="same"?d=rP([c.filterHeight,c.filterWidth],u):d=[[0,0],[0,0]];let p=u[0]===1&&u[1]===1,[h,f]=sP([c.inHeight,c.inWidth],u,d),m=p?s:"valid",g=p?i:Sf(i,u,h),x=(n==="avg"?()=>ff(g,t,a,m):()=>wf(g,t,a,m))(),y=p?x:mf(x,u,f);return l?H(y,[y.shape[1],y.shape[2],y.shape[3]]):y}function sP(e,t,n){let s=n.map(u=>u[0]),r=n.map(u=>u[1]),a=e.concat(s,r),o=t.map((u,d)=>(u-a[d]%u)%u),i=r.map((u,d)=>u+o[d]),l=t.map((u,d)=>[s[d],i[d]]),c=t.map((u,d)=>[0,o[d]]);return[l,c]}function rP(e,t){let s=e.map((o,i)=>o+(o-1)*(t[i]-1)).map(o=>o-1),r=s.map(o=>Math.floor(o/2)),a=s.map((o,i)=>o-r[i]);return s.map((o,i)=>[r[i],a[i]])}var aP=V({pool_:nP});function oP(e,t){let n=D(e,"base","pow"),s=D(t,"exp","pow");[n,s]=Ft(n,s);let r={a:n,b:s};return W.runKernel(ao,r)}var Po=V({pow_:oP});function iP(e,t){let n=D(e,"x","prelu"),s=D(t,"alpha","prelu"),r={x:n,alpha:s};return W.runKernel(oo,r)}var If=V({prelu_:iP});function lP(e,t=null,n=!1){let s=D(e,"x","prod");s.dtype==="bool"&&(s=me(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(zi,r,a)}var f1=V({prod_:lP});function uP(e,t,n){let s=Ht(e),r=null;if(n==null||n==="float32")r=new Float32Array(s);else if(n==="int32")r=new Int32Array(s);else if(n==="bool")r=new Uint8Array(s);else throw new Error(`Unknown data type ${n}`);for(let a=0;a<s;a++)r[a]=t();return W.makeTensor(r,e,n)}var cP=V({rand_:uP}),m1=di(Ah()),g1=class{constructor(e,t,n,s,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=s,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let a=r||Math.random();this.random=m1.alea(a.toString())}nextValue(){if(!isNaN(this.nextVal)){let s=this.nextVal;return this.nextVal=NaN,s}let e,t,n=!1;for(;!n;){let s,r,a;do s=2*this.random()-1,r=2*this.random()-1,a=s*s+r*r;while(a>=1||a===0);let o=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*s*o,t=this.mean+this.stdDev*r*o,(!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}},dP=class{constructor(e,t,n,s){this.alpha=e,this.beta=1/t,this.dtype=n;let r=s||Math.random();this.randu=m1.alea(r.toString()),this.randn=new g1(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,s,r,a;for(;;){do s=this.randn.nextValue(),a=1+this.c*s;while(a<=0);if(a*=a*a,e=s*s,t=1-.331*e*e,n=.5*e+this.d*(1-a+Math.log(a)),r=this.randu(),r<t||Math.log(r)<n)break}return a=1/this.beta*this.d*a,this.alpha<1&&(a*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(a)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},pP=class{constructor(e=0,t=1,n,s){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,s==null&&(s=Math.random()),typeof s=="number"&&(s=s.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=m1.alea(s)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function hP(e,t,n=1,s="float32",r){if(n==null&&(n=1),s==null&&(s="float32"),s!=="float32"&&s!=="int32")throw new Error(`Unsupported data type ${s}`);let a=new dP(t,n,s,r),o=ze(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var fP=V({randomGamma_:hP});function mP(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error(`Unsupported data type ${s}`);let a=new g1(t,n,s,!1,r),o=ze(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var zv=V({randomNormal_:mP});function gP(e,t=0,n=1,s="float32",r){let a=ze(e,s),o=new pP(t,n,null,r);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var ju=V({randomUniform_:gP});function qu(e,t,n=1,s="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:s};return W.runKernel(Cu,{},r)}function AP(e){let n={input:D(e,"input","real")};return W.runKernel(fd,n)}var zd=V({real_:AP});function yP(e){let n={x:D(e,"x","reciprocal")};return W.runKernel(Tu,n)}var Lv=V({reciprocal_:yP});function xP(e){let n={x:D(e,"x","relu")};return W.runKernel(io,n)}var Or=V({relu_:xP});function bP(e){let n={x:D(e,"x","relu6")};return W.runKernel(uo,n)}var A1=V({relu6_:bP});function vP(e,t){let s={x:D(e,"x","reverse")},r={dims:t};return W.runKernel(Bi,s,r)}var Fs=V({reverse_:vP});function wP(e){let t=D(e,"x","reverse");return M(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Fs(t,0)}var kP=V({reverse1d_:wP});function SP(e,t){let n=D(e,"x","reverse");return M(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Fs(n,t)}var IP=V({reverse2d_:SP});function CP(e,t){let n=D(e,"x","reverse");return M(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Fs(n,t)}var TP=V({reverse3d_:CP});function NP(e,t){let n=D(e,"x","reverse");return M(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Fs(n,t)}var EP=V({reverse4d_:NP});function RP(e){let n={x:D(e,"x","round")};return W.runKernel(Wi,n)}var y1=V({round_:RP});function $P(e){let n={x:D(e,"x","rsqrt","float32")};return W.runKernel(co,n)}var x1=V({rsqrt_:$P});function Ie(e,t){if((Dn(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"&&Dn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return No(e,[],[],t)}function _P(e){let n={x:D(e,"x","selu")};return W.runKernel(Eu,n)}var b1=V({selu_:_P});function DP(e,t,n,s,r,a=[1,1],o="NHWC"){let i=D(e,"x","separableConv2d"),l=D(t,"depthwiseFilter","separableConv2d"),c=D(n,"pointwiseFilter","separableConv2d"),u=i,d=!1;if(i.rank===3&&(d=!0,u=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),o==="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 p=l.shape[2],h=l.shape[3];M(c.shape[2]===p*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${p*h}, but got ${c.shape[2]}.`);let f=Dd(u,l,s,r,o,a),g=_o(f,c,1,"valid",o);return d?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var Bv=V({separableConv2d_:DP});async function PP(e,t){let n=D(e,"x","setdiff1d"),s=D(t,"y","setdiff1d");M(n.dtype===s.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`),M(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),M(s.rank===1,()=>`y should be 1D tensor, but got y (${s.shape}).`);let r=await n.data(),a=await s.data(),o=new Set(a),i=0;for(let u=0;u<r.length;u++)o.has(r[u])||i++;let l=new nn([i],n.dtype),c=new nn([i],"int32");for(let u=0,d=0;u<r.length;u++)o.has(r[u])||(l.values[d]=r[u],c.values[d]=u,d++);return[l.toTensor(),c.toTensor()]}var Wv=PP;function FP(e){let n={x:D(e,"x","sign")};return W.runKernel(Ru,n)}var Vv=V({sign_:FP});function OP(e){let n={x:D(e,"x","sin","float32")};return W.runKernel(po,n)}var v1=V({sin_:OP});function MP(e){let n={x:D(e,"x","sinh")};return W.runKernel(Hi,n)}var w1=V({sinh_:MP});function zP(e,t,n){let s=D(e,"x","slice1d");return M(s.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`),Pe(s,[t],[n])}var Cf=V({slice1d_:zP});function LP(e,t,n){let s=D(e,"x","slice2d");return M(s.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`),Pe(s,t,n)}var k1=V({slice2d_:LP});function BP(e,t,n){let s=D(e,"x","slice3d");return M(s.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`),Pe(s,t,n)}var fl=V({slice3d_:BP});function WP(e,t,n){let s=D(e,"x","slice4d");return M(s.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`),Pe(s,t,n)}var ml=V({slice4d_:WP});function VP(e,t=-1){let n=D(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 s={logits:n},r={dim:t};return W.runKernel(go,s,r)}var Xu=V({softmax_:VP});function UP(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return W.runKernel(Oh,t)}var Tf=V({fft_:UP});function GP(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return W.runKernel(Mh,t)}var Ld=V({ifft_:GP});function HP(e){let t=e.shape[e.shape.length-1],n=e.size/t,s;if(t<=2){let r=H(e,[n,t]);s=Ld(r)}else{let r=[n,2*(t-1)],a=H(zd(e),[n,t]),o=H(Af(e),[n,t]),i=Fs(Pe(a,[0,1],[n,t-2]),1),l=L(Fs(Pe(o,[0,1],[n,t-2]),1),Ie(-1)),c=kt([a,i],1),u=kt([o,l],1),d=H(To(c,u),[r[0],r[1]]);s=Ld(d)}if(s=zd(s),e.rank===3&&e.shape[0]!==0){let r=s,a=e.shape[0];s=H(s,[a,s.shape[0]/a,s.shape[1]]),r.dispose()}return s}var S1=V({irfft_:HP});function jP(e,t,n=0){let r={x:D(e,"x","split")},a={numOrSizeSplits:t,axis:n};return W.runKernel(qi,r,a)}var rn=V({split_:jP});function qP(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],s=e.size/n,r;if(t!=null&&t<n){let f=e.shape.map(g=>0),m=e.shape.map(g=>g);m[e.shape.length-1]=t,r=Pe(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,r=kt([e,jt(f)],e.shape.length-1),n=t}else r=e;let a=tt(r),o=H(To(r,a),[s,n]),i=Tf(o),l=Math.floor(n/2)+1,c=zd(i),u=Af(i),d=rn(c,[l,n-l],c.shape.length-1),p=rn(u,[l,n-l],u.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,H(To(d[0],p[0]),h)}var Nf=V({rfft_:qP});function XP(e){let n={x:D(e,"x","sqrt","float32")};return W.runKernel(fo,n)}var Pn=V({sqrt_:XP});function KP(e,t){let n=D(e,"a","squaredDifference"),s=D(t,"b","squaredDifference");[n,s]=Ft(n,s),At(n.shape,s.shape);let r={a:n,b:s},a={};return W.runKernel(Ao,r,a)}var I1=V({squaredDifference_:KP});function ZP(e,t){let n=D(e,"x","squeeze");return H(n,L5(n.shape,t).newShape)}var it=V({squeeze_:ZP});function YP(e,t=0){let n=Nd(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 s=n,r={axis:t};return W.runKernel(Mi,s,r)}var xn=V({stack_:YP});function JP(e,t=0){let s={x:D(e,"x","step")},r={alpha:t};return W.runKernel(vo,s,r)}var Bd=V({step_:JP});function QP(e,t,n,s,r=0,a=0,o=0,i=0,l=0){let u={x:D(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:s,beginMask:r,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return W.runKernel(Xi,u,d)}var Uv=V({stridedSlice_:QP});function eF(e){let n={x:D(e,"x","tan","float32")};return W.runKernel(Ki,n)}var Gv=V({tan_:eF});function Ut(e,t){pi(e);let n=_r(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return No(e,null,n,t)}function fr(e,t,n){if(pi(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let s=_r(e,n);if(s.length!==2&&s.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return No(e,t,s,n)}function tF(e,t,n){if(pi(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let s=_r(e,n);if(s.length!==4&&s.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return No(e,t,s,n)}function nF(e,t,n){if(pi(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let s=_r(e,n);if(s.length!==5&&s.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return No(e,t,s,n)}function sF(e,t,n){if(pi(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let s=_r(e,n);if(s.length!==6&&s.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||s,No(e,t,s,n)}function rF(e,t=1,n=!0){let s=D(e,"x","topk");if(s.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=s.shape[s.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>r)throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);let a={x:s},o={k:t,sorted:n},[i,l]=W.runKernel(Zi,a,o);return{values:i,indices:l}}var Hv=V({topk_:rF});function aF(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new g1(t,n,s,!0,r),o=ze(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Ef=V({truncatedNormal_:aF});function oF(e,t=0){let n=D(e,"x","unique","string_or_numeric");M(n.rank>0,()=>"The input tensor must be at least 1D");let s={x:n},r={axis:t},[a,o]=W.runKernel(Jh,s,r);return{values:a,indices:o}}var C1=V({unique_:oF});function iF(e,t,n){let s=D(e,"x","unsortedSegmentSum"),r=D(t,"segmentIds","unsortedSegmentSum","int32");M(mn(n),()=>"numSegments must be of dtype int");let a={x:s,segmentIds:r},o={numSegments:n};return W.runKernel(Ad,a,o)}var jv=V({unsortedSegmentSum_:iF});function lF(e,t=0){let n=D(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 s={value:n},r={axis:t};return W.runKernel(Ji,s,r)}var os=V({unstack_:lF});function qv(e,t=!0,n,s){return W.makeVariable(e,t,n,s)}function Xv(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let s=ze(e,"int32"),r=ze([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=s.indexToLoc(n[a]),i=a*e.length;r.values.set(o,i)}return r.toTensor()}async function uF(e){let t=D(e,"condition","whereAsync","bool"),n=await t.data(),s=Xv(t.shape,n);return e!==t&&t.dispose(),s}var T1=uF;async function cF(e,t,n){let s=D(e,"tensor","boolMask"),r=D(t,"mask","boolMask","bool"),a=n==null?0:n,o=r.rank,i=s.shape;M(o>0,()=>"mask cannot be scalar"),Ln(i.slice(a,a+o),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=a;m<a+o;m++)l*=i[m];let c=i.slice(0,a).concat([l],i.slice(a+o)),u=H(s,c),d=H(r,[-1]),p=await T1(d),h=it(p,[1]),f=Uu(u,h,a);return e!==s&&s.dispose(),t!==r&&r.dispose(),h.dispose(),u.dispose(),d.dispose(),p.dispose(),f}var dF=cF;function pF(e,t="euclidean",n=null,s=!1){e=D(e,"x","norm");let r=Kv(e,t,n),a=r.shape;if(s){let o=Xs(n,e.shape);a=hl(r.shape,o)}return H(r,a)}function Kv(e,t,n=null){if(e.rank===0)return sn(e);if(e.rank!==1&&n===null)return Kv(H(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return ke(sn(e),n);if(t===1/0)return yn(sn(e),n);if(t===-1/0)return Do(sn(e),n);if(t==="euclidean"||t===2)return Pn(ke(Po(sn(e),Ie(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return yn(ke(sn(e),n[0]),n[1]-1);if(t===1/0)return yn(ke(sn(e),n[1]),n[0]);if(t===-1/0)return Do(ke(sn(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Pn(ke(yt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var N1=V({norm_:pF});function hF(e,t,n,s,r=!0){let a=D(e,"v","movingAverage"),o=D(t,"x","movingAverage"),i=D(n,"decay","movingAverage");o3(a,o),M(Ta(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=Ie(1),c=fe(l,i),u=L(fe(o,a),c);if(r){M(s!=null,()=>"When using zeroDebias: true, step is required.");let d=D(s,"step","movingAverage");u=ge(u,fe(l,Po(i,d)))}return ue(a,u)}var fF=V({movingAverage_:hF});function mF(e,t,n){let s=D(e,"indices","scatterND","int32"),r=D(t,"updates","scatterND");U2(r,s,n);let a={indices:s,updates:r},o={shape:n};return W.runKernel(Vi,a,o)}var Zv=V({scatterND_:mF});function gF(e,t,n,s){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let r=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);let o=t.size;if(!(t.rank===0||t.rank===1&&o===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==s.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function AF(e,t,n,s=0){let r=D(e,"sparseIndices","sparseToDense","int32"),a=D(t,"sparseValues","sparseToDense"),o=D(s,"defaultValue","sparseToDense",a.dtype);gF(r,a,n,o);let i={sparseIndices:r,sparseValues:a,defaultValue:o},l={outputShape:n};return W.runKernel(md,i,l)}var E1=V({sparseToDense_:AF});function yF(e,t){let n=D(t,"indices","gatherND","int32"),r={params:D(e,"x","gatherND","string_or_numeric"),indices:n};return W.runKernel(Ii,r)}var Yv=V({gatherND_:yF});function xF(e,t){if(t==null)return e.shape.slice();if(Ta(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let s=0;s<e.shape.length;s++)t[s]==null&&e.shape[s]!=null?n.push(e.shape[s]):n.push(t[s]);return n}return t}function bF(e,t,n,s){let r=D(e,"x","dropout");if(M(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),M(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Qe?r.clone():r;let a=xF(r,n),o=1-t,i=ge(Fd(ue(ju(a,0,1,"float32",s),o)),o);return L(r,i)}var Jv=V({dropout_:bF});function Qv(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function R1(e,t,n){let s=1-e%2,r=new Float32Array(e);for(let a=0;a<e;++a){let o=2*Math.PI*a/(e+s-1);r[a]=t-n*Math.cos(o)}return Ut(r,"float32")}async function vF(e,t,n=1){let s=D(e,"predictions","inTopK"),r=D(t,"targets","inTopK");M(s.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`),M(s.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`),Ln(s.shape.slice(0,s.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=s.shape[s.shape.length-1];M(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);let o=await s.data(),i=await r.data(),[l,c]=[o.length/a,a],u=B5("bool",l);for(let d=0;d<l;d++){let p=d*c,h=o.subarray(p,p+c),f=[];for(let m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort((m,g)=>g.value-m.value),u[d]=0;for(let m=0;m<n;m++)if(f[m].index===i[d]){u[d]=1;break}}return e!==s&&s.dispose(),t!==r&&r.dispose(),ct(u,r.shape,"bool")}var wF=vF,Fo={};Oe(Fo,{conv2d:()=>IF,depthwiseConv2d:()=>EF,matMul:()=>$F});function kF(e,t,n,s,r,a="NHWC",o){let i=e;e.rank===3&&(i=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(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.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=a==="NHWC"?i.shape[3]:i.shape[1],u=a==="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]}).`),o!=null&&M(mn(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d={x:i,dy:l},p={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,filterShape:n};return W.runKernel(Ch,d,p)}var $1=V({conv2DBackpropFilter_:kF});function Rf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,Bd(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function $f(e,t){let n=t,s=Kt(e.shape,t.shape);return s.length>0&&(n=ke(n,s)),H(n,e.shape)}function _f(e,t,n,s){if(t==="linear")return e;if(t==="relu")return Or(e);if(t==="elu")return Pd(e);if(t==="relu6")return A1(e);if(t==="prelu")return If(e,n);if(t==="leakyrelu")return yf(e,s);if(t==="sigmoid")return ms(e);throw new Error(`Unknown fused activation ${t}.`)}var Df=(e,t)=>!(e>0)||t==="linear";function SF({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Df(W.state.gradientDepth,l)===!1){let w=_o(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),_f(w,l,c,u)}let d=D(e,"x","conv2d","float32"),p=D(t,"filter","conv2d","float32"),h=d,f=!1;d.rank===3&&(f=!0,h=H(d,[1,d.shape[0],d.shape[1],d.shape[2]])),M(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),M(p.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${p.rank}.`),o!=null&&M(mn(s),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(h.shape[3]===p.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${p.shape[2]}.`),M(Pr(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let m=$d(h.shape,p.shape,n,a,s,o),g;i!=null&&(g=D(i,"bias","fused conv2d"),[g]=Ft(g,d),At(m.outShape,g.shape));let A;c!=null&&(A=D(c,"prelu weights","fused conv2d"));let x=(w,k)=>{let[I,N,R,O]=k,$=Rf(w,R,l);M($o(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let P=n1(N.shape,$,I,n,s),T=$1(N,$,I.shape,n,s),F=[P,T];if(O!=null){let U=$f(O,$);F.push(U)}return F},y={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Fr((k,I,N)=>{let R=W.runKernel(ko,y,b);return N([I,k,R]),f&&(R=H(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,p):Fr((k,I,N,R)=>{let O=W.runKernel(ko,y,b);return R([I,k,O,N]),f&&(O=H(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:x}})(h,p,g)}var IF=V({fusedConv2d_:SF});function CF(e,t,n,s,r,a=[1,1],o){let i=e;e.rank===3&&(i=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:i,dy:l},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,filterShape:n};return W.runKernel(Rh,c,u)}var ew=V({depthwiseConv2dNativeBackpropFilter_:CF});function TF(e,t,n,s,r,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:i,filter:n},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,inputShape:e},d=W.runKernel($h,c,u);return l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var tw=V({depthwiseConv2dNativeBackpropInput_:TF});function NF({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(Df(W.state.gradientDepth,l)===!1){let w=Dd(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),_f(w,l,c,u)}let d=D(e,"x","depthwiseConv2d","float32"),p=D(t,"filter","depthwiseConv2d","float32"),h=d,f=!1;d.rank===3&&(f=!0,h=H(d,[1,d.shape[0],d.shape[1],d.shape[2]])),M(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),M(p.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`),M(h.shape[3]===p.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${p.shape[2]}.`),a==null&&(a=[1,1]),M(Pr(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&M(mn(s),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${s}.`);let m=$d(h.shape,p.shape,n,a,s,o,!0),g;i!=null&&(g=D(i,"bias","fused conv2d"),[g]=Ft(g,d),At(m.outShape,g.shape));let A;c!=null&&(A=D(c,"prelu weights","fused depthwiseConv2d"));let x=(w,k)=>{M($o(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[I,N,R,O]=k,$=Rf(w,R,l),P=tw(N.shape,$,I,n,s,a,o),T=ew(N,$,I.shape,n,s,a,o);if(O!=null){let F=$f(g,$);return[P,T,F]}return[P,T]},y={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Fr((k,I,N)=>{let R=W.runKernel(So,y,b);return N([I,k,R]),f&&(R=H(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,p):Fr((k,I,N,R)=>{let O=W.runKernel(So,y,b);return R([I,k,O,N]),f&&(O=H(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:x}})(h,p,g)}var EF=V({fusedDepthwiseConv2d_:NF});function RF({a:e,b:t,transposeA:n=!1,transposeB:s=!1,bias:r,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(Df(W.state.gradientDepth,a)===!1){let $=He(e,t,n,s);return r!=null&&($=ue($,r)),_f($,a,o,i)}let l=D(e,"a","fused matMul"),c=D(t,"b","fused matMul");[l,c]=Ft(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=s?c.shape[c.rank-1]:c.shape[c.rank-2],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=s?c.shape[c.rank-2]:c.shape[c.rank-1],f=l.shape.slice(0,-2),m=c.shape.slice(0,-2),g=Ht(f),A=Ht(m);M(u===d,()=>`Error in fused matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${s} must match.`);let y=At(l.shape.slice(0,-2),c.shape.slice(0,-2)).concat([p,h]),b=n?H(l,[g,u,p]):H(l,[g,p,u]),w=s?H(c,[A,h,d]):H(c,[A,d,h]),k;r!=null&&(k=D(r,"bias","fused matMul"),[k]=Ft(k,l),At(y,k.shape));let I;o!=null&&(I=D(o,"prelu weights","fused matMul"));let N=($,P)=>{let[T,F,U,q]=P,z=Rf(H($,U.shape),U,a),K,J;if(!n&&!s?(K=He(z,F,!1,!0),J=He(T,z,!0,!1)):!n&&s?(K=He(z,F,!1,!1),J=He(z,T,!0,!1)):n&&!s?(K=He(F,z,!1,!0),J=He(T,z,!1,!1)):(K=He(F,z,!0,!0),J=He(z,T,!0,!0)),r!=null){let Q=$f(q,z);return[K,J,Q]}else return[K,J]},R={a:b,b:w,bias:k,preluActivationWeights:I},O={transposeA:n,transposeB:s,activation:a,leakyreluAlpha:i};return r==null?Fr((P,T,F)=>{let U=W.runKernel(wo,R,O);return F([P,T,U]),{value:H(U,y),gradFunc:N}})(b,w):Fr((P,T,F,U)=>{let q=W.runKernel(wo,R,O);return U([P,T,q,F]),{value:H(q,y),gradFunc:N}})(b,w,k)}var $F=V({fusedMatMul_:RF});function _F(e){return R1(e,.54,.46)}var DF=V({hammingWindow_:_F});function PF(e){return R1(e,.5,.5)}var nw=V({hannWindow_:PF});function FF(e,t,n,s=!1,r=0){let a=0,o=[];for(;a+t<=e.size;)o.push(Pe(e,a,t)),a+=n;if(s)for(;a<e.size;){let i=a+t-e.size,l=kt([Pe(e,a,t-i),Vu([i],r)]);o.push(l),a+=n}return o.length===0?fr([],[0,t]):H(kt(o),[o.length,t])}var sw=V({frame_:FF});function OF(e,t,n,s,r=nw){s==null&&(s=Qv(t));let a=sw(e,t,n),o=L(a,r(t));return Nf(o,s)}var MF=V({stft_:OF});function zF(e,t,n,s,r="bilinear",a=0){let o=D(e,"image","cropAndResize"),i=D(t,"boxes","cropAndResize","float32"),l=D(n,"boxInd","cropAndResize","int32"),c=i.shape[0];M(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),M(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${i.shape}.`),M(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${i.shape}.`),M(s.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`),M(s[0]>=1&&s[1]>=1,()=>`cropSize must be atleast [1,1], but was ${s}`),M(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let u={image:o,boxes:i,boxInd:l},d={method:r,extrapolationValue:a,cropSize:s};return W.runKernel(yi,u,d)}var LF=V({cropAndResize_:zF});function BF(e){let t=D(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 W.runKernel(ki,n,{})}var WF=V({flipLeftRight_:BF});function VF(e){let t=D(e,"image","grayscaleToRGB"),n=t.rank-1,s=t.shape[n];M(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),M(s===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);let r=new Array(t.rank);return r.fill(1,0,n),r[n]=3,Ys(t,r)}var UF=V({grayscaleToRGB_:VF});function GF(e,t,n=0,s=.5){let r=D(e,"image","rotateWithOffset","float32");M(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let a={image:r},o={radians:t,fillValue:n,center:s};return W.runKernel(el,a,o)}var HF=V({rotateWithOffset_:GF});function Ku(e,t,n,s,r,a){s==null&&(s=.5),r==null&&(r=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),M(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${s}'`),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]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),M(0<=a&&a<=1,()=>`softNmsSigma must be in [0, 1], but was '${a}'`),{maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a}}function jF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppression","float32"),o=D(t,"scores","nonMaxSuppression","float32"),i=Ku(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:s,scoreThreshold:r};return W.runKernel(Di,{boxes:a,scores:o},l)}var qF=V({nonMaxSuppression_:jF});function XF(e,t,n){let s=KF(e,t,n),r=s<0?-(s+1):s;e.splice(r,0,t)}function KF(e,t,n){return YF(e,t,n||ZF)}function ZF(e,t){return e>t?1:e<t?-1:0}function YF(e,t,n){let s=0,r=e.length,a=0,o=!1;for(;s<r;){a=s+(r-s>>>1);let i=n(t,e[a]);i>0?s=a+1:(r=a,o=!i)}return o?s:-s-1}function rw(e,t,n,s,r){return _1(e,t,n,s,r,0)}function aw(e,t,n,s,r,a){return _1(e,t,n,s,r,0,!1,a,!0)}function ow(e,t,n,s,r,a){return _1(e,t,n,s,r,a,!0)}function _1(e,t,n,s,r,a,o=!1,i=!1,l=!1){let c=[];for(let g=0;g<t.length;g++)t[g]>r&&c.push({score:t[g],boxIndex:g,suppressBeginIndex:0});c.sort(iw);let u=a>0?-.5/a:0,d=[],p=[];for(;d.length<n&&c.length>0;){let g=c.pop(),{score:A,boxIndex:x,suppressBeginIndex:y}=g;if(A<r)break;let b=!1;for(let w=d.length-1;w>=y;--w){let k=JF(e,x,d[w]);if(k>=s){b=!0;break}if(g.score=g.score*QF(s,u,k),g.score<=r)break}g.suppressBeginIndex=d.length,b||(g.score===A?(d.push(x),p.push(g.score)):g.score>r&&XF(c,g,iw))}let h=d.length,f=n-h;i&&f>0&&(d.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=p),l&&(m.validOutputs=h),m}function JF(e,t,n){let s=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),a=Math.min(s[0],s[2]),o=Math.min(s[1],s[3]),i=Math.max(s[0],s[2]),l=Math.max(s[1],s[3]),c=Math.min(r[0],r[2]),u=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),p=Math.max(r[1],r[3]),h=(i-a)*(l-o),f=(d-c)*(p-u);if(h<=0||f<=0)return 0;let m=Math.max(a,c),g=Math.max(o,u),A=Math.min(i,d),x=Math.min(l,p),y=Math.max(A-m,0)*Math.max(x-g,0);return y/(h+f-y)}function QF(e,t,n){let s=Math.exp(t*n*n);return n<=e?s:0}function iw(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function eO(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppressionAsync"),o=D(t,"scores","nonMaxSuppressionAsync"),i=Ku(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),c=l[0],u=l[1],{selectedIndices:d}=rw(c,u,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Ut(d,"int32")}var tO=eO;function nO(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=Ku(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c={boxes:o,scores:i},u={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=W.runKernel(Pi,c,u);return{selectedIndices:d[0],selectedScores:d[1]}}var sO=V({nonMaxSuppressionWithScore_:nO});async function rO(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=Ku(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c=await Promise.all([o.data(),i.data()]),u=c[0],d=c[1],{selectedIndices:p,selectedScores:h}=ow(u,d,n,s,r,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Ut(p,"int32"),selectedScores:Ut(h)}}var aO=rO;function oO(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=Ku(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,p={boxes:o,scores:i},h={maxOutputSize:c,iouThreshold:u,scoreThreshold:d,padToMaxOutputSize:a},f=W.runKernel(Iu,p,h);return{selectedIndices:f[0],validOutputs:f[1]}}var iO=V({nonMaxSuppressionPadded_:oO});async function lO(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=Ku(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,[p,h]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=aw(p,h,c,u,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Ut(f,"int32"),validOutputs:Ie(m,"int32")}}var uO=lO;function cO(e,t,n=!1,s=!1){let r=D(e,"images","resizeBilinear");M(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),M(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),M(s===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(lo,i,l);return o?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var dO=V({resizeBilinear_:cO});function pO(e,t,n=!1,s=!1){let r=D(e,"images","resizeNearestNeighbor");M(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),M(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),M(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),M(s===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(Nu,i,l);return o?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var hO=V({resizeNearestNeighbor_:pO});function fO(e,t="binary",n=!1,s=.5){let r=D(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],c=L(Ut([s]),255),u,d,p,h;if(M(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),M(r.shape[2]===3||r.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`),M(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),M(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[u,d,p]=rn(r,[1,1,1],-1);let g=L(u,a),A=L(d,o),x=L(p,i);h=ue(ue(g,A),x)}else h=e;if(t==="otsu"){let g=e1(me(y1(h),"int32"),ct([]),256);c=mO(g,l)}let f=n?pl(h,c):As(h,c);return me(L(f,255),"int32")}function mO(e,t){let n=Ut([-1]),s=Ut([0]),r=Ut([0]),a,o,i,l,c,u;for(let d=0;d<e.size-1;d++){a=Pe(e,0,d+1),o=Pe(e,d+1),c=ge(ke(a),t),u=ge(ke(o),t);let p=ke(L(a,qu(0,a.size)));i=ge(p,ke(a));let h=Vu(o.shape,a.size),f=ue(qu(0,o.size),h),m=L(o,f);l=ge(ke(m),ke(o));let g=fe(i,l),A=fe(i,l),x=L(c,u);r=L(L(x,g),A);let y=As(r,s);s=Un(y,r,s),n=Un(y,Ut([d]),n)}return n}var gO=V({threshold_:fO});function AO(e,t,n="nearest",s="constant",r=0,a){let o=D(e,"image","transform","float32"),i=D(t,"transforms","transform","float32");M(o.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),M(i.rank===2&&(i.shape[0]===o.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),M(a==null||a.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${a}.`);let l={image:o,transforms:i},c={interpolation:n,fillMode:s,fillValue:r,outputShape:a};return W.runKernel(Yi,l,c)}var yO=V({transform_:AO});function xO(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 s=D(e,"a","bandPart");M(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=H(qu(0,a,1,"int32"),[-1,1]),l=qu(0,o,1,"int32"),c=fe(i,l),u=hr(pl(c,Ie(+t,"int32")),dl(c,Ie(-n,"int32"))),d=jt([a,o],s.dtype);return H(xn(os(H(s,[-1,a,o])).map(p=>Un(u,p,d))),r)}var bO=V({bandPart_:xO});function vO(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=rn(e,e.shape[0],0).map(r=>it(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(W.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=L(ke(L(n[o],a)),n[o]);a=fe(a,i)}return ge(a,N1(a,"euclidean"))}));return t?xn(n,0):n}var wO=V({gramSchmidt_:vO});function kO(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 lw(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),s=os(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[c,u]=lw(l,t);r.push(c),a.push(u)});let o=H(xn(r,0),e.shape),i=H(xn(a,0),e.shape);return[o,i]}}function lw(e,t=!1){return W.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=i1(n),a=Vn(e),o=fr([[1]],[1,1]),i=Vn(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=W.tidy(()=>{let h=Pe(a,[c,c],[n-c,1]),f=N1(h),m=Pe(a,[c,c],[1,1]),g=Un(As(m,0),fr([[-1]]),fr([[1]])),A=fe(m,L(g,f)),x=ge(h,A);x.shape[0]===1?i=Vn(o):i=kt([o,Pe(x,[1,0],[x.shape[0]-1,x.shape[1]])],0);let y=Mt(ge(He(g,A),f)),b=Pe(a,[c,0],[n-c,s]),w=L(y,i),k=et(i);if(c===0)a=fe(b,He(w,He(k,b)));else{let R=fe(b,He(w,He(k,b)));a=kt([Pe(a,[0,0],[c,s]),R],0)}let I=et(w),N=Pe(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=fe(N,He(He(N,i),I));else{let R=fe(N,He(He(N,i),I));r=kt([Pe(r,[0,0],[n,c]),R],1)}return[i,a,r]}),ne([u,d,p])}return!t&&n>s&&(r=Pe(r,[0,0],[n,s]),a=Pe(a,[0,0],[s,s])),[r,a]})}var SO=V({qr_:kO}),Gn;(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"})(Gn||(Gn={}));function IO(e,t,n=Gn.SUM_BY_NONZERO_WEIGHTS){let s=D(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=D(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===Gn.NONE)return a;if(n===Gn.SUM)return ke(a);if(n===Gn.MEAN){if(r==null)return Vt(a);{let o=s.size/r.size,i=ge(ke(a),ke(r));return o>1?ge(i,Ie(o)):i}}if(n===Gn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return ge(ke(a),Ie(s.size));{let o=L(r,ys(s.shape)),i=me(ke(Hu(o,Ie(0))),"float32");return ge(ke(a),i)}}throw Error(`Unknown reduction: ${n}`)}var ta=V({computeWeightedLoss_:IO});function CO(e,t,n,s=Gn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","absoluteDifference"),a=D(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=D(n,"weights","absoluteDifference")),Ln(r.shape,a.shape,"Error in absoluteDifference: ");let i=sn(fe(r,a));return ta(i,o,s)}var TO=V({absoluteDifference_:CO});function NO(e,t,n,s,r=Gn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","cosineDistance"),o=D(t,"predictions","cosineDistance"),i=null;s!=null&&(i=D(s,"weights","cosineDistance")),Ln(a.shape,o.shape,"Error in cosineDistance: ");let l=Ie(1),c=fe(l,ke(L(a,o),n,!0));return ta(c,i,r)}var EO=V({cosineDistance_:NO});function RO(e,t,n,s=Gn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","hingeLoss"),a=D(t,"predictions","hingeLoss"),o=null;n!=null&&(o=D(n,"weights","hingeLoss")),Ln(r.shape,a.shape,"Error in hingeLoss: ");let i=Ie(1);r=fe(L(Ie(2),r),i);let l=Or(fe(i,L(r,a)));return ta(l,o,s)}var $O=V({hingeLoss_:RO});function _O(e,t,n,s=1,r=Gn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","huberLoss"),o=D(t,"predictions","huberLoss"),i=null;n!=null&&(i=D(n,"weights","huberLoss")),Ln(a.shape,o.shape,"Error in huberLoss: ");let l=Ie(s),c=sn(fe(o,a)),u=Od(c,l),d=fe(c,u),p=ue(L(Ie(.5),yt(u)),L(l,d));return ta(p,i,r)}var DO=V({huberLoss_:_O});function PO(e,t,n,s=1e-7,r=Gn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","logLoss"),o=D(t,"predictions","logLoss"),i=null;n!=null&&(i=D(n,"weights","logLoss")),Ln(a.shape,o.shape,"Error in logLoss: ");let l=Ie(1),c=Ie(s),u=Mt(L(a,Ds(ue(o,c)))),d=L(fe(l,a),Ds(ue(fe(l,o),c))),p=fe(u,d);return ta(p,i,r)}var FO=V({logLoss_:PO});function OO(e,t,n,s=Gn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","meanSquaredError"),a=D(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=D(n,"weights","meanSquaredError")),Ln(r.shape,a.shape,"Error in meanSquaredError: ");let i=I1(r,a);return ta(i,o,s)}var MO=V({meanSquaredError_:OO});function zO(e,t){let n=D(e,"labels","sigmoidCrossEntropyWithLogits"),s=D(t,"logits","sigmoidCrossEntropyWithLogits");Ln(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Or(s),a=L(s,n),o=xf(_s(Mt(sn(s))));return ue(fe(r,a),o)}function LO(e,t,n,s=0,r=Gn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"multiClassLabels","sigmoidCrossEntropy"),o=D(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","sigmoidCrossEntropy")),Ln(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Ie(s),u=Ie(1),d=Ie(.5);a=ue(L(a,fe(u,c)),L(d,c))}let l=zO(a,o);return ta(l,i,r)}var BO=V({sigmoidCrossEntropy_:LO});function WO(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 Fr((r,a,o)=>{let l=Pv(a,[n],!0),c=fe(me(a,"float32"),l);o([r,c]);let u=Mt(L(c,r));return{value:ke(u,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=hl(h.shape,[n]);return[L(H(h,A),fe(me(m,"float32"),_s(g))),L(H(h,A),fe(_s(g),me(m,"float32")))]}}})(e,t)}function VO(e,t,n,s=0,r=Gn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"onehotLabels","softmaxCrossEntropy"),o=D(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","softmaxCrossEntropy")),Ln(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let c=Ie(s),u=Ie(1),d=Ie(a.shape[1]);a=ue(L(a,fe(u,c)),ge(c,d))}let l=WO(a,o);return ta(l,i,r)}var UO=V({softmaxCrossEntropy_:VO});function GO(e,t,n,s){let r=D(e,"indices","sparseFillEmptyRows"),a=D(t,"values","sparseFillEmptyRows"),o=D(n,"denseShape","sparseFillEmptyRows"),i=D(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
|
|
${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=W.runKernel(jh,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var HO=V({sparseFillEmptyRows_:GO});function jO(e,t,n){let s=D(e,"inputIndices","sparseReshape"),r=D(t,"inputShape","sparseReshape"),a=D(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
|
|
${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=W.runKernel(qh,o);return{outputIndices:i[0],outputShape:i[1]}}var qO=V({sparseReshape_:jO});function XO(e,t,n){let s=D(e,"data","sparseSegmentMean"),r=D(t,"indices","sparseSegmentMean"),a=D(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
|
|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
|
|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Xh,o)}var KO=V({sparseSegmentMean_:XO});function ZO(e,t,n){let s=D(e,"data","sparseSegmentSum"),r=D(t,"indices","sparseSegmentSum"),a=D(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
|
|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
|
|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Kh,o)}var YO=V({sparseSegmentSum_:ZO});function JO(e,t,n,s,r,a,o,i){let l=D(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let c=D(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:c},p=W.runKernel(gd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var QO=V({stringNGrams_:JO});function eM(e,t,n=!0){let s=D(e,"input","stringSplit","string"),r=D(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=W.runKernel(Zh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var tM=V({stringSplit_:eM});function nM(e,t){let n=D(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return W.runKernel(Yh,r,s)}var sM=V({stringToHashBucketFast_:nM}),rM={fft:Tf,ifft:Ld,rfft:Nf,irfft:S1},aM={hammingWindow:DF,hannWindow:nw,frame:sw,stft:MF},Ce={flipLeftRight:WF,grayscaleToRGB:UF,resizeNearestNeighbor:hO,resizeBilinear:dO,rotateWithOffset:HF,cropAndResize:LF,nonMaxSuppression:qF,nonMaxSuppressionAsync:tO,nonMaxSuppressionWithScore:sO,nonMaxSuppressionWithScoreAsync:aO,nonMaxSuppressionPadded:iO,nonMaxSuppressionPaddedAsync:uO,threshold:gO,transform:yO},uw={bandPart:bO,gramSchmidt:wO,qr:SO},oM={absoluteDifference:TO,computeWeightedLoss:ta,cosineDistance:EO,hingeLoss:$O,huberLoss:DO,logLoss:FO,meanSquaredError:MO,sigmoidCrossEntropy:BO,softmaxCrossEntropy:UO},Wd={sparseFillEmptyRows:HO,sparseReshape:qO,sparseSegmentMean:KO,sparseSegmentSum:YO},Pf={stringNGrams:QO,stringSplit:tM,stringToHashBucketFast:sM},na=class extends q3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return ne(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Rv(e,t)}dispose(){this.iterations_!=null&&ne(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(na,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Ff=class extends na{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:X(()=>tt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:X(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;X(()=>{let c=ue(L(i,this.rho),L(yt(o),1-this.rho)),u=L(ge(Pn(ue(l,this.epsilon)),Pn(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(yt(u),1-this.rho));i.assign(c),l.assign(d);let p=ue(L(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ne(this.accumulatedGrads.map(e=>e.variable)),ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ff.className="Adadelta";Ro(Ff);var Of=class extends na{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:X(()=>Vu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;X(()=>{let i=ue(o,yt(a));o.assign(i);let l=ue(L(ge(a,Pn(ue(i,W.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ne(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)}};Of.className="Adagrad";Ro(Of);var Mf=class extends na{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],X(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);X(()=>{let n=fe(1,this.accBeta1),s=fe(1,this.accBeta2);t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:X(()=>tt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:X(()=>tt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=ue(L(u,this.beta2),L(yt(l),1-this.beta2)),h=ge(d,n),f=ge(p,s);c.assign(d),u.assign(p);let m=ue(L(ge(h,ue(Pn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ne(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),X(()=>{this.accBeta1.assign(Po(this.beta1,this.iterations_+1)),this.accBeta2.assign(Po(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};Mf.className="Adam";Ro(Mf);var zf=class extends na{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],X(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);X(()=>{let n=fe(1,this.accBeta1),s=ge(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:tt(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:tt(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),h=sn(l),f=ea(p,h);c.assign(d),u.assign(f);let m=ue(L(ge(s,n),ge(d,ue(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ue(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ne(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)}};zf.className="Adamax";Ro(zf);var Vd=class extends na{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=W.registeredVariables[n];X(()=>{let o=ue(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=An(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Vd.className="SGD";Ro(Vd);var Lf=class extends Vd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ie(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:X(()=>tt(r).variable(i))}}let a=this.accumulations[s].variable,o=Array.isArray(e)?e[s].tensor:e[n];o!=null&&X(()=>{let i,l=ue(L(this.m,a),o);this.useNesterov?i=ue(L(this.c,ue(o,L(l,this.m))),r):i=ue(L(this.c,l),r),a.assign(l),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ne(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Lf.className="Momentum";Ro(Lf);var Bf=class extends na{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:X(()=>tt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:X(()=>tt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:X(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;X(()=>{let c=ue(L(i,this.decay),L(yt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=ue(L(u,this.decay),L(o,1-this.decay)),p=ge(L(o,this.learningRate),Pn(fe(c,ue(yt(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=fe(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(yt(o),1-this.decay)),d=ue(L(l,this.momentum),ge(L(o,this.learningRate),Pn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=fe(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(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(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.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)}};Bf.className="RMSProp";Ro(Bf);var Oo=class{static sgd(e){return new Vd(e)}static momentum(e,t,n=!1){return new Lf(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Bf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Mf(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Ff(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new zf(e,t,n,s,r)}static adagrad(e,t=.1){return new Of(e,t)}},gl={sgd:Oo.sgd,momentum:Oo.momentum,adadelta:Oo.adadelta,adagrad:Oo.adagrad,rmsprop:Oo.rmsprop,adamax:Oo.adamax,adam:Oo.adam},iM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function cw(){return new Promise(e=>iM(()=>e()))}var E={};Oe(E,{ERF_A1:()=>yM,ERF_A2:()=>xM,ERF_A3:()=>bM,ERF_A4:()=>vM,ERF_A5:()=>wM,ERF_P:()=>AM,PARALLELIZE_THRESHOLD:()=>D1,SELU_SCALE:()=>pw,SELU_SCALEALPHA:()=>dw,applyActivation:()=>_f,assertAndGetBroadcastShape:()=>At,assertAxesAreInnerMostDims:()=>xD,assertParamsConsistent:()=>lM,assignToTypedArray:()=>NM,axesAreInnerMostDims:()=>c1,calculateShapes:()=>O3,checkEinsumDimSizes:()=>PM,combineLocations:()=>$v,complexWithEvenIndex:()=>IM,complexWithOddIndex:()=>CM,computeConv2DInfo:()=>$d,computeConv3DInfo:()=>lv,computeDefaultPad:()=>Y2,computeDilation2DInfo:()=>L$,computeOptimalWindowSize:()=>cM,computeOutAndReduceShapes:()=>_v,computeOutShape:()=>uM,computePool2DInfo:()=>iv,computePool3DInfo:()=>B$,convertConv2DDataFormat:()=>uv,decodeEinsumEquation:()=>_M,eitherStridesOrDilationsAreOne:()=>Pr,expandShapeToKeepDim:()=>hl,exponent:()=>RM,exponents:()=>EM,fromStringArrayToUint8:()=>UM,fromUint8ToStringArray:()=>VM,getAxesPermutation:()=>Dv,getBroadcastDims:()=>_3,getComplexWithIndex:()=>TM,getEinsumComputePath:()=>FM,getEinsumPermutation:()=>DM,getFusedBiasGradient:()=>$f,getFusedDyActivation:()=>Rf,getImageCenter:()=>dM,getInnerMostAxes:()=>bD,getPermuted:()=>hM,getReductionAxes:()=>Kt,getReshaped:()=>pM,getReshapedPermuted:()=>fM,getSliceBeginCoords:()=>mM,getSliceSize:()=>gM,getUndoAxesPermutation:()=>d1,isIdentityPermutation:()=>OM,log:()=>h9,mergeRealAndImagArrays:()=>kM,prepareAndValidate:()=>F3,prepareSplitSize:()=>zM,segment_util:()=>mw,shouldFuse:()=>Df,slice_util:()=>Ot,splitRealAndImagArrays:()=>SM,tupleValuesAreOne:()=>$o,upcastType:()=>Wn,validateInput:()=>U2,validateUpdateShape:()=>V2,warn:()=>Io});function lM(e,t){let n=e[0].length;e.forEach((r,a)=>{M(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] 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 ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let o=0;o<n;o++)M(o===t||r[o]===s[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function uM(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var D1=30;function cM(e){return e<=D1?e:bh(e,Math.floor(Math.sqrt(e)))}function dM(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function pM(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function hM(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function fM(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function mM(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function gM(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var dw=1.7580993408473768,pw=1.0507009873554805,AM=.3275911,yM=.254829592,xM=-.284496736,bM=1.421413741,vM=-1.453152027,wM=1.061405429;function kM(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function SM(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function IM(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function CM(e){let t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function TM(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function NM(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function EM(e,t){let n=new Float32Array(e/2),s=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let a=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(a),s[r]=Math.sin(a)}return{real:n,imag:s}}function RM(e,t,n){let s=(n?2:-2)*Math.PI*(e/t),r=Math.cos(s),a=Math.sin(s);return{real:r,imag:a}}var P1="->",$M=/->/g,hw=",",fw="...";function _M(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace($M,"").length)/P1.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${P1}").`);let[s,r]=e.split(P1);M(s.indexOf(fw)===-1,()=>`The ellipsis notation ("${fw}") is not supported yet.`);let a=s.split(hw),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let p=0;p<r.length;++p){let h=r[p];if(!a.some(f=>f.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);i.indexOf(h)===-1&&i.push(h)}for(let p=0;p<s.length;++p){let h=s[p];i.indexOf(h)===-1&&h!==hw&&i.push(h)}let l=new Array(a.length);for(let p=0;p<o;++p){if(new Set(a[p].split("")).size!==a[p].length)throw new Error(`Found duplicate axes in input component ${a[p]}. Support for duplicate axes in input is not implemented yet.`);l[p]=[];for(let h=0;h<a[p].length;++h)l[p].push(i.indexOf(a[p][h]))}let c=i.length,u=r.length,d=[];for(let p=u;p<c;++p)d.push(p);return{allDims:i,summedDims:d,idDims:l}}function DM(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let s=[];for(let r=0;r<e;++r)n[r]===-1&&s.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:s}}function PM(e,t,n){let s=new Array(e);for(let r=0;r<n.length;++r){let a=n[r].shape;for(let o=0;o<t[r].length;++o)s[t[r][o]]===void 0?s[t[r][o]]=a[o]:M(s[t[r][o]]===a[o],()=>`Expected dimension ${s[t[r][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`)}}function FM(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let o=0;o<r;++o)s.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=MM(t,i);for(let c of l)a.indexOf(c)===-1&&(s[o].push(c),a.push(c))}return{path:n,steps:s}}function OM(e){return e.every((t,n)=>t===n)}function MM(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function zM(e,t,n=0){let s=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);M(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}M(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}var mw={};Oe(mw,{collectGatherOpShapeInfo:()=>WM,computeOutShape:()=>BM,segOpComputeOptimalWindowSize:()=>LM});function LM(e,t){let n=!1,s;for(e<=D1?(s=e,n=!0):s=bh(e,Math.floor(Math.sqrt(e)));!n;)s>t||s===e?n=!0:s=bh(e,s+1);return s}function BM(e,t,n){let s=[],r=e.length;for(let a=0;a<r;a++)a!==t?s.push(e[a]):s.push(n);return s}function WM(e,t,n,s){let r=t.shape.length,a=e.shape.length;if(s!==0&&(s<-r||s>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);if(s<0&&(s+=r),s>a)throw new Error(`batchDims (${s}) must be less than rank(x) (
|
|
${a}).`);if(n<s)throw new Error(`batchDims (${s}) must be less than or equal to axis (${n}).`);for(let d=0;d<s;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let o=e.shape[n],i=[],l=1,c=1,u=1;for(let d=0;d<s;++d)i.push(e.shape[d]),l*=e.shape[d];for(let d=s;d<n;d++)i.push(e.shape[d]),c*=e.shape[d];for(let d=s;d<r;d++)i.push(t.shape[d]);for(let d=n+1;d<a;d++)i.push(e.shape[d]),u*=e.shape[d];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:o,outputShape:i}}function VM(e){try{return e.map(t=>nf(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function UM(e){return e.map(t=>wd(t))}var Qs={};Oe(Qs,{nonMaxSuppressionV3Impl:()=>rw,nonMaxSuppressionV4Impl:()=>aw,nonMaxSuppressionV5Impl:()=>ow,whereImpl:()=>Xv});var gw={kernelName:fi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Bd(me(n,"float32"),-1))}}},GM={kernelName:iu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=yt(me(n,"float32")),r=Pn(fe(Ie(1),s));return Mt(ge(e,r))}}}},HM={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Pn(fe(yt(me(n,"float32")),1));return ge(e,s)}}}},jM={kernelName:Kr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=e,l=Kt(n.shape,r);return l.length>0&&(i=ke(i,l)),H(i,n.shape)},b:()=>{let i=e,l=Kt(s.shape,r);return l.length>0&&(i=ke(i,l)),H(i,s.shape)}}}},qM={kernelName:Ra,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((s,r)=>{n[r]=()=>e.clone()}),n}},XM={kernelName:$a,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>tt(n)}}},KM={kernelName:du,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>tt(n)}}},ZM={kernelName:pu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,Pn(fe(Ie(1),yt(me(n,"float32")))))}}},YM={kernelName:hu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Pn(ue(Ie(1),yt(me(n,"float32"))));return ge(e,s)}}}},JM={kernelName:gu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=ue(yt(n),yt(s)),l=L(e,ge(s,i)),c=Kt(n.shape,r);return c.length>0&&(l=ke(l,c)),H(l,n.shape)},b:()=>{let i=ue(yt(n),yt(s)),l=Mt(L(e,ge(n,i))),c=Kt(s.shape,r);return c.length>0&&(l=ke(l,c)),H(l,s.shape)}}}},QM={kernelName:fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ue(yt(me(n,"float32")),1))}}},ez={kernelName:mu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,fe(Ie(1),yt(me(n,"float32"))))}}};function tz(e,t,n,s,r,a){let o=D(e,"dy","avgPool3dGrad"),i=D(t,"input","avgPool3dGrad"),l=o,c=i,u=!1;i.rank===4&&(u=!0,l=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),c=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.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}.`),a!=null&&M(mn(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let d={dy:l,input:c},p={filterSize:n,strides:s,pad:r,dimRoundingMode:a},h=W.runKernel(kh,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var nz=V({avgPool3dGrad_:tz}),sz={kernelName:rd,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>nz(e,s,r,a,o,i)}}};function rz(e,t,n,s,r){let a=D(e,"dy","avgPoolGrad"),o=D(t,"input","avgPoolGrad");M(o.rank===a.rank,()=>`Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);let i=o,l=a,c=!1;o.rank===3&&(c=!0,i=H(o,[1,o.shape[0],o.shape[1],o.shape[2]]),l=H(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),M(i.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);let u={dy:l,input:i},d={filterSize:n,strides:s,pad:r},p=W.runKernel(wh,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var az=V({avgPoolGrad_:rz}),oz={kernelName:_a,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o}=n;return{x:()=>az(e,s,r,a,o)}}},iz={kernelName:Da,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[s,r]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>He(e,r,!1,!0),b:()=>He(s,e,!0,!1)}:!a&&o?{a:()=>He(e,r,!1,!1),b:()=>He(e,s,!0,!1)}:a&&!o?{a:()=>He(r,e,!1,!0),b:()=>He(s,e,!1,!1)}:{a:()=>He(r,e,!0,!0),b:()=>He(e,s,!0,!0)}}},lz={kernelName:mi,gradFunc:(e,t,n)=>{let{blockShape:s,crops:r}=n;return{x:()=>Sf(e,s,r)}}},uz={kernelName:Y5,gradFunc:(e,t,n)=>{let s=n,r=s.inputShape,a=s.shape,o=Array.from(a);for(let l=r.length-1;l>=0;l--)if(r[l]===a[l])o[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>ke(e,i,!0)}}},cz={kernelName:Pa,gradFunc:e=>({x:()=>e.clone()})},dz={kernelName:Fa,gradFunc:e=>({x:()=>tt(e)})},pz={kernelName:Zr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{clipValueMin:r,clipValueMax:a}=n;return{x:()=>Un(hr(dl(s,r),pl(s,a)),e,tt(e))}}},hz={kernelName:od,inputsToSave:["x"],gradFunc:gw.gradFunc},fz={kernelName:gi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let s=t.map(l=>l.shape),{axis:r}=n,a=Xs(r,t[0].shape)[0],o=s.map(l=>l[a]);return rn(e,o,a).map(l=>()=>l)}},mz={kernelName:Oa,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return M($o(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>n1(s.shape,e,r,o,i,l),filter:()=>$1(s,e,r.shape,o,i,l)}}},gz={kernelName:Ma,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>_o(e,r,a,o,i,1,l),filter:()=>$1(e,s,r.shape,a,o,i,l)}}};function Az(e,t,n,s,r){let a=e;e.rank===4&&(a=H(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let o=t;o.rank===4&&(o=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),M(a.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`),M(o.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${o.shape}.`),M(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),M(a.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`),M(o.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${o.shape[4]}) must match output depth for filter (${n[4]}).`);let i={x:a,dy:o},l={strides:s,pad:r,filterShape:n};return W.runKernel(Th,i,l)}var yz=V({conv3DBackpropFilter_:Az}),xz={kernelName:id,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a}=n;M($o(s),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let[o,i]=t;return{x:()=>yv(o.shape,e,i,r,a),filter:()=>yz(o,e,i.shape,r,a)}}},bz={kernelName:za,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Mt(v1(me(n,"float32"))),e)}}},vz={kernelName:La,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(w1(me(n,"float32")),e)}}},wz={kernelName:Ai,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r,exclusive:a,reverse:o}=n;return{x:()=>{let i=Dv([r],s.rank),l=o1(e,r,a,!o);return i!=null&&(l=et(l,i)),l}}}},kz={kernelName:Ba,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a,dimRoundingMode:o}=n,i=s==null?[1,1]:s;M($o(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);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(Pr(r,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${i}'.`),o!=null&&M(mn(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>tw(l.shape,e,c,r,a,i,o),filter:()=>ew(l,e,c.shape,r,a,i,o)}}},Sz={kernelName:ld,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,a={x:s,filter:r,dy:e},o={x:s,filter:r,dy:e};return{x:()=>W.runKernel(Dh,a,n),filter:()=>W.runKernel(Ph,o,n)}}},Iz={kernelName:Va,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,s={dy:e,y:n};return{x:()=>W.runKernel(Fh,s)}}},Cz={kernelName:Au,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(_s(Mt(yt(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,s)}}},Tz={kernelName:Ua,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},Nz={kernelName:vi,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},Ez={kernelName:wi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,_s(n))}}},Rz={kernelName:Ga,gradFunc:e=>({x:()=>tt(e)})},$z={kernelName:Ha,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=ge(e,me(s,"float32")),l=Kt(n.shape,r);return l.length>0?H(ke(i,l),n.shape):i},b:()=>{let i=L(e,me(n,"float32")),l=Kt(s.shape,r);l.length>0&&(i=H(ke(i,l),s.shape));let c=yt(s);return Mt(ge(i,me(c,"float32")))}}}},_z={kernelName:ja,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:s}=n,[r,a,o,i]=t,l=i==null?Ie(1):i,c=Kt(a.shape,r.shape),u=[];if(a.rank===1){for(let b=0;b<r.shape.length-1;++b)u.push(r.shape[b]);u.push(1)}let d=fe(r,a),p=L(e,l),h=x1(ue(o,Ie(s))),f=L(L(L(h,h),h),Ie(-.5));return{x:()=>a.rank===1?H(L(L(e,Ys(H(h,[1,1,1,a.shape[0]]),u)),l),r.shape):H(L(L(e,h),l),r.shape),mean:()=>{let b=L(L(h,Ie(-1)),p);return a.rank===1&&(b=ke(b,c)),H(b,a.shape)},variance:()=>{let b=L(L(f,d),p);return a.rank===1&&(b=ke(b,c)),H(b,a.shape)},scale:()=>{let b=L(d,h),w=L(e,b);return a.rank===1&&(w=ke(w,c)),H(w,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=ke(b,c)),H(b,a.shape)}}}},Dz={kernelName:Si,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[s,r]=t,{axis:a}=n,o=Xs(a,s.shape)[0];return{x:()=>{let l=s.shape,c=r.size,u=l.slice(0,o),d=u.length,p=l.slice(a,l.length).slice(1),h=p.length,f=Aw(0,d),m=Aw(d+1,d+1+h),g=yw([u,[c],p]),A=H(e,g),x=H(r,[c]),y=yw([[d],f,m]),b=et(A,y),w=jv(b,x,s.shape[o]),k=d1(y);return w=et(w,k),w},indices:()=>r}}};function Aw(e,t){let n=[];for(let s=e;s<t;++s)n.push(s);return n}function yw(e){let t=[];for(let n=0;n<e.length;++n)for(let s=0;s<e[n].length;++s)t.push(e[n][s]);return t}var Pz={kernelName:qa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>tt(n),b:()=>tt(s)}}},Fz={kernelName:Xa,gradFunc:e=>({x:()=>me(e,"float32")})},Oz={kernelName:xu,gradFunc:e=>({x:()=>tt(e)})},Mz={kernelName:bu,gradFunc:e=>({x:()=>tt(e)})},zz={kernelName:vu,gradFunc:e=>({x:()=>tt(e)})},Lz={kernelName:Ti,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{alpha:r}=n,a=As(s,0);return{x:()=>Un(a,e,L(e,r))}}},Bz={kernelName:wu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ue(n,1))}}},Wz={kernelName:Ka,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,me(n,"float32"))}}},Vz={kernelName:J5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n;return{logits:()=>{let a=!0,o=_s(s);return fe(e,L(ke(e,r,a),o))}}}};function Uz(e,t,n,s=5,r=1,a=1,o=.5){let i={x:e,y:t,dy:n},l={depthRadius:s,bias:r,alpha:a,beta:o};return W.runKernel(Lh,i,l)}var Gz=V({localResponseNormalizationBackprop_:Uz}),Hz={kernelName:pd,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{depthRadius:a,bias:o,alpha:i,beta:l}=n;return{x:()=>Gz(s,r,e,a,o,i,l)}}};function xw(e,t,n,s){return t.rank<n.rank&&(t=H(t,hl(t.shape,s))),e.rank<n.rank&&(e=H(e,hl(e.shape,s))),{x:()=>L(e,me($s(n,t),e.dtype))}}var bw={kernelName:Za,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{reductionIndices:r}=s,a=t[0],o=t[1],i=Xs(r,a.shape),l=xw(e,o,a,i);return{x:()=>l.x()}}},jz={kernelName:Ya,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,me(dl(n,s),"float32")),b:()=>L(e,me(l1(n,s),"float32"))}}};function qz(e,t,n,s,r,a,o){let i=D(e,"dy","maxPool3dGrad"),l=D(t,"input","maxPool3dGrad"),c=D(n,"output","maxPool3dGrad"),u=i,d=l,p=c,h=!1;l.rank===4&&(h=!0,u=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),d=H(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),p=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(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),M(p.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${p.rank}.`),o!=null&&M(mn(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:u,input:d,output:p},m={filterSize:s,strides:r,pad:a,dimRoundingMode:o},g=W.runKernel(Wh,f,m);return h?H(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var Xz=V({maxPool3dGrad_:qz}),Kz={kernelName:hd,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>Xz(e,s,r,a,o,i,l)}}};function Zz(e,t,n,s,r,a,o){let i=D(e,"dy","maxPoolGrad"),l=D(t,"input","maxPoolGrad"),c=D(n,"output","maxPoolGrad");M(l.rank===i.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${i.rank})`),M(i.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`),M(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),o!=null&&M(mn(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let u={dy:i,input:l,output:c},d={filterSize:s,strides:r,pad:a,dimRoundingMode:o};return W.runKernel(Bh,u,d)}var Yz=V({maxPoolGrad_:Zz}),Jz={kernelName:Ja,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>Yz(e,s,r,a,o,i)}}},Qz={kernelName:Qa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n,a=Xs(r,s.shape),i=_v(s.shape,a)[1],l=Ht(i);return{x:()=>{let u=s.shape.slice();a.forEach(h=>{u[h]=1});let d=H(e,u);return ge(L(d,ys(s.shape,"float32")),l)}}}},eL={kernelName:eo,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{axis:r}=s,[a,o]=t,i=Xs(r,a.shape),l=xw(e,o,a,i);return{x:()=>l.x()}}},tL={kernelName:to,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,me(pl(n,s),"float32")),b:()=>L(e,me(As(n,s),"float32"))}}},nL={kernelName:no,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>Pe(e,a,s.shape)}}},sL={kernelName:Su,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=Kt(n.shape,r);return i.length>0?H(ke(e,i),n.shape):e},b:()=>{let i=L(e,Mt(Fd(ge(n,s)))),l=Kt(s.shape,r);return l.length>0?H(ke(i,l),s.shape):i}}}},rL={kernelName:so,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=L(e,me(s,"float32")),l=Kt(n.shape,r);return l.length>0?H(ke(i,l),n.shape):i},b:()=>{let i=L(e,me(n,"float32")),l=Kt(s.shape,r);return l.length>0?H(ke(i,l),s.shape):i}}}},aL={kernelName:$i,gradFunc:e=>({x:()=>Mt(e)})},oL={kernelName:Oi,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>jt(n.shape,"float32")}}},iL={kernelName:Fi,gradFunc:e=>({x:()=>tt(e)})},lL={kernelName:Mi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:s}=n;return os(e,s).map(a=>()=>a)}},vw={kernelName:ro,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>Pe(e,a,s.shape)}}},uL={kernelName:ao,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,s,r]=t,a=n,o=s,i=At(a.shape,o.shape);return{a:()=>{let u=me(o,"float32"),d=L(e,L(u,Po(a,fe(u,Ie(1))))),p=Kt(a.shape,i);return p.length>0&&(d=ke(d,p)),H(d,a.shape)},b:()=>{let u=As(a,0),d=Un(u,Ds(a),tt(a)),p=L(e,L(r,d)),h=Kt(o.shape,i);return h.length>0&&(p=ke(p,h)),H(p,o.shape)}}}},cL={kernelName:oo,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,s]=t,r=As(n,0);return{x:()=>Un(r,e,L(e,s)),alpha:()=>{let a=Un(r,tt(e),L(e,n)),o=Kt(s.shape,e.shape);return o.length>0&&(a=ke(a,o)),H(a,s.shape)}}}},dL={kernelName:Wa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=ge(e,me(s,"float32")),l=Kt(n.shape,r);return l.length>0?H(ke(i,l),n.shape):i},b:()=>{let i=L(e,me(n,"float32")),l=Kt(s.shape,r);l.length>0&&(i=H(ke(i,l),s.shape));let c=yt(s);return Mt(ge(i,me(c,"float32")))}}}},pL={kernelName:Tu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,Mt(yt(n)))}}},hL={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(pl(n,6),Bd(n));return{x:()=>L(e,me(s,"float32"))}}},fL={kernelName:io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,me(Bd(n),"float32"))}}},mL={kernelName:Li,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},gL={kernelName:lo,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Hh,r,n)}}},AL={kernelName:Nu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Gh,r,n)}}},yL={kernelName:Bi,gradFunc:(e,t,n)=>{let{dims:s}=n,r=Xs(s,e.shape);return{x:()=>Fs(e,r)}}},xL={kernelName:Wi,gradFunc:e=>({x:()=>tt(e)})},bL={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Mt(ge(e,L(Po(n,1.5),2)))}}},vL={kernelName:Ui,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(tt(n),"float32"),t:()=>L(e,me(n,e.dtype)),e:()=>L(e,me(vf(n),e.dtype))}}},wL={kernelName:Eu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=As(n,Ie(0)),r=Ie(dw),a=Ie(pw),o=L(e,a),i=L(L(e,r),_s(me(n,"float32")));return Un(s,o,i)}}}},kL={kernelName:ho,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,fe(Ie(1),n)))}}},SL={kernelName:Ru,gradFunc:e=>({x:()=>tt(e)})},IL={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(gf(me(n,"float32")),e)}}},CL={kernelName:Hi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(a1(me(n,"float32")),e)}}},TL={kernelName:Gi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{begin:r,size:a}=n,o=s.shape,[i,l]=H3(s,r,a),c=[];for(let u=0;u<e.rank;u++)c.push([i[u],o[u]-i[u]-l[u]]);return{x:()=>Js(e,c)}}},NL={kernelName:go,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{dim:r}=n,a=!0,o=L(e,s);return{logits:()=>fe(o,L(ke(o,[r],a),s))}}},EL={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,ms(n))}}},ww={kernelName:ji,gradFunc:(e,t,n)=>{let{blockShape:s,paddings:r}=n;return{x:()=>mf(e,s,r)}}},kw={kernelName:qi,gradFunc:(e,t,n)=>{let{axis:s}=n;return{x:()=>kt(e,s)}}},RL={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,L(Pn(me(n,"float32")),2))}}},$L={kernelName:_u,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(me(n,"float32"),2))}}},_L={kernelName:Ao,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Ie(2);return{a:()=>L(e,L(r,fe(n,s))),b:()=>L(e,L(r,fe(s,n)))}}},DL={kernelName:vo,gradFunc:e=>({x:()=>tt(e)})},PL={kernelName:yo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=At(n.shape,s.shape);return{a:()=>{let i=e,l=Kt(n.shape,r);return l.length>0&&(i=ke(i,l)),H(i,n.shape)},b:()=>{let i=e,l=Kt(s.shape,r);return l.length>0&&(i=ke(i,l)),H(Mt(i),s.shape)}}}},FL={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,r=s.shape.slice(),{axis:a}=n;Xs(a,s.shape).forEach(c=>{r[c]=1});let i=H(e,r),l=L(i,ys(s.shape,"float32"));return{x:()=>l}}},OL={kernelName:Ki,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,yt(gf(n)))}}},ML={kernelName:xo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(fe(Ie(1),yt(n)),e)}}},zL={kernelName:Yr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{reps:r}=n;return{x:()=>{let o=tt(s);if(s.rank===1)for(let i=0;i<r[0];++i)o=ue(o,Pe(e,[i*s.shape[0]],[s.shape[0]]));else if(s.rank===2)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)o=ue(o,Pe(e,[i*s.shape[0],l*s.shape[1]],[s.shape[0],s.shape[1]]));else if(s.rank===3)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)o=ue(o,Pe(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else if(s.rank===4)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)for(let u=0;u<r[3];++u)o=ue(o,Pe(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2],u*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);return o}}}},LL={kernelName:bo,gradFunc:(e,t,n)=>{let s=n,{perm:r}=s,a=d1(r);return{x:()=>et(e,a)}}},BL={kernelName:Ji,gradFunc:(e,t,n)=>{let s=n,{axis:r}=s;return{value:()=>xn(e,r)}}},WL={kernelName:Ad,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>VL(e,n)}}};function VL(e,t){let n=ea(t,tt(t)),s=Uu(e,n),r=dl(t,Ie(0,"int32")),a=s.rank-r.rank;for(let i=0;i<a;++i)r=Zt(r,i+1);r=hr(r,ys(s.shape,"bool"));let o=tt(s);return Un(r,s,o)}var UL={kernelName:Qi,gradFunc:e=>({x:()=>tt(e)})},GL=[gw,GM,HM,jM,qM,XM,KM,ZM,YM,JM,QM,ez,sz,oz,iz,lz,uz,cz,dz,pz,hz,fz,gz,mz,xz,bz,vz,wz,kz,Sz,dL,Iz,Cz,Tz,Nz,Ez,$z,Rz,_z,Dz,Pz,Fz,Oz,Mz,zz,Lz,Bz,Wz,Vz,Hz,bw,bw,jz,Kz,Jz,Qz,eL,tL,nL,sL,rL,aL,oL,iL,lL,vw,vw,uL,cL,pL,hL,fL,mL,gL,AL,yL,xL,bL,vL,wL,kL,SL,IL,CL,TL,NL,EL,ww,ww,kw,kw,RL,_L,$L,DL,PL,FL,OL,ML,zL,LL,BL,WL,UL];for(let e of GL)Q5(e);var Sw={};Oe(Sw,{maxNorm:()=>XL,minMaxNorm:()=>YL,nonNeg:()=>ZL,unitNorm:()=>KL});var F1;function an(){return F1==null&&(F1=Dr().epsilon()),F1}function mr(){return"channelsLast"}var sa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,sa.prototype)}},gr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,gr.prototype)}},j=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,j.prototype)}},Le=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Le.prototype)}},Iw=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Iw.prototype)}};function Al(e,t){if(Array.isArray(e)){let n=[];for(let s=0;s<t;s++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Mr(e,t){if(!e)throw new Iw(t)}function Cw(e,t){let n=0;for(let s of e)s===t&&n++;return n}function is(e){return e.length===1?e[0]:e}function It(e){return Array.isArray(e)?e:[e]}function ra(e){let n=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return n[0]!=="_"?n:"private"+n}function yl(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var er={};function O1(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function M1(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>M1(t));else{let t=Object.keys(e);for(let n of t){let s=e[n];s!=null&&typeof s=="object"&&(!Array.isArray(s)&&s.type==="ndarray"&&typeof s.value=="number"?e[n]=s.value:M1(s))}}}function Ud(e,t={},n={},s="object",r=!1){if(typeof e=="string"){let a=e,o;if(a in n)o=n[a];else if(a in er)o=er[a];else if(o=t[a],o==null)throw new j(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
|
|
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return o}else{let a=e;if(a.className==null||a.config==null)throw new j(`${s}: Improper config format: ${JSON.stringify(a)}.
|
|
'className' and 'config' must set.`);let o=a.className,i,l;if(o in n?[i,l]=n[o]:o in er?[i,l]=er.className:o in t&&([i,l]=t[o]),i==null)throw new j(`Unknown ${s}: ${o}. This may be due to one of the following reasons:
|
|
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let c={};for(let h of Object.keys(er))c[h]=er[h];for(let h of Object.keys(n))c[h]=n[h];let u=a.config;u.customObjects=c;let d={...er};for(let h of Object.keys(n))er[h]=n[h];M1(a.config);let p=l(i,a.config,n,r);return er={...d},p}else{let c={...er};for(let d of Object.keys(n))er[d]=n[d];let u=new i(a.config);return er={...c},u}}}function HL(e,t){return e<t?-1:e>t?1:0}function Wf(e,t){return-1*HL(e,t)}function Mo(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function jL(e){if(e==null)throw new j(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function xl(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new j(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function z1(e,t,n=0,s=1/0){return Mr(n>=0),Mr(s>=n),Array.isArray(e)&&e.length>=n&&e.length<=s&&e.every(r=>typeof r===t)}function bn(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,s)=>bn(n,`element ${s+1} of ${t}`))):v.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Tw(e)}.`)}function Tw(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Tw(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function qL(e,t,n){let s=n!=null?n():v.now(),r;return(...o)=>{let i=n!=null?n():v.now();return i-s<t||(s=i,r=e(...o)),r}}function Nw(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function L1(e,t){return X(()=>Pn(ke(L(e,e),t,!0)))}var Gd=class extends ce.Serializable{getConfig(){return{}}},B1=class extends Gd{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 X(()=>{let t=L1(e,this.axis),n=gs(t,0,this.maxValue);return L(e,ge(n,ue(an(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};B1.className="MaxNorm";ce.registerClass(B1);var W1=class extends Gd{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return X(()=>ge(e,ue(an(),L1(e,this.axis))))}getConfig(){return{axis:this.axis}}};W1.className="UnitNorm";ce.registerClass(W1);var V1=class extends Gd{apply(e){return Or(e)}};V1.className="NonNeg";ce.registerClass(V1);var U1=class extends Gd{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 X(()=>{let t=L1(e,this.axis),n=ue(L(this.rate,gs(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,ge(n,ue(an(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};U1.className="MinMaxNorm";ce.registerClass(U1);var Ew={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function on(e){return O1(e)}function Rw(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"constraint")}function ln(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Ew?Ew[e]:e,config:{}};return Rw(n)}else return e instanceof Gd?e:Rw(e)}function XL(e){return new B1(e)}function KL(e){return new W1(e)}function ZL(){return new V1}function YL(e){return new U1(e)}var $w={};Oe($w,{constant:()=>bB,glorotNormal:()=>TB,glorotUniform:()=>CB,heNormal:()=>NB,heUniform:()=>EB,identity:()=>SB,leCunNormal:()=>RB,leCunUniform:()=>$B,ones:()=>xB,orthogonal:()=>_B,randomNormal:()=>wB,randomUniform:()=>vB,truncatedNormal:()=>kB,varianceScaling:()=>IB,zeros:()=>yB});var JL=["channelsFirst","channelsLast"],QL=["nearest","bilinear"],eB=["valid","same","causal"],tB=["max","avg"],nB=["sum","mul","concat","ave"],Zu=new Map;function qt(e){xl(JL,"DataFormat",e)}function sB(e){xl(QL,"InterpolationFormat",e)}function Os(e){xl(eB,"PaddingMode",e)}function _w(e){xl(tB,"PoolMode",e)}var Hd=[],Dw="/";function bl(e,t){Hd.push(e);try{let n=t();return Hd.pop(),n}catch(n){throw Hd.pop(),n}}function rB(){return Hd.length===0?"":Hd.join(Dw)+Dw}function Pw(e){if(!Ow(e))throw new Error("Not a valid tensor name: '"+e+"'");return rB()+e}function Fw(e){if(!Ow(e))throw new Error("Not a valid tensor name: '"+e+"'");Zu.has(e)||Zu.set(e,0);let t=Zu.get(e);if(Zu.set(e,Zu.get(e)+1),t>0){let n=`${e}_${t}`;return Zu.set(n,1),n}else return e}var aB=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Ow(e){return!!e.match(aB)}function oB(e){return e===parseInt(e.toString(),10)}function zo(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let s=1;for(let r=t;r<n;++r)s*=e[r];return s}function Yu(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s<t&&(t=s)}return t}function Lo(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s>t&&(t=s)}return t}function Ar(e,t){if(t<e)throw new j(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let s=e;s<t;++s)n.push(s);return n}function Vf(e,t){return me(e,t)}function jd(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),H(e,n)}function iB(e,t){return X(()=>{if(e.shape.length!==2)throw new j(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=jd(e,1);return j1(n,[1,t,1])})}function lB(e){let t=[zo(e.shape)];return H(e,t)}function uB(e){if(e.rank<=1)throw new j(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],zo(e.shape,1)];return H(e,t)}function vl(e,t,n){return X(()=>{switch(e.rank){case 1:return Cf(e,t,n);case 2:return k1(e,[t,0],[n,e.shape[1]]);case 3:return fl(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return ml(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Pe(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Pe(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 j(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function G1(e,t,n){return X(()=>{switch(e.rank){case 1:return Cf(e,t,n);case 2:return k1(e,[0,t],[e.shape[0],n]);case 3:return fl(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return ml(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new j(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Uf(e,t,n,s){return X(()=>{switch(e.rank){case 1:return Cf(e,t,n);case 2:switch(s){case 1:return vl(e,t,n);case 2:return G1(e,t,n);default:throw new j(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return vl(e,t,n);case 2:return fl(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return G1(e,t,n);default:throw new j(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return vl(e,t,n);case 2:return ml(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return ml(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return G1(e,t,n);default:throw new j(`The axis is not within the rank of the tensor ${s}`)}default:throw new j(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function H1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),kt(e,t)}function Mw(e,t){switch(e.rank){case 1:return mv([e,t]);case 2:return Wu([e,t],0);case 3:return gv([e,t],0);case 4:return Av([e,t],0);default:throw new j(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function j1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new j(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Ys(e,t)}function Gf(e,t=0,n=1,s,r){return zv(e,t,n,s,r)}function zr(e,t,n,s){if(e.rank<2||t.rank<2)throw new Le(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let r=e.shape.slice(-1)[0],a=t.shape.slice(-2)[0];if(r!==a)throw new Le(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let r=!1,a=!1;return Fo.matMul({a:e,b:t,transposeA:r,transposeB:a,bias:s?q1(e.rank,s,mr()):null,activation:n})}else{let r=e.shape.slice(),a=r.pop();e=H(e,[-1,a]);let o=t.shape.slice(),i=o.pop(),l=o.pop(),c=[...o,i],u=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=H(et(t,u),[l,-1]);let d=[...r,...c],p=!1,h=!1;return H(Fo.matMul({a:e,b:t,transposeA:p,transposeB:h,bias:s?q1(e.rank,s,mr()):null,activation:n}),d)}}function zw(e,t,n){return X(()=>(Array.isArray(t)?t=Ut(t,"int32"):t=me(t,"int32"),Uu(e,t,n)))}function qd(e){return L(e,e)}function q1(e,t,n){let s=t.shape;if(t.rank!==1&&t.rank!==e)throw new j(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return s.length===1?H(t,[1,s[0],1,1,1]):H(t,[1,s[3],s[0],s[1],s[2]]);if(n==="channelsLast")return s.length===1?H(t,[1,1,1,1,s[0]]):H(t,[1].concat(s))}else if(e===4){if(n==="channelsFirst")return s.length===1?H(t,[1,s[0],1,1]):H(t,[1,s[2],s[0],s[1]]);if(n==="channelsLast")return s.length===1?H(t,[1,1,1,s[0]]):H(t,[1].concat(s))}else if(e===3){if(n==="channelsFirst")return s.length===1?H(t,[1,s[0],1]):H(t,[1,s[1],s[0]]);if(n==="channelsLast")return s.length===1?H(t,[1,1,s[0]]):H(t,[1].concat(s))}else if(e<3)return t;throw new j(`Unsupported input rank by biasAdd: ${t.rank}`)}function yr(e,t,n){return X(()=>(n==null&&(n=mr()),qt(n),ue(e,q1(e.rank,t,n))))}function cB(e,t=1){if(t!==1)throw new Le(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Pd(e)}function dB(e){return X(()=>ge(e,ue(sn(e),1)))}function Lw(e,t,n,s){return X(()=>Jv(e,t,n,s))}function pB(e){return X(()=>{let t=ue(.5,L(.2,e));return gs(t,0,1)})}function Xd(e,t,n=!1){return n?e():t()}var hB=["fanIn","fanOut","fanAvg"],fB=["normal","uniform","truncatedNormal"];function mB(e){xl(hB,"FanMode",e)}function gB(e){xl(fB,"Distribution",e)}var tr=class extends ce.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},X1=class extends tr{apply(e,t){return jt(e,t)}};X1.className="Zeros";ce.registerClass(X1);var Hf=class extends tr{apply(e,t){return ys(e,t)}};Hf.className="Ones";ce.registerClass(Hf);var K1=class extends tr{constructor(e){super();if(typeof e!="object")throw new j(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new j(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return X(()=>L(Ie(this.value),ys(e,t)))}getConfig(){return{value:this.value}}};K1.className="Constant";ce.registerClass(K1);var Z1=class extends tr{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 ju(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};Z1.className="RandomUniform";ce.registerClass(Z1);var Y1=class extends tr{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 Le(`randomNormal does not support dType ${t}.`);return Gf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Y1.className="RandomNormal";ce.registerClass(Y1);var J1=class extends tr{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 Le(`truncatedNormal does not support dType ${t}.`);return Ef(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};J1.className="TruncatedNormal";ce.registerClass(J1);var Q1=class extends tr{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return X(()=>{if(e.length!==2||e[0]!==e[1])throw new j("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,i1(e[0]))})}getConfig(){return{gain:this.gain}}};Q1.className="Identity";ce.registerClass(Q1);function AB(e,t="channelsLast"){let n,s;if(qt(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=zo(e,2);n=e[1]*r,s=e[0]*r}else if(t==="channelsLast"){let r=zo(e,0,e.length-2);n=e[e.length-2]*r,s=e[e.length-1]*r}}else{let r=zo(e);n=Math.sqrt(r),s=Math.sqrt(r)}return[n,s]}var ls=class extends tr{constructor(e){super();if(e.scale<0)throw new j(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,mB(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,gB(this.distribution),this.seed=e.seed}apply(e,t){let n=AB(e),s=n[0],r=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,s):this.mode==="fanOut"?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Le(`${this.getClassName()} does not support dType ${t}.`);return Ef(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return ju(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};ls.className="VarianceScaling";ce.registerClass(ls);var jf=class extends ls{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ls.className}};jf.className="GlorotUniform";ce.registerClass(jf);var qf=class extends ls{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ls.className}};qf.className="GlorotNormal";ce.registerClass(qf);var Xf=class extends ls{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ls.className}};Xf.className="HeNormal";ce.registerClass(Xf);var Kf=class extends ls{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ls.className}};Kf.className="HeUniform";ce.registerClass(Kf);var Zf=class extends ls{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ls.className}};Zf.className="LeCunNormal";ce.registerClass(Zf);var Yf=class extends ls{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ls.className}};Yf.className="LeCunNormal";ce.registerClass(Yf);var eA=class extends tr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Le("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return X(()=>{if(e.length<2)throw new Le("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,s=Gf(n,0,1,"float32"),r=uw.gramSchmidt(s);return e[0]>e[1]&&(r=et(r)),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};eA.className="Orthogonal";ce.registerClass(eA);var Bw={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 Ww(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"initializer")}function zt(e){return O1(e)}function $t(e){if(typeof e=="string"){let t=e in Bw?Bw[e]:e;if(t==="GlorotNormal")return new qf;if(t==="GlorotUniform")return new jf;if(t==="HeNormal")return new Xf;if(t==="HeUniform")return new Kf;if(t==="LeCunNormal")return new Zf;if(t==="LeCunUniform")return new Yf;{let n={};return n.className=t,n.config={},Ww(n)}}else return e instanceof tr?e:Ww(e)}function yB(){return new X1}function xB(){return new Hf}function bB(e){return new K1(e)}function vB(e){return new Z1(e)}function wB(e){return new Y1(e)}function kB(e){return new J1(e)}function SB(e){return new Q1(e)}function IB(e){return new ls(e)}function CB(e){return new jf(e)}function TB(e){return new qf(e)}function NB(e){return new Xf(e)}function EB(e){return new Kf(e)}function RB(e){return new Zf(e)}function $B(e){return new Yf(e)}function _B(e){return new eA(e)}var Vw={};Oe(Vw,{Layer:()=>nt,RNN:()=>oa,RNNCell:()=>tp,activation:()=>hV,add:()=>wV,alphaDropout:()=>aU,average:()=>kV,averagePooling1d:()=>vy,averagePooling2d:()=>wy,averagePooling3d:()=>ky,avgPool1d:()=>_V,avgPool2d:()=>PV,avgPool3d:()=>OV,avgPooling1d:()=>DV,avgPooling2d:()=>FV,avgPooling3d:()=>MV,batchNormalization:()=>EV,bidirectional:()=>YV,concatenate:()=>SV,conv1d:()=>rV,conv2d:()=>aV,conv2dTranspose:()=>oV,conv3d:()=>iV,conv3dTranspose:()=>lV,convLstm2d:()=>qV,convLstm2dCell:()=>XV,cropping2D:()=>cV,dense:()=>fV,depthwiseConv2d:()=>pV,dot:()=>NV,dropout:()=>mV,elu:()=>JW,embedding:()=>vV,flatten:()=>AV,gaussianDropout:()=>rU,gaussianNoise:()=>sU,globalAveragePooling1d:()=>zV,globalAveragePooling2d:()=>LV,globalMaxPool1d:()=>QV,globalMaxPool2d:()=>eU,globalMaxPooling1d:()=>t7,globalMaxPooling2d:()=>n7,gru:()=>WV,gruCell:()=>VV,input:()=>vk,inputLayer:()=>YW,layerNormalization:()=>RV,leakyReLU:()=>eV,lstm:()=>UV,lstmCell:()=>GV,masking:()=>oU,maxPool1d:()=>tU,maxPool2d:()=>nU,maxPooling1d:()=>s7,maxPooling2d:()=>r7,maxPooling3d:()=>BV,maximum:()=>IV,minimum:()=>CV,multiply:()=>TV,permute:()=>bV,prelu:()=>tV,reLU:()=>QW,repeatVector:()=>yV,reshape:()=>xV,rnn:()=>KV,separableConv2d:()=>uV,simpleRNN:()=>HV,simpleRNNCell:()=>jV,softmax:()=>nV,spatialDropout1d:()=>gV,stackedRNNCells:()=>ZV,thresholdedReLU:()=>sV,timeDistributed:()=>JV,upSampling2d:()=>dV,zeroPadding2d:()=>$V});var DB=0;function Uw(){return DB++}var Jf={};function Qf(e=""){return e in Jf||(Jf[e]=0),Jf[e]+=1,e+Jf[e].toString()}function tA(e){return Array.isArray(e)&&Array.isArray(e[0])}function em(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ve(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ft(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new j(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function tm(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,r)=>s*r);return t}var Gw="Variable",Hw=class{constructor(e,t="float32",n=Gw,s=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Uw(),n=n==null?Gw:n,this.originalName=Pw(n),this.name=Fw(this.originalName),this.trainable_=s,this.constraint=r,this.val=qv(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),PB(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 PB(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function nA(e){return e.map(t=>t.read())}function sA(e){e.forEach(t=>{t[0].write(t[1])})}var Yt=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||{}}},xr=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=Uw(),a!=null&&(this.originalName=Pw(a),this.name=Fw(this.originalName)),this.rank=t.length}},FB=0,nm=class{constructor(e,t){this.callArgs=t,this.id=FB++,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}}},OB=0,nt=class extends ce.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=OB++,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=ra(n)+"_"+Qf(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}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 gr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return is(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return is(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new sa(`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 sa(`Layer ${this.name} is not connected, no input to return.`);return is(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new sa(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new sa(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return is(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=It(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=It(this.inputSpec);if(e.length!==t.length)throw new j(`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 s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),c=r.axes[i],u=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.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=It(e),s=!0;for(let a of n)if(!(a instanceof xr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof xr){r=!1;break}if(s===r)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return bl(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of It(e))a.push(o.shape);this.build(is(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=It(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=is(i),this.activityRegularizer!=null)throw new Le("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=MB(e),o=this.computeOutputShape(a),i,l=zB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((c,u)=>new xr(l,c,this,It(e),t,this.name,u)):i=new xr(l,o,this,It(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Le("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 sa(`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 sa(`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 gr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return tm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return nA(e?this.trainableWeights:this.weights)}setWeights(e){X(()=>{let t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${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=[],s=nA(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!v.arraysEqual(a.shape,i.shape))throw new j(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}sA(n)})}addWeight(e,t,n,s,r,a,o,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=i!=null?i():$t("zeros"));let l=s.apply(t,n),c=new Hw(l,n,e,a,o);return l.dispose(),r!=null&&this.addLoss(()=>r.apply(c.read())),a==null&&(a=!0),a?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=It(e);t=It(t),n=It(n),s=It(s),r=em(r),a=em(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new nm({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 MB(e){e=It(e);let t=[];for(let n of e)t.push(n.shape);return is(t)}function zB(e){return"float32"}function jw(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],c=jw(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Ju=class extends nt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Qf("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 j("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 j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("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 s=new xr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new nm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`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}}};Ju.className="InputLayer";ce.registerClass(Ju);function qw(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 j("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 Ju({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Bo(e){if(e==null)return;let t=[],n=[],s=[];for(let r in e){let a=e[r];if(typeof a!="number"){let o=a;t.push(o.data()),n.push(r),s.push(o)}}if(t.length>0){let r=await Promise.all(t);for(let a=0;a<r.length;++a)e[n[a]]=r[a][0];ne(s)}}function Xw(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var Kw;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Kw||(Kw={}));var LB=125,Qu=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){}},Zw=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)}},BB=class extends Qu{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 s in t){let r=t[s];if(typeof r=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+r*n;else{let a;s in this.totals?a=this.totals[s]:this.totals[s]=0;let o=X(()=>ue(this.totals[s],L(r,n)));this.totals[s]=o,a!=null&&a.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:X(()=>{let s=L(ge(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),An(t[n])}))}},Yw=class extends Qu{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let a=this.history[r];for(let o=0;o<a.length;++o)if(typeof a[o]!="number"){let i=a[o];e.push(i.data()),t.push(r),n.push(o)}}let s=await Promise.all(e);for(let r=0;r<s.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=s[r][0]}},Jw=class extends Qu{constructor(e,t){super();if(this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||cw,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=LB),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");v.isNumber(this.yieldEvery)&&(this.maybeWait=qL(this.maybeWait.bind(this),this.yieldEvery,this.nowFunc)),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 s=[];this.yield!=null&&(await Bo(n),s.push(this.yield(e,t,n))),s.push(this.nextFrameFunc()),await Promise.all(s)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Bo(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Bo(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(this.nextFrameFunc()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Bo(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Bo(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(this.nextFrameFunc()):v.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Bo(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Bo(e),await this.trainEnd(e))}};function Qw(e,t){return e==null&&(e={}),e instanceof Qu?[e]:Array.isArray(e)&&e[0]instanceof Qu?e:It(e).map(s=>new Jw(s,t))}var Lr=class{constructor(){}static registerCallbackConstructor(e,t){v.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),Lr.checkForDuplicate(t),Lr.constructors[e]==null&&(Lr.constructors[e]=[]),Lr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in Lr.constructors)Lr.constructors[+t].forEach(s=>{if(s===e)throw new j("Duplicate callback constructor.")})}static clear(){Lr.constructors={}}static createCallbacks(e){let t=[];for(let n in Lr.constructors){let s=+n;e>=s&&t.push(...Lr.constructors[s])}return t.map(n=>new n)}},rA=Lr;rA.constructors={};function ek(e,t,n,s,r,a,o,i,l){let c=new Yw,u=[new BB,...rA.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let d=new Zw(u);return d.setParams({epochs:n,initialEpoch:s,samples:r,steps:a,batchSize:o,verbose:t,doValidation:i,metrics:l}),{callbackList:d,history:c}}function br(e,t={},n=!1){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"layer",n)}function sm(e,t){return X(()=>{e.dtype!=="float32"&&(e=me(e,"float32"));let n=ke(qd(e),t,!0),s=Vu(n.shape,an()),r=Pn(ea(n,s));return ge(e,r)})}function wl(e,t){return X(()=>Vt(qd(fe(t,e)),-1))}function rm(e,t){return X(()=>Vt(sn(fe(t,e)),-1))}function ec(e,t){return X(()=>{let n=fe(e,t),s=gs(sn(e),an(),Number.MAX_VALUE),r=sn(ge(n,s));return L(100,Vt(r,-1))})}function WB(e,t){return X(()=>{let n=gs(t,an(),Number.MAX_VALUE),s=Ds(ue(1,n)),r=gs(e,an(),Number.MAX_VALUE),a=Ds(ue(1,r));return Vt(qd(fe(s,a)),-1)})}function VB(e,t){return X(()=>{let n=ea(0,fe(1,L(e,t)));return Vt(qd(n),-1)})}function UB(e,t){return X(()=>{let n=ea(0,fe(1,L(e,t)));return Vt(n,-1)})}function GB(e,t){return X(()=>{let n=ke(L(e,t),-1),s=yn(L(fe(1,e),t),-1);return ea(0,ue(1,fe(s,n)))})}function HB(e,t){return X(()=>{let n=Math.log(2),s=fe(t,e),r=fe(ue(s,Gu(L(-2,s))),n);return Vt(r,-1)})}function Kd(e,t,n=!1){return X(()=>{if(n)t=Xu(t);else{let s=ke(t,t.shape.length-1,!0);t=ge(t,s)}return t=gs(t,an(),1-an()),Mt(ke(L(me(e,"float32"),Ds(t)),t.shape.length-1))})}function am(e,t,n=!1){return X(()=>{let s=me(Fd(lB(e)),"int32");t=gs(t,an(),1-an());let r=t.shape,a=H(Rd(s,r[r.length-1]),r);return Kd(a,t,n)})}function jB(e,t){if(!v.arraysEqual(e.shape,t.shape))throw new j(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return X(()=>{let n=Or(t),s=Mt(sn(t));return ue(fe(n,L(t,e)),xf(_s(s)))})}function om(e,t){return X(()=>{let n;return n=gs(t,an(),1-an()),n=Ds(ge(n,fe(1,n))),Vt(jB(e,n),-1)})}function qB(e,t){return X(()=>{let n=gs(e,an(),1),s=gs(t,an(),1);return ke(L(e,Ds(ge(n,s))),-1)})}function XB(e,t){return X(()=>{let n=Ds(ue(an(),t));return Vt(fe(t,L(e,n)),-1)})}function aA(e,t){return X(()=>{let n=sm(e,-1),s=sm(t,-1),r=L(n,s);return Mt(ke(r,-1))})}var im={meanSquaredError:wl,meanAbsoluteError:rm,meanAbsolutePercentageError:ec,meanSquaredLogarithmicError:WB,squaredHinge:VB,hinge:UB,categoricalHinge:GB,logcosh:HB,categoricalCrossentropy:Kd,sparseCategoricalCrossentropy:am,binaryCrossentropy:om,kullbackLeiblerDivergence:qB,poisson:XB,cosineProximity:aA};function oA(e){if(typeof e=="string"){if(e in im)return im[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new j(t)}else return e}function iA(e,t){return X(()=>{let n=L(.5,Ps(t)),s=Vf(As(t,n),e.dtype);return Vt($s(e,s),-1)})}function lA(e,t){return X(()=>Vf($s(Zs(e,-1),Zs(t,-1)),"float32"))}function tk(e,t){return X(()=>me(ke(hr($s(e,1),$s(t,1))),"float32"))}function KB(e,t){return X(()=>me(ke(hr($s(e,1),$s(t,0))),"float32"))}function ZB(e,t){return X(()=>me(ke(hr($s(e,0),$s(t,1))),"float32"))}function nk(e,t){return X(()=>{let n=tk(e,t),s=ZB(e,t),r=ue(n,s);return me(Un(As(r,0),ge(n,r),0),"float32")})}function YB(e,t){return X(()=>{let n=tk(e,t),s=KB(e,t),r=ue(n,s);return me(Un(As(r,0),ge(n,r),0),"float32")})}function sk(e,t){return om(e,t)}function rk(e,t){return e.rank===t.rank&&(e=it(e,[e.rank-1])),t=Zs(t,-1),t.dtype!==e.dtype&&(t=me(t,e.dtype)),me($s(e,t),"float32")}var JB=wl,QB=wl,eW=rm,tW=rm,nW=ec,sW=ec,uA=Kd,rW=aA,ak=am,lm={binaryAccuracy:iA,categoricalAccuracy:lA,precision:nk,categoricalCrossentropy:uA,sparseCategoricalCrossentropy:ak,mse:JB,MSE:QB,mae:eW,MAE:tW,mape:nW,MAPE:sW,cosine:rW};function aW(e){if(typeof e=="string"&&e in lm)return lm[e];if(typeof e!="string"&&e!=null)return e;throw new j(`Unknown metric ${e}`)}function um(e){if(Mr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(im))if(im[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(lm))if(lm[n]===e){t=n;break}return t!==void 0?t:e.name}}function oW(e){let t={Adagrad:()=>gl.adagrad(.01),Adadelta:()=>gl.adadelta(1,.95,an()),Adam:()=>gl.adam(.001,.9,.999,an()),Adamax:()=>gl.adamax(.002,.9,.999,an(),0),RMSProp:()=>gl.rmsprop(.001,.9,0,an()),SGD:()=>gl.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 j(`Unknown Optimizer ${e}`)}var ok=1*1024*1024;function ik(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!cA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let s=JSON.stringify(e);s.length>ok&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${ok}.`)}}function cA(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"||!cA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!cA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function iW(e,t,n,s=console.log){let r=uW(e),a=["Layer (type)","Output shape","Param #"];r?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(u=>Math.floor(t*u)));let o;if(!r){a.push("Receives inputs"),o=[];for(let u in e.nodesByDepth)o.push(...e.nodesByDepth[u])}s("_".repeat(t)),cm(a,n,s),s("=".repeat(t));let i=e.layers;for(let u=0;u<i.length;++u)r?cW(i[u],n,s):dW(i[u],n,o,s),s((u===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=lW(e),c=tm(e.nonTrainableWeights);s(`Total params: ${l+c}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${c}`),s("_".repeat(t))}function lW(e){let t;return e.collectedTrainableWeights!=null?t=tm(e.collectedTrainableWeights):t=tm(e.trainableWeights),t}function uW(e){let t=!0,n=[],s=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}s.push(...r)}if(t)for(let r of e.layers){let a=!1;for(let o of r.inboundNodes)if(s.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function cm(e,t,n=console.log){let s="";for(let r=0;r<e.length;++r)r>0&&(s=s.slice(0,s.length-1)+" "),s+=e[r],s=s.slice(0,t[r]),s+=" ".repeat(t[r]-s.length);n(s)}function cW(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(i){s="multiple"}let r=e.name,a=e.getClassName(),o=[`${r} (${a})`,s,e.countParams().toString()];cm(o,t,n)}function dW(e,t,n,s){let r;try{r=JSON.stringify(e.outputShape)}catch(u){r="multiple"}let a=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let d=0;d<u.inboundLayers.length;++d){let p=u.inboundLayers[d].name,h=u.nodeIndices[d],f=u.tensorIndices[d];a.push(`${p}[${h}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],c=[`${o} (${i})`,r,e.countParams().toString(),l];cm(c,t,s);for(let u=1;u<a.length;++u)cm(["","","",a[u]],t,s)}function lk(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Zd(e,t){if(e===null)return null;if(typeof e=="string")return yl(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];lk(t,r,a)?n.push(a):n.push(Zd(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s];if(s==="name"&&typeof r=="string")n[s]=r;else{let a=yl(s);n[a]=Zd(r,a)}}return n}}function dA(e,t){if(e==null)return null;if(typeof e=="string")return ra(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];lk(t,r,a)?n.push(a):n.push(dA(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s],a=ra(s);(s==="name"||s==="className")&&typeof r=="string"?n[a]=r:n[a]=dA(r,s)}return n}}var pA="0.0.0";function pW(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(n){throw new j(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var kl=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof kl)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=pW(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new j(`Duplicate key: name=${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 xr){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof xr){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&ne(this.id2Mask)}},hA={},uk={};function Yd(e,t,n,s){let r=n==null?!1:n.training,a=Array.isArray(e),o=a?e:[e],i=o.map(f=>f.name),l=[],c=t.names();for(let f of i)c.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);s!=null&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);let u=i.join(",")+"|"+t.names().join(","),d,p;if(hA[u]==null){let f=hW(o,t);d=f.sorted,p=f.recipientCounts,hA[u]=d,uk[u]=p}d=hA[u],p={},r||Object.assign(p,uk[u]);let h=new kl(t);for(let f=0;f<d.length;++f){if(s!=null){let R=lf().numTensors;R>s.maxNumTensors&&(s.maxNumTensors=R),R<s.minNumTensors&&(s.minNumTensors=R)}let m=d[f],g=m.sourceLayer;if(g instanceof Ju)continue;let A=[],x=[],y=[],b=!1;for(let R of m.inputs){let O=h.getValue(R),$=h.getMask(R);A.push(O),x.push($),$!=null&&(b=!0),r||(p[R.name]--,p[R.name]===0&&!t.hasKey(R)&&i.indexOf(R.name)===-1&&!O.isDisposed&&R.sourceLayer.stateful!==!0&&y.push(O))}b&&(n=n||{},n.mask=x[0]);let w=It(g.apply(A,n)),k=null;g.supportsMasking&&(k=g.computeMask(A,x));let I=mW(m),N=Array.isArray(I)?I:[I];for(let R=0;R<N.length;++R){h.hasKey(N[R])||h.add(N[R],w[R],Array.isArray(k)?k[0]:k);let O=i.indexOf(N[R].name);O!==-1&&(l[O]=w[R])}r||ne(y)}return h.disposeMasks(),a?l:l[0]}function hW(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=ck(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:o,recipientMap:i}=ck(a,t);for(let l of o)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in i)s[l]==null&&(s[l]=new Set),i[l].forEach(c=>s[l].add(c))}}return{sorted:n,recipientCounts:fW(s)}}function fW(e){let t={};for(let n in e)t[n]=e[n].size;return t}function ck(e,t){let n=new Set,s=[],r={};for(let i of t.names())n.add(i);let a=[],o=[];for(a.push(e);a.length>0;){let i=a[a.length-1];if(n.has(i.name)){a.pop();continue}let l=o[o.length-1]===a.length-1;if(i.inputs.length===0||l)a.pop(),s.push(i),n.add(i.name),l&&o.pop();else{o.push(a.length-1);for(let c of i.inputs)r[c.name]==null&&(r[c.name]=new Set),r[c.name].add(i.name),!n.has(c.name)&&a.push(c)}}return{sorted:s,recipientMap:r}}function mW(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Br=class extends nt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=Qf(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],Mo(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);Mo(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 x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(x),this.outputLayersNodeIndices.push(y),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;Mr(y===0,"input layer has >1 nodes"),Mr(b===0,"input layer has >1 tensors"),this.inputLayers.push(x),this.inputLayersNodeIndices.push(y),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let x=this.inputLayers[A];if(!(x instanceof Ju))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${x.getClassName()}.`);this.inputNames.push(x.name),this.feedInputShapes.push(x.batchInputShape),this.feedInputNames.push(x.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={},s={},r={},a={},o=[],i=(A,x,y,b,w,k)=>{(b==null||w==null||k==null)&&(b=A.sourceLayer,w=A.nodeIndex,k=A.tensorIndex);let I=b.inboundNodes[w];if(y.indexOf(I)!==-1)throw new gr(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(x.indexOf(I)!==-1)return;this.containerNodes.add(Br.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),y.indexOf(I)===-1&&y.push(I);let N=I.inboundLayers.length;for(let R=0;R<N;R++){let O=I.inputTensors[R],$=I.inboundLayers[R],P=I.nodeIndices[R],T=I.tensorIndices[R];i(O,x,y,$,P,T)}for(x.push(I);y.indexOf(I)>=0;)y.splice(y.indexOf(I),1);o.push(I)},l=[],c=[];for(let A of this.outputs)i(A,l,c);let u=o.slice().reverse();for(let A of u){n[A.id]=A,A.id in t||(t[A.id]=0);let x=t[A.id],y=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];x=Math.max(x,y),s[A.outboundLayer.id]=x,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=x;for(let b=0;b<A.inboundLayers.length;b++){let w=A.inboundLayers[b],k=A.nodeIndices[b],I=w.inboundNodes[k],N=t[I.id]==null?0:t[I.id];t[I.id]=Math.max(x+1,N),n[I.id]=I}}let d={};for(let A in t){let x=t[A];x in d||(d[x]=[]),d[x].push(n[A])}let p={};for(let A in s){let x=s[A];x in p||(p[x]=[]),p[x].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(Wf);this.layers=[];for(let A of h){let x=p[A];x.sort((y,b)=>{let w=a[y.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let y of x)y instanceof Br&&this.internalContainerRefs.push(y),this.layers.push(y)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(Wf);let f=this.inputs.slice(),m=[];for(let A of h)for(let x of d[A]){let y=x.outboundLayer;if(y!=null){for(let b of x.inputTensors)if(f.indexOf(b)===-1)throw new gr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${y.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(y.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let x=g.filter(y=>y===A).length;if(x!==1)throw new gr(`The name "${A}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new nm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 j("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={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new j(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new j(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new j(`${a.length} of ${s} weights are not set: ${a}`)}sA(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${pA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=dA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return X(()=>{e=It(e);let n=new kl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Yd(this.outputs,n,t)})}computeMask(e,t){return X(()=>{e=It(e);let n;return t==null?n=Al(null,e.length):n=It(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=em(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Wf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],x=`${m.name}_${g}_${A}`,y=n[x];u.push(y)}let d=c.computeOutputShape(is(u)),p=em(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];Mr(i in n),r.push(n[i])}return is(r)}runInternalGraph(e,t){t==null&&(t=Al(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Wf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,x;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[y,b]=h[0];f.mask==null&&(f.mask=b),A=It(u.call(y,f)),x=It(u.computeMask(y,b)),m=[y],g=[b]}else m=h.map(y=>y[0]),g=h.map(y=>y[1]),f.mask==null&&(f.mask=g),A=It(u.call(m,f)),x=It(u.computeMask(m,g));if(u.activityRegularizer)throw new Le("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let y=0;y<p.length;++y){let b=p[y],w=A[y],k=x[y];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Mr(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Br?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Br.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`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 j("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return X(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Br.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=Br.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],A=d.nodeIndices[m],x=d.tensorIndices[m],y=Br.nodeKey(g,A),b=t[y];b==null&&(b=0),f.push([g.name,b,x,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Br.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Br.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let A=[],x;for(let y of g){let b=y[0],w=y[1],k=y[2];if(x=y[3]==null?{}:y[3],!(b in r)){o(m,g);return}let I=r[b];if(I.inboundNodes.length<=w){o(m,g);return}let N=I.inboundNodes[w];A.push(N.outputTensors[k])}A.length>0&&m.apply(is(A),x)}function l(m){let g=m.name,A=br(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(y=>{if(!(y instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${y}`);o(A,y)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!jL(a);)for(let m of u){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let x of A)i(g,x)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],x=m[2];Mr(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],x=m[2];Mr(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[x])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new j("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(){X(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gW(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===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!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function dk(e,t){return gW(e,t,"classWeight")}async function pk(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=X(()=>{if(e.shape.length===1)return Vn(e);if(e.shape.length===2){if(e.shape[1]>1)return Zs(e,1);if(e.shape[1]===1)return H(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),a=Array.from(await r.data());ne(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Ut(o,"float32")}else return null}function AW(e,t){return L(e,t)}var yW=32;function hk(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=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 a=fk("input",e.inputNames,n),o=fk("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function fk(e,t,n){if(n instanceof Qe)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function xW(e){if(e.length===3)throw new Le("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function bW(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(mk(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=xW(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Qw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=ek(u,d,n.epochs,null,null,vW(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let A=0,x=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let y=await m.next();if(s&&y.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(y.value!=null){let{xs:b,ys:w}=hk(e,y.value),k={};k.batch=x,k.size=b[0].shape[0],await p.onBatchBegin(x,k);let I=[];if(n.classWeight!=null){let O=dk(n.classWeight,e.outputNames);for(let $=0;$<O.length;++$)I.push(await pk(w[$],null,O[$]))}let N=b.concat(w).concat(I),R=i(N);ne(N);for(let O=0;O<l.length;++O){let $=l[O],P=R[O];k[$]=P,An(P)}await p.onBatchEnd(x,k),Xw(k),x++,A++}if(s?A>=n.batchesPerEpoch:y.done){if(r){let b;mk(n.validationData)?b=It(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=It(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?yW:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,g),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function vW(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function mk(e){return typeof e.iterator=="function"}function wW(e){return typeof e.next=="function"}async function kW(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Le("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=wW(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let c=await o.next();if(a=X(()=>{if(c.value){let{xs:u,ys:d}=hk(e,c.value),p=u.concat(d),h=X(()=>r(p));if(ne(p),l===0)for(let m=0;m<h.length;++m)a.push(Ie(0));let f=p[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],A=a[m];a[m]=X(()=>ue(a[m],L(f,g))),l>0&&ne(A)}ne(h),i+=f,++l}return a}),c.done){s&&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<a.length;++c){let u=a[c];a[c]=ge(a[c],i),ne(u)}return is(a)}function fA(e){v.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Jd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>vl(s,t,n-t)):vl(e,t,n-t)}function mA(e,t){return X(()=>e==null?null:Array.isArray(e)?e.map(n=>mA(n,t)):zw(e,t.dtype==="int32"?t:me(t,"int32")))}function gA(e,t){let n=[],s=0,r=null;for(;s<e;)r=s+t,r>=e&&(r=e),n.push([s,r]),s=r;return n}async function SW(e,t,n,s,r,a,o,i,l,c,u,d,p,h,f){r==null&&(r=32),a==null&&(a=1),u==null&&(u=!0),p==null&&(p=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,h==null))throw new j("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),A;g!=null&&(A=Ar(0,g)),o==null&&(o=1);let{callbackList:x,history:y}=ek(i,o,a,p,g,h,r,m,d);x.setModel(e),e.history=y,await x.onTrainBegin(),e.stopTraining_=!1;for(let b=p;b<a;++b){await x.onEpochBegin(b);let w={};if(h!=null)throw new Le("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new Le("batch shuffling is not implemneted yet");u&&v.shuffle(A);let k=Ut(A),I=gA(g,r);for(let N=0;N<I.length;++N){let R={};if(await x.onBatchBegin(N,R),X(()=>{let O=I[N][0],$=I[N][1],P=vl(k,O,$-O);R.batch=N,R.size=$-O;let T=mA(n,P),F=t(T);for(let U=0;U<s.length;++U){let q=s[U],z=F[U];R[q]=z,An(z)}if(N===I.length-1&&m){let U=e.testLoop(l,c,r);for(let q=0;q<s.length;++q){let z=s[q],K=U[q];An(K),w["val_"+z]=K}}}),await x.onBatchEnd(N,R),Xw(R),e.stopTraining_)break}k.dispose()}if(await x.onEpochEnd(b,w),e.stopTraining_)break}return await x.onTrainEnd(),await e.history.syncData(),e.history}async function IW(e,t,n,s={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,a,o,i,l,c,u;try{let d=s.batchSize==null?32:s.batchSize;fA(d);let p=!1,h=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,p,d);r=h[0],a=h[1],u=h[2];let f=!1,m;if(s.validationData!=null&&s.validationData.length>0){if(f=!0,s.validationData.length===2)o=s.validationData[0],i=s.validationData[1];else throw s.validationData.length===3?new Le("validationData including sample weights is not supported yet."):new j(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);let I=!0,N=await e.standardizeUserData(o,i,null,null,I,d);l=N[0],c=N[1],m=l.concat(c)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){f=!0;let I=Math.floor(r[0].shape[0]*(1-s.validationSplit)),N=r[0].shape[0];l=Jd(r,I,N),r=Jd(r,0,I),c=Jd(a,I,N),a=Jd(a,0,I),m=l.concat(c)}else s.validationSteps!=null&&(f=!0);let g=r.concat(a).concat(u);e.checkTrainableWeightsConsistency();let A=e.makeTrainFunction(),x=e.getDedupedMetricsNames(),y,b;f?(e.makeTestFunction(),y=e.testFunction,b=x.slice().concat(x.map(I=>"val_"+I))):(y=null,m=[],b=x.slice());let w=Qw(s.callbacks,s.yieldEvery);return await SW(e,A,g,x,d,s.epochs,s.verbose,w,y,m,s.shuffle,b,s.initialEpoch,null,null)}finally{e.isTraining=!1,Sl(r,t),Sl(a,n),Sl(l,o),Sl(c,i),u!=null&&ne(u)}}function gk(e){let t=[];e instanceof Qe&&(e=[e]);for(let n=0;n<e.length;++n){let s=e[n];if(s.rank===1)t.push(jd(s,1));else{if(s.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(s)}}return t}function Sl(e,t){if(e==null)return;let n=[];if(t instanceof Qe)n.push(t.id);else if(Array.isArray(t))t.forEach(r=>n.push(r.id));else if(t!=null)for(let r in t){let a=t[r];n.push(a.id)}let s=[];if(e instanceof Qe)n.indexOf(e.id)===-1&&s.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&s.push(r)});else if(e!=null)for(let r in e){let a=e[r];n.indexOf(a.id)===-1&&s.push(a)}s.forEach(r=>{r.isDisposed||r.dispose()})}function CW(e){return e instanceof Qe}function AA(e){return Array.isArray(e)}function Ak(e){return!CW(e)&&!AA(e)}function yk(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(AA(e)&&e.length>0)o=!0;else if(Ak(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new j(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(Ak(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new j(`No data provided for "${o}". Need data for each key in: ${t}`);a.push(e[o])}}else if(AA(e)){if(e=e,e.length!==t.length)throw new j(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(e=e,t.length>1)throw new j(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=gk(a),n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new j(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let c=i.shape[l],u=n[o][l];if(u!=null&&u>=0&&c!==u)throw new j(`${r} expected a batch of elements where each example has shape [${n[o].slice(1,n[o].length)}] (i.e.,tensor shape [*,${n[o].slice(1,n[o].length)}]) but the ${r} received an input with ${i.shape[0]} examples, each with shape [${i.shape.slice(1,i.shape.length)}] (tensor shape [${i.shape}])`)}}return a}function TW(e,t,n){let s=Mo(e.map(a=>a.shape[0]));s.sort();let r=Mo(t.map(a=>a.shape[0]));if(r.sort(),s.length>1)throw new j(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(a=>a.shape))}`);if(r.length>1)throw new j(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(a=>a.shape))}`);if(s.length>0&&r.length>0&&!v.arraysEqual(s,r))throw new j(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`)}function NW(e,t,n){let s=[wl,om,Kd];for(let r=0;r<e.length;++r){let a=e[r],o=t[r],i=n[r];if(o!=null){if(o===Kd&&a.shape[a.shape.length-1]===1)throw new j(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(s.indexOf(o)!==-1){let l=a.shape.slice(1),c=i.slice(1);for(let u=0;u<l.length;++u){let d=l[u],p=c[u];if(p!=null&&d!==p)throw new j(`A target Tensor with shape ${a.shape} was passed for an output of shape ${i}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function xk(e,t,n,s=!0,r=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new j(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);a=e}else{if(t.length>1)throw new j(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new j(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let c=i.shape[l],u=n[o][l];if(u!=null&&u!==c)throw new j(`Error when checking ${r}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}function EW(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>[]);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: ${e}`);if(Array.isArray(n))return t.map(s=>n);{let s=[];for(let r of t){let a=n.hasOwnProperty(r)?n[r]:[];Array.isArray(a)||(a=[a]),s.push(a)}return s}}var RW="layers-model",aa=class extends Br{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new j("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");iW(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=oW(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof na))throw new j("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new j(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(oA(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new j(`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(o=>oA(o))}else{let a=oA(e.loss);this.outputs.forEach(o=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a<this.outputs.length;++a){let o=this.internalOutputShapes[a],i=this.outputNames[a];this.feedOutputNames.push(i),this.feedOutputShapes.push(o),this.feedLossFns.push(this.lossFunctions[a])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],bl("loss",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=this.lossFunctions[a];this.outputs.length>1&&(this.metricsTensors.push([o,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let s=EW(e.metrics,this.outputNames),r=(a,o,i)=>{this.outputNames.length>1&&(o=this.outputNames[a]+"_"+o),this.metricsNames.push(o),this.metricsTensors.push([i,a])};bl("metric",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=s[a];(l=>{let c="",u,d,p;for(let h of l){if(typeof h=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(h)!==-1){let m=this.internalOutputShapes[a];m[m.length-1]===1||this.lossFunctions[a]===om?["accuracy","acc"].indexOf(h)!==-1?d=iA:["crossentropy","ce"].indexOf(h)!==-1&&(d=sk):this.lossFunctions[a]===am?["accuracy","acc"].indexOf(h)!==-1?d=rk:["crossentropy","ce"].indexOf(h)!==-1&&(d=ak):["accuracy","acc"].indexOf(h)!==-1?d=lA:["crossentropy","ce"].indexOf(h)!==-1&&(d=uA);let g;["accuracy","acc"].indexOf(h)!==-1?g="acc":["crossentropy","ce"].indexOf(h)!==-1&&(g="ce"),p=d,u=c+g}else p=aW(h),u=c+um(h);let f;bl(u,()=>{f=p}),r(a,u,f)}})(o)}}),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 s=n.batchSize==null?32:n.batchSize;fA(s);let r=!0,a=this.standardizeUserDataXY(e,t,r,s);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,s,n.verbose,n.steps);return is(l)}finally{Sl(a[0],e),Sl(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),kW(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new j(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new j(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new j("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new kl;if(e instanceof Qe&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new j(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)a.add(this.inputs[i],e[i])}else for(let i of this.inputs){let l=e[i.name];if(l==null)throw new j(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=Yd(r,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=Al(null,e.length),n=e.length;for(let s of this.layers){let r=Array.isArray(s.output)?s.output:[s.output],a=r.map(o=>o.name);for(let o=0;o<e.length;++o){let i=a.indexOf(e[o]);if(i!==-1&&(t[o]=r[i],n--),n===0)break}if(n===0)break}if(n>0){let s=[];throw t.forEach((r,a)=>{r==null&&s.push(e[a])}),new j(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return X(()=>{let s=this.checkNumSamples(e);if(n)throw new Le("Verbose predictLoop() is not implemented yet.");let r=gA(s,t),a=this.outputs.map(o=>[]);for(let o=0;o<r.length;++o)X(()=>{let l=r[o][0],c=r[o][1],u=Jd(e,l,c),d=[];if(Array.isArray(u))for(let h=0;h<u.length;++h)d.push({key:this.inputs[h],value:u[h]});else d.push({key:this.inputs[0],value:u});let p=new kl(d);return Yd(this.outputs,p)}).forEach((l,c)=>a[c].push(l));return is(a.map(o=>kt(o,0)))})}predict(e,t={}){let n=gk(e);xk(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return fA(s),this.predictLoop(n,s)}finally{Sl(n,e)}}predictOnBatch(e){xk(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,s){if(this.optimizer_==null)throw new gr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let o=this.feedOutputShapes[a];this.feedLossFns[a]===am?r.push(o.slice(0,o.length-1).concat([1])):r.push(o)}if(e=yk(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=yk(t,this.feedOutputNames,r,!1,"target"),TW(e,t,null),NW(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!=0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let c=dk(s,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await pk(i[u],null,c[u]))}return[o,i,l]}testLoop(e,t,n,s=0,r){return X(()=>{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new Le("Verbose mode is not implemented yet.");if(r!=null)throw new Le("steps mode in testLoop() is not implemented yet");{let i=gA(a,n),l=Ut(Ar(0,a));for(let c=0;c<i.length;++c){let u=i[c][0],d=i[c][1],p=vl(l,u,d-u),h=mA(t,p),f=e(h);if(c===0)for(let m=0;m<f.length;++m)o.push(Ie(0));for(let m=0;m<f.length;++m){let g=f[m];o[m]=ue(o[m],L(d-u,g))}}for(let c=0;c<o.length;++c)o[c]=ge(o[c],a)}return o})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Cw(e,s)>1&&(r+=`_${Cw(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let u=[];for(let f=0;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new kl(u),p=Yd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=AW(g,r[f]));let A=Vt(g);t.push(A),f===0?h=g:h=ue(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],A=this.metricsTensors[f][1];m=Vt(g(s[A],p[A]))}An(m),a.push(m)}return h=Vt(h),this.calculateLosses().forEach(f=>{h=ue(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>X(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new kl(a),i=Yd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Vt(c(r[l],i[l]));l===0?n=u:n=ue(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],d=Vt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return IW(this,e,t,n)}async fitDataset(e,t){return bW(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let c=await l.data();i.push(c[0])}return ne(o),is(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});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=lf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-lf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ra(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=>ra(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=ra(n[s]);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[ra(um(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ra(um(e)));{let e={};for(let t in this.metrics)e[t]=ra(um(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=Zd(e.optimizer_config),n=br(t),s;if(typeof e.loss=="string")s=yl(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>yl(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=yl(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>yl(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=yl(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=rs.getSaveHandlers(e);if(l.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new j(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await rs.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:RW,generatedBy:`TensorFlow.js tfjs-layers v${pA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await rs.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=rs.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;ik(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){ik(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};aa.className="Model";ce.registerClass(aa);var bk=class extends aa{};bk.className="Functional";ce.registerClass(bk);async function $W(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zd(n),r=br(s,t);if(e.weightsManifest!=null){let a=await rs.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ne(a)}return r}async function _W(e,t){if(t==null&&(t={}),typeof e=="string"){let n=rs.getLoadHandlers(e,t);if(n.length===0)n.push(rs.browserHTTPRequest(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DW(e,void 0,t)}async function DW(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=br(Zd(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=PW(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),ne(c),ne(u.map(d=>d.tensor))}return i}function PW(e,t){let n=rs.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var yA=class extends aa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Qf("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof yA||e instanceof aa,n;if(t){if(n=e,n.outputs.length!==1)throw new j("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 j("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 j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=qw({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`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 j("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=jw(this.outputs[0])}this.inboundNodes=[],new nm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Al(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))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=[s],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(ft(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 aa({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 gr("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 gr("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 gr("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 gr("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={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof yA))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=br(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new j("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 j("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}}},dm=yA;dm.className="Sequential";ce.registerClass(dm);function FW(e){return new aa(e)}function OW(e){return new dm(e)}function MW(e,t){return t==null&&(t={}),_W(e,t)}function vk(e){return qw(e)}function zW(e,t){rA.registerCallbackConstructor(e,t)}var us=class extends ce.Serializable{getConfig(){return{}}},wk=class extends us{apply(e,t=1){return cB(e,t)}};wk.className="elu";ce.registerClass(wk);var kk=class extends us{apply(e){return b1(e)}};kk.className="selu";ce.registerClass(kk);var Sk=class extends us{apply(e){return Or(e)}};Sk.className="relu";ce.registerClass(Sk);var Ik=class extends us{apply(e){return X(()=>Od(6,Or(e)))}};Ik.className="relu6";ce.registerClass(Ik);var Ck=class extends us{apply(e){return e}};Ck.className="linear";ce.registerClass(Ck);var Tk=class extends us{apply(e){return ms(e)}};Tk.className="sigmoid";ce.registerClass(Tk);var Nk=class extends us{apply(e){return pB(e)}};Nk.className="hardSigmoid";ce.registerClass(Nk);var Ek=class extends us{apply(e){return Gu(e)}};Ek.className="softplus";ce.registerClass(Ek);var Rk=class extends us{apply(e){return dB(e)}};Rk.className="softsign";ce.registerClass(Rk);var $k=class extends us{apply(e){return Lu(e)}};$k.className="tanh";ce.registerClass($k);var xA=class extends us{apply(e,t=-1){return Xu(e,t)}};xA.className="softmax";ce.registerClass(xA);var _k=class extends us{apply(e,t=-1){return u1(e,t)}};_k.className="logSoftmax";ce.registerClass(_k);var Dk=class extends us{apply(e,t=1){return X(()=>L(ms(L(e,t)),e))}};Dk.className="swish";ce.registerClass(Dk);var Pk=class extends us{apply(e){return X(()=>L(e,Lu(Gu(e))))}};Pk.className="mish";ce.registerClass(Pk);function Wo(e){return e.getClassName()}function bA(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"activation")}function Vo(e){if(e==null){let t={};return t.className="linear",t.config={},bA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},bA(t)}else return e instanceof us?e:bA(e)}function vA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Fk=class extends ce.Serializable{},Qd=class extends Fk{constructor(e){super();vA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return X(()=>{let t=jt([1]);return this.hasL1&&(t=ue(t,ke(L(this.l1,sn(e))))),this.hasL2&&(t=ue(t,ke(L(this.l2,qd(e))))),H(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Qd.className="L1L2";ce.registerClass(Qd);function LW(e){return vA(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function BW(e){return vA(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var Ok={l1l2:"L1L2"};function xt(e){return O1(e)}function Mk(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Ok?Ok[e]:e,config:{}};return Mk(n)}else return e instanceof Fk?e:Mk(e)}var wA=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ve(e);let n=Or(e);return this.maxValue!=null&&(n=gs(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};wA.className="ReLU";ce.registerClass(wA);var kA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return yf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};kA.className="LeakyReLU";ce.registerClass(kA);var SA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=$t(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=ln(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 j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Yt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),If(e,this.alpha.read())}getConfig(){let e={alphaInitializer:zt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:on(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};SA.className="PReLU";ce.registerClass(SA);var IA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Le(`Non-default alpha value (${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=Ve(e);return Pd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};IA.className="ELU";ce.registerClass(IA);var CA=class extends nt{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=Ve(e);return L(n,me(As(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ThresholdedReLU";ce.registerClass(CA);var TA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new xA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ve(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}};TA.className="Softmax";ce.registerClass(TA);function tc(e,t,n){if(typeof e=="number")return Al(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!oB(r))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function vr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Wr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Lo([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function NA(e,t){return X(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function zk(e,t){return X(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function WW(e,t,n,s=1,r="valid",a,o=1){return X(()=>{if(a==null&&(a=mr()),qt(a),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=et(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=t1(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=yr(i,n)),i})}function Lk(e,t,n,s=[1,1],r="valid",a,o,i=null){return X(()=>{if(a==null&&(a=mr()),qt(a),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=NA(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Fo.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=et(l,[0,3,1,2])),l})}function VW(e,t,n,s=[1,1,1],r="valid",a,o){return X(()=>{if(a==null&&(a=mr()),qt(a),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=zk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=r1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=yr(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var EA=class extends nt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",EA.verifyArgs(t),this.rank=e,bn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=tc(t.kernelSize,e,"kernelSize"),this.strides=tc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Os(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,qt(this.dataFormat),this.activation=Vo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=$t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ln(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=tc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`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 j(`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 j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Mr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!z1(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Wo(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:on(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ep=class extends EA{constructor(e,t){super(e,t);this.kernel=null,ep.verifyArgs(t),this.filters=t.filters,bn(this.filters,"filters"),this.kernelInitializer=$t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ln(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 X(()=>{e=Ve(e);let n,s=this.bias==null?null:this.bias.read(),r=Nw(this.activation.getClassName());if(r!=null&&this.rank===2)n=Lk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=WW(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Lk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=VW(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ft(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=vr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:zt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:on(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 j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Bk=class extends ep{constructor(e){super(2,e);Bk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!z1(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},pm=Bk;pm.className="Conv2D";ce.registerClass(pm);var Wk=class extends ep{constructor(e){super(3,e);Wk.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 j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},hm=Wk;hm.className="Conv3D";ce.registerClass(hm);var RA=class extends pm{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Wr(i,d,c,this.padding),f=Wr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=s1(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=yr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Wr(t[s],i,a,this.padding),t[r]=Wr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv2DTranspose";ce.registerClass(RA);var $A=class extends hm{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=Wr(l,f,d,this.padding),x=Wr(c,m,p,this.padding),y=Wr(u,g,h,this.padding),b=[r,A,x,y,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let w=xv(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=et(w,[0,4,1,2,3])),this.bias!==null&&(w=yr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Wr(t[s],c,o,this.padding),t[r]=Wr(t[r],u,i,this.padding),t[a]=Wr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};$A.className="Conv3DTranspose";ce.registerClass($A);var Vk=class extends ep{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 j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("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 j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=ln(t.depthwiseConstraint),this.pointwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=ln(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${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 j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{e=Ve(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=Bv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=et(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=zt(this.depthwiseInitializer),e.pointwiseInitializer=zt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=on(this.depthwiseConstraint),e.pointwiseConstraint=on(this.pointwiseConstraint),e}};Vk.className="SeparableConv";var _A=class extends Vk{constructor(e){super(2,e)}};_A.className="SeparableConv2D";ce.registerClass(_A);var Uk=class extends ep{constructor(e){super(1,e);Uk.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"&&!z1(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},DA=Uk;DA.className="Conv1D";ce.registerClass(DA);var PA=class extends nt{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 X(()=>{if(e=Ve(e),this.dataFormat==="channelsLast"){let n=Uf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uf(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}};PA.className="Cropping2D";ce.registerClass(PA);var FA=class extends nt{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,qt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sB(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 X(()=>{let n=Ve(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=et(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a]);return et(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};FA.className="UpSampling2D";ce.registerClass(FA);function UW(e,t,n=[1,1],s="valid",r,a){return X(()=>{r==null&&(r=mr()),qt(r);let o=NA(e,r);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Dd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var OA=class extends EA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ln(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 X(()=>{e=Ve(e);let n=UW(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=vr(t,this.kernelSize[0],this.padding,this.strides[0]),a=vr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=on(this.depthwiseRegularizer),e}};OA.className="DepthwiseConv2D";ce.registerClass(OA);function Gk(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function Hk(e,t,n,s=!1,r,a,o=!1,i=!1){return X(()=>{let l=t.shape.length;if(l<3)throw new j(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Ar(2,l));if(t=et(t,c),a!=null)throw new Le("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=me(me(r,"bool"),"float32"),r.rank===l-1&&(r=Zt(r,-1)),r=et(r,c)),s&&(t=Fs(t,0),r!=null&&(r=Fs(r,0)));let u=[],d,p=n,h=t.shape[0],f=os(t),m;r!=null&&(m=os(r));for(let A=0;A<h;++A){let x=f[A],y=X(()=>e(x,p));if(r==null)d=y[0],p=y[1];else{let b=X(()=>{let w=m[A],k=fe(Ps(w),w),I=ue(L(y[0],w),L(p[0],k)),N=p.map((R,O)=>ue(L(y[1][O],w),L(R,k)));return{output:I,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=xn(u,1)),[d,g,p]})}var jk=class extends nt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new gm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("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 Yt({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 Ar(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){tA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return X(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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 Le("Constants support is not implemented in RNN yet.");tA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Le("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new j(`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=a.map(o=>new Yt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("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(s=>jt([n,s])):this.states_=[jt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_[0]=jt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):ne(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>An(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Gk(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Yt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof xr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ve(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new j(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=Hk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return X(()=>{let t=jt(e.shape);return t=ke(t,[1,2]),t=jd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?j1(t,[1,n]):t):this.cell.stateSize>1?[j1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jk.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=br(s,n);return new e(Object.assign(t,{cell:r}))}},oa=jk;oa.className="RNN";ce.registerClass(oa);var tp=class extends nt{},fm=class extends tp{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,bn(this.units,"units"),this.activation=Vo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Yu([1,Lo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Lo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(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 X(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Uo({ones:()=>Ps(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Uo({ones:()=>Ps(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=zr(L(e,a),this.kernel.read()):r=zr(e,this.kernel.read()),this.bias!=null&&(r=yr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,zr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Wo(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:on(this.kernelConstraint),recurrentConstraint:on(this.recurrentConstraint),biasConstraint:on(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};fm.className="SimpleRNNCell";ce.registerClass(fm);var MA=class extends oa{constructor(e){e.cell=new fm(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};MA.className="SimpleRNN";ce.registerClass(MA);var mm=class extends tp{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,bn(this.units,"units"),this.activation=Vo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Yu([1,Lo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Lo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(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 X(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Uo({ones:()=>Ps(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Uo({ones:()=>Ps(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=zr(e,this.kernel.read());this.useBias&&(c=yr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=rn(u,[2*this.units,this.units],u.rank-1),h=zr(s,d),[f,m,g]=rn(c,3,c.rank-1),[A,x]=rn(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,A)),i=this.recurrentActivation.apply(ue(m,x));let y=zr(L(i,s),p);l=this.activation.apply(ue(g,y));let b=ue(L(o,s),L(ue(1,Mt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Wo(this.activation),recurrentActivation:Wo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:on(this.kernelConstraint),recurrentConstraint:on(this.recurrentConstraint),biasConstraint:on(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};mm.className="GRUCell";ce.registerClass(mm);var zA=class extends oa{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 mm(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zA.className="GRU";ce.registerClass(zA);var np=class extends tp{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,bn(this.units,"units"),this.activation=Vo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Yu([1,Lo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Lo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends tr{apply(o,i){let l=r.apply([a]),c=new Hf().apply([a]),u=r.apply([a*2]);return Mw(Mw(l,c),u)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return X(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Uo({ones:()=>Ps(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Uo({ones:()=>Ps(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=zr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ue(d,zr(s,this.recurrentKernel.read())),this.useBias&&(d=yr(d,this.bias.read()));let[p,h,f,m]=rn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ue(L(l,r),L(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Wo(this.activation),recurrentActivation:Wo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:on(this.kernelConstraint),recurrentConstraint:on(this.recurrentConstraint),biasConstraint:on(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};np.className="LSTMCell";ce.registerClass(np);var LA=class extends oa{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 np(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};LA.className="LSTM";ce.registerClass(LA);var gm=class extends tp{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return X(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){tA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{bl(`RNNCell_${s}`,()=>{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()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(br(r,n));return new e({cells:s})}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 nA(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}sA(t)}};gm.className="StackedRNNCells";ce.registerClass(gm);function Uo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Lw(t(),n),i=()=>Xd(o,t,s);return!r||r<=1?An(i().clone()):Array(r).fill(void 0).map(i).map(c=>An(c.clone()))}var qk=class extends oa{constructor(e){if(e.unroll)throw new Le("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Le("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return X(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return X(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=jt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new j("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(()=>jt(r)):this.states_=[jt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_[0]=jt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new j(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>An(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=vr(l,s[0],r,a[0],o[0]),d=vr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};qk.className="ConvRNN2D";var Am=class extends np{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,bn(this.filters,"filters"),this.kernelSize=tc(n,2,"kernelSize"),this.kernelSize.forEach(i=>bn(i,"kernelSize")),this.strides=tc(s||1,2,"strides"),this.strides.forEach(i=>bn(i,"strides")),this.padding=r||"valid",Os(this.padding),this.dataFormat=a||"channelsLast",qt(this.dataFormat),this.dilationRate=tc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>bn(i,"dilationRate"))}build(e){var t;e=ft(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends tr{apply(u,d){let p=l.apply([c]),h=ys([c]),f=l.apply([c*2]);return H1([p,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return X(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Uo({ones:()=>Ps(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(J,Q,te)=>!Q||!Q[te]?J:L(Q[te],J),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Uo({ones:()=>Ps(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),x=3,[y,b,w,k]=rn(this.kernel.read(),o,x),[I,N,R,O]=this.useBias?rn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,y,I,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,O,this.padding);let[$,P,T,F]=rn(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,$),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),A=this.recurrentConv(A,F);let U=this.recurrentActivation.apply(ue(c,f)),q=this.recurrentActivation.apply(ue(u,m)),z=ue(L(q,a),L(U,this.activation.apply(ue(d,g)))),K=L(this.recurrentActivation.apply(ue(p,A)),this.activation.apply(z));return[K,K,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=_o(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yr(r,n,this.dataFormat):r}recurrentConv(e,t){return _o(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Am.className="ConvLSTM2DCell";ce.registerClass(Am);var BA=class extends qk{constructor(e){let t=new Am(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};BA.className="ConvLSTM2D";ce.registerClass(BA);var ym=class extends nt{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 s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Xd(()=>Lw(n,this.rate,r,this.seed),()=>n,s)}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()}};ym.className="Dropout";ce.registerClass(ym);var WA=class extends ym{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};WA.className="SpatialDropout1D";ce.registerClass(WA);var VA=class extends nt{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,bn(this.units,"units"),this.activation=Vo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ln(e.kernelConstraint),this.biasConstraint=ln(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(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=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=Nw(this.activation.getClassName()),r;return s!=null?r=zr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=zr(n,this.kernel.read()),this.bias!=null&&(r=yr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Wo(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:on(this.kernelConstraint),biasConstraint:on(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};VA.className="Dense";ce.registerClass(VA);var UA=class extends nt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new j(`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],zo(e,1)]}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=et(n,s)}return uB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};UA.className="Flatten";ce.registerClass(UA);var GA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vo(e.activation)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Wo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};GA.className="Activation";ce.registerClass(GA);var HA=class extends nt{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 X(()=>(e=Ve(e),iB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};HA.className="RepeatVector";ce.registerClass(HA);var jA=class extends nt{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.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new j("Can only specifiy one unknown dimension.");else r*=l}let o=zo(e);if(a!==null){if(r===0||o%r!=0)throw new j(n);s[a]=o/r}else if(o!==r)throw new j(n);return s}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 X(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return H(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};jA.className="Reshape";ce.registerClass(jA);var qA=class extends nt{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 ${e.dims} instead.`);let t=Ar(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return et(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Permute";ce.registerClass(qA);var XA=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ve(e),s=-1;return pf(Hu(n,this.maskValue),s)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=pf(Hu(n,this.maskValue),s,r);return L(n,me(a,n.dtype))})}};XA.className="Masking";ce.registerClass(XA);var KA=class extends nt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,bn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,bn(this.outputDim,"outputDim"),this.embeddingsInitializer=$t(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=ln(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 X(()=>this.maskZero?(e=Ve(e),Hu(e,tt(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);n.dtype!=="int32"&&(n=Vf(n,"int32"));let s=zw(this.embeddings.read(),H(n,[n.size]));return H(s,ft(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:zt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:on(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Embedding";ce.registerClass(KA);var Il=class extends nt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Le}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 s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Mo(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Mo(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return X(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Lo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=jd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=H(i,[u].concat(zo(c.slice(1))));p=et(p,[1,0]),p=H(p,d),n.push(p),r=!0}else if(l>1){let c=Ar(1,l).concat([0]);n.push(et(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=H(et(H(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(Ar(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Mo(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return X(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Zt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=hr(n,t[s]);return n})}},ZA=class extends Il{constructor(e){super(e)}mergeFunction(e){return X(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return t})}};ZA.className="Add";ce.registerClass(ZA);var YA=class extends Il{constructor(e){super(e)}mergeFunction(e){return X(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};YA.className="Multiply";ce.registerClass(YA);var JA=class extends Il{constructor(e){super(e)}mergeFunction(e){return X(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return L(1/e.length,t)})}};JA.className="Average";ce.registerClass(JA);var QA=class extends Il{constructor(e){super(e)}mergeFunction(e){return X(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ea(t,e[n]);return t})}};QA.className="Maximum";ce.registerClass(QA);var ey=class extends Il{constructor(e){super(e)}mergeFunction(e){return X(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Od(t,e[n]);return t})}};ey.className="Minimum";ce.registerClass(ey);var ty=class extends Il{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 j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return X(()=>H1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return X(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(me(Ps(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Zt(t[a],-1)):s.push(t[a]);let r=kt(s,this.axis);return Z2(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ty.className="Concatenate";ce.registerClass(ty);function sp(e,t){for(;e<0;)e+=t;return e}function GW(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Le("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Le("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return X(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=H(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=H(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(L(e,t),a[0]):i=ke(L(et(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=He(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=it(i,c)}return i.shape.length===1&&(i=Zt(i,1)),i})}var ny=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>sp(r,e[a].shape.length)):s=[sp(this.axes,t.shape.length),sp(this.axes,n.shape.length)],this.normalize&&(t=sm(t,s[0]),n=sm(n,s[1])),GW(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[sp(this.axes,e.length),sp(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ny.className="Dot";ce.registerClass(ny);var sy=class extends nt{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 X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return Xd(()=>ue(Gf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};sy.className="GaussianNoise";ce.registerClass(sy);var ry=class extends nt{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 X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.rate>0&&this.rate<1?Xd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Gf(n.shape,1,r))},()=>n,t.training||!1):n})}};ry.className="GaussianDropout";ce.registerClass(ry);var ay=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ve(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 X(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Xd(()=>{let r=Ve(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=dl(ju(n),this.rate);l=Vf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ue(L(r,l),L(ue(l,-1),i));return ue(L(d,c),u)},()=>Ve(e),t.training||!1)}return e})}};ay.className="AlphaDropout";ce.registerClass(ay);function rp(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=cv(e,t,n,s,r,a);else if(e.rank===3)o=dv(e,t,n,s,r,a);else if(e.rank===4)o=pv(e,t,n,s,r,a);else throw new Le(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function HW(e,t,n,s,r=.001){return X(()=>{let a=kf(e,s),o=a.mean,i=a.variance;return[rp(e,o,i,n,t,r),o,i]})}function jW(e,t,n,s,r=.001){return X(()=>{let a=kf(e,s),o=a.mean,i=a.variance,l=[];for(let f of Ar(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=H(o,l),u=H(i,l),d=t==null?null:H(t,l),p=n==null?null:H(n,l);return[rp(e,c,u,p,d,r),o,i]})}function qW(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),Ar(0,e.rank-1))?HW(e,t,n,s,r):jW(e,t,n,s,r)}var oy=class extends nt{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.movingMeanInitializer=$t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=$t(e.movingVarianceInitializer||"ones"),this.betaConstraint=ln(e.betaConstraint),this.gammaConstraint=ln(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return X(()=>{let n=t.training==null?!1:t.training,s=Ve(e),r=s.shape,a=r.length,o=Ar(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Al(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,Ar(0,a).slice(0,a-1)),d=()=>{if(u){let A=H(this.movingMean.read(),l),x=H(this.movingVariance.read(),l),y=this.center?H(this.beta.read(),l):null,b=this.scale?H(this.gamma.read(),l):null;return rp(s,A,x,y,b,this.epsilon)}else return rp(s,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 d();let[p,h,f]=qW(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,x,y)=>{X(()=>{let b=1-y,w=A.read(),k=L(fe(w,x),b);A.write(fe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),movingMeanInitializer:zt(this.movingMeanInitializer),movingVarianceInitializer:zt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:on(this.betaConstraint),gammaConstraint:on(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};oy.className="BatchNormalization";ce.registerClass(oy);var iy=class extends nt{constructor(e){e==null&&(e={});super(e);if(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 ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Mo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ve(e),s=n.shape,r=s.length;return X(()=>{let a=!0,{mean:o,variance:i}=kf(n,this.axis,a),l=Al(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?H(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Ys(o,p),i=Ys(i,p),u=Ys(u,h),d=Ys(d,h),rp(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};iy.className="LayerNormalization";ce.registerClass(iy);function XW(e,t,n){return X(()=>{if(e.rank!==4)throw new j(`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 j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=mr()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Js(e,s)})}var ly=class extends nt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?mr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ft(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 X(()=>XW(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ly.className="ZeroPadding2D";ce.registerClass(ly);function xm(e,t,n,s,r,a){return X(()=>{qt(r),_w(a),Os(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=NA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=wf(e,t,n,i):o=ff(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function Xk(e,t,n,s,r,a){return X(()=>{qt(r),_w(a),Os(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=zk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=h1(e,t,n,i):o=Q2(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var Kk=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(bn(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 j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Os(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=vr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return X(()=>{this.invokeCallHook(e,t),e=jd(Ve(e),2);let n=this.poolingFunction(Ve(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return it(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},uy=class extends Kk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),xm(e,t,n,s,r,"max")}};uy.className="MaxPooling1D";ce.registerClass(uy);var cy=class extends Kk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),xm(e,t,n,s,r,"avg")}};cy.className="AveragePooling1D";ce.registerClass(cy);var Zk=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(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 j(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Os(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(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 X(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},dy=class extends Zk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),xm(e,t,n,s,r,"max")}};dy.className="MaxPooling2D";ce.registerClass(dy);var py=class extends Zk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),xm(e,t,n,s,r,"avg")}};py.className="AveragePooling2D";ce.registerClass(py);var Yk=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 j(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Os(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),s=vr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},hy=class extends Yk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),Xk(e,t,n,s,r,"max")}};hy.className="MaxPooling3D";ce.registerClass(hy);var fy=class extends Yk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Os(s),Xk(e,t,n,s,r,"avg")}};fy.className="AveragePooling3D";ce.registerClass(fy);var Jk=class extends nt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},my=class extends Jk{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return Vt(n,1)})}};my.className="GlobalAveragePooling1D";ce.registerClass(my);var gy=class extends Jk{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return yn(n,1)})}};gy.className="GlobalMaxPooling1D";ce.registerClass(gy);var Qk=class extends nt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Le}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ay=class extends Qk{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Vt(n,[1,2]):Vt(n,[2,3])})}};Ay.className="GlobalAveragePooling2D";ce.registerClass(Ay);var yy=class extends Qk{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?yn(n,[1,2]):yn(n,[2,3])})}};yy.className="GlobalMaxPooling2D";ce.registerClass(yy);var e7=class extends nt{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 s=t.layer,r=br(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},xy=class extends e7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new j(`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=ft(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return X(()=>(e=Ve(e),Hk((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};xy.className="TimeDistributed";ce.registerClass(xy);function KW(e){xl(nB,"BidirectionalMergeMode",e)}var ZW="concat",by=class extends e7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=br(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=br(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?ZW:e.mergeMode,KW(this.mergeMode),e.weights)throw new Le("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,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):is(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Gk(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Yt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Le("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof xr;for(let l of a)if(l instanceof xr!==i)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Fs(r,1));let o;return this.mergeMode==="concat"?o=H1([s,r]):this.mergeMode==="sum"?o=ue(s,r):this.mergeMode==="ave"?o=L(.5,ue(s,r)):this.mergeMode==="mul"?o=L(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){bl(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),bl(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=br(t.layer);if(delete t.layer,t.numConstants!=null)throw new Le("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};by.className="Bidirectional";ce.registerClass(by);function YW(e){return new Ju(e)}function JW(e){return new IA(e)}function QW(e){return new wA(e)}function eV(e){return new kA(e)}function tV(e){return new SA(e)}function nV(e){return new TA(e)}function sV(e){return new CA(e)}function rV(e){return new DA(e)}function aV(e){return new pm(e)}function oV(e){return new RA(e)}function iV(e){return new hm(e)}function lV(e){return new $A(e)}function uV(e){return new _A(e)}function cV(e){return new PA(e)}function dV(e){return new FA(e)}function pV(e){return new OA(e)}function hV(e){return new GA(e)}function fV(e){return new VA(e)}function mV(e){return new ym(e)}function gV(e){return new WA(e)}function AV(e){return new UA(e)}function yV(e){return new HA(e)}function xV(e){return new jA(e)}function bV(e){return new qA(e)}function vV(e){return new KA(e)}function wV(e){return new ZA(e)}function kV(e){return new JA(e)}function SV(e){return new ty(e)}function IV(e){return new QA(e)}function CV(e){return new ey(e)}function TV(e){return new YA(e)}function NV(e){return new ny(e)}function EV(e){return new oy(e)}function RV(e){return new iy(e)}function $V(e){return new ly(e)}function vy(e){return new cy(e)}function _V(e){return vy(e)}function DV(e){return vy(e)}function wy(e){return new py(e)}function PV(e){return wy(e)}function FV(e){return wy(e)}function ky(e){return new fy(e)}function OV(e){return ky(e)}function MV(e){return ky(e)}function zV(e){return new my(e)}function LV(e){return new Ay(e)}function t7(e){return new gy(e)}function n7(e){return new yy(e)}function s7(e){return new uy(e)}function r7(e){return new dy(e)}function BV(e){return new hy(e)}function WV(e){return new zA(e)}function VV(e){return new mm(e)}function UV(e){return new LA(e)}function GV(e){return new np(e)}function HV(e){return new MA(e)}function jV(e){return new fm(e)}function qV(e){return new BA(e)}function XV(e){return new Am(e)}function KV(e){return new oa(e)}function ZV(e){return new gm(e)}function YV(e){return new by(e)}function JV(e){return new xy(e)}var QV=t7,eU=n7,tU=s7,nU=r7;function sU(e){return new sy(e)}function rU(e){return new ry(e)}function aU(e){return new ay(e)}function oU(e){return new XA(e)}var a7={};Oe(a7,{MAPE:()=>AU,MSE:()=>bU,binaryAccuracy:()=>iU,binaryCrossentropy:()=>lU,categoricalAccuracy:()=>cU,categoricalCrossentropy:()=>dU,cosineProximity:()=>fU,mape:()=>yU,meanAbsoluteError:()=>mU,meanAbsolutePercentageError:()=>gU,meanSquaredError:()=>xU,mse:()=>vU,precision:()=>pU,recall:()=>hU,sparseCategoricalAccuracy:()=>uU});function iU(e,t){return iA(e,t)}function lU(e,t){return sk(e,t)}function uU(e,t){return rk(e,t)}function cU(e,t){return lA(e,t)}function dU(e,t){return uA(e,t)}function pU(e,t){return nk(e,t)}function hU(e,t){return YB(e,t)}function fU(e,t){return aA(e,t)}function mU(e,t){return rm(e,t)}function gU(e,t){return ec(e,t)}function AU(e,t){return ec(e,t)}function yU(e,t){return ec(e,t)}function xU(e,t){return wl(e,t)}function bU(e,t){return wl(e,t)}function vU(e,t){return wl(e,t)}var o7={};Oe(o7,{modelFromJSON:()=>$W});var i7={};Oe(i7,{l1:()=>kU,l1l2:()=>wU,l2:()=>SU});function wU(e){return new Qd(e)}function kU(e){return LW(e)}function SU(e){return BW(e)}var l7=class extends Qu{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof aa))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function bm(e,t){return e<t}function u7(e,t){return e>t}var c7=class extends l7{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Le("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=bm:this.mode==="max"?this.monitorFunc=u7:this.monitor.indexOf("acc")!==-1?this.monitorFunc=u7:this.monitorFunc=bm,this.monitorFunc===bm&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===bm?1/0:-1/0}async onEpochEnd(e,t){await Bo(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 IU(e){return new c7(e)}var CU={earlyStopping:IU},TU=Y();TU.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,e=>{e&&console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.")});var wr;(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"})(wr||(wr={}));var d7;(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={}))})(d7||(d7={}));var Sy={};function NU(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Sy[e]=n}function p7(e){return Sy[e]}function EU(e){delete Sy[e]}function S(e,t,n,s,r){let a=t.inputParams[e];if(a&&a.inputIndexStart!==void 0){let i=a.inputIndexStart,l=a.inputIndexEnd===0?void 0:a.inputIndexEnd===void 0?i+1:a.inputIndexEnd;if(a.type==="tensor")return Hn(t.inputNames[a.inputIndexStart],n,s,r);if(a.type==="tensors")return t.inputNames.slice(i,l).map(p=>Hn(p,n,s,r));let c=Hn(t.inputNames.slice(i)[0],n,s,r),u=c.dataSync();return a.type==="number"?u[0]:v.toNestedArray(c.shape,u)}let o=t.attrParams[e];return o&&o.value}function Hn(e,t,n,s){let[r,a]=xs(e);if(s!=null){let i=s.getHashTableHandleByName(r);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[vm(r,i)]);return o!==void 0?t[vm(r,o)][a]:void 0}function RU(e,t,n){return t[vm(e,n.currentContextId)]}function Vr(e,t){let[n,s,r]=xs(e);return[vm(n,t&&t.currentContextId),s,r]}function vm(e,t){return t?`${e}-${t}`:e}function xs(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],s=t.length===3?t[1]:void 0,r=Number(t[t.length-1]);return[n,r,s]}function wm(e,t,n){let s=S("pad",e,t,n);if(s==="explicit"){s=S("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let a=0;a<4;a++)r[a][0]=s[a*2],r[a][1]=s[a*2+1];return r}return s}function ia(e){return e.kept?e:Vn(e)}var h7={};Oe(h7,{json:()=>$U});var $U=[{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}]}],f7={};Oe(f7,{json:()=>_U});var _U=[{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}]}],m7={};Oe(m7,{json:()=>DU});var DU=[{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"}]}],g7={};Oe(g7,{json:()=>PU});var PU=[{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"}]}],A7={};Oe(A7,{json:()=>FU});var FU=[{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"}]}],y7={};Oe(y7,{json:()=>OU});var OU=[{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}]}],x7={};Oe(x7,{json:()=>MU});var MU=[{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"}]}],b7={};Oe(b7,{json:()=>zU});var zU=[{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"}]}],v7={};Oe(v7,{json:()=>LU});var LU=[{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"}]}],w7={};Oe(w7,{json:()=>BU});var BU=[{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"}]}],k7={};Oe(k7,{json:()=>WU});var WU=[{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}]}],S7={};Oe(S7,{json:()=>VU});var VU=[{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"}]}],I7={};Oe(I7,{json:()=>UU});var UU=[{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}]}],C7={};Oe(C7,{json:()=>GU});var GU=[{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"}]}],T7={};Oe(T7,{json:()=>HU});var HU=[{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}]}],N7={};Oe(N7,{json:()=>jU});var jU=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}],E7={};Oe(E7,{json:()=>qU});var qU=[{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}]}],R7={};Oe(R7,{json:()=>XU});var XU=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}],$7={};Oe($7,{json:()=>KU});var KU=[{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:[]},{tfOpName:"BroadcastArgs",category:"transformation",inputs:[{start:0,name:"s0",type:"tensor"},{start:1,name:"s1",type:"tensor"}],attrs:[]}],_7=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[h7,f7,m7,g7,A7,y7,x7,b7,v7,w7,k7,S7,I7,C7,T7,N7,E7,R7,$7],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,s)=>(n[s.tfOpName]=s,n),{})}transformGraph(e,t={}){let n=e.node,s=[],r=[],a=[],o=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?s.push(f[m.name]):m.op==="Const"?r.push(f[m.name]):(m.input==null||m.input.length===0)&&a.push(f[m.name]),f),{}),i=[],l=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=this.mapSignatureEntries(t.outputs));let d=Object.keys(o);d.forEach(f=>{let m=o[f];m.inputNames.forEach((g,A)=>{let[x,,y]=Vr(g),b=o[x];if(b.outputs!=null){let w=b.outputs.indexOf(y);if(w!==-1){let k=`${x}:${w}`;m.inputNames[A]=k}}m.inputs.push(b),b.children.push(m)})}),Object.keys(u).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=Vr(f),g=o[m];g!=null&&(g.signatureKey=u[f],l.push(g))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=Vr(f),g=o[m];g&&(g.signatureKey=c[f],i.push(g))}):i=s;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let h={nodes:o,inputs:i,outputs:l,weights:r,placeholders:s,signature:t,functions:p};return a.length>0&&(h.initNodes=a),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=p7(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(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,r)=>(s[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,r)=>{let a=r.type,o;switch(r.type){case"string":o=Iy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Iy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":o=Dy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Dy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":o=Ty(e.attr,r.tfName,r.defaultValue||0),o===void 0&&!!r.tfDeprecatedName&&(o=Ty(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":o=_y(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=_y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":o=Cy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Cy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":o=Fy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Fy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":o=$y(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=$y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":o=Py(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Py(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":o=Ey(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Ey(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":o=Ry(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Ry(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":o=P7(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=P7(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return s[r.name]={value:o,type:a},s},{})),n}mapFunction(e){let t=e.nodeDef,n=[],s=[],r={};t!=null&&(r=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&s.push(u[d.name]),u),{}));let a=[],o=[];e.signature.inputArg.forEach(u=>{let[d]=Vr(u.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Ny(u.type),type:"dtype"}},children:[]};p.signatureKey=u.name,a.push(p),r[d]=p}),Object.keys(r).forEach(u=>{let d=r[u];d.inputNames.forEach((p,h)=>{let[f,,m]=Vr(p),g=r[f];if(g.outputs!=null){let A=g.outputs.indexOf(m);if(A!==-1){let x=`${f}:${A}`;d.inputNames[h]=x}}d.inputs.push(g),g.children.push(d)})});let l=e.ret;e.signature.outputArg.forEach(u=>{let[d,p]=Vr(l[u.name]),h=r[d];h!=null&&(h.defaultOutput=p,o.push(h))});let c=this.mapArgsToSignature(e);return{nodes:r,inputs:a,outputs:o,weights:s,placeholders:n,signature:c}}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 ZU(e){let t=Y().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 D7(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):ZU(e);return t?n:n.toLowerCase()}function Iy(e,t,n,s=!1){let r=e[t];return r!=null?D7(r.s,s):n}function Cy(e,t,n){let s=e[t];return s?s.b:n}function Ty(e,t,n){let s=e[t]||{},r=s.i!=null?s.i:s.f!=null?s.f:n;return typeof r=="number"?r:parseInt(r,10)}function Ny(e){switch(typeof e=="string"&&(e=wr[e]),e){case wr.DT_FLOAT:return"float32";case wr.DT_INT32:case wr.DT_INT64:case wr.DT_INT8:case wr.DT_UINT8:return"int32";case wr.DT_BOOL:return"bool";case wr.DT_DOUBLE:return"float32";case wr.DT_STRING:return"string";default:return null}}function P7(e,t,n){let s=e[t];return s&&s.func?s.func.name:n}function Ey(e,t,n){let s=e[t];return s&&s.type?Ny(s.type):n}function Ry(e,t,n){let s=e[t];return s&&s.list&&s.list.type?s.list.type.map(r=>Ny(r)):n}function F7(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function $y(e,t,n){let s=e[t];return s&&s.shape?F7(s.shape):n}function _y(e,t,n){let s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function Dy(e,t,n,s=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(a=>D7(a,s)):n}function Py(e,t,n){let s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(r=>F7(r)):n}function Fy(e,t,n){let s=e[t];return s&&s.list&&s.list.b?s.list.b:n}var YU=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,r)=>(s[r]=this.getAttr(r),s),{}))}getInput(e){return Hn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Hn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Ty(this.node.rawAttrs,e,t);if(n.s!=null)return Iy(this.node.rawAttrs,e,t);if(n.b!=null)return Cy(this.node.rawAttrs,e,t);if(n.shape!=null)return $y(this.node.rawAttrs,e,t);if(n.type!=null)return Ey(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return _y(this.node.rawAttrs,e,t);if(n.list.s!=null)return Dy(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Py(this.node.rawAttrs,e,t);if(n.list.b!=null)return Fy(this.node.rawAttrs,e,t);if(n.list.type!=null)return Ry(this.node.rawAttrs,e,t)}return t}},JU=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ue(S("a",e,t,n),S("b",e,t,n))];case"AddN":return[df(S("tensors",e,t,n))];case"FloorMod":case"Mod":return[Md(S("a",e,t,n),S("b",e,t,n))];case"Mul":return[L(S("a",e,t,n),S("b",e,t,n))];case"RealDiv":case"Div":return[ge(S("a",e,t,n),S("b",e,t,n))];case"DivNoNan":return[kv(S("a",e,t,n),S("b",e,t,n))];case"FloorDiv":return[cf(S("a",e,t,n),S("b",e,t,n))];case"Sub":return[fe(S("a",e,t,n),S("b",e,t,n))];case"Minimum":return[Od(S("a",e,t,n),S("b",e,t,n))];case"Maximum":return[ea(S("a",e,t,n),S("b",e,t,n))];case"Pow":return[Po(S("a",e,t,n),S("b",e,t,n))];case"SquaredDifference":return[I1(S("a",e,t,n),S("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QU=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[sn(S("x",e,t,n))];case"Acos":return[Q3(S("x",e,t,n))];case"Acosh":return[ev(S("x",e,t,n))];case"Asin":return[nv(S("x",e,t,n))];case"Asinh":return[sv(S("x",e,t,n))];case"Atan":return[rv(S("x",e,t,n))];case"Atan2":return[av(S("x",e,t,n),S("y",e,t,n))];case"Atanh":return[ov(S("x",e,t,n))];case"Ceil":return[fv(S("x",e,t,n))];case"Complex":return[To(S("real",e,t,n),S("imag",e,t,n))];case"Cos":return[gf(S("x",e,t,n))];case"Cosh":return[a1(S("x",e,t,n))];case"Elu":return[Pd(S("x",e,t,n))];case"Erf":return[Iv(S("x",e,t,n))];case"Exp":return[_s(S("x",e,t,n))];case"Expm1":return[Cv(S("x",e,t,n))];case"Floor":return[Fd(S("x",e,t,n))];case"Log":return[Ds(S("x",e,t,n))];case"Log1p":return[xf(S("x",e,t,n))];case"Imag":return[Af(S("x",e,t,n))];case"Neg":return[Mt(S("x",e,t,n))];case"Reciprocal":return[Lv(S("x",e,t,n))];case"Real":return[zd(S("x",e,t,n))];case"Relu":return[Or(S("x",e,t,n))];case"Round":return[y1(S("x",e,t,n))];case"Selu":return[b1(S("x",e,t,n))];case"Sigmoid":return[ms(S("x",e,t,n))];case"Sin":return[v1(S("x",e,t,n))];case"Sign":return[Vv(S("x",e,t,n))];case"Sinh":return[w1(S("x",e,t,n))];case"Softplus":return[Gu(S("x",e,t,n))];case"Sqrt":return[Pn(S("x",e,t,n))];case"Square":return[yt(S("x",e,t,n))];case"Tanh":return[Lu(S("x",e,t,n))];case"Tan":return[Gv(S("x",e,t,n))];case"ClipByValue":return[gs(S("x",e,t,n),S("clipValueMin",e,t,n),S("clipValueMax",e,t,n))];case"Relu6":return[A1(S("x",e,t,n))];case"Rsqrt":return[x1(Hn(e.inputNames[0],t,n))];case"Prod":return[f1(S("x",e,t,n),S("axes",e,t,n))];case"LeakyRelu":return[yf(S("x",e,t,n),S("alpha",e,t,n))];case"Prelu":return[If(S("x",e,t,n),S("alpha",e,t,n))];case"IsNan":return[Tv(Hn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function nr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];v.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function O7(e){return!(typeof e=="number"||e.some(t=>t<0))}function ap(e,t,n){let s=Oy(e,n),r=!O7(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=Oy(a.shape,s)}),!O7(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Oy(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 s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var eG=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Ie(0),An(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),nr(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,An(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,s)=>this.write(n,t[s]))}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 s=0;s<this.size();s++)e.push(s)}if(e.length===0)return ct([],[0].concat(this.elementShape));let n=this.readMany(e);return nr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),xn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return ct([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return nr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),kt(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,os(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,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];X(()=>{t=H(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],c=[0,l,0],u=[1,e[i],r];a[i]=H(Pe(t,c,u),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},op=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);nr(t,r.shape,"TensorList shape mismatch: "),An(r)}),this.idTensor=Ie(0),this.maxNumElements=s,An(this.idTensor)}get id(){return this.idTensor.id}copy(){return new op([...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.`);nr(e,this.elementShape,"TensorList shape mismatch: ");let s=ap(this.elementShape,this.tensors,e);return X(()=>{let r=this.tensors.map(a=>H(a,s));return xn(r,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=ap(this.elementShape,this.tensors,e),s=this.tensors.pop();return nr(s.shape,e,"TensorList shape mismatch: "),H(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(nr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");An(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.`);nr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=ap(this.elementShape,this.tensors,t);return H(this.tensors[e],s)}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.`);nr(this.elementShape,t.shape,"TensorList shape mismatch: "),An(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}`);nr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=ap(this.elementShape,this.tensors,n);return e.length===0?ct([],[0].concat(s)):X(()=>{let r=e.map(a=>H(this.tensors[a],s));return xn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);nr(this.elementShape,t,"TensorList shape mismatch: ");let n=ap(this.elementShape,this.tensors,t);return this.size()===0?ct([],[0].concat(n)):X(()=>{let s=this.tensors.map(r=>H(r,n));return kt(s,0)})}};function tG(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);nr(r,t,"TensorList shape mismatch: ");let a=os(e);return new op(a,t,s)}function nG(e,t,n){return new op([],e,t,n)}function sG(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r} vs. ${s})`);let a=new op([],n,e.dtype,s),o=os(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function rG(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=Oy(a,n),i=s===0?0:e.size/s,l=X(()=>{let u=[];e=H(e,[1,s,i]);for(let d=0;d<t.length;++d){let p=d===0?0:r[d-1],h=[0,p,0],f=[1,t[d],i];u[d]=H(Pe(e,h,f),o)}return e.dispose(),u}),c=new op([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var aG=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=S("thenBranch",e,t,n),r=S("elseBranch",e,t,n),a=S("cond",e,t,n),o=S("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=S("body",e,t,n),r=S("cond",e,t,n),a=S("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(u=>u.id),l=await o[0].data();o.forEach(u=>{!u.kept&&i.indexOf(u.id)===-1&&u.dispose()});let c=a;for(;l[0];){let u=c;c=await n.functionMap[s].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let d=c.map(h=>h.id);u.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let p=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await p[0].data(),p.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let s=S("pred",e,t,n);return[ia(s)]}case"Switch":{let s=S("pred",e,t,n),r=S("data",e,t,n);return r.kept||(r=ia(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>Hn(r,t,n)!==void 0);if(s){let r=Hn(s,t,n);return[ia(r)]}return}case"Enter":{let s=S("frameName",e,t,n),r=S("tensor",e,t,n);return n.enterFrame(s),[ia(r)]}case"Exit":{let s=S("tensor",e,t,n);return n.exitFrame(),[ia(s)]}case"NextIteration":{let s=S("tensor",e,t,n);return n.nextIteration(),[ia(s)]}case"TensorArrayV3":{let s=S("size",e,t,n),r=S("dtype",e,t,n),a=S("elementShape",e,t,n),o=S("dynamicSize",e,t,n),i=S("clearAfterRead",e,t,n),l=S("identicalElementShapes",e,t,n),c=S("name",e,t,n),u=new eG(c,r,s,a,l,o,i);return n.addTensorArray(u),[u.idTensor,Ie(1)]}case"TensorArrayWriteV3":{let s=S("tensorArrayId",e,t,n),r=S("index",e,t,n),a=S("tensor",e,t,n),o=n.getTensorArray(s.id);return o.write(r,a),[o.idTensor]}case"TensorArrayReadV3":{let s=S("tensorArrayId",e,t,n),r=S("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),a=S("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),a=S("tensor",e,t,n),o=n.getTensorArray(s.id);return o.scatter(r,a),[o.idTensor]}case"TensorArrayConcatV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=S("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=S("tensorArrayId",e,t,n),r=S("tensor",e,t,n),a=S("lengths",e,t,n),o=n.getTensorArray(s.id);return o.split(a,r),[o.idTensor]}case"TensorArraySizeV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Ie(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=S("tensorListId",e,t,n),r=S("index",e,t,n),a=S("tensor",e,t,n),o=n.getTensorList(s.id);return o.setItem(r,a),[o.idTensor]}case"TensorListGetItem":{let s=S("tensorListId",e,t,n),r=S("index",e,t,n),a=S("elementShape",e,t,n),o=S("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let s=S("indices",e,t,n),r=S("tensor",e,t,n),a=S("elementShape",e,t,n),o=S("numElements",e,t,n),i=sG(r,s,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=S("elementShape",e,t,n),r=S("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=S(a,e,t,n),i=nG(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=S("tensorListId",e,t,n),r=S("indices",e,t,n),a=S("elementShape",e,t,n),o=S("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n),o=S("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=S("tensor",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n),o=tG(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=S("tensorListId",e,t,n),r=n.getTensorList(s.id),a=S("dtype",e,t,n),o=S("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=S("tensorListId",e,t,n),r=S("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=S("tensor",e,t,n),r=S("elementShape",e,t,n),a=S("lengths",e,t,n),o=rG(s,a,r);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function M7(e,t,n){let[s,r]=S("fusedOps",e,t,n),a=s==="biasadd",o=!a,i=r==="prelu",l=s==="fusedbatchnorm",c=S("numArgs",e,t,n);if(a){if(i&&c!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&c!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let u=S("strides",e,t,n),d=wm(e,t,n),p=S("dataFormat",e,t,n).toUpperCase(),h=S("dilations",e,t,n),[f,m]=S("args",e,t,n);o&&(m=f,f=void 0);let g=S("leakyreluAlpha",e,t,n);return{stride:u,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var oG=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=S("stride",e,t,n),r=S("pad",e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilation",e,t,n);return[t1(S("x",e,t,n),S("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let s=S("strides",e,t,n),r=wm(e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilations",e,t,n);return[_o(S("x",e,t,n),S("filter",e,t,n),[s[1],s[2]],r,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:c,leakyreluAlpha:u}=M7(e,t,n);return[Fo.conv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:c,leakyreluAlpha:u}=M7(e,t,n);return[Fo.depthwiseConv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let s=S("outputShape",e,t,n),r=S("strides",e,t,n),a=wm(e,t,n);return[s1(S("x",e,t,n),S("filter",e,t,n),s,[r[1],r[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let s=S("strides",e,t,n),r=wm(e,t,n),a=S("dilations",e,t,n),o=S("dataFormat",e,t,n).toUpperCase();return[Dd(S("input",e,t,n),S("filter",e,t,n),[s[1],s[2]],r,o,[a[1],a[2]])]}case"Conv3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilations",e,t,n);return[r1(S("x",e,t,n),S("filter",e,t,n),[s[1],s[2],s[3]],r,a,[o[1],o[2],o[3]])]}case"AvgPool":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[ff(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPool":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[wf(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPoolWithArgmax":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n),o=S("includeBatchInIndex",e,t,n),{result:i,indexes:l}=Fv(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,o);return[i,l]}case"AvgPool3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[Q2(S("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[h1(S("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("dilations",e,t,n),o=s[1],i=s[2],l=a[1],c=a[2];return[wv(S("x",e,t,n),S("filter",e,t,n),[o,i],r,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},iG=(e,t,n)=>{switch(e.op){case"Fill":{let s=S("shape",e,t,n),r=S("dtype",e,t,n),a=S("value",e,t,n);return[Vu(s,a,r)]}case"LinSpace":{let s=S("start",e,t,n),r=S("stop",e,t,n),a=S("num",e,t,n);return[Nv(s,r,a)]}case"Multinomial":{let s=S("logits",e,t,n),r=S("numSamples",e,t,n),a=S("seed",e,t,n);return[Mv(s,r,a)]}case"OneHot":{let s=S("indices",e,t,n),r=S("depth",e,t,n),a=S("onValue",e,t,n),o=S("offValue",e,t,n);return[Rd(s,r,a,o)]}case"Ones":return[ys(S("shape",e,t,n),S("dtype",e,t,n))];case"OnesLike":return[Ps(S("x",e,t,n))];case"RandomUniform":return[ju(S("shape",e,t,n),S("minval",e,t,n),S("maxval",e,t,n),S("dtype",e,t,n))];case"Range":{let s=S("start",e,t,n),r=S("stop",e,t,n),a=S("step",e,t,n);return[qu(s,r,a,S("dtype",e,t,n))]}case"TruncatedNormal":{let s=S("shape",e,t,n),r=S("mean",e,t,n),a=S("stdDev",e,t,n),o=S("seed",e,t,n);return[Ef(s,r,a,S("dtype",e,t,n),o)]}case"Zeros":return[jt(S("shape",e,t,n),S("dtype",e,t,n))];case"ZerosLike":return[tt(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function My(e,t,n){let s=S("boxes",e,t,n),r=S("scores",e,t,n),a=S("maxOutputSize",e,t,n),o=S("iouThreshold",e,t,n),i=S("scoreThreshold",e,t,n),l=S("softNmsSigma",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}}var lG=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=My(e,t,n),c=await Ce.nonMaxSuppressionWithScoreAsync(s,r,a,o,i,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=My(e,t,n),l=S("padToMaxOutputSize",e,t,n),c=await Ce.nonMaxSuppressionPaddedAsync(s,r,a,o,i,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=My(e,t,n);return[await Ce.nonMaxSuppressionAsync(s,r,a,o,i)]}case"Where":{let s=me(S("condition",e,t,n),"bool"),r=[await T1(s)];return s.dispose(),r}case"ListDiff":return Wv(S("x",e,t,n),S("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},uG=(e,t,n)=>{switch(e.op){case"TopKV2":{let s=S("x",e,t,n),r=S("k",e,t,n),a=S("sorted",e,t,n),o=Hv(s,r,a);return[o.values,o.indices]}case"Unique":{let s=S("x",e,t,n),r=C1(s);return[r.values,r.indices]}case"UniqueV2":{let s=S("x",e,t,n),r=S("axis",e,t,n),a=C1(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cG=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let s=S("default",e,t,n);return[Hn(e.name,t,n)||s];case"Placeholder":return[Hn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=S("x",e,t,n);return[ia(c)]}case"IdentityN":return S("x",e,t,n).map(c=>ia(c));case"Snapshot":let r=S("x",e,t,n);return[ia(r)];case"Shape":return[Ut(S("x",e,t,n).shape,"int32")];case"ShapeN":return S("x",e,t,n).map(c=>Ut(c.shape));case"Size":return[Ie(S("x",e,t,n).size,"int32")];case"Rank":return[Ie(S("x",e,t,n).rank,"int32")];case"NoOp":return[Ie(1)];case"Print":let a=S("x",e,t,n),o=S("data",e,t,n),i=S("message",e,t,n),l=S("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let c=0;c<o.length;c++)console.log(Array.prototype.slice.call(o[c].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dG=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,An(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ie(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),X(()=>{let s=os(t),r=n.length,a=s.length;v.assert(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let o=0;o<r;o++){let i=n[o],l=s[o];An(l),this.tensorMap.set(i,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return X(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],o=this.findWithDefault(a,t);s.push(o)}return xn(s)})}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}`)}},pG=async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=S("keyDType",e,t,n),a=S("valueDType",e,t,n),o=new dG(r,a);return s.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=S("tableHandle",e,t,n,s),a=S("keys",e,t,n),o=S("values",e,t,n);return[await s.getHashTableById(r.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let r=S("tableHandle",e,t,n,s),a=S("keys",e,t,n),o=S("defaultValue",e,t,n);return[await s.getHashTableById(r.id).find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=S("tableHandle",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hG=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let s=S("images",e,t,n),r=S("size",e,t,n),a=S("alignCorners",e,t,n),o=S("halfPixelCenters",e,t,n);return[Ce.resizeBilinear(s,[r[0],r[1]],a,o)]}case"ResizeNearestNeighbor":{let s=S("images",e,t,n),r=S("size",e,t,n),a=S("alignCorners",e,t,n),o=S("halfPixelCenters",e,t,n);return[Ce.resizeNearestNeighbor(s,[r[0],r[1]],a,o)]}case"CropAndResize":{let s=S("image",e,t,n),r=S("boxes",e,t,n),a=S("boxInd",e,t,n),o=S("cropSize",e,t,n),i=S("method",e,t,n),l=S("extrapolationValue",e,t,n);return[Ce.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fG=(e,t,n)=>{switch(e.op){case"Equal":return[$s(S("a",e,t,n),S("b",e,t,n))];case"NotEqual":return[Hu(S("a",e,t,n),S("b",e,t,n))];case"Greater":return[As(S("a",e,t,n),S("b",e,t,n))];case"GreaterEqual":return[dl(S("a",e,t,n),S("b",e,t,n))];case"Less":return[l1(S("a",e,t,n),S("b",e,t,n))];case"LessEqual":return[pl(S("a",e,t,n),S("b",e,t,n))];case"LogicalAnd":return[hr(S("a",e,t,n),S("b",e,t,n))];case"LogicalNot":return[vf(S("a",e,t,n))];case"LogicalOr":return[p1(S("a",e,t,n),S("b",e,t,n))];case"Select":case"SelectV2":return[Un(S("condition",e,t,n),S("a",e,t,n),S("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mG=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[He(S("a",e,t,n),S("b",e,t,n),S("transposeA",e,t,n),S("transposeB",e,t,n))];case"Einsum":return[Sv(S("equation",e,t,n),...S("tensors",e,t,n))];case"Transpose":return[et(S("x",e,t,n),S("perm",e,t,n))];case"_FusedMatMul":let[s,r]=S("fusedOps",e,t,n),a=s==="biasadd",o=r==="prelu",i=S("numArgs",e,t,n),l=S("leakyreluAlpha",e,t,n);if(a){if(o&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,u]=S("args",e,t,n);return[Fo.matMul({a:S("a",e,t,n),b:S("b",e,t,n),transposeA:S("transposeA",e,t,n),transposeB:S("transposeB",e,t,n),bias:c,activation:r,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gG=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Bu(S("x",e,t,n),S("mean",e,t,n),S("variance",e,t,n),S("offset",e,t,n),S("scale",e,t,n),S("epsilon",e,t,n))];case"FusedBatchNormV3":return[Bu(S("x",e,t,n),S("mean",e,t,n),S("variance",e,t,n),S("offset",e,t,n),S("scale",e,t,n),S("epsilon",e,t,n))];case"LRN":return[Ev(S("x",e,t,n),S("radius",e,t,n),S("bias",e,t,n),S("alpha",e,t,n),S("beta",e,t,n))];case"Softmax":return[Xu(S("x",e,t,n))];case"LogSoftmax":return[u1(S("x",e,t,n))];case"SparseToDense":return[E1(S("sparseIndices",e,t,n),S("outputShape",e,t,n),S("sparseValues",e,t,n),S("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},AG=(e,t,n)=>{switch(e.op){case"Max":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[yn(S("x",e,t,n),o,i)]}case"Mean":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Vt(S("x",e,t,n),o,i)]}case"Min":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Do(S("x",e,t,n),o,i)]}case"Sum":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[ke(S("x",e,t,n),o,i)]}case"All":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Z2(S("x",e,t,n),o,i)]}case"Any":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[pf(S("x",e,t,n),o,i)]}case"ArgMax":{let o=S("axis",e,t,n);return[Zs(S("x",e,t,n),o)]}case"ArgMin":{let o=S("axis",e,t,n);return[tv(S("x",e,t,n),o)]}case"Prod":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[f1(S("x",e,t,n),o,i)]}case"Cumsum":{let o=S("axis",e,t,n),i=S("exclusive",e,t,n),l=S("reverse",e,t,n);return[o1(S("x",e,t,n),o,i,l)]}case"Bincount":let s=S("x",e,t,n),r=S("weights",e,t,n),a=S("size",e,t,n);return[e1(s,r,a)];case"DenseBincount":{let o=S("x",e,t,n),i=S("weights",e,t,n),l=S("size",e,t,n),c=S("binaryOutput",e,t,n);return[bv(o,i,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},yG=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=S("n",e,t,n),r=S("axis",e,t,n),a=S("tensors",e,t,n);return a=a.slice(0,s),[kt(a,r)]}case"Gather":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[Uu(s,me(r,"int32"),0)]}case"GatherV2":{let s=S("axis",e,t,n),r=S("batchDims",e,t,n),a=S("x",e,t,n),o=S("indices",e,t,n);return[Uu(a,me(o,"int32"),s,r)]}case"Reverse":{let s=S("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=S("x",e,t,n);return[Fs(a,r)]}case"ReverseV2":{let s=S("axis",e,t,n),r=S("x",e,t,n);return[Fs(r,s)]}case"Slice":{let s=S("begin",e,t,n),r=S("size",e,t,n);return[Pe(S("x",e,t,n),s,r)]}case"StridedSlice":{let s=S("begin",e,t,n),r=S("end",e,t,n),a=S("strides",e,t,n),o=S("beginMask",e,t,n),i=S("endMask",e,t,n),l=S("ellipsisMask",e,t,n),c=S("newAxisMask",e,t,n),u=S("shrinkAxisMask",e,t,n),d=S("x",e,t,n);return[Uv(d,s,r,a,o,i,l,c,u)]}case"Pack":return X(()=>{let s=S("axis",e,t,n),r=S("tensors",e,t,n),a=r[0].shape,o=it(r[0]).shape,i=r.map(l=>{let c=v.arraysEqual(l.shape,a);if(!c&&!v.arraysEqual(it(l).shape,o))throw new Error("the input tensors shape does not match");return c?l:H(l,a)});return[xn(i,s)]});case"Unpack":{let s=S("axis",e,t,n),r=S("tensor",e,t,n);return os(r,s)}case"Tile":{let s=S("reps",e,t,n);return[Ys(S("x",e,t,n),s)]}case"Split":case"SplitV":{let s=S("axis",e,t,n),r=S("numOrSizeSplits",e,t,n),a=S("x",e,t,n);return rn(a,r,s)}case"ScatterNd":{let s=S("indices",e,t,n),r=S("values",e,t,n),a=S("shape",e,t,n);return[Zv(s,r,a)]}case"GatherNd":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[Yv(s,r)]}case"SparseToDense":{let s=S("sparseIndices",e,t,n),r=S("outputShape",e,t,n),a=S("sparseValues",e,t,n),o=S("defaultValue",e,t,n);return[E1(s,a,r,a.dtype===o.dtype?o:me(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xG=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=Wd.sparseFillEmptyRows(S("indices",e,t,n),S("values",e,t,n),S("denseShape",e,t,n),S("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Wd.sparseReshape(S("inputIndices",e,t,n),S("inputShape",e,t,n),S("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Wd.sparseSegmentMean(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];case"SparseSegmentSum":return[Wd.sparseSegmentSum(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bG=(e,t,n)=>{switch(e.op){case"FFT":return[Tf(S("x",e,t,n))];case"IFFT":return[Ld(S("x",e,t,n))];case"RFFT":return[Nf(S("x",e,t,n))];case"IRFFT":return[S1(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Pf.stringNGrams(S("data",e,t,n),S("dataSplits",e,t,n),S("separator",e,t,n),S("nGramWidths",e,t,n),S("leftPad",e,t,n),S("rightPad",e,t,n),S("padWidth",e,t,n),S("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Pf.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Pf.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wG=(e,t,n)=>{switch(e.op){case"Cast":return[me(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Zt(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[it(S("x",e,t,n),s)]}case"Reshape":return[H(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[Ov(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[Js(S("x",e,t,n),S("padding",e,t,n),S("constantValue",e,t,n))];case"SpaceToBatchND":{let s=S("blockShape",e,t,n),r=S("paddings",e,t,n);return[Sf(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[mf(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[vv(S("x",e,t,n),s,r)]}case"BroadcastTo":return[_d(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[hv(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function z7(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return X(()=>JU(a,o,i));case"basic_math":return X(()=>QU(a,o,i));case"control":return aG(a,o,i);case"convolution":return X(()=>oG(a,o,i));case"creation":return X(()=>iG(a,o,i));case"dynamic":return lG(a,o,i);case"evaluation":return X(()=>uG(a,o,i));case"image":return X(()=>hG(a,o,i));case"graph":return X(()=>cG(a,o,i));case"logical":return X(()=>fG(a,o,i));case"matrices":return X(()=>mG(a,o,i));case"normalization":return X(()=>gG(a,o,i));case"reduction":return X(()=>AG(a,o,i));case"slice_join":return X(()=>yG(a,o,i));case"sparse":return X(()=>xG(a,o,i));case"spectral":return X(()=>bG(a,o,i));case"string":return X(()=>vG(a,o,i));case"transformation":return X(()=>wG(a,o,i));case"hash_table":return pG(a,o,i,s);case"custom":let l=p7(a.op);if(l&&l.customExecutor)return l.customExecutor(new YU(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var L7=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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 B7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>xs(p)[0]),u=[];s!=null&&(u=s.map(p=>xs(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((W7(p)||TG(p)||NG(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function kG(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>xs(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var SG=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],IG=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],CG=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function W7(e){return SG.indexOf(e.op)>=0}function TG(e){return IG.indexOf(e.op)>=0}function NG(e){return CG.indexOf(e.op)>=0}var zy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,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 zy(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(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=B7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return kG(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 s=n.map(u=>this.graph.nodes[xs(u)[0]]),r=t.map(u=>xs(u)[0]),a=r.map(u=>this.graph.nodes[u]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return X(()=>{let u=new L7(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=xs(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=z7(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Hn(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=RU(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];if(u===1){if(!this.keepTensorForDebug)c.dispose();else{let[d,p]=Vr(t.name,s);this.intermediateTensors[d]?this.intermediateTensors[d][p]=c:(this.intermediateTensors[d]=[],this.intermediateTensors[d][p]=c)}delete o[c.id]}else u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let a=new L7(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(c=>Hn(c,this.tensorsMap,a)),i=o.map(c=>c.id),l=Object.keys(e).map(c=>e[c].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[xs(x)[0]]),o=n.map(x=>xs(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=B7(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(x=>{let[y,b]=xs(x),w=[];w[b]=e[x],h[y]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let x=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(x)}u==null&&!s&&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=i.filter(x=>!W7(x)&&!Hn(x.name,h,t)).map(x=>x.name);if(A.length>0){let x="";throw u!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&S("isConstant",u.node,s,n)&&([d]=Vr(u.node.name,n)),s[u.node.name]==null){let p=z7(u.node,s,n,this._resourceManager);d||([d]=Vr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Vr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Hn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Hn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=xs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=xs(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=xs(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},EG=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},RG="?tfjs-format=file",$G="model.json",V7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new EG}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=rs.browserHTTPRequest(e,this.loadOptions);else{let t=rs.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(rs.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 s=rs.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new zy(_7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=_7.Instance.transformGraph(e.modelInitializer);this.initializer=new zy(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=rs.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 Qe)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Be(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${$G}${RG}`);let n=new V7(e,t);return await n.load(),n}var _G="0.0.0",U7={};Oe(U7,{CSVDataset:()=>sS,Dataset:()=>sc,FileDataSource:()=>cS,TextLineDataset:()=>eS,URLDataSource:()=>dS,array:()=>tH,csv:()=>pH,func:()=>hH,generator:()=>fH,microphone:()=>gH,version_data:()=>AH,webcam:()=>mH,zip:()=>nH});var DG=di(Ah()),PG=di(Ah());function FG(e,t){return km(e,t)}function km(e,t,n=new Map,s=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(nc(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=km(i,t,n,s);a[o]=l}return s.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function OG(e,t=H7){return G7(e,t)}function G7(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(nc(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(c=>c[o]),l=G7(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function H7(e){return e===null?null:nc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function j7(e,t){let n=new Map;km(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let o=await a;n.set(r,o)}}return km(e,t,n)}function nc(e){let t=!1;if(Y().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=M5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Qe)&&!(e instanceof Promise)&&!t)}function MG(e){return e==null||zG(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Qe||v.isTypedArray(e)}function zG(e){return e===null||typeof e!="object"&&typeof e!="function"}function LG(e){return FG(e,BG)}function BG(e){return e instanceof Qe?{value:e.clone(),recurse:!1}:nc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var q7=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}},X7=class extends q7{constructor(){super(X7.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 s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},K7=X7;K7.INITIAL_CAPACITY=32;function Z7(e){return new UG(e)}function Ly(e){return new GG(e)}function WG(e,t){return new J7(e,t)}function VG(e,t=Sm.FAIL){return new QG(e,t)}var vn=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 YG(this,e)}filter(e){return new KG(this,e)}map(e){return new ZG(this,e)}mapAsync(e){return new Y7(this,e)}serialMapAsync(e){return new Y7(this,e).serial()}flatmap(e){return new JG(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 XG(this,e,t)}columnMajorBatch(e,t=!0,n=H7){return this.rowMajorBatch(e,t).map(r=>OG(r,n))}concatenate(e,t){return new J7(Z7([this,e]),t)}take(e){return e<0||e==null?this:new qG(this,e)}skip(e){return e<0||e==null?this:new jG(this,e)}prefetch(e){return new Q7(this,e)}shuffle(e,t){return new eH(this,e,t)}serial(){return new HG(this)}},UG=class extends vn{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:LG(e),done:!1}}},GG=class extends vn{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: ${e.message}`,e}}},HG=class extends vn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},jG=class extends vn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;ne(e.value)}return this.upstream.next()}},qG=class extends vn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},XG=class extends vn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},KG=class extends vn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;ne(e.value)}}},ZG=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},YG=class extends vn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Y7=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},By=class extends vn{constructor(){super();this.outputQueue=new K7,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},JG=class extends By{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return!0}},J7=class extends vn{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}},Sm;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Sm||(Sm={}));var QG=class extends vn{constructor(e,t=0){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 s(a){return a instanceof vn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await j7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Q7=class extends vn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new q7(t)}summary(){return`${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()}},eH=class extends Q7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=PG.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},sc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),bs(async()=>(await n.iterator()).columnMajorBatch(e,t,sH),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,bs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,bs(async()=>(await t.iterator()).filter(s=>X(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return bs(async()=>(await t.iterator()).map(n=>X(()=>e(n))),this.size)}mapAsync(e){let t=this;return bs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return bs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,bs(async()=>{let s=Ly(async()=>({value:await t.iterator(),done:!1}));return WG(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,bs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=DG.alea(t||v.now().toString());return bs(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.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,bs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};sc.MAX_BUFFER_SIZE=1e4;function bs(e,t=null){return new class extends sc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function tH(e){return bs(async()=>Z7(e),e.length)}function nH(e){if(!nc(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 bs(async()=>{let n=await j7(e,s=>{if(s instanceof sc)return{value:s.iterator(),recurse:!1};if(nc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return VG(n,Sm.SHORTEST)},t)}function sH(e){if(e===null)return null;let t=e[0];return MG(t)?{value:rH(e),recurse:!1}:{value:null,recurse:!0}}function rH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Qe?xn(e):ct(e)}var eS=class extends sc{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
|
|
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Im='"',ip=Symbol("out"),tS=Symbol("field"),Cm=Symbol("quote"),Wy=Symbol("quoteafterquote"),nS=Symbol("quoteinquote"),sS=class extends sc{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 eS(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!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={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let c=Number(i);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(i);break;default:l=c}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=ip;for(let o=0;o<r;o++)switch(a){case ip:switch(e.charAt(o)){case Im:s=o+1,a=Cm;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=ip;break;default:a=tS,s=o;break}break;case tS:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=ip,s=o+1;break;default:}break;case Cm:switch(e.charAt(o)){case Im:a=Wy;break;default:}break;case Wy:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=ip,s=o+1;break;case Im:a=Cm;break;default:a=nS;break}break;case nS:switch(e.charAt(o)){case Im:a=Cm;break;default:}break;default:}if(a===Wy?n.push(e.substring(s,r-1)):n.push(e.substring(s)),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}},rS=class extends vn{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(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new rS(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 s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[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(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({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((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),ct(n,t)}},aS=class extends vn{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=Ut([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=fr([a,r,i,o],[1,4])}else this.cropBox=fr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().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 aS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ks.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Zt(me(e,"float32"),0),n;n=Ce.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return H(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},oS=class{},iS=class extends vn{split(e){return new aH(this,e)}},aH=class extends iS{constructor(e,t){super();this.upstream=e,this.impl=new oH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},oH=class extends By{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},iH=class extends vn{decodeUTF8(){return new lH(this)}},lH=class extends iS{constructor(e){super();this.upstream=e,this.impl=new uH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uH=class extends By{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=M5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},lS=class extends iH{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function cH(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=dH(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new lS(o,t)}else throw new Error(a.statusText)}var dH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function uS(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var cS=class extends oS{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(uS(this.input)&&Y().get("IS_NODE")){let e=js("fs");this.input=e.readFileSync(this.input.substr(7))}return new lS(this.input,this.options)}},dS=class extends oS{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return uS(this.url)?new cS(this.url,this.fileOptions).iterator():cH(this.url,this.fileOptions)}};function pH(e,t={}){return new sS(new dS(e),t)}function hH(e){let t=Ly(e);return bs(async()=>t)}function fH(e){return bs(async()=>{let t=await e();return Ly(()=>t.next())})}async function mH(e,t){return aS.create(e,t)}async function gH(e){return rS.create(e)}var AH="0.0.0";function Re(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yH=Qs.whereImpl,pS=class extends ru{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new td(this,as())}nextDataId(){return pS.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&E.warn(`
|
|
============================
|
|
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
|
|
============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,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,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}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 s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return E.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return as().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Re([e],"where");let t=this.readSync(e.dataId);return yH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},Vy=pS;Vy.nextDataId=0;var Tm={};Oe(Tm,{addImpl:()=>fS,bincountImpl:()=>Gy,bincountReduceImpl:()=>mS,ceilImpl:()=>gS,concatImpl:()=>Hy,equalImpl:()=>AS,expImpl:()=>xS,expm1Impl:()=>vS,floorImpl:()=>wS,gatherNdImpl:()=>kS,gatherV2Impl:()=>SS,greaterEqualImpl:()=>CS,greaterImpl:()=>IS,lessEqualImpl:()=>NS,lessImpl:()=>TS,linSpaceImpl:()=>ES,logImpl:()=>RS,maxImpl:()=>$S,maximumImpl:()=>_S,minimumImpl:()=>DS,multiplyImpl:()=>jy,negImpl:()=>PS,notEqualImpl:()=>FS,prodImpl:()=>OS,rangeImpl:()=>Xy,rsqrtImpl:()=>MS,sigmoidImpl:()=>oj,simpleAbsImpl:()=>hS,sliceImpl:()=>Rm,sparseFillEmptyRowsImpl:()=>LS,sparseReshapeImpl:()=>BS,sparseSegmentReductionImpl:()=>Ky,sqrtImpl:()=>uj,squaredDifferenceImpl:()=>WS,stridedSliceImpl:()=>VS,stringNGramsImpl:()=>US,stringSplitImpl:()=>GS,stringToHashBucketFastImpl:()=>HS,subImpl:()=>jS,tileImpl:()=>qS,topKImpl:()=>KS,transposeImpl:()=>qy,uniqueImpl:()=>ZS});function hS(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var xH=e=>{let{x:t}=e.inputs,n=e.backend;Re(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=hS(r),n.makeOutput(s,t.shape,t.dtype)},bH={kernelName:fi,backendName:"cpu",kernelFunc:xH};function Jt(e){return(t,n,s,r,a)=>{let o=E.assertAndGetBroadcastShape(t,n),i=o.length,l=v.computeStrides(o),c=v.sizeFromShape(o),u=v.getTypedArrayFromDType(a,c),d=t.length,p=n.length,h=v.computeStrides(t),f=v.computeStrides(n),m=E.getBroadcastDims(t,o),g=E.getBroadcastDims(n,o);if(m.length+g.length===0)for(let A=0;A<u.length;++A)u[A]=e(s[A%s.length],r[A%r.length]);else for(let A=0;A<u.length;++A){let x=v.indexToLoc(A,i,l),y=x.slice(-d);m.forEach(I=>y[I]=0);let b=v.locToIndex(y,d,h),w=x.slice(-p);g.forEach(I=>w[I]=0);let k=v.locToIndex(w,p,f);u[A]=e(s[b],r[k])}return[u,o]}}function vs(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",o)},i}var vH={kernelName:ad,backendName:"cpu",kernelFunc:vs};function Nm(e,t,n="float32"){if(n==="complex64"){let r=Nm(e,t,"float32"),a=Nm(e,t,"float32");return vs({inputs:{real:r,imag:a},backend:e})}let s=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function Ur(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var wH={kernelName:Xa,backendName:"cpu",kernelFunc:Ur};function Cl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var kH={kernelName:fd,backendName:"cpu",kernelFunc:Cl};function Go(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Ur({inputs:{x:r},backend:n});let o=Nm(n,r.shape,r.dtype),i=Go({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=vs({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Cl({inputs:{input:r},backend:n}),i=Go({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Ur({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,"int32",i)}if(a==="bool"){let o=n.data.get(r.dataId).values,i=v.toTypedArray([0],r.dtype),[l,c]=Jt((u,d)=>u!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var SH={kernelName:Pa,backendName:"cpu",kernelFunc:Go};function wn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Re([o,i],e);let c=l.data.get(o.dataId).values,u=l.data.get(i.dataId).values,d=o.dtype==="string"?E.fromUint8ToStringArray(c):c,p=o.dtype==="string"?E.fromUint8ToStringArray(u):u,h=s||o.dtype,[f,m]=t(o.shape,i.shape,d,p,h);return l.makeTensorInfo(m,h,f)}:({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let c=Go({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),d=u.complexTensorInfos.real,p=u.complexTensorInfos.imag,h=l.data.get(d.dataId).values,f=l.data.get(p.dataId).values,m=Go({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),A=g.complexTensorInfos.real,x=g.complexTensorInfos.imag,y=l.data.get(A.dataId).values,b=l.data.get(x.dataId).values,[w,k,I]=n(o.shape,i.shape,h,f,y,b),N=l.makeTensorInfo(I,"float32",w),R=l.makeTensorInfo(I,"float32",k),O=vs({inputs:{real:N,imag:R},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(N),l.disposeIntermediateTensorInfo(R),O}else{let c=l.data.get(o.dataId).values,u=l.data.get(i.dataId).values,d=s||o.dtype,[p,h]=t(o.shape,i.shape,c,u,d);return l.makeTensorInfo(h,d,p)}}}function Uy(e){return(t,n,s,r,a,o)=>{let i=E.assertAndGetBroadcastShape(t,n),l=v.sizeFromShape(i),c=i.length,u=v.computeStrides(i),d=v.getTypedArrayFromDType("float32",l),p=v.getTypedArrayFromDType("float32",l),h=E.getBroadcastDims(t,i),f=E.getBroadcastDims(n,i),m=E.mergeRealAndImagArrays(s,r),g=E.mergeRealAndImagArrays(a,o),A=t.length,x=v.computeStrides(t),y=n.length,b=v.computeStrides(n);if(h.length+f.length===0)for(let w=0;w<d.length;w++){let k=w%m.length,I=w%g.length,N=e(m[k*2],m[k*2+1],g[I*2],g[I*2+1]);d[w]=N.real,p[w]=N.imag}else for(let w=0;w<d.length;w++){let k=v.indexToLoc(w,c,u),I=k.slice(-A);h.forEach(P=>I[P]=0);let N=v.locToIndex(I,A,x),R=k.slice(-y);f.forEach(P=>R[P]=0);let O=v.locToIndex(R,y,b),$=e(m[N*2],m[N*2+1],g[O*2],g[O*2+1]);d[w]=$.real,p[w]=$.imag}return[d,p,i]}}var fS=Jt((e,t)=>e+t),IH=Uy((e,t,n,s)=>({real:e+n,imag:t+s})),lp=wn(Kr,fS,IH),CH={kernelName:Kr,backendName:"cpu",kernelFunc:lp};function Gy(e,t,n,s,r){let a=v.sizeFromShape(s),o=v.makeZerosTypedArray(r,n);for(let i=0;i<e.length;i++){let l=e[i];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(a>0?o[l]+=t[i]:o[l]+=1)}return o}function mS(e,t,n,s=!1){let r=e.shape[0],a=e.shape[1],o=ze([r,n],t.dtype);for(let i=0;i<r;i++)for(let l=0;l<a;l++){let c=e.get(i,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(s?o.set(1,i,c):t.size>0?o.set(o.get(i,c)+t.get(i,l),i,c):o.set(o.get(i,c)+1,i,c))}return o}function Ho(e){return(t,n,s)=>{let r=v.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)r[a]=e(t[a],s);return r}}function mt(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Re(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,c=v.sizeFromShape(o.shape),u=n||o.dtype,d=v.getArrayFromDType(u,c);for(let p=0;p<c;++p)d[p]=t(l[p],r);return i.makeTensorInfo(o.shape,u,d)}}function rc(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Re(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,c=n||o.dtype,u=t(l,c,r);return i.makeTensorInfo(o.shape,c,u)}}var gS=Ho(e=>Math.ceil(e)),TH=rc(Fa,gS),NH={kernelName:Fa,backendName:"cpu",kernelFunc:TH};function Hy(e,t,n,s){let r=v.getArrayFromDType(n,v.sizeFromShape(t));if(s&&n!=="string"){let a=0;e.forEach(o=>{let i=v.sizeFromShape(o.shape);r.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?E.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let c=0;c<o.shape[0];++c){let u=c*t[1]+a;for(let d=0;d<o.shape[1];++d)r[u+d]=i[l++]}a+=o.shape[1]})}return r}var AS=Jt((e,t)=>e===t?1:0),yS=wn(bi,AS,null,"bool"),EH={kernelName:bi,backendName:"cpu",kernelFunc:yS},xS=Ho(e=>Math.exp(e)),bS=rc(Ua,xS,"float32"),RH={kernelName:Ua,backendName:"cpu",kernelFunc:bS},vS=Ho(e=>Math.expm1(e)),$H=rc(wi,vS),_H={kernelName:wi,backendName:"cpu",kernelFunc:$H},wS=Ho(e=>Math.floor(e)),DH=rc(Ga,wS),PH={kernelName:Ga,backendName:"cpu",kernelFunc:DH};function kS(e,t,n,s,r,a,o,i,l){let c=ze([s,a],n);for(let u=0;u<s;u++){let d=[],p=0;for(let h=0;h<r;h++){let f=e[u*r+h];p+=f*o[h],d.push(f)}if(p<0||p>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let h=0;h<a;h++)c.values[u*a+h]=t.get(...t.indexToLoc(p*a+h))}return c}function SS(e,t,n){let s=ze(n,e.dtype);for(let r=0;r<s.size;++r){let o=s.indexToLoc(r).slice(),i=o[0],l=o[2],c=t.locToIndex([i,l]);o[2]=t.values[c];let u=e.locToIndex(o);s.values[r]=e.values[u]}return s}var IS=Jt((e,t)=>e>t?1:0),FH=wn(Ci,IS,null,"bool"),OH={kernelName:Ci,backendName:"cpu",kernelFunc:FH},CS=Jt((e,t)=>e>=t?1:0),MH=wn(qa,CS,null,"bool"),zH={kernelName:qa,backendName:"cpu",kernelFunc:MH},TS=Jt((e,t)=>e<t?1:0),LH=wn(Ni,TS,null,"bool"),BH={kernelName:Ni,backendName:"cpu",kernelFunc:LH},NS=Jt((e,t)=>e<=t?1:0),WH=wn(Ei,NS,null,"bool"),VH={kernelName:Ei,backendName:"cpu",kernelFunc:WH};function ES(e,t,n){let s=(t-e)/(n-1),r=v.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var RS=Ho(e=>Math.log(e)),UH=rc(Ka,RS),GH={kernelName:Ka,backendName:"cpu",kernelFunc:UH};function $S(e,t,n,s){let r=v.getTypedArrayFromDType(s,v.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let c=e[o+l];(Number.isNaN(c)||c>i)&&(i=c)}r[a]=i}return r}var _S=Jt((e,t)=>Math.max(e,t)),HH=wn(Ya,_S),jH={kernelName:Ya,backendName:"cpu",kernelFunc:HH},DS=Jt((e,t)=>Math.min(e,t)),qH=wn(to,DS),XH={kernelName:to,backendName:"cpu",kernelFunc:qH},jy=Jt((e,t)=>e*t),KH=Uy((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Em=wn(so,jy,KH),ZH={kernelName:so,backendName:"cpu",kernelFunc:Em};function PS(e,t,n){let s=v.createScalarValue(-1,n);return jy([],t,s,e,n)}function YH(e){let{inputs:t,backend:n}=e,{x:s}=t;Re(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=PS(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var JH={kernelName:$i,backendName:"cpu",kernelFunc:YH},FS=Jt((e,t)=>e!==t?1:0),QH=wn(_i,FS,null,"bool"),ej={kernelName:_i,backendName:"cpu",kernelFunc:QH};function qy(e,t,n,s,r){let a=t.length,o=v.sizeFromShape(t),i=v.computeStrides(t),l=v.computeStrides(r),c=v.getTypedArrayFromDType(n,v.sizeFromShape(r));for(let u=0;u<o;++u){let d=v.indexToLoc(u,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[s[f]];let h=v.locToIndex(p,a,l);c[h]=e[u]}return c}function Ms(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;Re(r,"transpose");let o=r.shape.length,i=new Array(o);for(let d=0;d<i.length;d++)i[d]=r.shape[a[d]];let l=s.data.get(r.dataId).values,c=qy(l,r.shape,r.dtype,a,i);return{dataId:s.write(c,i,r.dtype),shape:i,dtype:r.dtype}}var tj={kernelName:bo,backendName:"cpu",kernelFunc:Ms};function OS(e,t,n,s){let[r,a]=E.computeOutAndReduceShapes(e,s),o=Wn(t,"int32"),i=v.makeZerosTypedArray(v.sizeFromShape(r),o),l=v.sizeFromShape(a);for(let c=0;c<i.length;++c){let u=c*l,d=1;for(let p=0;p<l;++p)d*=n[u+p];i[c]=d}return{outVals:i,outShape:r,outDtype:o}}function nj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Re(r,"prod");let i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=E.getAxesPermutation(l,i),u=l,d=r,p=[];c!=null&&(d=Ms({inputs:{x:r},backend:n,attrs:{perm:c}}),p.push(d),u=E.getInnerMostAxes(u.length,i));let h=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:g}=OS(d.shape,d.dtype,h,u),A=m;return o&&(A=E.expandShapeToKeepDim(m,l)),p.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.makeTensorInfo(A,g,f)}var sj={kernelName:zi,backendName:"cpu",kernelFunc:nj};function Xy(e,t,n,s){let r=e===t,a=e<t&&n<0,o=t<e&&n>1;if(r||a||o)return v.makeZerosTypedArray(0,s);let i=Math.abs(Math.ceil((t-e)/n)),l=v.makeZerosTypedArray(i,s);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 MS=Ho(e=>1/Math.sqrt(e)),rj=rc(co,MS),aj={kernelName:co,backendName:"cpu",kernelFunc:rj},oj=Ho(e=>1/(1+Math.exp(-e))),zS=mt(ho,e=>1/(1+Math.exp(-e))),ij={kernelName:ho,backendName:"cpu",kernelFunc:zS};function Rm(e,t,n,s,r){let a=Ot.isSliceContinous(s,t,n),o=v.sizeFromShape(n),i=v.computeStrides(s);if(a){let d=Ot.computeFlatOffset(t,i);return r==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=r==="string"?E.fromUint8ToStringArray(e):e,c=ze(s,r,l),u=ze(n,r);for(let d=0;d<u.size;++d){let p=u.indexToLoc(d),h=p.map((f,m)=>f+t[m]);u.set(c.get(...h),...p)}return r==="string"?E.fromStringArrayToUint8(u.values):u.values}function Tl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Re(r,"slice");let[i,l]=Ot.parseSliceParams(r,a,o);Ot.assertParamsValid(r,i,l);let c=n.data.get(r.dataId).values,u=Rm(c,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var lj={kernelName:Gi,backendName:"cpu",kernelFunc:Tl};function LS(e,t,n,s,r,a,o){let i=t[0],l=a[0],c=new Array(l),u=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
|
|
indices.shape[0] = ${i}`);let g=v.getArrayFromDType(n,0),A=v.getArrayFromDType(r,0);return[g,[0,d],A,c,u]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let A=e[g*d];if(A<0)throw new Error(`indices(${g}, 0) is invalid: ${A} < 0`);if(A>=l)throw new Error(`indices(${g}, 0) is invalid: ${A} >= ${l}`);++f[A],p=p&&A>=h,h=A}let m=!0;for(let g=0;g<l;++g){let A=f[g]===0;c[g]=A,m=m&&!A,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,A=s;for(let x=0;x<i;++x)u[x]=x;return[g,[i,d],A,c,u]}else{let g=f[l-1],A=v.getArrayFromDType(n,g*d),x=v.getArrayFromDType(r,g),y=new Array(l).fill(0);for(let b=0;b<i;++b){let w=e[b*d],k=y[w],I=(w===0?0:f[w-1])+k;y[w]++;for(let N=0;N<d;++N)A[I*d+N]=e[b*d+N];x[I]=s[b],u[b]=I}for(let b=0;b<l;++b)if(y[b]===0){let k=b===0?0:f[b-1];A[k*d+0]=b;for(let I=1;I<d;++I)A[k*d+I]=0;x[k]=o}return[A,[g,d],x,c,u]}}function BS(e,t,n,s,r){let a=v.sizeFromShape(s),o=t[0],i=r.length,l=[],c=1,u=-1;for(let g=0;g<i;++g){let A=r[g];if(A===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${g}`);u=g,l.push(1)}else{if(A<0)throw new Error(`size ${g} must be non-negative, not ${A}`);c*=A,l.push(A)}}if(u!==-1){if(c<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/c);if(c*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
|
|
dense values, but the requested shape requires a multiple of ${c}. inputShape=${s} outputShape= ${l}`);l[u]=g}let d=v.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let p=s.length,h=[];if(p>0){h[p-1]=1;for(let g=p-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=v.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let A=0;for(let x=0;x<p;++x)A+=e[g*p+x]*h[x];for(let x=0;x<i;++x)m[g*i+x]=Math.trunc(A/f[x]),A%=f[x]}return[m,[o,i],l]}function Ky(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],c=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let h=p.reduce((y,b)=>y*b,1),f=v.getArrayFromDType(n,h);if(i===0)return d>0&&f.fill(o),[f,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,A=0,x=r[m];for(;;){let y=0;if(g<i){if(y=r[g],x===y){++g;continue}if(x>=y)throw new Error("segment ids are not increasing")}if(x<0||x>=d)throw new Error(`Segment id ${x} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);x>A&&f.fill(o,A*c,x*c);for(let b=m;b<g;++b){let w=s[b];if(w<0||w>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<c;k++)f[x*c+k]+=e[w*c+k]}if(a)for(let b=0;b<c;b++)f[x*c+b]/=g-m;if(m=g,++g,A=x+1,x=y,g>i)break}return A<d&&f.fill(o,A*c,d*c),[f,p]}var uj=Ho(e=>Math.sqrt(e)),cj=mt(fo,e=>Math.sqrt(e)),dj={kernelName:fo,backendName:"cpu",kernelFunc:cj},WS=Jt((e,t)=>{let n=e-t;return n*n}),pj=wn(Ao,WS),hj={kernelName:Ao,backendName:"cpu",kernelFunc:pj};function VS(e,t,n,s){let r=ze(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var fj=class{constructor(e,t,n,s,r,a){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(n),this.rightPad=v.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),c=Math.max(0,i-(r-(o+1))),u=a-(l+c),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let A=0;A<u;++A)p+=e[d+A].length;p+=c*this.rightPad.length,p+=(l+c+u-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=A=>A.forEach(x=>f[m++]=x);for(let A=0;A<l;++A)g(this.leftPad),g(this.separator);for(let A=0;A<u-1;++A)g(e[d+A]),g(this.separator);if(u>0){g(e[d+u-1]);for(let A=0;A<c;++A)g(this.separator),g(this.rightPad)}else{for(let A=0;A<c-1;++A)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let c=t[l]>=i;if(c=c&&t[l]<=n,!c)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`)}let r=s-1,a=v.getArrayFromDType("int32",s);if(n===0||s===0){let i=new Array(n);for(let l=0;l<=r;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=r;++i){let l=t[i]-t[i-1],c=0;this.nGramWidths.forEach(u=>{c+=this.getNumNGrams(l,u)}),this.preserveShort&&l>0&&c===0&&(c=1),a[i]=a[i-1]+c}let o=new Array(a[r]);for(let i=0;i<r;++i){let l=t[i],c=a[i];if(this.nGramWidths.forEach(u=>{let d=t[i+1]-t[i],p=this.getNumNGrams(d,u);this.createNGrams(e,l,o,c,p,u),c+=p}),this.preserveShort&&c===a[i]){let u=t[i+1]-t[i];if(u===0)continue;let d=u+2*this.padWidth,p=1;this.createNGrams(e,l,o,c,p,d)}}return[o,a]}};function US(e,t,n,s,r,a,o,i){return new fj(n,s,r,a,o,i).compute(e,t)}function mj(e,t,n,s){if(!e.length)return;if(t.length===0){for(let a=0;a<e.length;++a)s.push(e.subarray(a,a+1));return}if(t.length===1){let a=t[0],o=e.indexOf(a);for(;o!==-1;){let i=e.subarray(0,o);(!n||i.length!==0)&&s.push(i),e=e.subarray(o+1),o=e.indexOf(a)}(!n||e.length!==0)&&s.push(e);return}let r=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}}function GS(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let p=0;p<s;++p){let h=r.length;mj(e[p],t,n,r);let f=r.length-h;i[p]=f,a+=f,o=Math.max(o,f)}let l=v.getArrayFromDType("int32",a*2),c=new Array(a),u=[s,o],d=0;for(let p=0;p<s;++p)for(let h=0;h<i[p];++h)l[d*2]=p,l[d*2+1]=h,c[d]=r[d],++d;return[l,c,u]}function HS(e,t){let n=v.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=v.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var jS=Jt((e,t)=>e-t),gj=Uy((e,t,n,s)=>({real:e-n,imag:t-s})),Zy=wn(yo,jS,gj),Aj={kernelName:yo,backendName:"cpu",kernelFunc:Zy};function qS(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let s=ze(n,e.dtype);for(let r=0;r<s.values.length;++r){let a=s.indexToLoc(r),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);s.values[r]=e.values[i]}return s}var up=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function XS(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,c=Math.log(i),u=.5*Math.exp(2*c/3),d=.5*Math.sqrt(c*u*(i-u)/i)*Math.sign(l-i/2),p=Math.max(n,Math.floor(t-l*u/i+d)),h=Math.min(s,Math.floor(t+(i-l)*u/i+d));XS(e,t,p,h)}let r=e[t],a=n,o=s;for(v.swap(e,n,t),up(e[s],r)>0&&v.swap(e,n,s);a<o;){for(v.swap(e,a,o),a++,o--;up(e[a],r)<0;)a=a+1;for(;up(e[o],r)>0;)o=o-1}up(e[n],r)===0?v.swap(e,n,o):(o=o+1,v.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function KS(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=v.getTypedArrayFromDType(n,o*s),c=v.getTypedArrayFromDType("int32",o*s);for(let d=0;d<o;d++){let p=d*i,h=e.subarray(p,p+i),f=new Array(h.length);h.forEach((x,y)=>f[y]={value:x,index:y}),s<f.length&&(XS(f,s),f=f.slice(0,s)),r&&f.sort(up);let m=d*s,g=l.subarray(m,m+s),A=c.subarray(m,m+s);for(let x=0;x<s;x++)g[x]=f[x].value,A[x]=f[x].index}let u=t.slice();return u[u.length-1]=s,[ze(u,n,l),ze(u,"int32",c)]}function ZS(e,t,n,s){let r=v.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new nn(a,s,e),c=[],u=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(u)m=e[f].toString();else{let g=[];for(let A=0;A<a[0];A++)for(let x=0;x<a[2];x++)g.push(l.get(A,f,x));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,c.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new nn(d,s);c.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let A=0;A<a[2];A++)p.set(l.get(g,f,A),g,m,A)});let h=n.slice();return h[r]=d[1],{outputValues:p.values,outputShape:h,indices:i}}var yj="0.0.0";ul("cpu",()=>new Vy,1);var YS=mt(Va,e=>e>=0?e:Math.exp(e)-1),xj={kernelName:Va,backendName:"cpu",kernelFunc:YS};function JS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Re([r],"leakyRelu");let o=v.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=v.getTypedArrayFromDType("float32",o);for(let c=0;c<i.length;c++)l[c]=i[c]<0?a*i[c]:i[c];return n.makeTensorInfo(r.shape,"float32",l)}var bj={kernelName:Ti,backendName:"cpu",kernelFunc:JS},vj=Jt((e,t)=>e<0?t*e:e);function QS(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Re([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=vj(s.shape,r.shape,a,o,"float32");return n.makeTensorInfo(l,"float32",i)}var wj={kernelName:oo,backendName:"cpu",kernelFunc:QS},eI=mt(io,e=>Math.max(0,e)),kj={kernelName:io,backendName:"cpu",kernelFunc:eI},tI=mt(uo,e=>Math.min(Math.max(0,e),6)),Sj={kernelName:uo,backendName:"cpu",kernelFunc:tI};function Yy(e,t,n,s,r){if(n==="linear")return Ur({inputs:{x:t},backend:e});if(n==="relu")return eI({inputs:{x:t},backend:e});if(n==="elu")return YS({inputs:{x:t},backend:e});if(n==="relu6")return tI({inputs:{x:t},backend:e});if(n==="prelu")return QS({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return JS({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return zS({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Dt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,o),l=v.sizeFromShape(i);v.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let c=n.data.get(r.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,d=c.complexTensorInfos.imag;u.shape=i,d.shape=i}return{dataId:r.dataId,shape:i,dtype:r.dtype}}var Ij={kernelName:Li,backendName:"cpu",kernelFunc:Dt};function nI(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;Re([r,a],"matMul");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[A,h,d]:[A,d,h],k=Dt({inputs:{x:r},backend:n,attrs:{shape:b}}),I=Dt({inputs:{x:a},backend:n,attrs:{shape:w}}),N=o?k.shape[1]:k.shape[2],R=o?k.shape[2]:k.shape[1],O=i?I.shape[1]:I.shape[2],$=Math.max(g,A),P=n.data.get(k.dataId).values,T=n.data.get(I.dataId).values,F=v.computeStrides(k.shape),U=v.computeStrides(I.shape),[q,z,K]=o?[F[0],1,F[1]]:[F[0],F[1],1],[J,Q,te]=i?[1,U[1],U[0]]:[U[1],1,U[0]],re=R*O,G=ze([$,R,O],k.dtype),se=G.values,oe=n.blockSize;for(let pe=0;pe<$;pe++)for(let ye=0;ye<R;ye+=oe)for(let we=0;we<O;we+=oe)for(let Ne=0;Ne<N;Ne+=oe){let Me=Math.min(ye+oe,R),Ue=Math.min(we+oe,O),qe=Math.min(Ne+oe,N);for(let Ke=ye;Ke<Me;Ke++)for(let pt=we;pt<Ue;pt++){let ht=0;for(let at=Ne;at<qe;at++){let St=Math.min(pe,g-1)*q,gt=Math.min(pe,A-1)*te,Et=P[St+Ke*z+at*K],Pt=T[at*J+pt*Q+gt];ht+=Et*Pt}se[pe*re+(Ke*O+pt)]+=ht}}return n.disposeIntermediateTensorInfo(k),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(y,G.dtype,G.values)}var Cj={kernelName:Da,backendName:"cpu",kernelFunc:nI};function Tj(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p,h,f,m=[];p=nI({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=lp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=Yy(n,p,u,i,d),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var Nj={kernelName:wo,backendName:"cpu",kernelFunc:Tj},Ej=mt(iu,e=>Math.acos(e)),Rj={kernelName:iu,backendName:"cpu",kernelFunc:Ej},$j=mt(lu,e=>Math.acosh(e)),_j={kernelName:lu,backendName:"cpu",kernelFunc:$j};function Dj(e){let{inputs:t,backend:n}=e,s=t;Re(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=ze(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let c=0;c<o.length;c++)o[c]+=l[c]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var Pj={kernelName:Ra,backendName:"cpu",kernelFunc:Dj};function Fj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Re(r,"all");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Ms({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("all",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let x=A*h,y=m[x];for(let b=0;b<h;++b){let w=m[x+b];y=y&&w}f[A]=y}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let A=E.expandShapeToKeepDim(d,i),x=Dt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var Oj={kernelName:uu,backendName:"cpu",kernelFunc:Fj};function Mj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Re(r,"any");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Ms({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("any",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let x=A*h,y=m[x];for(let b=0;b<h;++b){let w=m[x+b];y=y||w}f[A]=y}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let A=E.expandShapeToKeepDim(d,i),x=Dt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var zj={kernelName:cu,backendName:"cpu",kernelFunc:Mj};function Lj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Re(r,"argMax");let o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ms({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],E.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[u,d]=E.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,x=m[A],y=0;for(let b=0;b<f;++b){let w=m[A+b];w>x&&(x=w,y=b)}h[g]=y}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var Bj={kernelName:$a,backendName:"cpu",kernelFunc:Lj};function Wj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Re(r,"argMin");let o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ms({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],E.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[u,d]=E.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,x=m[A],y=0;for(let b=0;b<f;++b){let w=m[A+b];w<x&&(x=w,y=b)}h[g]=y}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var Vj={kernelName:du,backendName:"cpu",kernelFunc:Wj},Uj=mt(pu,e=>Math.asin(e)),Gj={kernelName:pu,backendName:"cpu",kernelFunc:Uj},Hj=mt(hu,e=>Math.asinh(e)),jj={kernelName:hu,backendName:"cpu",kernelFunc:Hj},qj=mt(fu,e=>Math.atan(e)),Xj={kernelName:fu,backendName:"cpu",kernelFunc:qj},Kj=Jt((e,t)=>Math.atan2(e,t)),Zj=wn(gu,Kj),Yj={kernelName:gu,backendName:"cpu",kernelFunc:Zj},Jj=mt(mu,e=>Math.atanh(e)),Qj={kernelName:mu,backendName:"cpu",kernelFunc:Jj};function Jy(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=ze(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],y=r.outShape[3];for(let b=0;b<r.batchSize;++b){let w=b*A,k=b*s[0];for(let I=0;I<r.inChannels;++I)for(let N=0;N<r.outHeight;++N){let R=N*o-p,O=Math.max(0,R),$=Math.min(r.inHeight,u+R),P=w+N*x;for(let T=0;T<r.outWidth;++T){let F=T*i-h,U=Math.max(0,F),q=Math.min(r.inWidth,d+F),z=f,K=0,J=0;for(let te=O;te<$;te+=l){let re=k+te*s[1];for(let G=U;G<q;G+=c){let se=re+G*s[2],oe=e[se+I];a==="max"&&oe>z?z=oe:a==="avg"&&(K+=oe,J++)}if(isNaN(z))break}let Q=P+T*y+I;g[Q]=a==="avg"?K/J:z}}}return m}function sI(e,t,n,s,r=!1,a=!1){let o=ze(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=ze(t,n,e);for(let g=0;g<s.batchSize;++g)for(let A=0;A<s.inChannels;++A)for(let x=0;x<s.outHeight;++x){let y=x*i-h,b=y;for(;b<0;)b+=c;let w=Math.min(s.inHeight,d+y);for(let k=0;k<s.outWidth;++k){let I=k*l-f,N=I;for(;N<0;)N+=u;let R=Math.min(s.inWidth,p+I),O=Number.NEGATIVE_INFINITY,$=-1;for(let P=b;P<w;P+=c){let T=P-y;for(let F=N;F<R;F+=u){let U=F-I,q=m.get(g,P,F,A);q>O&&(O=q,r?$=a?((g*s.inHeight+P)*s.inWidth+F)*s.inChannels+A:(P*s.inWidth+F)*s.inChannels+A:$=T*p+U)}}o.set($,g,x,k,A)}}return o}function rI(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,c=r.dilationDepth,u=r.dilationHeight,d=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,A=r.padInfo.left,x=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,y=ze(r.outShape,n),b=y.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],I=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let R=0;R<r.batchSize;++R){let O=R*w,$=R*s[0];for(let P=0;P<r.inChannels;++P)for(let T=0;T<r.outDepth;++T){let F=T*o-m,U=F;for(;U<0;)U+=c;let q=Math.min(r.inDepth,p+F),z=O+T*k;for(let K=0;K<r.outHeight;++K){let J=K*i-g,Q=J;for(;Q<0;)Q+=u;let te=Math.min(r.inHeight,h+J),re=z+K*I;for(let G=0;G<r.outWidth;++G){let se=G*l-A,oe=se;for(;oe<0;)oe+=d;let pe=Math.min(r.inWidth,f+se),ye=re+G*N,we=x,Ne=0,Me=0;for(let qe=U;qe<q;qe+=c){let Ke=$+qe*s[1];for(let pt=Q;pt<te;pt+=u){let ht=Ke+pt*s[2];for(let at=oe;at<pe;at+=d){let St=ht+at*s[3],gt=e[St+P];if(a==="max"&>>we?we=gt:a==="avg"&&(Ne+=gt,Me++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Ue=ye+P;b[Ue]=a==="avg"?Ne/Me:we}}}}return y}function eq(e,t){let n=ze(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let A=0;A<t.outDepth;++A){let x=A*s-p,y=x;for(;y<0;)y+=o;let b=Math.min(t.inDepth,c+x);for(let w=0;w<t.outHeight;++w){let k=w*r-h,I=k;for(;I<0;)I+=i;let N=Math.min(t.inHeight,u+k);for(let R=0;R<t.outWidth;++R){let O=R*a-f,$=O;for(;$<0;)$+=l;let P=Math.min(t.inWidth,d+O),T=Number.NEGATIVE_INFINITY,F=-1;for(let U=y;U<b;U+=o){let q=U-x;for(let z=I;z<N;z+=i){let K=z-k;for(let J=$;J<P;J+=l){let Q=J-O,te=e.get(m,U,z,J,g);te>=T&&(T=te,F=q*u*d+K*u+Q)}}}n.set(F,m,A,w,R,g)}}}return n}function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Re(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l),d;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))d=Ur({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=v.computeStrides(r.shape),f=Jy(p,r.shape,r.dtype,h,u,"avg");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var nq={kernelName:_a,backendName:"cpu",kernelFunc:tq};function sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Re(r,"avgPool3d");let u=E.computePool3DInfo(r.shape,a,o,1,i,l,c),d=n.data.get(r.dataId).values,p=rI(d,r.shape,r.dtype,v.computeStrides(r.shape),u,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var rq={kernelName:rd,backendName:"cpu",kernelFunc:sq};function aq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Re([r,a],"avgPool3DGrad");let u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,A=u.dilationDepth,x=u.dilationHeight,y=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,I=b-1-u.padInfo.front,N=k-1-u.padInfo.left,R=w-1-u.padInfo.top,O=ze(a.shape,"float32"),$=1/(f*m*g),P=n.bufferSync(r);for(let T=0;T<u.batchSize;++T)for(let F=0;F<u.inChannels;++F)for(let U=0;U<u.inDepth;++U)for(let q=0;q<u.inHeight;++q)for(let z=0;z<u.inWidth;++z){let K=U-I,J=q-R,Q=z-N,te=0;for(let re=0;re<b;re+=A){let G=(K+re)/d;if(!(G<0||G>=u.outDepth||Math.floor(G)!==G))for(let se=0;se<w;se+=x){let oe=(J+se)/p;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let pe=0;pe<k;pe+=y){let ye=(Q+pe)/h;if(ye<0||ye>=u.outWidth||Math.floor(ye)!==ye)continue;te+=P.get(T,G,oe,ye,F)}}}O.set(te*$,T,U,q,z,F)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var oq={kernelName:kh,backendName:"cpu",kernelFunc:aq};function iq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Re([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,A=u.effectiveFilterHeight,x=u.effectiveFilterWidth,y=x-1-u.padInfo.left,b=A-1-u.padInfo.top,w=ze(o.shape,"float32"),k=1/(h*f),I=n.data.get(r.dataId).values,N=ze(r.shape,"float32",I);for(let R=0;R<u.batchSize;++R)for(let O=0;O<u.inChannels;++O)for(let $=0;$<u.inHeight;++$)for(let P=0;P<u.inWidth;++P){let T=$-b,F=P-y,U=0;for(let q=0;q<A;q+=m){let z=(T+q)/d;if(!(z<0||z>=u.outHeight||Math.floor(z)!==z))for(let K=0;K<x;K+=g){let J=(F+K)/p;if(J<0||J>=u.outWidth||Math.floor(J)!==J)continue;U+=N.get(R,z,J,O)}}w.set(U*k,R,$,P,O)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var lq={kernelName:wh,backendName:"cpu",kernelFunc:iq};function uq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;v.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Re([r,i,l,a,o],"batchNorm");let{varianceEpsilon:c}=s;c==null&&(c=.001);let u=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(l.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),g=f.length,A=h.length,x=p.length,y=d.length,b=0,w=0,k=0,I=0;for(let N=0;N<u.length;++N)m[N]=f[b++]+(u[N]-d[w++])*h[k++]/Math.sqrt(p[I++]+c),b>=g&&(b=0),w>=y&&(w=0),k>=A&&(k=0),I>=x&&(I=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var cq={kernelName:ja,backendName:"cpu",kernelFunc:uq};function dq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Re([r],"batchToSpaceND");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=Dt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ms({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Dt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Tl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var pq={kernelName:mi,backendName:"cpu",kernelFunc:dq};function hq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=Gy(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var fq={kernelName:Sh,backendName:"cpu",kernelFunc:hq};function mq(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var gq={kernelName:Ih,backendName:"cpu",kernelFunc:mq},Aq=mt(Zr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),yq={kernelName:Zr,backendName:"cpu",kernelFunc:Aq},xq=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},bq={kernelName:od,backendName:"cpu",kernelFunc:xq};function ac(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var vq={kernelName:cd,backendName:"cpu",kernelFunc:ac};function oc(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(m=>m.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>v.sizeFromShape(m.shape)>0);if(i.length===1)return Ur({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(E.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>Cl({inputs:{input:b},backend:n})),g=i.map(b=>ac({inputs:{input:b},backend:n})),A=oc({inputs:m,backend:n,attrs:{axis:a}}),x=oc({inputs:g,backend:n,attrs:{axis:a}}),y=vs({inputs:{real:A,imag:x},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x),y}let c=i.map(m=>{let g=v.sizeFromShape(m.shape.slice(a));return Dt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=E.computeOutShape(c.map(m=>m.shape),1);let d=c[0].shape[0]===1,p=Hy(u,o,t[0].dtype,d),h=E.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var wq={kernelName:gi,backendName:"cpu",kernelFunc:oc};function aI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s;Re([r,a],"conv2d");let d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,x=p.padInfo.top,y=p.dataFormat==="channelsLast",b=new nn(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),I=w[0],N=y?w[1]:w[2],R=y?w[2]:1,O=y?1:w[1],$=b.strides[0],P=y?b.strides[1]:b.strides[2],T=y?b.strides[2]:1,F=y?1:b.strides[1],U=n.data.get(r.dataId).values,q=n.data.get(a.dataId).values,z=b.values;for(let K=0;K<p.batchSize;++K){let J=K*I,Q=K*$;for(let te=0;te<p.outHeight;++te){let re=Q+te*P,G=te*p.strideHeight-x;for(let se=0;se<h;++se){let oe=G+se*m;if(oe<0||oe>=p.inHeight)continue;let pe=se*k[0],ye=J+oe*N;for(let we=0;we<p.outWidth;++we){let Ne=re+we*T,Me=we*p.strideWidth-A;for(let Ue=0;Ue<f;++Ue){let qe=Me+Ue*g;if(qe<0||qe>=p.inWidth)continue;let Ke=pe+Ue*k[1],pt=ye+qe*R,ht=Ke;for(let at=0;at<p.inChannels;++at){let St=U[pt+at*O];for(let gt=0;gt<p.outChannels;++gt)z[Ne+gt*F]+=St*q[ht+gt];ht+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var kq={kernelName:Oa,backendName:"cpu",kernelFunc:aI};function Sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s;Re([r,a],"conv2dBackpropFilter");let d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,A=p.dataFormat==="channelsLast",x=new nn(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,I=new nn(r.shape,r.dtype,w),N=new nn(a.shape,a.dtype,k);for(let R=0;R<m;++R){let O=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outHeight,(p.inHeight+b-R)/h);for(let P=0;P<g;++P){let T=Math.max(0,Math.ceil((y-P)/f)),F=Math.min(p.outWidth,(p.inWidth+y-P)/f);for(let U=0;U<p.inChannels;++U)for(let q=0;q<p.outChannels;++q){let z=0;for(let K=0;K<p.batchSize;++K)for(let J=O;J<$;++J){let Q=R+J*h-b;for(let te=T;te<F;++te){let re=P+te*f-y;A?z+=I.get(K,Q,re,U)*N.get(K,J,te,q):z+=I.get(K,U,Q,re)*N.get(K,q,J,te)}}x.set(z,R,P,U,q)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var Iq={kernelName:Ch,backendName:"cpu",kernelFunc:Sq};function Cq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s;Re([r,a],"conv2dBackpropInput");let d=v.computeStrides(a.shape),p=v.computeStrides(r.shape),h=E.convertConv2DDataFormat(c),f=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,h),m=new nn(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,x=n.data.get(a.dataId).values,[y,b,w]=d,{batchSize:k,filterHeight:I,filterWidth:N,inChannels:R,inHeight:O,inWidth:$,outChannels:P,outHeight:T,outWidth:F,strideHeight:U,strideWidth:q}=f;h=f.dataFormat;let z=I-1-f.padInfo.top,K=N-1-f.padInfo.left,J=h==="channelsLast",Q=m.strides[0],te=J?m.strides[1]:m.strides[2],re=J?m.strides[2]:1,G=J?1:m.strides[1],se=p[0],oe=J?p[1]:p[2],pe=J?p[2]:1,ye=J?1:p[1];for(let we=0;we<k;++we)for(let Ne=0;Ne<R;++Ne)for(let Me=0;Me<O;++Me){let Ue=Me-z,qe=Math.max(0,Math.ceil(Ue/U)),Ke=Math.min(T,(I+Ue)/U);for(let pt=0;pt<$;++pt){let ht=pt-K,at=Math.max(0,Math.ceil(ht/q)),St=Math.min(F,(N+ht)/q),gt=0;for(let Pt=qe;Pt<Ke;++Pt){let Ts=Pt*U-Ue;for(let Sn=at;Sn<St;++Sn){let lr=Sn*q-ht,Mn=se*we+oe*Pt+pe*Sn,hs=y*(I-1-Ts)+b*(N-1-lr)+w*Ne;for(let Gs=0;Gs<P;++Gs){let Ns=A[Mn+ye*Gs],In=x[hs+Gs];gt+=Ns*In}}}let Et=Q*we+te*Me+re*pt+G*Ne;g[Et]=gt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Tq={kernelName:Ma,backendName:"cpu",kernelFunc:Cq};function Nq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Re([r,a],"conv3d");let c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:u,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=c,A=g.front,x=g.left,y=g.top,b=new nn(c.outShape,r.dtype),w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,I=b.values,N=v.computeStrides(r.shape),R=v.computeStrides(a.shape);for(let O=0;O<c.batchSize;++O){let $=O*N[0],P=O*b.strides[0];for(let T=0;T<c.outDepth;++T){let F=P+T*b.strides[1],U=T*c.strideDepth-A;for(let q=0;q<u;++q){let z=U+q*h;if(z<0||z>=c.inDepth)continue;let K=q*R[0],J=$+z*N[1];for(let Q=0;Q<c.outHeight;++Q){let te=F+Q*b.strides[2],re=Q*c.strideHeight-y;for(let G=0;G<d;++G){let se=re+G*f;if(se<0||se>=c.inHeight)continue;let oe=K+G*R[1],pe=J+se*N[2];for(let ye=0;ye<c.outWidth;++ye){let we=te+ye*c.outChannels,Ne=ye*c.strideWidth-x;for(let Me=0;Me<p;++Me){let Ue=Ne+Me*m;if(Ue<0||Ue>=c.inWidth)continue;let qe=oe+Me*R[2],Ke=pe+Ue*c.inChannels,pt=qe;for(let ht=0;ht<c.inChannels;++ht){let at=w[Ke+ht];for(let St=0;St<c.outChannels;++St)I[we+St]+=at*k[pt+St];pt+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Eq={kernelName:id,backendName:"cpu",kernelFunc:Nq};function Rq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Re([r,a],"conv3dBackpropFilterV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,A=d.filterWidth,x=new nn(d.filterShape,"float32"),y=x.values,[b,w,k,I]=x.strides,N=n.data.get(a.dataId).values,[R,O,$,P]=u,T=n.data.get(r.dataId).values,[F,U,q,z]=c,K=d.padInfo.front,J=d.padInfo.left,Q=d.padInfo.top;for(let te=0;te<m;++te){let re=Math.max(0,Math.ceil((K-te)/p)),G=Math.min(d.outDepth,(d.inDepth+K-te)/p),se=te*b;for(let oe=0;oe<g;++oe){let pe=Math.max(0,Math.ceil((Q-oe)/h)),ye=Math.min(d.outHeight,(d.inHeight+Q-oe)/h),we=oe*w+se;for(let Ne=0;Ne<A;++Ne){let Me=Math.max(0,Math.ceil((J-Ne)/f)),Ue=Math.min(d.outWidth,(d.inWidth+J-Ne)/f),qe=Ne*k+we;for(let Ke=0;Ke<d.inChannels;++Ke){let pt=Ke*I+qe;for(let ht=0;ht<d.outChannels;++ht){let at=0;for(let St=0;St<d.batchSize;++St){let gt=St*F,Et=St*R;for(let Pt=re;Pt<G;++Pt){let Sn=(te+Pt*p-K)*U+gt,lr=Pt*O+Et;for(let Mn=pe;Mn<ye;++Mn){let Gs=(oe+Mn*h-Q)*q+Sn,Ns=Mn*$+lr;for(let In=Me;In<Ue;++In){let $n=(Ne+In*f-J)*z+Gs,Nr=In*P+Ns;at+=T[$n+Ke]*N[Nr+ht]}}}}y[pt+ht]=at}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var $q={kernelName:Th,backendName:"cpu",kernelFunc:Rq};function _q(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Re([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(l,a.shape,i,1,o),p=new nn(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,x=n.data.get(r.dataId).values,[y,b,w,k]=c,I=n.data.get(a.dataId).values,[N,R,O,$]=u,{batchSize:P,filterDepth:T,filterHeight:F,filterWidth:U,inChannels:q,inDepth:z,inHeight:K,inWidth:J,outChannels:Q,outDepth:te,outHeight:re,outWidth:G,strideDepth:se,strideHeight:oe,strideWidth:pe}=d,ye=T-1-d.padInfo.front,we=F-1-d.padInfo.top,Ne=U-1-d.padInfo.left;for(let Me=0;Me<P;++Me)for(let Ue=0;Ue<q;++Ue)for(let qe=0;qe<z;++qe){let Ke=qe-ye,pt=Math.max(0,Math.ceil(Ke/se)),ht=Math.min(te,(T+Ke)/se);for(let at=0;at<K;++at){let St=at-we,gt=Math.max(0,Math.ceil(St/oe)),Et=Math.min(re,(F+St)/oe);for(let Pt=0;Pt<J;++Pt){let Ts=Pt-Ne,Sn=Math.max(0,Math.ceil(Ts/pe)),lr=Math.min(G,(U+Ts)/pe),Mn=0;for(let hs=pt;hs<ht;++hs){let Gs=hs*se-Ke;for(let Ns=gt;Ns<Et;++Ns){let In=Ns*oe-St;for(let Tr=Sn;Tr<lr;++Tr){let $n=Tr*pe-Ts,Nr=y*Me+b*hs+w*Ns+k*Tr,Er=N*(T-1-Gs)+R*(F-1-In)+O*(U-1-$n)+$*Ue;for(let ya=0;ya<Q;++ya){let Lc=x[Nr+ya],ur=I[Er+ya];Mn+=Lc*ur}}}}h[f*Me+m*qe+g*at+A*Pt+Ue]=Mn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var Dq={kernelName:Nh,backendName:"cpu",kernelFunc:_q},Pq=mt(za,e=>Math.cos(e)),Fq={kernelName:za,backendName:"cpu",kernelFunc:Pq},Oq=mt(La,e=>Math.cosh(e)),Mq={kernelName:La,backendName:"cpu",kernelFunc:Oq};function zq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=ze([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,y=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(A.shape);for(let I=0;I<f;I++){let N=I*4,R=x[N],O=x[N+1],$=x[N+2],P=x[N+3],T=y[I];if(T>=u)continue;let F=m>1?($-R)*(d-1)/(m-1):0,U=g>1?(P-O)*(p-1)/(g-1):0;for(let q=0;q<m;q++){let z=m>1?R*(d-1)+q*F:.5*(R+$)*(d-1);if(z<0||z>d-1){for(let K=0;K<g;K++)for(let J=0;J<h;J++){let Q=J+K*k[2]+q*k[1]+I*k[0];A.values[Q]=c}continue}if(l==="bilinear"){let K=Math.floor(z),J=Math.ceil(z),Q=z-K;for(let te=0;te<g;te++){let re=g>1?O*(p-1)+te*U:.5*(O+P)*(p-1);if(re<0||re>p-1){for(let pe=0;pe<h;pe++){let ye=pe+te*k[2]+q*k[1]+I*k[0];A.values[ye]=c}continue}let G=Math.floor(re),se=Math.ceil(re),oe=re-G;for(let pe=0;pe<h;pe++){let ye=pe+G*w[2]+K*w[1]+T*w[0],we=b[ye];ye=pe+se*w[2]+K*w[1]+T*w[0];let Ne=b[ye];ye=pe+G*w[2]+J*w[1]+T*w[0];let Me=b[ye];ye=pe+se*w[2]+J*w[1]+T*w[0];let Ue=b[ye],qe=we+(Ne-we)*oe,Ke=Me+(Ue-Me)*oe;ye=pe+te*k[2]+q*k[1]+I*k[0],A.values[ye]=qe+(Ke-qe)*Q}}}else for(let K=0;K<g;++K){let J=g>1?O*(p-1)+K*U:.5*(O+P)*(p-1);if(J<0||J>p-1){for(let re=0;re<h;re++){let G=re+K*k[2]+q*k[1]+I*k[0];A.values[G]=c}continue}let Q=Math.round(J),te=Math.round(z);for(let re=0;re<h;re++){let G=re+Q*w[2]+te*w[1]+T*w[0],se=re+K*k[2]+q*k[1]+I*k[0];A.values[se]=b[G]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Lq={kernelName:yi,backendName:"cpu",kernelFunc:zq};function Bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Re(r,"cumsum");let l=E.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Ms({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let d=Wn(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(A,x)=>A+f-x-1:(A,x)=>A+x;for(let A=0;A<h.length;A+=f)for(let x=0;x<f;x++){let y=m(A,x);if(x===0)p[y]=o?0:h[y];else{let b=m(A,x-1);p[y]=o?h[b]+p[b]:h[y]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let A=E.getUndoAxesPermutation(l),x=Ms({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),x}return g}var Wq={kernelName:Ai,backendName:"cpu",kernelFunc:Bq};function Vq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Gy(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=mS(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Uq={kernelName:Eh,backendName:"cpu",kernelFunc:Vq};function Gq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],d=l*a,p=c*a,h=u/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let A=0;A<i;++A)for(let x=0;x<d;++x){let y=Math.floor(x/a),b=x%a;for(let w=0;w<p;++w){let k=Math.floor(w/a),I=w%a,N=(b*a+I)*h;for(let R=0;R<h;++R){let $=R+N+u*(k+c*(y+l*A));m[g++]=f[$]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var Hq={kernelName:xi,backendName:"cpu",kernelFunc:Gq};function oI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s;Re([r,a],"depthwiseConv2DNative");let u=v.computeStrides(r.shape),d=v.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=E.computeConv2DInfo(r.shape,a.shape,o,p,i,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:x}=h,y=x.left,b=x.top,w=h.outChannels/h.inChannels,k=new nn(h.outShape,r.dtype),I=n.data.get(r.dataId).values,N=n.data.get(a.dataId).values,R=k.values;for(let O=0;O<h.batchSize;++O){let $=O*u[0],P=O*k.strides[0];for(let T=0;T<h.outHeight;++T){let F=P+T*k.strides[1],U=T*h.strideHeight-b;for(let q=0;q<f;++q){let z=U+q*g;if(z<0||z>=h.inHeight)continue;let K=q*d[0],J=$+z*u[1];for(let Q=0;Q<h.outWidth;++Q){let te=F+Q*k.strides[2],re=Q*h.strideWidth-y;for(let G=0;G<m;++G){let se=re+G*A;if(se<0||se>=h.inWidth)continue;let oe=K+G*d[1],pe=J+se*h.inChannels,ye=te,we=oe;for(let Ne=0;Ne<h.inChannels;++Ne){let Me=I[pe+Ne];for(let Ue=0;Ue<w;++Ue)R[ye+Ue]+=Me*N[we+Ue];ye+=w,we+=w}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var jq={kernelName:Ba,backendName:"cpu",kernelFunc:oI};function qq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s;Re([r,a],"depthwiseConv2dNativeBackpropFilter");let d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new nn(d.filterShape,"float32"),A=d.padInfo.left,x=d.padInfo.top,y=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new nn(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,I=new nn(a.shape,a.dtype,k);for(let N=0;N<f;++N){let R=Math.max(0,Math.ceil((x-N)/p)),O=Math.min(d.outHeight,(d.inHeight+x-N)/p);for(let $=0;$<m;++$){let P=Math.max(0,Math.ceil((A-$)/h)),T=Math.min(d.outWidth,(d.inWidth+A-$)/h);for(let F=0;F<d.outChannels;++F){let U=Math.trunc(F/y),q=F%y,z=0;for(let K=0;K<d.batchSize;++K)for(let J=R;J<O;++J){let Q=N+J*p-x;for(let te=P;te<T;++te){let re=$+te*h-A;z+=w.get(K,Q,re,U)*I.get(K,J,te,F)}}g.set(z,N,$,U,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Xq={kernelName:Rh,backendName:"cpu",kernelFunc:qq};function Kq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Re([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new nn(h.inShape,"float32"),m=f.values,[g,A,x]=f.strides,y=n.data.get(r.dataId).values,[b,w,k]=d,I=n.data.get(a.dataId).values,[N,R,O]=p,{batchSize:$,filterHeight:P,filterWidth:T,inChannels:F,inHeight:U,inWidth:q,outChannels:z,outHeight:K,outWidth:J,strideHeight:Q,strideWidth:te}=h,re=P-1-h.padInfo.top,G=T-1-h.padInfo.left,se=z/F;for(let oe=0;oe<$;++oe)for(let pe=0;pe<F;++pe)for(let ye=0;ye<U;++ye){let we=ye-re,Ne=Math.max(0,Math.ceil(we/Q)),Me=Math.min(K,(P+we)/Q);for(let Ue=0;Ue<q;++Ue){let qe=Ue-G,Ke=Math.max(0,Math.ceil(qe/te)),pt=Math.min(J,(T+qe)/te),ht=0;for(let at=Ne;at<Me;++at){let St=at*Q-we;for(let gt=Ke;gt<pt;++gt){let Et=gt*te-qe,Pt=b*oe+w*at+k*gt,Ts=N*(P-1-St)+R*(T-1-Et)+O*pe;for(let Sn=0;Sn<se;++Sn){let lr=pe*se+Sn,Mn=y[Pt+lr],hs=I[Ts+Sn];ht+=Mn*hs}}}m[g*oe+A*ye+x*Ue+pe]=ht}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var Zq={kernelName:$h,backendName:"cpu",kernelFunc:Kq};function Yq(e){let{inputs:t,backend:n}=e,{x:s}=t,r=v.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=ze([r,r],s.dtype),i=o.values;for(let c=0;c<a.length;c++)i[c*r+c]=a[c];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var Jq={kernelName:_h,backendName:"cpu",kernelFunc:Yq},Qq={kernelName:ld,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,c=l.data.get(s.dataId).values,u=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:A,outWidth:x,padInfo:y,strideHeight:b,strideWidth:w,filterHeight:k,filterWidth:I,dilationHeight:N,dilationWidth:R,outShape:O}=E.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),$=v.sizeFromShape(O),P=O.length,T=v.getArrayFromDType(s.dtype,$);for(let U=0;U<h;++U)for(let q=0;q<A;++q){let z=q*b-y.top;for(let K=0;K<x;++K){let J=K*w-y.left;for(let Q=0;Q<g;++Q){let te=Number.MIN_SAFE_INTEGER;for(let G=0;G<k;++G){let se=z+G*N;if(se>=0&&se<f)for(let oe=0;oe<I;++oe){let pe=J+oe*R;if(pe>=0&&pe<m){let ye=v.locToIndex([U,se,pe,Q],u,v.computeStrides(s.shape)),we=v.locToIndex([G,oe,Q],p,v.computeStrides(r.shape)),Ne=c[ye]+d[we];Ne>te&&(te=Ne)}}}let re=v.locToIndex([U,q,K,Q],P,v.computeStrides(O));T[re]=te}}}return{dataId:l.write(v.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},eX={kernelName:Ph,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:x,strideHeight:y,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:I,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Ph}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let O=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<p;++T)for(let F=0;F<g;++F){let U=F*y-x.top;for(let q=0;q<A;++q){let z=q*b-x.left;for(let K=0;K<m;++K){let J=Number.MIN_SAFE_INTEGER,Q=0,te=0;for(let re=0;re<w;++re){let G=U+re*I;if(G>=0&&G<h)for(let se=0;se<k;++se){let oe=z+se*N;if(oe>=0&&oe<f){let pe=u[T][G][oe][K]+d[re][se][K];pe>J&&(J=pe,Q=re,te=se)}}}$[Q][te][K]+=O[T][F][q][K]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},tX={kernelName:Dh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:x,strideHeight:y,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:I,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Dh}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let O=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T<p;++T)for(let F=0;F<g;++F){let U=F*y-x.top;for(let q=0;q<A;++q){let z=q*b-x.left;for(let K=0;K<m;++K){let J=Number.MIN_SAFE_INTEGER,Q=U<0?0:U,te=z<0?0:z;for(let re=0;re<w;++re){let G=U+re*I;if(G>=0&&G<h)for(let se=0;se<k;++se){let oe=z+se*N;if(oe>=0&&oe<f){let pe=u[T][G][oe][K]+d[re][se][K];pe>J&&(J=pe,Q=G,te=oe)}}}$[T][Q][te][K]+=O[T][F][q][K]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function cp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Re(r,"sum");let i;r.dtype==="bool"?i=Go({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=Ur({inputs:{x:r},backend:n});let l=i.shape.length,c=v.parseAxisParam(a,i.shape),u=E.getAxesPermutation(c,l),d=c,p=i;u!=null&&(p=Ms({inputs:{x:i},backend:n,attrs:{perm:u}}),d=E.getInnerMostAxes(d.length,l)),E.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=E.computeOutAndReduceShapes(p.shape,d),m=E.upcastType(p.dtype,"int32"),g=Nm(n,h,m),A=v.sizeFromShape(f),x=n.data.get(g.dataId).values,y=n.data.get(p.dataId).values;for(let b=0;b<x.length;++b){let w=b*A,k=0;for(let I=0;I<A;++I)k+=y[w+I];x[b]=k}if(o){let b=E.expandShapeToKeepDim(g.shape,c),w=g;g=Dt({inputs:{x:g},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(i),u!=null&&n.disposeIntermediateTensorInfo(p),g}var nX={kernelName:mo,backendName:"cpu",kernelFunc:cp};function sX(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=E.getEinsumPermutation(h,l[g]),y;E.isIdentityPermutation(A)?y=a[g]:(y=Ms({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=Dt({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=Em({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=cp({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var rX={kernelName:ud,backendName:"cpu",kernelFunc:sX};function aX(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Re([s,r],"eluGrad");let a=new Float32Array(v.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l<o.length;++l){let c=o[l];c>=1?a[l]=i[l]:a[l]=i[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",a)}var oX={kernelName:Fh,backendName:"cpu",kernelFunc:aX},iX=E.ERF_P,lX=E.ERF_A1,uX=E.ERF_A2,cX=E.ERF_A3,dX=E.ERF_A4,pX=E.ERF_A5,hX=mt(Au,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+iX*n);return t*(1-((((pX*s+dX)*s+cX)*s+uX)*s+lX)*s*Math.exp(-n*n))}),fX={kernelName:Au,backendName:"cpu",kernelFunc:hX};function $m(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Dt({inputs:{x:r},backend:n,attrs:{shape:i}})}var mX={kernelName:vi,backendName:"cpu",kernelFunc:$m},gX=Jt((e,t)=>e/t),Qy=wn(Wa,gX),ex={kernelName:Wa,backendName:"cpu",kernelFunc:Qy};function iI(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,c=[r,a],u=v.sizeFromShape(c),d=v.getTypedArrayFromDType("float32",u),p=v.getTypedArrayFromDType("float32",u);for(let g=0;g<r;g++){let A=Tl({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),x=Tl({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=vs({inputs:{real:A,imag:x},backend:n}),{real:b,imag:w}=AX(y,t,n),k=E.mergeRealAndImagArrays(b,w);for(let I=0;I<a;I++){let N=E.getComplexWithIndex(k,I);d[g*a+I]=N.real,p[g*a+I]=N.imag}n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(y)}let h=n.makeTensorInfo(c,"float32",d),f=n.makeTensorInfo(c,"float32",p),m=vs({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function AX(e,t,n){let s=v.sizeFromShape(e.shape),r=n.data.get(e.dataId),a=n.data.get(r.complexTensorInfos.real.dataId).values,o=n.data.get(r.complexTensorInfos.imag.dataId).values;if(yX(s)){let i=tx(a,o,s,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",i.real),u=n.makeTensorInfo(l,"float32",i.imag),d=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),p=Ur({inputs:{x:d},backend:n}),h=ex.kernelFunc({inputs:{a:c,b:d},backend:n}),f=ex.kernelFunc({inputs:{a:u,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=E.mergeRealAndImagArrays(a,o),l=xX(i,s,t);return E.splitRealAndImagArrays(l)}}function yX(e){return(e&e-1)==0}function tx(e,t,n,s,r){if(n===1)return{real:e,imag:t};let a=E.mergeRealAndImagArrays(e,t),o=n/2,i=E.complexWithEvenIndex(a),l=i.real,c=i.imag,u=[l.length],d=r.makeTensorInfo(u,"float32",l),p=r.makeTensorInfo(u,"float32",c),h=vs({inputs:{real:d,imag:p},backend:r}),f=E.complexWithOddIndex(a),m=f.real,g=f.imag,A=[m.length],x=r.makeTensorInfo(A,"float32",m),y=r.makeTensorInfo(A,"float32",g),b=vs({inputs:{real:x,imag:y},backend:r}),w=tx(l,c,o,s,r),k=w.real,I=w.imag,N=[k.length],R=r.makeTensorInfo(N,"float32",k),O=r.makeTensorInfo(N,"float32",I),$=vs({inputs:{real:R,imag:O},backend:r}),P=tx(m,g,o,s,r),T=P.real,F=P.imag,U=[T.length],q=r.makeTensorInfo(U,"float32",T),z=r.makeTensorInfo(U,"float32",F),K=vs({inputs:{real:q,imag:z},backend:r}),J=E.exponents(n,s),Q=[J.real.length],te=r.makeTensorInfo(Q,"float32",J.real),re=r.makeTensorInfo(Q,"float32",J.imag),G=vs({inputs:{real:te,imag:re},backend:r}),se=Em({inputs:{a:G,b:K},backend:r}),oe=lp({inputs:{a:$,b:se},backend:r}),pe=Zy({inputs:{a:$,b:se},backend:r}),ye=Cl({inputs:{input:oe},backend:r}),we=Cl({inputs:{input:pe},backend:r}),Ne=ac({inputs:{input:oe},backend:r}),Me=ac({inputs:{input:pe},backend:r}),Ue=oc({inputs:[ye,we],backend:r,attrs:{axis:0}}),qe=oc({inputs:[Ne,Me],backend:r,attrs:{axis:0}}),Ke=r.data.get(Ue.dataId).values,pt=r.data.get(qe.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(R),r.disposeIntermediateTensorInfo(O),r.disposeIntermediateTensorInfo($),r.disposeIntermediateTensorInfo(q),r.disposeIntermediateTensorInfo(z),r.disposeIntermediateTensorInfo(K),r.disposeIntermediateTensorInfo(te),r.disposeIntermediateTensorInfo(re),r.disposeIntermediateTensorInfo(G),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(pe),r.disposeIntermediateTensorInfo(ye),r.disposeIntermediateTensorInfo(Ne),r.disposeIntermediateTensorInfo(we),r.disposeIntermediateTensorInfo(Me),r.disposeIntermediateTensorInfo(Ue),r.disposeIntermediateTensorInfo(qe),{real:Ke,imag:pt}}function xX(e,t,n){let s=new Float32Array(t*2);for(let r=0;r<t;r++){let a=0,o=0;for(let i=0;i<t;i++){let l=E.exponent(r*i,t,n),c=E.getComplexWithIndex(e,i);a+=c.real*l.real-c.imag*l.imag,o+=c.real*l.imag+c.imag*l.real}n&&(a/=t,o/=t),E.assignToTypedArray(s,a,o,r)}return s}function bX(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=Dt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=iI(i,!1,n),c=Dt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var vX={kernelName:Oh,backendName:"cpu",kernelFunc:bX};function nx(e){let{backend:t,attrs:n}=e,{shape:s,value:r,dtype:a}=n,o=a||v.inferDtype(r),i=v.getArrayFromDType(o,v.sizeFromShape(s));return kX(i,r,o),t.makeTensorInfo(s,o,i)}var wX={kernelName:yu,backendName:"cpu",kernelFunc:nx};function kX(e,t,n){e.fill(t)}var SX={kernelName:ki,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,r=n,a=v.getTypedArrayFromDType(s.dtype,v.sizeFromShape(s.shape)),[o,i,l,c]=s.shape,u=r.data.get(s.dataId).values;for(let p=0;p<o;p++){let h=p*l*i*c;for(let f=0;f<i;f++){let m=f*(l*c);for(let g=0;g<l;g++){let A=g*c;for(let x=0;x<c;x++){let y=Math.round(l-g-1),b=h+m+A+x,w=u[b];if(y>=0&&y<l){let k=y*c,I=h+m+k+x;w=u[I]}a[b]=w}}}}return{dataId:r.write(a,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},IX=Jt((e,t)=>Math.floor(e/t)),CX=wn(Ha,IX,null,"int32"),TX={kernelName:Ha,backendName:"cpu",kernelFunc:CX};function NX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=aI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=lp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Yy(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var EX={kernelName:ko,backendName:"cpu",kernelFunc:NX};function RX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=oI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=lp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Yy(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var $X={kernelName:So,backendName:"cpu",kernelFunc:RX};function _X(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=v.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,c,u,d]=E.prepareAndValidate(s,r);if(c===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),f=kS(p,h,s.dtype,c,i,u,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var DX={kernelName:Ii,backendName:"cpu",kernelFunc:_X};function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Re([r,a],"gatherV2");let l=v.parseAxisParam(o,r.shape)[0],c=n.data.get(a.dataId).values,u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=i;i==null&&(d=0);let p=v.sizeFromShape(a.shape),h=E.segment_util.collectGatherOpShapeInfo(r,a,l,d),f=Dt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=Dt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,p/h.batchSize]}}),g=[h.batchSize,h.outerSize,p/h.batchSize,h.sliceSize],A=n.bufferSync(m),x=n.bufferSync(f),y=SS(x,A,g);return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var FX={kernelName:Si,backendName:"cpu",kernelFunc:PX};function OX(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=Dt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=iI(i,!0,n),c=Dt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var MX={kernelName:Mh,backendName:"cpu",kernelFunc:OX},zX=mt(xu,e=>Number.isFinite(e)?1:0,"bool"),LX={kernelName:xu,backendName:"cpu",kernelFunc:zX},BX=mt(bu,e=>Math.abs(e)===1/0?1:0,"bool"),WX={kernelName:bu,backendName:"cpu",kernelFunc:BX},VX=mt(vu,e=>Number.isNaN(e)?1:0,"bool"),UX={kernelName:vu,backendName:"cpu",kernelFunc:VX};function GX(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=ES(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var HX={kernelName:zh,backendName:"cpu",kernelFunc:GX},jX=mt(wu,e=>Math.log1p(e)),qX={kernelName:wu,backendName:"cpu",kernelFunc:jX},XX=Jt((e,t)=>e&&t),KX=wn(Ri,XX,null,"bool"),ZX={kernelName:Ri,backendName:"cpu",kernelFunc:KX},YX=mt(ku,e=>e?0:1,"bool"),JX={kernelName:ku,backendName:"cpu",kernelFunc:YX},QX=Jt((e,t)=>e||t),eK=wn(dd,QX,null,"bool"),tK={kernelName:dd,backendName:"cpu",kernelFunc:eK};function nK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Re(r,"LRN");let c=r.shape[3],u=c-1,d=n.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),h=new Float32Array(p);function f(m){let g=m%c,A=m-g+Math.max(0,g-a),x=m-g+Math.min(g+a,u),y=0;for(;A<=x;A++){let b=d[A];y+=b*b}return y}for(let m=0;m<p;m++){let g=f(m),A=d[m]*Math.pow(o+i*g,-l);h[m]=A}return n.makeTensorInfo(r.shape,r.dtype,h)}var sK={kernelName:pd,backendName:"cpu",kernelFunc:nK};function rK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s;Re(o,"LRNGrad");let d=v.sizeFromShape(o.shape),p=o.shape[3],h=n.data.get(o.dataId).values,f=n.data.get(r.dataId).values,m=n.data.get(a.dataId).values,g=new Float32Array(d),A=d;for(let x=0;x<A;x++){let y=x%p,b=x-y+Math.max(0,y-i),w=x-y+Math.min(p,y+i+1),k=0;for(let I=b;I<w;I++)k+=Math.pow(f[I],2);k=c*k+l;for(let I=b;I<w;I++){let N=-2*c*u*f[I]*m[x]/k;x===I&&(N+=Math.pow(k,-u)),N*=h[x],g[I]+=N}}return n.makeTensorInfo(o.shape,r.dtype,g)}var aK={kernelName:Lh,backendName:"cpu",kernelFunc:rK};function lI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=n,l=r.shape,c=l.length,u=v.parseAxisParam(a,l),d=u,p=E.getAxesPermutation(d,c),h=i.data.get(r.dataId).values;if(p!=null){let b=new Array(c);for(let w=0;w<b.length;w++)b[w]=l[p[w]];h=qy(h,l,r.dtype,p,b),d=E.getInnerMostAxes(d.length,c),l=b}Re(r,"max"),E.assertAxesAreInnerMostDims("max",d,c);let[f,m]=E.computeOutAndReduceShapes(l,d),g=v.sizeFromShape(m),A=$S(h,g,f,r.dtype),x=i.write(A,f,r.dtype),y=f;return o&&(y=E.expandShapeToKeepDim(f,u)),{dataId:x,shape:y,dtype:r.dtype}}var oK={kernelName:Za,backendName:"cpu",kernelFunc:lI};function iK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Re(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l),d;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))d=Ur({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=v.computeStrides(r.shape),f=Jy(p,r.shape,r.dtype,h,u,"max");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var lK={kernelName:Ja,backendName:"cpu",kernelFunc:iK};function uK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Re(r,"maxPool3d");let u=E.computePool3DInfo(r.shape,a,o,1,i,l,c),d=n.data.get(r.dataId).values,p=rI(d,r.shape,r.dtype,v.computeStrides(r.shape),u,"max");return n.makeTensorInfo(p.shape,"float32",p.values)}var cK={kernelName:hd,backendName:"cpu",kernelFunc:uK};function dK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Re([r,a],"maxPool3DGrad");let u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=n.bufferSync(a),p=eq(d,u),h=u.strideDepth,f=u.strideHeight,m=u.strideWidth,g=u.dilationDepth,A=u.dilationHeight,x=u.dilationWidth,y=u.effectiveFilterDepth,b=u.effectiveFilterHeight,w=u.effectiveFilterWidth,k=y-1-u.padInfo.front,I=w-1-u.padInfo.left,N=b-1-u.padInfo.top,R=ze(a.shape,"float32"),O=n.bufferSync(r);for(let $=0;$<u.batchSize;++$)for(let P=0;P<u.inChannels;++P)for(let T=0;T<u.inDepth;++T)for(let F=0;F<u.inHeight;++F)for(let U=0;U<u.inWidth;++U){let q=T-k,z=F-N,K=U-I,J=0;for(let Q=0;Q<y;Q+=g){let te=(q+Q)/h;if(!(te<0||te>=u.outDepth||Math.floor(te)!==te))for(let re=0;re<b;re+=A){let G=(z+re)/f;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let se=0;se<w;se+=x){let oe=(K+se)/m;if(oe<0||oe>=u.outWidth||Math.floor(oe)!==oe)continue;let pe=y*b*w-1-p.get($,te,G,oe,P),ye=Q*b*w+re*w+se,we=pe===ye?1:0;if(we===0)continue;J+=O.get($,te,G,oe,P)*we}}}R.set(J,$,T,F,U,P)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var pK={kernelName:Wh,backendName:"cpu",kernelFunc:dK};function hK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Re([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=n.data.get(i.dataId).values,f=ze(p.outShape,i.dtype,sI(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,A=p.dilationHeight,x=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,w=b-1-p.padInfo.left,k=y-1-p.padInfo.top,I=ze(i.shape,"float32"),N=n.data.get(r.dataId).values,R=ze(r.shape,"float32",N);for(let O=0;O<p.batchSize;++O)for(let $=0;$<p.inChannels;++$)for(let P=0;P<p.inHeight;++P)for(let T=0;T<p.inWidth;++T){let F=P-k,U=T-w,q=0;for(let z=0;z<y;z+=A){let K=(F+z)/m;if(!(K<0||K>=p.outHeight||Math.floor(K)!==K))for(let J=0;J<b;J+=x){let Q=(U+J)/g;if(Q<0||Q>=p.outWidth||Math.floor(Q)!==Q)continue;let te=y*b-1-f.get(O,K,Q,$),re=z*b+J,G=te===re?1:0;if(G===0)continue;q+=R.get(O,K,Q,$)*G}}I.set(q,O,P,T,$)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var fK={kernelName:Bh,backendName:"cpu",kernelFunc:hK};function mK(e,t,n,s,r){let a=v.computeStrides(t),o=Jy(e,t,n,a,r,"max"),i=sI(e,t,n,r,!0,s);return[o.values,i.values]}var gK={kernelName:Vh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Re(s,"MaxPoolWithArgmax");let c=l.data.get(s.dataId).values,u=E.computePool2DInfo(s.shape,r,a,[1,1],o),[d,p]=mK(c,s.shape,s.dtype,i,u),h=l.write(d,u.outShape,s.dtype),f=l.write(p,u.outShape,s.dtype);return[{dataId:h,shape:u.outShape,dtype:s.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function AK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=v.parseAxisParam(a,r.shape),c=E.computeOutAndReduceShapes(r.shape,i)[1],u=v.sizeFromShape(c),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([u]));d.push(p);let h=Go({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(h);let f=Qy({inputs:{a:h,b:p},backend:n});d.push(f);let m=cp({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),m}var yK={kernelName:Qa,backendName:"cpu",kernelFunc:AK};function xK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Re(r,"min");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Ms({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let x=A*h,y=m[x];for(let b=0;b<h;++b){let w=m[x+b];(Number.isNaN(w)||w<y)&&(y=w)}f[A]=y}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let A=E.expandShapeToKeepDim(d,i),x=Dt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var bK={kernelName:eo,backendName:"cpu",kernelFunc:xK};function vK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,mode:o}=s;Re(r,"mirrorPad");let i=a.map((y,b)=>y[0]+r.shape[b]+y[1]),l=a.map(y=>y[0]),c=a.map((y,b)=>y[0]+r.shape[b]),u=o==="reflect"?0:1,d=n.data.get(r.dataId).values,p=r.shape.length,h=v.computeStrides(r.shape),f=v.sizeFromShape(i),m=i.length,g=v.computeStrides(i),A=v.getTypedArrayFromDType(r.dtype,f);for(let y=0;y<f;y++){let b=v.indexToLoc(y,m,g);for(let k=0;k<m;k++)b[k]<l[k]?b[k]=l[k]*2-b[k]-u:b[k]>=c[k]&&(b[k]=(c[k]-1)*2-b[k]+u);b=b.map((k,I)=>k-l[I]);let w=v.locToIndex(b,p,h);A[y]=d[w]}return{dataId:n.write(A,i,r.dtype),shape:i,dtype:r.dtype}}var wK={kernelName:no,backendName:"cpu",kernelFunc:vK},kK=Jt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),SK=wn(Su,kK),IK={kernelName:Su,backendName:"cpu",kernelFunc:SK},CK=di(Ah());function uI(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=r.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);let l=v.parseAxisParam([i],r.shape),c=lI({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=E.expandShapeToKeepDim(c.shape,l),d=Dt({inputs:{x:c},backend:n,attrs:{shape:u}}),p=Zy({inputs:{a:r,b:d},backend:n}),h=bS({inputs:{x:p},backend:n}),f=cp({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),m=Dt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Qy({inputs:{a:h,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var TK={kernelName:go,backendName:"cpu",kernelFunc:uI};function NK(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;Re(r,"multinomial");let l=i?r:uI({inputs:{logits:r},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],d=n.data.get(l.dataId).values,p=[c,a],h=v.makeZerosTypedArray(v.sizeFromShape(p),"int32");for(let f=0;f<c;++f){let m=f*u,g=new Float32Array(u-1);g[0]=d[m];for(let y=1;y<g.length;++y)g[y]=g[y-1]+d[m+y];let A=CK.alea(o.toString()),x=f*a;for(let y=0;y<a;++y){let b=A();h[x+y]=g.length;for(let w=0;w<g.length;w++)if(b<g[w]){h[x+y]=w;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",h)}var EK={kernelName:Uh,backendName:"cpu",kernelFunc:NK},RK=Qs.nonMaxSuppressionV3Impl;function $K(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;Re(r,"NonMaxSuppression");let c=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,{selectedIndices:d}=RK(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var _K={kernelName:Di,backendName:"cpu",kernelFunc:$K},DK=Qs.nonMaxSuppressionV4Impl;function PK(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s;Re(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:p,validOutputs:h}=DK(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var FK={kernelName:Iu,backendName:"cpu",kernelFunc:PK},OK=Qs.nonMaxSuppressionV5Impl;function MK(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s;Re(r,"NonMaxSuppressionWithScore");let u=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=OK(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var zK={kernelName:Pi,backendName:"cpu",kernelFunc:MK};function LK(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s;Re(r,"oneHot");let l=v.sizeFromShape(r.shape),c=new Float32Array(l*a);c.fill(i);let u=n.data.get(r.dataId).values;for(let d=0;d<l;++d)u[d]>=0&&u[d]<a&&(c[d*a+u[d]]=o);return n.makeTensorInfo([...r.shape,a],"int32",c)}var BK={kernelName:Oi,backendName:"cpu",kernelFunc:LK};function _m(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(s.dtype==="complex64"){let r=Cl({inputs:{input:s},backend:n}),a=_m({inputs:{x:r},backend:n}),o=ac({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=vs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return nx({backend:n,attrs:{shape:s.shape,value:0,dtype:s.dtype}})}var WK={kernelName:Qi,backendName:"cpu",kernelFunc:_m};function cI(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(s.dtype==="complex64"){let r=Cl({inputs:{input:s},backend:n}),a=cI({inputs:{x:r},backend:n}),o=ac({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=vs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return nx({backend:n,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}var VK={kernelName:Fi,backendName:"cpu",kernelFunc:cI};function dI(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return $m({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=$m({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=oc({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var UK={kernelName:Mi,backendName:"cpu",kernelFunc:dI};function GK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;Re(r,"pad");let i=a.map((x,y)=>x[0]+r.shape[y]+x[1]),l=a.map(x=>x[0]),c=n.data.get(r.dataId).values,u=v.sizeFromShape(r.shape),d=r.shape.length,p=v.computeStrides(r.shape),h=v.sizeFromShape(i),f=i.length,m=v.computeStrides(i),g=v.getTypedArrayFromDType(r.dtype,h);o!==0&&g.fill(o);for(let x=0;x<u;x++){let b=v.indexToLoc(x,d,p).map((k,I)=>k+l[I]),w=v.locToIndex(b,f,m);g[w]=c[x]}return{dataId:n.write(g,i,r.dtype),shape:i,dtype:r.dtype}}var pI={kernelName:ro,backendName:"cpu",kernelFunc:GK},HK=Jt((e,t)=>Math.pow(e,t)),jK=wn(ao,HK),qK={kernelName:ao,backendName:"cpu",kernelFunc:jK};function XK(e){let{backend:t,attrs:n}=e,{start:s,stop:r,dtype:a,step:o}=n,i=Xy(s,r,o,a);return t.makeTensorInfo([i.length],a,i)}var KK={kernelName:Cu,backendName:"cpu",kernelFunc:XK},ZK=mt(Tu,e=>1/e),YK={kernelName:Tu,backendName:"cpu",kernelFunc:ZK};function JK(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Re(r,"resizeBilinear");let l=v.computeStrides(r.shape),[c,u]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(v.sizeFromShape([d,c,u,f])),A=[a&&c>1?p-1:p,a&&u>1?h-1:h],x=[a&&c>1?c-1:c,a&&u>1?u-1:u],y=0,b=A[0]/x[0],w=A[1]/x[1];for(let k=0;k<d;k++)for(let I=0;I<c;I++){let N;o?N=b*(I+.5)-.5:N=b*I;let R=Math.max(0,Math.floor(N)),O=N-R,$=Math.min(p-1,Math.ceil(N)),P=k*l[0]+R*l[1],T=k*l[0]+$*l[1];for(let F=0;F<u;F++){let U;o?U=w*(F+.5)-.5:U=w*F;let q=Math.max(0,Math.floor(U)),z=U-q,K=Math.min(h-1,Math.ceil(U)),J=P+q*l[2],Q=T+q*l[2],te=P+K*l[2],re=T+K*l[2];for(let G=0;G<f;G++){let se=m[J+G],oe=m[Q+G],pe=m[te+G],ye=m[re+G],we=se+(pe-se)*z,Ne=oe+(ye-oe)*z,Me=we+(Ne-we)*O;g[y++]=Me}}}return n.makeTensorInfo([d,c,u,f],"float32",g)}var QK={kernelName:lo,backendName:"cpu",kernelFunc:JK};function eZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Re([a,r],"resizeBilinearGrad");let i=v.computeStrides(r.shape),[l,c,u,d]=r.shape,[,p,h]=a.shape,f=new Float32Array(l*c*u*d),m=[o&&p>1?c-1:c,o&&h>1?u-1:u],g=[o&&p>1?p-1:p,o&&h>1?h-1:h],A=m[0]/g[0],x=m[1]/g[1],y=n.data.get(a.dataId).values,b=0;for(let w=0;w<l;w++){let k=w*i[0];for(let I=0;I<p;I++){let N=I*A,R=Math.floor(N),O=Math.min(Math.ceil(N),c-1),$=k+R*i[1],P=k+O*i[1],T=N-R,F=1-T;for(let U=0;U<h;U++){let q=U*x,z=Math.floor(q),K=Math.min(Math.ceil(q),u-1),J=q-z,Q=1-J,te=$+z*i[2],re=$+K*i[2],G=P+z*i[2],se=P+K*i[2],oe=F*Q,pe=F*J,ye=T*Q,we=T*J;for(let Ne=0;Ne<d;Ne++){let Me=y[b++];f[te+Ne]+=Me*oe,f[re+Ne]+=Me*pe,f[G+Ne]+=Me*ye,f[se+Ne]+=Me*we}}}}return n.makeTensorInfo([l,u,c,d],"float32",f)}var tZ={kernelName:Hh,backendName:"cpu",kernelFunc:eZ};function nZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Re(r,"resizeNearestNeighbor");let l=v.computeStrides(r.shape),[c,u]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(d*c*u*f),A=[a&&c>1?p-1:p,a&&u>1?h-1:h],x=[a&&c>1?c-1:c,a&&u>1?u-1:u],y=A[0]/x[0],b=A[1]/x[1],w=0;for(let k=0;k<d;k++){let I=k*l[0];for(let N=0;N<c;N++){let R=o?y*(N+.5):y*N,O=Math.min(p-1,a?Math.round(R):Math.floor(R));o&&(O=Math.max(0,O));let $=I+O*l[1];for(let P=0;P<u;P++){let T=o?b*(P+.5):b*P,F=Math.min(h-1,a?Math.round(T):Math.floor(T));o&&(F=Math.max(0,F));let U=$+F*l[2];for(let q=0;q<f;q++){let z=m[U+q];g[w++]=z}}}}return n.makeTensorInfo([d,c,u,f],r.dtype,g)}var sZ={kernelName:Nu,backendName:"cpu",kernelFunc:nZ};function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Re([a,r],"resizeNearestNeighborGrad");let i=v.computeStrides(r.shape),l=v.computeStrides(a.shape),[c,u,d,p]=r.shape,[,h,f]=a.shape,m=new Float32Array(c*u*d*p),g=n.data.get(a.dataId).values,A=[o&&h>1?u-1:u,o&&f>1?d-1:d],x=[o&&h>1?h-1:h,o&&f>1?f-1:f],y=A[0]/x[0],b=A[1]/x[1],w=1/y,k=1/b,I=Math.ceil(w)*2+2,N=Math.ceil(k)*2+2;for(let R=0;R<c;R++){let O=R*i[0];for(let $=0;$<u;$++){let P=O+$*i[1],T=Math.floor($*w),F=Math.floor(T-I/2);for(let U=0;U<d;U++){let q=P+U*i[2],z=Math.floor(U*k),K=Math.floor(z-N/2);for(let J=0;J<p;J++){let Q=0;for(let te=0;te<I;te++){let re=te+F;if(re<0||re>=h)continue;let G=O+re*l[1],se=re*y,oe=Math.min(u-1,o?Math.round(se):Math.floor(se));if($===oe)for(let pe=0;pe<N;pe++){let ye=pe+K;if(ye<0||ye>=f)continue;let we=G+ye*l[2],Ne=ye*b,Me=Math.min(d-1,o?Math.round(Ne):Math.floor(Ne));U===Me&&(Q+=g[we+J])}}m[q+J]=Q}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var aZ={kernelName:Gh,backendName:"cpu",kernelFunc:rZ};function oZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;Re(r,"reverse");let o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Ur({inputs:{x:r},backend:n});let l=new nn(r.shape,r.dtype),c=n.bufferSync(r);for(let u=0;u<l.size;u++){let d=l.indexToLoc(u),p=d.slice();i.forEach(h=>p[h]=r.shape[h]-1-p[h]),l.set(c.get(...p),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var iZ={kernelName:Bi,backendName:"cpu",kernelFunc:oZ},lZ={kernelName:el,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=v.getTypedArrayFromDType(s.dtype,v.sizeFromShape(s.shape)),[c,u,d,p]=s.shape,[h,f]=E.getImageCenter(o,u,d),m=255,g=Math.sin(r),A=Math.cos(r),x=i.data.get(s.dataId).values;for(let b=0;b<c;b++){let w=b*d*u*p;for(let k=0;k<u;k++){let I=k*(d*p);for(let N=0;N<d;N++){let R=N*p;for(let O=0;O<p;O++){let $=[c,k,N,O],P=$[2],T=$[1],F=(P-h)*A-(T-f)*g,U=(P-h)*g+(T-f)*A;F=Math.round(F+h),U=Math.round(U+f);let q=a;if(typeof a!="number"&&(O===3?q=m:q=a[O]),F>=0&&F<d&&U>=0&&U<u){let K=U*(d*p),J=F*p,Q=w+K+J+O;q=x[Q]}let z=w+I+R+O;l[z]=q}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},uZ=mt(Wi,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}),cZ={kernelName:Wi,backendName:"cpu",kernelFunc:uZ};function hI(e,t,n,s,r,a,o,i,l,c){let u=[s/r,r],d=e.values,p=t.values;if(s===0)return ze(n,t.dtype);let h=ze(u,t.dtype);h.values.fill(l);for(let f=0;f<a;f++){let m=[],g=0;for(let A=0;A<o;A++){let x=d[f*o+A];m.push(x),g+=x*i[A]}if(g<0||g>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let A=0;A<r;A++)c?h.values[g*r+A]+=p[f*r+A]:h.values[g*r+A]=t.rank===0?p[0]:p[f*r+A]}return h}function dZ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=!0,h=n.bufferSync(r),f=n.bufferSync(a),m=hI(h,f,o,d,c,l,i,u,0,p);return n.makeTensorInfo(o,m.dtype,m.values)}var pZ={kernelName:Vi,backendName:"cpu",kernelFunc:dZ};function hZ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Re([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Wn(r.dtype,a.dtype),d=v.makeZerosTypedArray(v.sizeFromShape(r.shape),u),p=0,h=o===0||o>1||r.shape.length===1?1:v.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=c[f];return n.makeTensorInfo(r.shape,u,d)}var fZ={kernelName:Ui,backendName:"cpu",kernelFunc:hZ},mZ=E.SELU_SCALEALPHA,gZ=E.SELU_SCALE,AZ=mt(Eu,e=>e>=0?gZ*e:mZ*(Math.exp(e)-1)),yZ={kernelName:Eu,backendName:"cpu",kernelFunc:AZ},xZ=mt(Ru,e=>e<0?-1:e>0?1:0),bZ={kernelName:Ru,backendName:"cpu",kernelFunc:xZ},vZ=mt(po,e=>Math.sin(e)),wZ={kernelName:po,backendName:"cpu",kernelFunc:vZ},kZ=mt(Hi,e=>Math.sinh(e)),SZ={kernelName:Hi,backendName:"cpu",kernelFunc:kZ},IZ=11920928955078125e-23,fI=Math.log(IZ)+2,CZ=mt($u,e=>{let t=e>-fI,n=e<fI,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),TZ={kernelName:$u,backendName:"cpu",kernelFunc:CZ};function NZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Re([r],"spaceToBatchND");let i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=pI.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,a,i,!1),d=E.getPermuted(u.length,a.length,!1),p=E.getReshapedPermuted(c.shape,a,i,!1),m=Dt({inputs:{x:c},backend:n,attrs:{shape:u}}),x=Ms({inputs:{x:m},backend:n,attrs:{perm:d}}),w=Dt({inputs:{x},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(x),w}var EZ={kernelName:ji,backendName:"cpu",kernelFunc:NZ};function RZ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=LS(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var $Z={kernelName:jh,backendName:"cpu",kernelFunc:RZ};function _Z(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[c,u,d]=BS(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var DZ={kernelName:qh,backendName:"cpu",kernelFunc:_Z};function PZ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=Ky(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var FZ={kernelName:Xh,backendName:"cpu",kernelFunc:PZ};function OZ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=Ky(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var MZ={kernelName:Kh,backendName:"cpu",kernelFunc:OZ};function zZ(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=E.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=hI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var LZ={kernelName:md,backendName:"cpu",kernelFunc:zZ};function BZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let h=Tl({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=d,h})}var WZ={kernelName:qi,backendName:"cpu",kernelFunc:BZ},VZ={kernelName:_u,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Re(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},UZ=mt(vo,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),GZ={kernelName:vo,backendName:"cpu",kernelFunc:UZ};function HZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s;Re(r,"stridedSlice");let{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ot.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=Dt({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ot.computeOutShape(x,y,b),I=Tl({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=Dt({inputs:{x:I},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(I)}else{let k=n.bufferSync(r),I=VS(h,k,b,x);w=n.makeTensorInfo(f,I.dtype,I.values)}return w}var jZ={kernelName:Xi,backendName:"cpu",kernelFunc:HZ};function qZ(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.data.get(u.dataId).values,h=n.data.get(d.dataId).values,[f,m]=US(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var XZ={kernelName:gd,backendName:"cpu",kernelFunc:qZ};function KZ(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values[0],[c,u,d]=GS(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var ZZ={kernelName:Zh,backendName:"cpu",kernelFunc:KZ};function YZ(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.data.get(a.dataId).values,i=HS(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var JZ={kernelName:Yh,backendName:"cpu",kernelFunc:YZ},QZ=mt(Ki,e=>Math.tan(e)),eY={kernelName:Ki,backendName:"cpu",kernelFunc:QZ},tY=mt(xo,e=>Math.tanh(e)),nY={kernelName:xo,backendName:"cpu",kernelFunc:tY};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;Re(r,"tile");let o=qS(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var rY={kernelName:Yr,backendName:"cpu",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;Re(r,"topk");let i=n.data.get(r.dataId).values,[l,c]=KS(i,r.shape,r.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var oY={kernelName:Zi,backendName:"cpu",kernelFunc:aY};function iY(e){let{inputs:t,attrs:n,backend:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=n,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=v.computeStrides(r.shape),x=A[0],y=A[1],b=A[2],w=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(g));w.fill(l);let k=s.data.get(r.dataId).values,I=s.data.get(a.dataId).values;for(let R=0;R<u;++R){let O=a.shape[0]===1?I:I.subarray(R*8,R*8+8);for(let $=0;$<f;++$)for(let P=0;P<m;++P)for(let T=0;T<h;++T){let F,U=O[6]*P+O[7]*$+1;if(U===0)continue;let q=(O[0]*P+O[1]*$+O[2])/U,z=(O[3]*P+O[4]*$+O[5])/U,K=mI(q,p,i),J=mI(z,d,i);switch(o){case"nearest":F=hY(k,d,p,x,y,b,R,J,K,T,l);break;case"bilinear":F=fY(k,d,p,x,y,b,R,J,K,T,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let Q=R*x+$*y+P*b+T;w[Q]=F}return s.makeTensorInfo(g,r.dtype,w)}return{dataId:s.write(w,g,r.dtype),shape:r.shape,dtype:r.dtype}}var lY={kernelName:Yi,backendName:"cpu",kernelFunc:iY};function mI(e,t,n){switch(n){case"reflect":return uY(e,t);case"wrap":return cY(e,t);case"nearest":return pY(e,t);case"constant":default:return dY(e,t)}}function uY(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=2*t;n<s&&(n=s*Math.trunc(-n/s)+n),n=n<-t?n+s:-n-1}else if(n>t-1)if(t<=1)n=0;else{let s=2*t;n-=s*Math.trunc(n/s),n>=t&&(n=s-n-1)}return v.clamp(0,n,t-1)}function cY(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=t-1;n+=t*(Math.trunc(-n/s)+1)}else if(n>t-1)if(t<=1)n=0;else{let s=t-1;n-=t*Math.trunc(n/s)}return v.clamp(0,n,t-1)}function dY(e,t){return e}function pY(e,t){return v.clamp(0,e,t-1)}function dp(e,t,n,s,r,a,o,i,l,c,u){let d=o*s+i*r+l*a+c;return 0<=i&&i<t&&0<=l&&l<n?e[d]:u}function hY(e,t,n,s,r,a,o,i,l,c,u){let d=Math.round(i),p=Math.round(l);return dp(e,t,n,s,r,a,o,d,p,c,u)}function fY(e,t,n,s,r,a,o,i,l,c,u){let d=Math.floor(i),p=Math.floor(l),h=d+1,f=p+1,m=(f-l)*dp(e,t,n,s,r,a,o,d,p,c,u)+(l-p)*dp(e,t,n,s,r,a,o,d,f,c,u),g=(f-l)*dp(e,t,n,s,r,a,o,h,p,c,u)+(l-p)*dp(e,t,n,s,r,a,o,h,f,c,u);return(h-i)*m+(i-d)*g}function mY(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Re(a,"unique");let o=s.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:c}=ZS(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var gY={kernelName:Jh,backendName:"cpu",kernelFunc:mY};function AY(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape.length,i=r.shape[a],l=new Array(o-1),c=0;for(let h=0;h<o;h++)h!==a&&(l[c++]=r.shape[h]);let u=new Array(o).fill(0),d=r.shape.slice();d[a]=1;let p=new Array(i);for(let h=0;h<p.length;h++){u[a]=h;let f=Tl({inputs:{x:r},backend:n,attrs:{begin:u,size:d}});p[h]=Dt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return p}var yY={kernelName:Ji,backendName:"cpu",kernelFunc:AY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s;Re(r,"unsortedSegmentSum");let i=r.shape.length,l=a.shape.length,c=[],u=[],d=i-l,p=a;for(let f=0;f<d;++f){let m=$m({inputs:{input:p},backend:n,attrs:{dim:f+1}});p=m,u.push(m)}for(let f=0;f<o;++f){let m=v.createScalarValue(f,"int32"),g=n.makeTensorInfo([],"int32",m),A=yS({inputs:{a:g,b:p},backend:n}),x=Go({inputs:{x:A},backend:n,attrs:{dtype:"float32"}}),y=Em({inputs:{a:x,b:r},backend:n}),b=cp({inputs:{x:y},backend:n,attrs:{axis:0,keepDims:!1}});c.push(b),u.push(g),u.push(A),u.push(x),u.push(y),u.push(b)}let h=dI({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var bY={kernelName:Ad,backendName:"cpu",kernelFunc:xY},vY=[Nj,bH,Rj,_j,CH,Pj,Oj,zj,Bj,Vj,Gj,jj,Xj,Yj,Qj,nq,rq,oq,lq,Cj,cq,pq,fq,gq,SH,NH,yq,vH,bq,wq,Iq,Tq,kq,$q,Dq,Eq,Fq,Mq,Lq,Wq,Uq,Hq,jq,Xq,Zq,Jq,Qq,tX,eX,ex,rX,xj,oX,EH,fX,RH,mX,_H,vX,wX,SX,PH,TX,EX,$X,DX,FX,OH,zH,wH,MX,vq,LX,WX,UX,bj,BH,VH,HX,GH,qX,ZX,JX,tK,sK,aK,jH,lK,cK,pK,fK,gK,oK,yK,bK,XH,wK,IK,EK,ZH,JH,_K,FK,zK,ej,BK,VK,UK,pI,qK,wj,sj,KK,kH,YK,kj,Sj,Ij,QK,tZ,sZ,aZ,iZ,lZ,cZ,aj,pZ,fZ,yZ,ij,bZ,wZ,SZ,lj,TK,TZ,EZ,$Z,DZ,FZ,MZ,LZ,WZ,dj,VZ,hj,GZ,jZ,XZ,ZZ,JZ,Aj,nX,eY,nY,rY,oY,tj,lY,gY,yY,bY,WK];for(let e of vY)cr(e);var gI={};Oe(gI,{assertNotComplex:()=>lc,bindCanvasToFramebuffer:()=>DY,bindColorTextureToFramebuffer:()=>Om,bindTextureToProgramUniformSampler:()=>$I,bindTextureUnit:()=>NI,bindVertexBufferToProgramAttribute:()=>ax,callAndCheck:()=>Te,canBeRepresented:()=>AI,createFragmentShader:()=>bI,createFramebuffer:()=>TI,createProgram:()=>vI,createStaticIndexBuffer:()=>SI,createStaticVertexBuffer:()=>kI,createTexture:()=>II,createVertexShader:()=>xI,getBatchDim:()=>El,getExtensionOrThrow:()=>fp,getFramebufferErrorMessage:()=>_I,getMaxTexturesInShader:()=>OI,getNumChannels:()=>$Y,getProgramUniformLocation:()=>RI,getProgramUniformLocationOrThrow:()=>EI,getRowsCols:()=>Rl,getShapeAs3D:()=>Mm,getTextureShapeFromLogicalShape:()=>PI,getWebGLDisjointQueryTimerVersion:()=>MI,getWebGLErrorMessage:()=>yI,getWebGLMaxTextureSize:()=>FI,hasExtension:()=>Ls,isCapableOfRenderingToFloatTexture:()=>zI,isDownloadFloatTextureEnabled:()=>LI,isReshapeFree:()=>gp,isWebGLFenceEnabled:()=>BI,isWebGLVersionEnabled:()=>ix,linkProgram:()=>wI,resetMaxTextureSize:()=>PY,resetMaxTexturesInShader:()=>FY,unbindColorTextureFromFramebuffer:()=>ox,unbindTextureUnit:()=>_Y,validateFramebuffer:()=>mp,validateProgram:()=>Fm,validateTextureSize:()=>CI});var Nl={},sx={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Dm(e,t){Nl[e]=t}function Gr(e){if(!(e in Nl)){let n=kY(e);if(n!==null)Nl[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Nl[e];return t==null||t.isContextLost()?(delete Nl[e],Gr(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),Nl[e])}function wY(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 kY(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=wY(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Nl[e]},!1),e===1?t.getContext("webgl",sx)||t.getContext("experimental-webgl",sx):t.getContext("webgl2",sx)}var pp;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(pp||(pp={}));var zs;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(zs||(zs={}));var Tn;(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"})(Tn||(Tn={}));function hp(e,t){return[t,e]}function SY(e,t){return e*t}function Pm(e){let t=v.sizeFromShape(e),n=Math.ceil(t/4);return v.sizeToSquarishShape(n)}function ic(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function IY(e,t){let[n,s]=ic(e,t);return n*s*4}function rx(e,t){let n=e,s,r,a,o,i,l,c,u,d,p;return Y().getNumber("WEBGL_VERSION")===2?(s=n.R32F,r=n.R16F,a=n.RGBA16F,o=n.RGBA32F,i=n.RED,c=4,u=1,d=n.HALF_FLOAT,p=n.FLOAT):(s=e.RGBA,r=e.RGBA,a=e.RGBA,o=n.RGBA,i=e.RGBA,c=4,u=4,d=t!=null?t.HALF_FLOAT_OES:null,p=e.FLOAT),l=e.RGBA,{internalFormatFloat:s,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:a,internalFormatPackedFloat:o,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:d,textureTypeFloat:p}}function Te(e,t){let n=t();return Y().getBool("DEBUG")&&CY(e),n}function CY(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+yI(e,t))}var TY=596e-10,NY=65504;function AI(e){return!!(Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||TY<Math.abs(e)&&Math.abs(e)<NY)}function yI(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 fp(e,t){return la(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function xI(e,t){let n=la(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Te(e,()=>e.shaderSource(n,t)),Te(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function bI(e,t){let n=la(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Te(e,()=>e.shaderSource(n,t)),Te(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw RY(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var EY=/ERROR: [0-9]+:([0-9]+):/g;function RY(e,t){let n=EY.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let s=+n[1],r=e.split(`
|
|
`),a=r.length.toString().length+2,o=r.map((d,p)=>v.rightPad((p+1).toString(),a)+d),i=0;for(let d=0;d<o.length;d++)i=Math.max(o[d].length,i);let l=o.slice(0,s-1),c=o.slice(s-1,s),u=o.slice(s);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${v.rightPad(c[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function vI(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function wI(e,t){if(Te(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Fm(e,t){if(Te(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function kI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Te(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function SI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Te(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Te(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function $Y(){return Y().getNumber("WEBGL_VERSION")===2?1:4}function II(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function CI(e,t){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let s=`[${e}x${t}]`;throw new Error("Requested texture size "+s+" is invalid.")}if(e>n||t>n){let s=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+s+" greater than WebGL maximum on this browser / GPU "+r+".")}}function TI(e){return la(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function ax(e,t,n,s,r,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s)),Te(e,()=>e.vertexAttribPointer(i,r,e.FLOAT,!1,a,o)),Te(e,()=>e.enableVertexAttribArray(i)),!0)}function NI(e,t,n){DI(e,n),Te(e,()=>e.activeTexture(e.TEXTURE0+n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function _Y(e,t){DI(e,t),Te(e,()=>e.activeTexture(e.TEXTURE0+t)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function EI(e,t,n){return la(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function RI(e,t,n){return e.getUniformLocation(t,n)}function $I(e,t,n,s){Te(e,()=>NI(e,t,s)),Te(e,()=>e.uniform1i(n,s))}function DY(e){Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Te(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),Te(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function Om(e,t,n){Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Te(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function ox(e,t){Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Te(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function mp(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+_I(e,t))}function _I(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 ${t}`}}function la(e,t,n){let s=Te(e,()=>t());if(s==null)throw new Error(n);return s}function DI(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function El(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function Rl(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 Mm(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[El(e),...Rl(e)]),t}function PI(e,t=!1){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?v.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let s=v.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=El(e),a=2,o=2;return e.length&&([a,o]=Rl(e)),s=r*(a/2)*(o/2),v.sizeToSquarishShape(s).map(i=>i*2)}return v.sizeToSquarishShape(s)}function zm(e){return e%2==0}function gp(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s||zm(n)&&zm(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&zm(e[0])&&zm(t[0])}var Lm,Bm;function FI(e){if(Lm==null){let t=Gr(e);Lm=t.getParameter(t.MAX_TEXTURE_SIZE)}return Lm}function PY(){Lm=null}function FY(){Bm=null}function OI(e){if(Bm==null){let t=Gr(e);Bm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Bm)}function MI(e){if(e===0)return 0;let t,n=Gr(e);return Ls(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Ls(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Ls(e,t){return e.getExtension(t)!=null}function ix(e){try{if(Gr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function zI(e){if(e===0)return!1;let t=Gr(e);if(e===1){if(!Ls(t,"OES_texture_float"))return!1}else if(!Ls(t,"EXT_color_buffer_float"))return!1;return lx(t)}function LI(e){if(e===0)return!1;let t=Gr(e);if(e===1){if(!Ls(t,"OES_texture_float")||!Ls(t,"WEBGL_color_buffer_float"))return!1}else{if(Ls(t,"EXT_color_buffer_float"))return lx(t);let s="EXT_color_buffer_half_float";if(Ls(t,s)){let r=t.getExtension(s);return OY(t,r)}return!1}return lx(t)}function lx(e){let t=rx(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function OY(e,t){let n=rx(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function BI(e){return e!==2?!1:Gr(e).fenceSync!=null}function lc(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var _e=Y();_e.registerFlag("HAS_WEBGL",()=>_e.getNumber("WEBGL_VERSION")>0);_e.registerFlag("WEBGL_VERSION",()=>ix(2)?2:ix(1)?1:0);_e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);_e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>_e.get("WEBGL_VERSION")===2);_e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);_e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);_e.registerFlag("WEBGL_PACK",()=>_e.getBool("HAS_WEBGL"));_e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_CLIP",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_REDUCE",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_LAZILY_UNPACK",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_CONV_IM2COL",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>FI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>OI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=_e.getNumber("WEBGL_VERSION");return e===0?0:MI(e)});_e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>_e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Fu.isMobile());_e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>zI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>_e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:_e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));_e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>LI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>BI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>_e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);_e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});_e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Fu.isMobile()?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}.`)});_e.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);_e.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);_e.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);_e.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function jn(){let e,t,n,s,r,a,o,i,l,c;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
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",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
|
|
#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:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function $l(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Wm(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function MY(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function zY(e,t,n="index"){let s=e.map((a,o)=>o),r=MY(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function ux(e){let t=v.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function cx(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var WI=`
|
|
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;
|
|
}
|
|
`,{getBroadcastDims:VI}=E;function LY(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=dx(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>BY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=jn(),l=UY(i),c,u,d=jY(i);return t.isPacked?(c=WY(t.logicalShape,o,n.enableShapeUniforms),u=HY(i)):(c=VY(t.logicalShape,o,n.enableShapeUniforms),u=GY(i)),n.packedInputs&&(d+=ZY),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function uc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return lJ(e,t);case 1:return cJ(e,t);case 2:return pJ(e,t);case 3:return fJ(e,t);case 4:return gJ(e,t);case 5:return AJ(e);case 6:return yJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function UI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return iJ(e);case 1:return uJ(e,t);case 2:return dJ(e,t);case 3:return hJ(e,t);default:return mJ(e,t)}}function BY(e,t,n=!1,s){let r="";n?r+=UI(e,s):r+=uc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=xJ(e,t):r+=bJ(e,t)),r}function WY(e,t,n){switch(e.length){case 0:return GI();case 1:return YY(e,t,n);case 2:return aJ(e,t,n);case 3:return QY(e,t,n);default:return tJ(e,t,n)}}function VY(e,t,n){switch(e.length){case 0:return GI();case 1:return JY(e,t,n);case 2:return oJ(e,t,n);case 3:return eJ(e,t,n);case 4:return nJ(e,t,n);case 5:return sJ(e,t);case 6:return rJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function UY(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function GY(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function HY(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function jY(e){return`${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);
|
|
}
|
|
|
|
${qY}
|
|
${XY}
|
|
${KY}
|
|
`}var qY=`
|
|
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);
|
|
}
|
|
`,XY=`
|
|
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);
|
|
}
|
|
`,KY=`
|
|
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);
|
|
}
|
|
`,ZY=`
|
|
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 GI(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function YY(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${s[1]}.0);
|
|
}
|
|
`:s[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${s[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
|
|
}
|
|
`}function JY(e,t,n){return t[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function QY(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[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 eJ(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Wm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=$l(["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;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function tJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${c} = index / ${o};
|
|
index -= b${c} * ${o};
|
|
`+i,l=`b${c}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[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}(${l});
|
|
}
|
|
`}function nJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Wm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=$l(["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;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function sJ(e,t){let n=$l(["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;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function rJ(e,t){let n=$l(["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;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function aJ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function oJ(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function _l(e){return`offset${e}`}function iJ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=jn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function lJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${s}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=_l(n);if(t)return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[i,l]=e.shapeInfo.texShape;return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function uJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=jn();if(t)return`
|
|
vec4 ${s}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${s}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function cJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${cc(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=_l(n);return o===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:a===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function dJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=jn();if(a!=null&&v.arraysEqual(n,a))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function pJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let p=a[0],h=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=dc(e,l),h=["row","col"];return`
|
|
${uc(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${pc(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${cc(e)}
|
|
}
|
|
`;let c=a[0],u=a[1],d=_l(s);return u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:c===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s}Shape[1] + col + ${d};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${c}, ${u}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function hJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=dc(e,p),m=["b","row","col"];return`
|
|
${UI(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${pc(m,h)});
|
|
}
|
|
`}let i=jn();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${c}, ${d}, ${u}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function fJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=dc(e,c),g=["row","col","depth"];return`
|
|
${uc(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${pc(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${o}, 1)));
|
|
${cc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=_l(s);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${s}Shape[1] * ${s}Shape[2];
|
|
int stride1 = ${s}Shape[2];
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function mJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=jn();if(t)return`
|
|
vec4 ${s}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
|
|
vec4 ${s}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function gJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let x=dc(e,l),y=["row","col","depth","depth2"];return`
|
|
${uc(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${pc(y,c)});
|
|
}
|
|
`}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}, ${o}, ${a}, 1)));
|
|
${cc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${o}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(h===a&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let A=_l(s);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
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 * ${o} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function AJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=dc(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${uc(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${pc(g,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, ${r})) +
|
|
depth3;
|
|
${cc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=_l(n);return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} + depth * ${a} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function yJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=dc(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${uc(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${pc(A,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${c}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${cc(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${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, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=_l(n);return`
|
|
float ${s}(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 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function cc(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function xJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=VI(e.shapeInfo.logicalShape,t.logicalShape),l=bt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${d[x+c]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((x,y)=>`coords.${d[y+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,A=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!A)o===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let x=a-2,y=a-1;i.indexOf(x)>-1&&i.indexOf(y)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function bJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let c=bt(l),u=VI(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
|
|
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
|
|
float ${r}() {
|
|
${c} coords = getOutputCoords();
|
|
${p}
|
|
return get${s}(${f});
|
|
}
|
|
`}function bt(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 ${e} is not yet supported`)}function dx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function dc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pc(e,t){return t.map(n=>e[n]).join(", ")}function vJ(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=LY(r,o,t),l=bI(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${w}`]=e.getUniformLocation(c,`offset${w}`,p),t.enableShapeUniforms&&(f[`${w}Shape`]=e.getUniformLocation(c,`${w}Shape`,p),m[`${w}TexShape`]=e.getUniformLocation(c,`${w}TexShape`,p))}let g,A,x;t.enableShapeUniforms&&(g=e.getUniformLocation(c,"outShape",p),x=e.getUniformLocation(c,"outShapeStrides",p),A=e.getUniformLocation(c,"outTexShape",p));let y=[];return t.customUniforms&&t.customUniforms.forEach((b,w)=>{y[w]=e.getUniformLocation(c,b.name,p)}),{program:t,fragmentShader:l,source:i,webGLProgram:c,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:x,outTexShapeLocation:A}}function HI(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,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function wJ(e,t,n,s,r){t.program.enableShapeUniforms||(HI(t.inShapeInfos,n),HI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=dx(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function kJ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=dx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),A=v.sizeFromShape(o.shape)===1,x=E.getBroadcastDims(o.shape,n.shape),y=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${y}_${c?d:""}_${u.length}_${A}_${x}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function Bs(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var SJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=pp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=jn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Wm(["r","c","d"],e):$l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[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);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},IJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=pp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=jn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Wm(["r","c","d"],e):$l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[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));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},CJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=zs.DOWNLOAD;let t=jn();this.outputShape=e,this.userCode=`
|
|
${WI}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},TJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=zs.DOWNLOAD;let t=jn();this.outputShape=e,this.userCode=`
|
|
${WI}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},NJ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=jn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?cx():ux(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${n.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];
|
|
}
|
|
|
|
${n.output} = vec4(${s}, 0., 0., 0.);
|
|
}
|
|
`}},EJ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=jn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${o};
|
|
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${a};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${i}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${i}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${i}] = values[2];
|
|
} else {
|
|
result[${i}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?cx():ux(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},jI={};Oe(jI,{bindVertexProgramAttributeStreams:()=>t4,createBufferFromOutputTexture:()=>r4,createFloat16MatrixTexture:()=>YI,createFloat16PackedMatrixTexture:()=>e4,createFloat32MatrixTexture:()=>ZI,createIndexBuffer:()=>KI,createPackedMatrixTexture:()=>QI,createUnsignedBytesMatrixTexture:()=>JI,createVertexBuffer:()=>XI,createVertexShader:()=>qI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>o4,downloadFloat32MatrixFromBuffer:()=>a4,downloadMatrixFromPackedOutputTexture:()=>l4,downloadPackedMatrixFromBuffer:()=>i4,getInternalFormatForFloat16MatrixTexture:()=>hx,getInternalFormatForFloat16PackedMatrixTexture:()=>gx,getInternalFormatForFloat32MatrixTexture:()=>px,getInternalFormatForPackedMatrixTexture:()=>mx,getInternalFormatForUnsignedBytesMatrixTexture:()=>fx,uploadDenseMatrixToTexture:()=>n4,uploadPixelDataToTexture:()=>s4});function qI(e){let t=jn(),n=`${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 xI(e,n)}function XI(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 kI(e,t)}function KI(e){let t=new Uint16Array([0,1,2,2,1,3]);return SI(e,t)}function Ap(e,t,n,s,r,a){CI(t,n);let o=II(e),i=e.TEXTURE_2D;return Te(e,()=>e.bindTexture(i,o)),Te(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Te(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Te(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function px(e){return e.internalFormatFloat}function ZI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,px(s),s.textureFormatFloat,e.FLOAT)}function hx(e){return e.internalFormatHalfFloat}function YI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,hx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function fx(e){return e.downloadTextureFormat}function JI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,fx(s),e.RGBA,e.UNSIGNED_BYTE)}function mx(e){return e.internalFormatPackedFloat}function QI(e,t,n,s){let[r,a]=ic(t,n);return Ap(e,r,a,mx(s),e.RGBA,e.FLOAT)}function gx(e){return e.internalFormatPackedHalfFloat}function e4(e,t,n,s){let[r,a]=ic(t,n);return Ap(e,r,a,gx(s),e.RGBA,s.textureTypeHalfFloat)}function t4(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ax(e,t,"clipSpacePos",n,3,a,s)&&ax(e,t,"uv",n,2,a,r)}function n4(e,t,n,s,r,a){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function s4(e,t,n){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function r4(e,t,n,s){let r=e.createBuffer();Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Te(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function a4(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function o4(e,t,n,s){let[r,a]=hp(t,n),o=4,i=new Uint8Array(SY(t*n,o));return Te(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function i4(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(IY(a,o));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 l4(e,t,n){let s=new Float32Array(t*n*4);return Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Vm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Dm(t,e)):this.gl=Gr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=fp(this.gl,r),Ls(this.gl,a))this.textureHalfFloatExtension=fp(this.gl,a);else if(Y().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),Ls(this.gl,s))this.colorBufferHalfFloatExtension=fp(this.gl,s);else if(Y().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",Ls(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ls(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=XI(this.gl),this.indexBuffer=KI(this.gl),this.framebuffer=TI(this.gl),this.textureConfig=rx(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Te(e,()=>e.finish()),Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Te(e,()=>e.deleteFramebuffer(this.framebuffer)),Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Te(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Te(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),ZI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),YI(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),JI(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),s4(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),n4(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),e4(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),QI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(ox(this.gl,this.framebuffer),this.outputTexture=null),Te(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>o4(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return i4(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return a4(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=r4(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>l4(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=qI(t));let n=vI(t);return Te(t,()=>t.attachShader(n,this.vertexShader)),Te(t,()=>t.attachShader(n,e)),wI(t,n),this.debug&&Fm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=t4(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Te(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fm(this.gl,this.program),Te(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?EI(this.gl,e,t):RI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Te(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),$I(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=ic(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Fm(this.gl,this.program),mp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Te(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Te(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,Y().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(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().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,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=RJ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Om(this.gl,e,this.framebuffer),this.debug&&mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Om(this.gl,this.outputTexture,this.framebuffer),this.debug&&mp(this.gl)):ox(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;Om(s,e,this.framebuffer),this.debug&&mp(s),this.outputTexture=e,Te(s,()=>s.viewport(0,0,t,n)),Te(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Te(this.gl,()=>this.gl.scissor(e,t,n,s))}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 RJ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:$J,bincountImpl:u4,bincountReduceImpl:_J,ceilImpl:DJ,concatImpl:PJ,equalImpl:FJ,expImpl:OJ,expm1Impl:MJ,floorImpl:zJ,gatherNdImpl:LJ,gatherV2Impl:BJ,greaterImpl:WJ,greaterEqualImpl:VJ,lessImpl:UJ,lessEqualImpl:GJ,linSpaceImpl:HJ,logImpl:jJ,maxImpl:qJ,maximumImpl:XJ,minimumImpl:KJ,multiplyImpl:ZJ,negImpl:YJ,notEqualImpl:JJ,prodImpl:QJ,rangeImpl:eQ,rsqrtImpl:tQ,sigmoidImpl:nQ,simpleAbsImpl:c4,sliceImpl:sQ,sparseFillEmptyRowsImpl:rQ,sparseReshapeImpl:aQ,sparseSegmentReductionImpl:d4,sqrtImpl:oQ,stridedSliceImpl:iQ,stringNGramsImpl:lQ,stringSplitImpl:uQ,stringToHashBucketFastImpl:cQ,subImpl:dQ,tileImpl:pQ,topKImpl:hQ,transposeImpl:Ax,uniqueImpl:fQ}=Tm;function p4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function qn(e,t){return t===1?[e]:p4(e,t)}function mQ(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var gQ=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=qn("rc",t),s=bt(t),r=yQ(t,e,n),a=xQ(t,e[e.length-1],e[e.length-2],n),o=bQ(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function AQ(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function yQ(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function xQ(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function bQ(e,t){let n=e.length,s=AQ(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${s[0]}),
|
|
cEdge ? 0. : getA(${s[1]}),
|
|
rEdge ? 0. : getA(${s[2]}),
|
|
rEdge || cEdge ? 0. : getA(${s[3]})`}var h4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${s}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${s>0?"}":""}
|
|
`}this.userCode=`
|
|
${vQ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?cx():ux(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function vQ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?zY(["r","c","d"],"inputShape"):$l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var wQ=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 s=m4(t,n),r=g4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=f4(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Tn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Tn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Tn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Tn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Tn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=m4(n,s),a=g4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=f4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],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 kQ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function f4(e,t,n,s,r){let a=SQ(t,s),o;if(r){let[l,c]=ic(e[0],e[1]);o=l*c}else{let[l,c]=hp(e[0],e[1]);o=l*c}let i=kQ(n,a);return o*i}function SQ(e,t){switch(e){case Tn.PACKED_2X2_FLOAT32:return mx(t);case Tn.PACKED_2X2_FLOAT16:return gx(t);case Tn.UNPACKED_FLOAT32:return px(t);case Tn.UNPACKED_FLOAT16:return hx(t);case Tn.PACKED_4X1_UNSIGNED_BYTE:return fx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function IQ(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Tn.PACKED_2X2_FLOAT32:Tn.UNPACKED_FLOAT32:e?Tn.PACKED_2X2_FLOAT16:Tn.UNPACKED_FLOAT16}function m4(e,t){if(e===zs.UPLOAD)return Tn.PACKED_2X2_FLOAT32;if(e===zs.RENDER||e==null)return IQ(t);if(e===zs.DOWNLOAD||e===zs.PIXELS)return Tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function g4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var jo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},kr="if (isnan(x)) return x;",CQ="return x;",A4="return abs(x);",TQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",NQ=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,EQ=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Um="return x;",RQ="return 1.0 / (1.0 + exp(-1.0 * x));",$Q="return x;",_Q=`
|
|
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;
|
|
`,DQ=`
|
|
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;
|
|
`,PQ=`
|
|
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;
|
|
`,FQ="return 1.0 / (1.0 + exp(-1.0 * x));",hc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},OQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=qn("rc",t),s=bt(t),r=mQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},MQ=Qs.whereImpl,zQ=1e-7,LQ=1e-4,Gm={};function BQ(e){return e in Gm||(Gm[e]={}),Gm[e]}var WQ=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),VQ=600;function UQ(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*VQ/1024/1024}var y4=class extends ru{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,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Gr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=BQ(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new Vm(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 wQ(this.gpgpu),this.numMBBeforeWarning=UQ(),this.texData=new td(this,as())}nextDataId(){return y4.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().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 s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:zs.UPLOAD,refCount:1}),s}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,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:zs.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new hc(o,Um):d=new jo(o,Um);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=E.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new hc(s,Um):h=new jo(s,Um);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Pm(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=E.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;Te(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&as().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!AI(n))throw Y().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Pm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Mm(t):t,i=a?new TJ(o):new CJ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().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:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));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=WQ){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return MQ(e.shape,t)}packedUnaryOp(e,t,n){let s=new hc(e.shape,t),r=this.compileAndRun(s,[e],n);return as().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=c4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,A4,e.dtype);let t=new jo(e.shape,A4),n=this.compileAndRun(t,[e]);return as().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return as().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new gQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[El(e.shape),...Rl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[El(t),...Rl(t)],a=new h4(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Mm(s),o,i=Pm(a);n?o=new IJ(a):o=new SJ(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===pp.DENSE){let m=Pm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],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 g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!gp(g.shape,m.shape)){let A=m,x=m.shape;m.shape=g.shape,m=this.packedReshape(m,x),i.push(m),g=this.texData.get(m.dataId),A.shape=x}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=kJ(e,l,c),d=this.getAndSaveBinary(u,()=>vJ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),wJ(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=X(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zQ:LQ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=PI(n,i),t.texShape=u),r!=null){let d=Mm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;i?([h,f]=ic(u[0],u[1]),p=new EJ(d,m)):p=new NJ(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=zs.PIXELS:this.texData.get(g.dataId).usage=zs.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],x=!0,y=this.runWebGLProgram(p,[g],s,A,x),b=this.texData.get(y.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(y.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=GQ(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}},yp=y4;yp.nextDataId=0;function GQ(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 s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var HQ="0.0.0";function x4(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Fu.isBrowser()&&ul("webgl",()=>new yp,2);var jQ={forceHalfFloat:x4},b4=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,fc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},Hm=`
|
|
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;
|
|
`,xp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Bs(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${bt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?a+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=qn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function ws(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var qQ={kernelName:Xa,backendName:"webgl",kernelFunc:ws};function qo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=ws({inputs:{x:s},backend:n}),l=ws({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var XQ={kernelName:ad,backendName:"webgl",kernelFunc:qo},v4="return (a < 0.) ? b * a : a;",w4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function KQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new xp(w4,r.shape,o.shape):new fc(v4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var ZQ={kernelName:Ti,backendName:"webgl",kernelFunc:KQ},k4="return (a < 0.) ? b * a : a;",S4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function YQ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new xp(S4,s.shape,r.shape):new fc(k4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var JQ={kernelName:oo,backendName:"webgl",kernelFunc:YQ},I4="if (isnan(x)) return x;",QQ=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,eee=`
|
|
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 rt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new hc(o.shape,t):u=new jo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(y=>{let[b,w]=y,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new fc(e,l.shape,c.shape);return u.runWebGLProgram(N,[k,I],Wn(b.dtype,w.dtype))}),x=qo({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),x}let d=a||Wn(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?E.fromUint8ToStringArray(f):f,A=l.dtype==="string"?E.fromUint8ToStringArray(m):m,[x,y]=r(l.shape,c.shape,g,A,d),b=u.makeTensorInfo(y,d),w=u.texData.get(b.dataId);return w.values=x,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new xp(t,l.shape,c.shape,n):h=new fc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function jm(e,t=!1){if(e==="linear")return t?$Q:CQ;if(e==="relu")return t?DQ:NQ;if(e==="elu")return t?_Q:TQ;if(e==="relu6")return t?PQ:EQ;if(e==="prelu")return t?S4:k4;if(e==="leakyrelu")return t?w4:v4;if(e==="sigmoid")return t?FQ:RQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var C4=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Bs(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",y="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(y=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${u}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${u}; i++) {
|
|
int batchA = ${x};
|
|
int batchB = ${y};
|
|
vec4 a = getMatrixA(batchA, ${d});
|
|
vec4 b = getMatrixB(batchB, ${p});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${A}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},T4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},N4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},E4="return a * b;";function yx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=E.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new N4(T4.REAL,s.shape,r.shape),u=new N4(T4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=qo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=ZJ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new xp(E4,s.shape,r.shape):o=new fc(E4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var tee={kernelName:so,backendName:"webgl",kernelFunc:yx};function nee(e,t,n){let s=[El(e.shape),...Rl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[El(t),...Rl(t)],o=new h4(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!gp(r.shape,l)&&!(u.texture!==null&&gp(u.shape,l))?nee(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var see={kernelName:Li,backendName:"webgl",kernelFunc:ve},R4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${o}; 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 + ${o};
|
|
if (${i===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},ree=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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,d=`
|
|
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 = ${i}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,p="vec4";t==="all"?(o="1.0",d=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,p="bvec4"):t==="any"&&(o="0.0",d=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,p="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${o});
|
|
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;
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${u===1}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===2}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===3}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function aee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Dl(e,t,n,s){let r=aee(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new R4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new R4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new ree({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var oee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=bt(this.rank),r=iee(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function iee(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"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var lee=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 ${this.rank} is not yet supported.`);let s=bt(this.rank),r=p4("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${i}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${i}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function qm(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lee(e.shape,t):new oee(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function uee(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=E.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=qm(e,l,s),i=E.getInnerMostAxes(i.length,a)),E.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=E.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,A=ve({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=Td(e.dtype),y=Dl(A,x,"sum",s),b=ve({inputs:{x:y},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(y),c&&s.disposeIntermediateTensorInfo(u),b}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uee(r,a,o,n)}var cee={kernelName:mo,backendName:"webgl",kernelFunc:Xm};function Xn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=Ax(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=qm(r,a,o);return c}var dee={kernelName:bo,backendName:"webgl",kernelFunc:Xn},$4=1e3;function Km({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],I=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),N=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,N],O=Math.max(A,x),$=n?I.shape[1]:I.shape[2],P=a!=null,T=o!=null,F=l==="leakyrelu",U=l!=null?jm(l,!0):null,q=P||T||F||U!=null,z;if((h===1||f===1)&&$>$4&&q===!1){let J=I,Q=N;n&&(J=Xn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),R.push(J)),s&&(Q=Xn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),R.push(Q));let te=f!==1,re=f===1,G=J;te&&(G=ve({inputs:{x:J},backend:r,attrs:{shape:[O,$,1]}}),R.push(G));let se=f===1?2:1,oe=Q;re&&(oe=ve({inputs:{x:Q},backend:r,attrs:{shape:[O,1,$]}}),R.push(oe));let pe=yx({inputs:{a:G,b:oe},backend:r});z=Xm({inputs:{x:pe},backend:r,attrs:{axis:se,keepDims:!0}}),R.push(pe)}else{let J=Wn(e.dtype,t.dtype),Q=new C4(w,k,[O,h,f],n,s,P,U,T,F),te=[I,N];if(a!=null&&te.push(a),T&&te.push(o),F){let re=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));te.push(re),R.push(re)}z=r.runWebGLProgram(Q,te,J)}let K=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let J of R)r.disposeIntermediateTensorInfo(J);return K}function pee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Km({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var hee={kernelName:wo,backendName:"webgl",kernelFunc:pee},_4="return abs(x);";function fee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=c4(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,_4):r=new jo(s.shape,_4),n.runWebGLProgram(r,[s],s.dtype)}var mee={kernelName:fi,backendName:"webgl",kernelFunc:fee},gee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,Aee=rt({opSnippet:gee}),yee={kernelName:iu,backendName:"webgl",kernelFunc:Aee},xee=kr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,bee=rt({opSnippet:xee}),vee={kernelName:lu,backendName:"webgl",kernelFunc:bee},D4="return a + b;",wee=Nn({opSnippet:D4,packedOpSnippet:D4,supportsComplex:!0,cpuKernelImpl:$J}),kee={kernelName:Kr,backendName:"webgl",kernelFunc:wee},See=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},Iee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}};function Zm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ws({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Zm({inputs:s.slice(0,l),backend:n}),u=Zm({inputs:s.slice(l),backend:n});return Zm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Wn(l,c)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new Iee(s[0].shape,a):new See(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Cee={kernelName:Ra,backendName:"webgl",kernelFunc:Zm};function Tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=Xn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("all",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Dl(m,m.dtype,"all",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var Nee={kernelName:uu,backendName:"webgl",kernelFunc:Tee};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=Xn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("any",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Dl(m,m.dtype,"any",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var Ree={kernelName:cu,backendName:"webgl",kernelFunc:Eee},$ee=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=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 * ${s};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
int inIdx = ${i};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},_ee=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=bt(i),c=qn("coords",i),u,d;if(a===1){d=i+1;let I=bt(d);u=`
|
|
${I} sourceLocR = ${I}(${c.join()}, 0);
|
|
++${c[i-1]};
|
|
${I} sourceLocG = ${I}(${c.join()}, 0);
|
|
++${c[i-2]};
|
|
${I} sourceLocA = ${I}(${c.join()}, 0);
|
|
--${c[i-1]};
|
|
${I} sourceLocB = ${I}(${c.join()}, 0);
|
|
--${c[i-2]};`}else d=i,u=`
|
|
${l} sourceLocR = coords;
|
|
++${c[i-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[i-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[i-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(I=>"int "+I),m=qn("sourceLocR",d-1).concat("inIdx.r"),g=qn("sourceLocG",d-1).concat("inIdx.g"),A=qn("sourceLocB",d-1).concat("inIdx.b"),x=qn("sourceLocA",d-1).concat("inIdx.a"),y=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${x.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,k=s?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}
|
|
${k}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
|
|
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${w};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${y}(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 P4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=E.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new $ee(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=P4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function F4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=E.computeOptimalWindowSize(a),i=new _ee(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=F4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function O4(e,t,n,s){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=E.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=P4(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return F4(e,t,s)}function Dee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Xn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=O4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var Pee={kernelName:$a,backendName:"webgl",kernelFunc:Dee};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Xn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=O4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var Oee={kernelName:du,backendName:"webgl",kernelFunc:Fee},Mee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,zee=rt({opSnippet:Mee}),Lee={kernelName:pu,backendName:"webgl",kernelFunc:zee},Bee=kr+"return log(x + sqrt(x * x + 1.0));",Wee=rt({opSnippet:Bee}),Vee={kernelName:hu,backendName:"webgl",kernelFunc:Wee},Uee=kr+`
|
|
return atan(x);
|
|
`,Gee=rt({opSnippet:Uee}),Hee={kernelName:fu,backendName:"webgl",kernelFunc:Gee},jee=QQ+`
|
|
return atan(a, b);
|
|
`,qee=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+eee+`
|
|
return result;
|
|
`,Xee=Nn({opSnippet:jee,packedOpSnippet:qee}),Kee={kernelName:gu,backendName:"webgl",kernelFunc:Xee},Zee=kr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Yee=rt({opSnippet:Zee}),Jee={kernelName:mu,backendName:"webgl",kernelFunc:Yee},bp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let I=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
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 ${I} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let x="max",y=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(y="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${x}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
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 < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${y});
|
|
}
|
|
`}},xx=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",y="0.0";if(x||(y="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${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 < ${p};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${d}) {
|
|
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 ${R} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,I=a%4,N=`
|
|
if (${x}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${k}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
|
|
);
|
|
|
|
${N}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${I===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${I===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${I===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function Qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;lc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ws({inputs:{x:r},backend:n});let d=new bp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var ete={kernelName:_a,backendName:"webgl",kernelFunc:Qee};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new xx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var nte={kernelName:rd,backendName:"webgl",kernelFunc:tte},ste=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${u});
|
|
const float avgMultiplier = float(${d});
|
|
|
|
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 < ${i};
|
|
wR += ${a}) {
|
|
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 < ${l};
|
|
wC+= ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},rte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${u};
|
|
wD += ${i}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${c}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.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 ate(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new rte(p);return n.runWebGLProgram(h,[r],o.dtype)}var ote={kernelName:kh,backendName:"webgl",kernelFunc:ate};function ite(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;lc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new ste(u);return n.runWebGLProgram(d,[r],o.dtype)}var lte={kernelName:wh,backendName:"webgl",kernelFunc:ite};function ute(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Km({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var cte={kernelName:Da,backendName:"webgl",kernelFunc:ute},dte=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${o};
|
|
float scale = ${i};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},pte=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${o};
|
|
vec4 scale = ${i};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},hte=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new pte(s.shape,r.shape,a.shape,u,d,l):new dte(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},fte={kernelName:ja,backendName:"webgl",kernelFunc:hte},mte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=gte(this.rank),s,r=e.map((a,o)=>`sourceLoc.${bx[o]} = start[${o}] + coords.${bx[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},bx=["x","y","z","w","u","v"];function gte(e){if(e===1)return"sourceLoc";if(e<=6)return bx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ate=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=bt(this.rank),n=qn("coords",this.rank),s=qn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.y = ${a};
|
|
--${s[this.rank-1]};
|
|
}
|
|
`,i=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${s[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function yte(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Ot.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function mc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ot.parseSliceParams(r,a,o);if(Ot.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=sQ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Ot.isSliceContinous(r.shape,i,l);if(c||!u){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ate(l):new mte(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),yte(r,i,l,n)}var xte={kernelName:Gi,backendName:"webgl",kernelFunc:mc},bte=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Xn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),A=mc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),A},vte={kernelName:mi,backendName:"webgl",kernelFunc:bte};function wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=u4(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var kte={kernelName:Sh,backendName:"webgl",kernelFunc:wte};function Ste(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Ite={kernelName:Ih,backendName:"webgl",kernelFunc:Ste},Cte="return float(a != b);",M4=Nn({opSnippet:Cte,cpuKernelImpl:JJ,dtype:"bool"}),Tte={kernelName:_i,backendName:"webgl",kernelFunc:M4};function vp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ws({inputs:{x:r.complexTensorInfos.real},backend:n})}var Nte={kernelName:fd,backendName:"webgl",kernelFunc:vp},Ete="return float(int(x));";function Rte(e,t){let n=new jo(e.shape,Ete),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function vx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ws({inputs:{x:r},backend:n});let o=jt(r.shape),i=vx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=qo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=vp({inputs:{input:r},backend:n}),i=vx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=ws({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Rte(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=M4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var $te={kernelName:Pa,backendName:"webgl",kernelFunc:vx},z4="return ceil(x);",_te=rt({opSnippet:z4,packedOpSnippet:z4,cpuKernelImpl:DJ}),Dte={kernelName:Fa,backendName:"webgl",kernelFunc:_te},Pte=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},Fte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function Ote(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new Fte(r.shape):i=new Pte(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Mte={kernelName:Zr,backendName:"webgl",kernelFunc:Ote},zte=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 L4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Lte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new zte(s.shape),o=[L4(s,r.complexTensorInfos.real),L4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var Bte={kernelName:od,backendName:"webgl",kernelFunc:Lte},Wte=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},Vte=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=bt(s),a=qn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${c.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Ym(o,l,m)}),
|
|
vec2(${Ym(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${Ym(o,l,h)}),
|
|
vec2(${Ym(c,l,h)}));`,this.userCode=`
|
|
float getValue(${o.map(f=>"int "+f)}) {
|
|
${d}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[s-1]} = ${a[s-1]} + 1;
|
|
if (${a[s-1]} < ${n[s-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[s-2]} = ${a[s-2]} + 1;
|
|
if (${a[s-2]} < ${n[s-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[s-1]} = ${a[s-1]} - 1;
|
|
if (${a[s-2]} < ${n[s-2]} &&
|
|
${a[s-1]} < ${n[s-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Ym(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Jm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ws({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Ute={kernelName:cd,backendName:"webgl",kernelFunc:Jm};function gc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>vp({inputs:{input:m},backend:n})),d=e.map(m=>Jm({inputs:{input:m},backend:n})),p=gc(u,t,n),h=gc(d,t,n),f=qo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=PJ(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=gc(e.slice(0,u),t,n),p=gc(e.slice(u),t,n),h=gc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new Vte(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=Gte(e,t,n),i=new Wte(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function Gte(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function B4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return ws({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),gc(i,a,n)}var Hte={kernelName:gi,backendName:"webgl",kernelFunc:B4},W4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,x=m?3:1,y="",b="";n&&(s?y=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?y=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:y=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${x}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${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 < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${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, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},jte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${a}, ${o});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${s});
|
|
|
|
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 * ${i};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},qte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length);let{dataFormat:n}=t,s=jn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
|
|
blockIndex = rc.y + ${u};
|
|
pos = rc.x + ${c};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${a}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${o}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${c*2+u}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${c*2+u}] = 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;
|
|
|
|
${l}
|
|
|
|
${s.output} = result;
|
|
}
|
|
`}};function V4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||p===1)&&u>$4)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(gp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let I=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(I);let N=Km({a:w,b:I,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=ws({inputs:{x:N},backend:s}),g.shape=n.outShape,A.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Km({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:I},backend:s,attrs:{shape:n.outShape}}),A.push(w),A.push(k),A.push(I)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function U4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,A=[m,g],x=!0,y=!1,b=[],w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let I=new qte(A,n),N=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[w],"float32",N),O=ve({inputs:{x:R},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(R),b.push(O);let $=r!=null,P=a!=null,T=i==="leakyrelu",F=i?jm(i,!0):null,U=new C4(O.shape,k.shape,[1,g,n.outChannels],x,y,$,F,P,T),q=[O,k];if(r&&q.push(r),P&&q.push(a),T){let Q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));q.push(Q),b.push(Q)}let z=s.runWebGLProgram(U,q,"float32"),K=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],J=ve({inputs:{x:z},backend:s,attrs:{shape:K}});b.push(z);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return J}function Xte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=V4({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=U4({x:r,filter:a,convInfo:p,backend:n});else{let m=new W4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Kte={kernelName:Oa,backendName:"webgl",kernelFunc:Xte},Zte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${a}) {
|
|
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);
|
|
}
|
|
`}},Yte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${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) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${a}) {
|
|
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);
|
|
}
|
|
`}},Jte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${s} - ${o};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Qte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${i}, ${l}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.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 < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${s} - 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 ene(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new Zte(p);return n.runWebGLProgram(h,[r,a],"float32")}var tne={kernelName:Ch,backendName:"webgl",kernelFunc:ene};function nne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new Yte(p);return n.runWebGLProgram(h,[r,a],"float32")}var sne={kernelName:Ma,backendName:"webgl",kernelFunc:nne};function rne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new jte(c);return n.runWebGLProgram(u,[r,a],"float32")}var ane={kernelName:id,backendName:"webgl",kernelFunc:rne};function one(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new Jte(c);return n.runWebGLProgram(u,[r,a],"float32")}var ine={kernelName:Th,backendName:"webgl",kernelFunc:one};function lne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new Qte(c);return n.runWebGLProgram(u,[r,a],"float32")}var une={kernelName:Nh,backendName:"webgl",kernelFunc:lne},cne=I4+`
|
|
return cos(x);
|
|
`,dne=rt({opSnippet:cne}),pne={kernelName:za,backendName:"webgl",kernelFunc:dne},hne=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,fne=rt({opSnippet:hne}),mne={kernelName:La,backendName:"webgl",kernelFunc:fne},gne=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,y,b]=d>1?[`${(i-1)/(d-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(${x});
|
|
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 >= ${a}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${y};
|
|
|
|
float in_y = ${A};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Ane=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new gne(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},yne={kernelName:yi,backendName:"webgl",kernelFunc:Ane},G4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${H4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${bt(s)} coords = getOutputCoords();
|
|
int end = ${j4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${j4(s,"coords")} = idx;
|
|
val += getX(${H4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function H4(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 ${e} is not yet supported`)}function j4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function xne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=Xn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=ws({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new G4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new G4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=Xn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var bne={kernelName:Ai,backendName:"webgl",kernelFunc:xne};function vne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=u4(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=_J(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var wne={kernelName:Eh,backendName:"webgl",kernelFunc:vne},kne=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 Sne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new kne(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Ine={kernelName:xi,backendName:"webgl",kernelFunc:Sne},q4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Bs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${i};
|
|
int q = d2 - d1 * ${i};
|
|
|
|
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 < ${a}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${o}; wC++) {
|
|
int xC = xCCorner + wC * dilations[1];
|
|
|
|
if (xC < 0 || xC >= inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${u}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},X4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Bs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;p+=`
|
|
for (int r = 0; r < ${c}; r++) {
|
|
`;for(let g=0;g<u;g++)p+=`
|
|
xTexelC${g*2} = vec4(0.0);
|
|
xTexelC${g*2}Ready = 0;
|
|
xTexelC${g*2+1} = vec4(0.0);
|
|
xTexelC${g*2+1}Ready = 0;
|
|
xC${g} = vec4(0.0);`;p+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let g=0;g<(d+1)/2;g++){let A=g*2;if(p+=`
|
|
xC = xCCorner + ${A*l};
|
|
`,i===1){if(A<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
`,l===1&&A>0?p+=`
|
|
xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.xy);
|
|
`:p+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${A} = vec4(previous.zw, xTexelC${A}.xy);
|
|
} else {
|
|
xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
xC${A} = xTexelC${A};
|
|
`,A+1<u)){let x=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${A+1} = xTexelC${A};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A+1} = xTexelC${A+1};
|
|
`}}else A<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
|
|
`,A+1<u&&(p+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A} = vec4(
|
|
xTexelC${A}.xy, xTexelC${A+1}.xy);
|
|
`,A+1<u&&(p+=`
|
|
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
|
|
`)));A<u&&(p+=`
|
|
wTexel = getW(r, ${A}, d1, q);
|
|
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
|
|
`,A+1<u&&(p+=`
|
|
wTexel = getW(r, ${A+1}, d1, q);
|
|
dotProd += xC${A+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}p+=`
|
|
}
|
|
`,p+=`
|
|
}
|
|
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${a};
|
|
int q = d2 - d1 * ${a};
|
|
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);
|
|
|
|
${p}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function Cne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new X4(d):p=new q4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Tne={kernelName:Ba,backendName:"webgl",kernelFunc:Cne},Nne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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 * ${a} + 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} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Ene=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
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) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${i}; dm++) {
|
|
int d2 = d1 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Rne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Nne(d);return n.runWebGLProgram(p,[r,a],"float32")}var $ne={kernelName:Rh,backendName:"webgl",kernelFunc:Rne};function _ne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Ene(d);return n.runWebGLProgram(p,[r,a],"float32")}var Dne={kernelName:$h,backendName:"webgl",kernelFunc:_ne},Pne=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 Fne(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Pne(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var One={kernelName:_h,backendName:"webgl",kernelFunc:Fne},Mne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${a});
|
|
const ivec2 pads = ivec2(${u}, ${d});
|
|
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 < ${o}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${i}; 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 zne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Mne(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Lne={kernelName:ld,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=E.getEinsumPermutation(h,l[g]),y;E.isIdentityPermutation(A)?y=a[g]:(y=Xn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=ve({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=yx({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Xm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var Wne={kernelName:ud,backendName:"webgl",kernelFunc:Bne},Vne="return (x >= 0.0) ? x : (exp(x) - 1.0);",Une=`
|
|
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;
|
|
`,Gne=rt({opSnippet:Vne,packedOpSnippet:Une}),Hne={kernelName:Va,backendName:"webgl",kernelFunc:Gne},jne="return (b >= 1.0) ? a : a * (b + 1.0);",qne=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,Xne=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new xp(qne,s.shape,r.shape):new fc(jne,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},Kne={kernelName:Fh,backendName:"webgl",kernelFunc:Xne},Zne=`
|
|
return vec4(equal(a, b));
|
|
`,Yne="return float(a == b);",Jne=Nn({opSnippet:Yne,packedOpSnippet:Zne,dtype:"bool",cpuKernelImpl:FJ}),Qne={kernelName:bi,backendName:"webgl",kernelFunc:Jne},ese=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${E.ERF_P};
|
|
float a1 = ${E.ERF_A1};
|
|
float a2 = ${E.ERF_A2};
|
|
float a3 = ${E.ERF_A3};
|
|
float a4 = ${E.ERF_A4};
|
|
float a5 = ${E.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,tse=rt({opSnippet:ese}),nse={kernelName:Au,backendName:"webgl",kernelFunc:tse},K4="return exp(x);",Z4=rt({opSnippet:K4,packedOpSnippet:K4,cpuKernelImpl:OJ,dtype:"float32"}),sse={kernelName:Ua,backendName:"webgl",kernelFunc:Z4};function wx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var rse={kernelName:vi,backendName:"webgl",kernelFunc:wx},Y4="return exp(x) - 1.0;",ase=rt({opSnippet:Y4,packedOpSnippet:Y4,cpuKernelImpl:MJ}),ose={kernelName:wi,backendName:"webgl",kernelFunc:ase},J4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${o}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${s});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${s}; 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) / ${a};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function Q4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new J4("real",l,t),u=new J4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=qo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function ise(e){let{inputs:t,backend:n}=e,{input:s}=t;return Q4(s,!1,n)}var lse={kernelName:Oh,backendName:"webgl",kernelFunc:ise},use=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function wp(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new use(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var cse={kernelName:yu,backendName:"webgl",kernelFunc:wp},dse=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 - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},pse={kernelName:ki,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new dse(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},eC="return floor(x);",hse=rt({opSnippet:eC,packedOpSnippet:eC,cpuKernelImpl:zJ}),fse={kernelName:Ga,backendName:"webgl",kernelFunc:hse},mse=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,gse=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Ase=Nn({opSnippet:mse,packedOpSnippet:gse,dtype:"int32"}),yse={kernelName:Ha,backendName:"webgl",kernelFunc:Ase},xse=class{constructor(e){this.variableNames=["A"];let t=jn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},bse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=jn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}.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;
|
|
}
|
|
`}},vse={kernelName:yd,backendName:"webgl",kernelFunc:wse},Ac;function wse(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(Ac==null&&(Ac=document.createElement("canvas").getContext("2d")),Ac.canvas.width=l,Ac.canvas.height=c,Ac.drawImage(r,0,0,l,c),r=Ac.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=zs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new bse(d):new xse(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function kse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A,x=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))A=V4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=U4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",I=h?jm(h,!1):null,N=new W4(g,b,I,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let O=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(O),x.push(O)}A=n.runWebGLProgram(N,R,"float32")}let y=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return x.push(A),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Sse={kernelName:ko,backendName:"webgl",kernelFunc:kse};function Ise(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=E.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),A=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?jm(p,A):null,y=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&y.push(o),w&&y.push(i),k){let O=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));y.push(O),f.push(O)}let I;A?I=new X4(g,b,x,w,k):I=new q4(g,b,x,w,k);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(I,y,"float32",N);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),R}var Cse={kernelName:So,backendName:"webgl",kernelFunc:Ise},Tse=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=bt(t.length),r=bt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${s} strides = ${s}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${a};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function Nse(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),y=LJ(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,y.values)}let f=new Tse(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Ese={kernelName:Ii,backendName:"webgl",kernelFunc:Nse},Rse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=bt(this.rank),s=$se(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function $se(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function tC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=BJ(w,b,g);return h.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new Rse(f.shape,g),x=n.runWebGLProgram(A,[f,m],f.dtype);h.push(x);let y=ve({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var _se={kernelName:Si,backendName:"webgl",kernelFunc:tC},Dse="return float(a > b);",Pse=`
|
|
return vec4(greaterThan(a, b));
|
|
`,Fse=Nn({opSnippet:Dse,packedOpSnippet:Pse,cpuKernelImpl:WJ,dtype:"bool"}),Ose={kernelName:Ci,backendName:"webgl",kernelFunc:Fse},Mse="return float(a >= b);",zse=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,Lse=Nn({opSnippet:Mse,packedOpSnippet:zse,dtype:"bool",cpuKernelImpl:VJ}),Bse={kernelName:qa,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:n}=e,{input:s}=t;return Q4(s,!0,n)}var Vse={kernelName:Mh,backendName:"webgl",kernelFunc:Wse},Use="return float(!isnan(x) && !isinf(x));",Gse=rt({opSnippet:Use,dtype:"bool"}),Hse={kernelName:xu,backendName:"webgl",kernelFunc:Gse},jse="return float(isinf(x));",qse=rt({opSnippet:jse,dtype:"bool"}),Xse={kernelName:bu,backendName:"webgl",kernelFunc:qse},Kse="return float(isnan(x));",Zse=rt({opSnippet:Kse,dtype:"bool"}),Yse={kernelName:vu,backendName:"webgl",kernelFunc:Zse},Jse="return float(a < b);",Qse=`
|
|
return vec4(lessThan(a, b));
|
|
`,ere=Nn({opSnippet:Jse,packedOpSnippet:Qse,cpuKernelImpl:UJ,dtype:"bool"}),tre={kernelName:Ni,backendName:"webgl",kernelFunc:ere},nre="return float(a <= b);",sre=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,rre=Nn({opSnippet:nre,packedOpSnippet:sre,cpuKernelImpl:GJ,dtype:"bool"}),are={kernelName:Ei,backendName:"webgl",kernelFunc:rre};function ore(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=HJ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var ire={kernelName:zh,backendName:"webgl",kernelFunc:ore},lre=`if (x < 0.0) return NAN;
|
|
return log(x);`,ure=`
|
|
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;
|
|
`,cre=rt({opSnippet:lre,packedOpSnippet:ure,cpuKernelImpl:jJ}),dre={kernelName:Ka,backendName:"webgl",kernelFunc:cre},pre="return log(1.0 + x);",hre=rt({opSnippet:pre}),fre={kernelName:wu,backendName:"webgl",kernelFunc:hre},mre="return float(a >= 1.0 && b >= 1.0);",gre=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Are=Nn({opSnippet:mre,packedOpSnippet:gre,dtype:"bool"}),yre={kernelName:Ri,backendName:"webgl",kernelFunc:Are},xre="return float(!(x >= 1.0));",bre=rt({opSnippet:xre}),vre={kernelName:ku,backendName:"webgl",kernelFunc:bre},wre="return float(a >= 1.0 || b >= 1.0);",kre=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Sre=Nn({opSnippet:wre,packedOpSnippet:kre,dtype:"bool"}),Ire={kernelName:dd,backendName:"webgl",kernelFunc:Sre},Cre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${a}; j <= ${a}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${o}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Tre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${a};
|
|
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 = - ${a}; j <= ${a}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
|
|
|
|
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 * ${i};
|
|
setOutput(result);
|
|
}
|
|
`}},Nre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Tre(r.shape,a,o,i,l):new Cre(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Ere={kernelName:pd,backendName:"webgl",kernelFunc:Nre},Rre=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${s}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${s})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},$re=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Rre(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},_re={kernelName:Lh,backendName:"webgl",kernelFunc:$re};function Dre(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Dl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function nC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let y=n.texData.get(h.dataId).values,b=new Array(i);for(let I=0;I<b.length;I++)b[I]=r.shape[u[I]];let w=Ax(y,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=qm(r,u,n);c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("max",c,i);let[f,m]=E.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=E.expandShapeToKeepDim(f,l));let A;if(p){let y=n.texData.get(h.dataId).values,b=qJ(y,v.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(A.dataId);w.values=b}else A=Dre(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Pre={kernelName:Za,backendName:"webgl",kernelFunc:nC},Fre=b4+`
|
|
return max(a, b);
|
|
`,Ore=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Hm+`
|
|
return result;
|
|
`,Mre=Nn({opSnippet:Fre,packedOpSnippet:Ore,cpuKernelImpl:XJ}),zre={kernelName:Ya,backendName:"webgl",kernelFunc:Mre};function Lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;lc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ws({inputs:{x:r},backend:n});let d=new bp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Bre={kernelName:Ja,backendName:"webgl",kernelFunc:Lre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new xx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Vre={kernelName:hd,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${s}) {
|
|
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 < ${a}; 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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Gre=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${d}, ${p});
|
|
|
|
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 < ${i};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${a}) {
|
|
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 += ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Hre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new xx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Gre(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var jre={kernelName:Wh,backendName:"webgl",kernelFunc:Hre};function qre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;lc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new bp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Ure(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Xre={kernelName:Bh,backendName:"webgl",kernelFunc:qre};function Kre(e,t,n,s){let r=new bp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new bp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Zre={kernelName:Vh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=Kre(s,i,u,l);return[d,p]}};function Yre(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Dl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Jre={kernelName:Qa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let N=0;N<w.length;N++)w[N]=s.shape[u[N]];let k=Ax(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let I=o.texData.get(f.dataId);I.values=k}else f=qm(s,u,o);h.push(f),c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=E.computeOutAndReduceShapes(f.shape,c),A=m;r&&(A=E.expandShapeToKeepDim(m,l));let x=Yre(f,g,A,o);for(let y of h)o.disposeIntermediateTensorInfo(y);return x}};function Qre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=Xn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Dl(m,m.dtype,"min",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var eae={kernelName:eo,backendName:"webgl",kernelFunc:Qre},tae=b4+`
|
|
return min(a, b);
|
|
`,nae=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Hm+`
|
|
return result;
|
|
`,sae=Nn({opSnippet:tae,packedOpSnippet:nae,cpuKernelImpl:KJ}),rae={kernelName:to,backendName:"webgl",kernelFunc:sae},aae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=bt(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${s}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}},oae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=bt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=qn("rc",s),l=qn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${d};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${d};
|
|
}
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${d}) +
|
|
gte * ((end - 1) * 2 - source + ${d});
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},iae=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oae(s.shape,r,a):new aae(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},lae={kernelName:no,backendName:"webgl",kernelFunc:iae},uae=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,cae=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Hm+`
|
|
return result;
|
|
`,dae=Nn({opSnippet:uae,packedOpSnippet:cae}),pae={kernelName:Su,backendName:"webgl",kernelFunc:dae},hae=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
|
|
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}));
|
|
}
|
|
`}},fae=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,mae=`
|
|
// 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;
|
|
`,sC=Nn({opSnippet:fae,packedOpSnippet:mae,checkOutOfBounds:!0}),gae={kernelName:Wa,backendName:"webgl",kernelFunc:sC},rC="return a - b;",aC=Nn({opSnippet:rC,packedOpSnippet:rC,supportsComplex:!0,cpuKernelImpl:dQ}),Aae={kernelName:yo,backendName:"webgl",kernelFunc:aC};function oC(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=nC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=aC({inputs:{a:r,b:c},backend:n}),d=Z4({inputs:{x:u},backend:n}),p=Xm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=sC({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var yae={kernelName:go,backendName:"webgl",kernelFunc:oC};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:oC({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new hae(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var bae={kernelName:Uh,backendName:"webgl",kernelFunc:xae},iC="return -x;";function vae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=YJ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,iC):r=new jo(s.shape,iC),n.runWebGLProgram(r,[s],s.dtype)}var wae={kernelName:$i,backendName:"webgl",kernelFunc:vae},kae=Qs.nonMaxSuppressionV3Impl;function Sae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=kae(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Iae={kernelName:Di,backendName:"webgl",kernelFunc:Sae},Cae=Qs.nonMaxSuppressionV4Impl;function Tae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Cae(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Nae={kernelName:Iu,backendName:"webgl",kernelFunc:Tae},Eae=Qs.nonMaxSuppressionV5Impl;function Rae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Eae(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var $ae={kernelName:Pi,backendName:"webgl",kernelFunc:Rae},_ae=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},Dae=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new _ae(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Pae={kernelName:Oi,backendName:"webgl",kernelFunc:Dae};function Qm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=vp({inputs:{input:s},backend:n}),a=Qm({inputs:{x:r},backend:n}),o=Jm({inputs:{input:s},backend:n}),i=Qm({inputs:{x:o},backend:n}),l=qo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return wp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Fae={kernelName:Qi,backendName:"webgl",kernelFunc:Qm};function lC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=vp({inputs:{input:s},backend:n}),a=lC({inputs:{x:r},backend:n}),o=Jm({inputs:{input:s},backend:n}),i=Qm({inputs:{x:o},backend:n}),l=qo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return wp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Oae={kernelName:Fi,backendName:"webgl",kernelFunc:lC};function Mae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return wx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=wx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=B4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var zae={kernelName:Mi,backendName:"webgl",kernelFunc:Mae},Lae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=bt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
}
|
|
`}},Bae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=bt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=qn("rc",s),l=qn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${c}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
|
|
${d[f]}
|
|
if (${p}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},uC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return wp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Bae(r.shape,a,o):new Lae(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Wae={kernelName:ro,backendName:"webgl",kernelFunc:uC},Vae=`
|
|
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);
|
|
`,Uae=`
|
|
// 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));
|
|
`+Hm+`
|
|
return result;
|
|
`,Gae=Nn({opSnippet:Vae,packedOpSnippet:Uae}),Hae={kernelName:ao,backendName:"webgl",kernelFunc:Gae};function jae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=E.getAxesPermutation(u,i),p=r;d!=null&&(p=Xn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=QJ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=Td(r.dtype),y=Dl(A,x,"prod",n);h=ve({inputs:{x:y},backend:n,attrs:{shape:f}}),l.push(A),l.push(y)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var qae={kernelName:zi,backendName:"webgl",kernelFunc:jae},cC=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=eQ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Xae={kernelName:Cu,backendName:"webgl",kernelFunc:cC},Kae="return 1.0 / x;",Zae=rt({opSnippet:Kae}),Yae={kernelName:Tu,backendName:"webgl",kernelFunc:Zae},Jae=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Qae=`
|
|
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;
|
|
`,eoe=rt({opSnippet:Jae,packedOpSnippet:Qae}),toe={kernelName:io,backendName:"webgl",kernelFunc:eoe},noe=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,soe=`
|
|
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;
|
|
`,roe=rt({opSnippet:noe,packedOpSnippet:soe}),aoe={kernelName:uo,backendName:"webgl",kernelFunc:roe},ooe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.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 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);
|
|
}
|
|
`}},ioe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.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 = ${d};
|
|
|
|
// 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 loe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ioe(r.shape,l,c,a,o):new ooe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var uoe={kernelName:lo,backendName:"webgl",kernelFunc:loe},coe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*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(${u});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
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 >= ${a}) {
|
|
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 >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function doe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new coe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var poe={kernelName:Hh,backendName:"webgl",kernelFunc:doe},hoe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},foe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function moe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new foe(r.shape,l,c,a,o):new hoe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var goe={kernelName:Nu,backendName:"webgl",kernelFunc:moe},Aoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
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 >= ${a}) {
|
|
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 >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${i[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${i[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function yoe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Aoe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var xoe={kernelName:Gh,backendName:"webgl",kernelFunc:yoe},boe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=bt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},voe=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-${n} tensor is not yet supported`);this.outputShape=e;let s=qn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=bt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${i(s.slice())};
|
|
if(${r}){
|
|
result.g = ${l(s.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${c(s.slice())};
|
|
if(${r}) {
|
|
result.a = ${u(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,x)=>p(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function woe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return ws({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new voe(r.shape,i):new boe(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var koe={kernelName:Bi,backendName:"webgl",kernelFunc:woe},Soe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - 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]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Ioe={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Soe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Coe=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Toe=rt({opSnippet:Coe}),Noe={kernelName:Wi,backendName:"webgl",kernelFunc:Toe},Eoe="return inversesqrt(x);",Roe=rt({opSnippet:Eoe,cpuKernelImpl:tQ}),$oe={kernelName:co,backendName:"webgl",kernelFunc:Roe},dC=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=bt(r.length),l=bt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${i} strides = ${i}(${r});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${p};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function _oe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new dC(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),x}var Doe={kernelName:Vi,backendName:"webgl",kernelFunc:_oe},Poe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=bt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Foe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Poe(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Wn(r.dtype,a.dtype))}var Ooe={kernelName:Ui,backendName:"webgl",kernelFunc:Foe},Moe=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${E.SELU_SCALEALPHA};
|
|
float scale = ${E.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,zoe=rt({opSnippet:Moe}),Loe={kernelName:Eu,backendName:"webgl",kernelFunc:zoe},pC="return 1.0 / (1.0 + exp(-1.0 * x));",Boe=rt({opSnippet:pC,packedOpSnippet:pC,cpuKernelImpl:nQ}),Woe={kernelName:ho,backendName:"webgl",kernelFunc:Boe},Voe=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Uoe=rt({opSnippet:Voe}),Goe={kernelName:Ru,backendName:"webgl",kernelFunc:Uoe},Hoe=I4+`
|
|
return sin(x);
|
|
`,joe=rt({opSnippet:Hoe}),qoe={kernelName:po,backendName:"webgl",kernelFunc:joe},Xoe=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Koe=rt({opSnippet:Xoe}),Zoe={kernelName:Hi,backendName:"webgl",kernelFunc:Koe},Yoe=`
|
|
float epsilon = 1.1920928955078125e-7;
|
|
float threshold = log(epsilon) + 2.0;
|
|
|
|
bool too_large = x > -threshold;
|
|
bool too_small = x < threshold;
|
|
|
|
float result;
|
|
float exp_x = exp(x);
|
|
|
|
if (too_large){
|
|
result = x;
|
|
}
|
|
else if (too_small){
|
|
result = exp_x;
|
|
}
|
|
else{
|
|
result = log(exp_x + 1.0);
|
|
}
|
|
return result;
|
|
`,Joe=rt({opSnippet:Yoe}),Qoe={kernelName:$u,backendName:"webgl",kernelFunc:Joe},eie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=uC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=ve({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Xn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},tie={kernelName:ji,backendName:"webgl",kernelFunc:eie};function nie(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=rQ(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var sie={kernelName:jh,backendName:"webgl",kernelFunc:nie};function rie(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=aQ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var aie={kernelName:qh,backendName:"webgl",kernelFunc:rie};function oie(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=d4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var iie={kernelName:Xh,backendName:"webgl",kernelFunc:oie};function lie(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=d4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var uie={kernelName:Kh,backendName:"webgl",kernelFunc:lie};function cie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=new dC(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var die={kernelName:md,backendName:"webgl",kernelFunc:cie};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=mc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var hie={kernelName:qi,backendName:"webgl",kernelFunc:pie},hC="return sqrt(x);",fie=rt({opSnippet:hC,packedOpSnippet:hC,cpuKernelImpl:oQ}),mie={kernelName:fo,backendName:"webgl",kernelFunc:fie},gie="return x * x;",Aie=rt({opSnippet:gie}),yie={kernelName:_u,backendName:"webgl",kernelFunc:Aie},fC="return (a - b) * (a - b);",xie=Nn({opSnippet:fC,packedOpSnippet:fC}),bie={kernelName:Ao,backendName:"webgl",kernelFunc:xie};function vie({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=kr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new jo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var wie={kernelName:vo,backendName:"webgl",kernelFunc:vie},kie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=bt(n.length),a=bt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ot.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=ve({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Ot.computeOutShape(x,y,b),N=mc({inputs:{x:r},backend:n,attrs:{begin:x,size:I}});w=ve({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(N)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),R=ze(r.shape,r.dtype,N),O=iQ(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,O.values)}else{let N=new kie(x,b,h);w=n.runWebGLProgram(N,[r],r.dtype)}let k=ve({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Iie={kernelName:Xi,backendName:"webgl",kernelFunc:Sie};function Cie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=lQ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Tie={kernelName:gd,backendName:"webgl",kernelFunc:Cie};function Nie(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=uQ(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Eie={kernelName:Zh,backendName:"webgl",kernelFunc:Nie};function Rie(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=cQ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var $ie={kernelName:Yh,backendName:"webgl",kernelFunc:Rie},_ie="return tan(x);",Die=rt({opSnippet:_ie}),Pie={kernelName:Ki,backendName:"webgl",kernelFunc:Die},Fie=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Oie=rt({opSnippet:Fie}),Mie={kernelName:xo,backendName:"webgl",kernelFunc:Oie},zie=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=bt(this.rank),r=Lie(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function Lie(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function mC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=ze(r.shape,r.dtype,c),d=pQ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new zie(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Bie={kernelName:Yr,backendName:"webgl",kernelFunc:mC},Wie=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced above,
|
|
// Figure5(a) shows that element[1] is in the
|
|
// second half of the group when group size is 2, but it is in the
|
|
// first half of the group when group size is 4.
|
|
|
|
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
|
|
int i = isFirstInPair ? elemIdx : elemIdx - inc;
|
|
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
|
|
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
|
|
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
|
|
|
|
// Denotes which direction indices are in (ascending or descending).
|
|
bool reverse = imod(elemIdx, 2 * dir) >= dir;
|
|
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) { // Elements in opposite order of direction
|
|
int iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutput(float(i0));
|
|
} else {
|
|
setOutput(float(i1));
|
|
}
|
|
}
|
|
`}},Vie=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
|
|
// we only need to output the indices at positions |, the indices at
|
|
// positions _ can be thrown away, see Figure5(b) After Phase 2
|
|
// (Merge phase) in the Bitonic Top K paper referenced above.
|
|
// For example, the paper shows we only need to output the orange bars.
|
|
// The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back
|
|
// to the previous sequence to find the corresponding value,
|
|
// we need to double the index. When we double the index,
|
|
// we basically interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
|
|
// of each 2k positions by - elemIdx % k. E.g. for output at
|
|
// index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
|
|
|
|
float x0 = getX(batch, i0);
|
|
float x1 = i1 < n ? getX(batch, i1) : x0;
|
|
|
|
setOutput(x0 >= x1 ? float(i0) : float(i1));
|
|
}
|
|
`}};function Pl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function gC(e){let t=1;for(;t<e;)t*=2;return t}function Uie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let O=n.readSync(r.dataId),[$,P]=hQ(O,c,r.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,wp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&Pl(n,h);let A=gC(a),x=gC(u),y=null,b=()=>y===null?[g,g]:[g,y],w=(O,$,P)=>{let T=b(),F=new Wie(P),q=[[u],[y===null?1:0],[Number.NEGATIVE_INFINITY],[O],[$]],z=y;y=n.runWebGLProgram(F,T,"int32",q),Pl(n,z)};for(let O=1;O<A;O*=2){let $=O*2;for(let P=O;P>=1;P/=2)w($,P,[m,x])}for(let O=x;O>A;O/=2){let $=b(),P=new Vie([m,O/2]),F=[[u],[y===null?1:0],[A]],U=y;y=n.runWebGLProgram(P,$,"int32",F),Pl(n,U);let q=A/2,z=q*2;for(let K=q;K>=1;K/=2)w(z,K,y.shape)}let k=y;y=mc({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,a]}}),Pl(n,k);let I=tC({inputs:{x:g,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Pl(n,g);let N=c.slice(0,-1);N.push(a),k=y,y=ve({inputs:{x:y},attrs:{shape:N},backend:n}),Pl(n,k);let R=I;return I=ve({inputs:{x:I},attrs:{shape:N},backend:n}),Pl(n,R),[I,y]}var Gie={kernelName:Zi,backendName:"webgl",kernelFunc:Uie},Hie=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${i} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 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 (${i} == 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 < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
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(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${o} == 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 jie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Hie(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var qie={kernelName:Yi,backendName:"webgl",kernelFunc:jie};function Xie(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;lc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=fQ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var Kie={kernelName:Jh,backendName:"webgl",kernelFunc:Xie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=mc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Yie={kernelName:Ji,backendName:"webgl",kernelFunc:Zie},Jie=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=`
|
|
sumValue += dot(values, segFilter);
|
|
`,p="";r%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${p}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${a})));
|
|
|
|
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
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
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
|
|
);
|
|
|
|
${d}
|
|
} else if (${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
|
|
);
|
|
|
|
${d}
|
|
} else if (${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
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function Qie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],i),d=r;u!=null&&(d=Xn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Td(r.dtype),g=(b,w,k,I,N)=>{let R=b.shape[0],O=b.shape[1],$=E.segment_util.segOpComputeOptimalWindowSize(O,N),P={windowSize:$,inSize:O,batchSize:R,numSegments:N},T=new Jie(P,w),F=n.compileAndRun(T,[b,k],I);if(l.push(F),F.shape[1]===N)return F;let U=cC({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),q=mC({inputs:{x:U},backend:n,attrs:{reps:[O/$]}});return l.push(U),l.push(q),g(F,w,q,I,N)},A=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),y=x;if(u!=null){l.push(x);let b=E.getUndoAxesPermutation(u);y=Xn({inputs:{x:y},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var ele={kernelName:Ad,backendName:"webgl",kernelFunc:Qie},tle=[Ere,_re,hee,mee,yee,vee,kee,Cee,Nee,Ree,Pee,Oee,Lee,Vee,Kee,Hee,Jee,nte,ete,ote,lte,cte,fte,vte,kte,Ite,$te,Dte,Mte,Bte,XQ,Hte,tne,sne,Kte,ine,une,ane,pne,mne,yne,bne,wne,Ine,$ne,Dne,Tne,One,Lne,Wne,Hne,Kne,Qne,nse,sse,rse,ose,lse,cse,pse,fse,yse,vse,Sse,Cse,Ese,_se,Ose,Bse,qQ,Vse,Ute,Hse,Xse,Yse,ZQ,tre,are,ire,fre,dre,yre,vre,Ire,Pre,Vre,Bre,jre,Xre,Zre,zre,Jre,eae,rae,lae,pae,bae,tee,wae,Iae,Nae,$ae,Tte,Pae,Oae,zae,Wae,Hae,JQ,qae,Xae,Nte,gae,Yae,aoe,toe,see,uoe,poe,goe,xoe,koe,Ioe,Noe,$oe,Doe,Ooe,Loe,Woe,Goe,qoe,Zoe,xte,yae,Qoe,tie,sie,aie,iie,uie,die,hie,mie,yie,bie,wie,Iie,Tie,Eie,$ie,Aae,cee,Pie,Mie,Bie,Gie,qie,dee,Kie,Yie,ele,Fae];for(let e of tle)cr(e);var Hr=Y();Hr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Hr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Hr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Hr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Hr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Hr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Hr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Hr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Hr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Hr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function nle(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function kn(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function e0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function t0(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function kx(){return`
|
|
${t0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] numWorkgroups: vec3<u32>)
|
|
`}function Xo(){return`
|
|
${t0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>)
|
|
`}function st(){return`
|
|
${kx()} {
|
|
let index = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
`}function sle(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=xC(t.shape),f=`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${e0(t.dtype,n.isVec4)}>;
|
|
};
|
|
[[block]] struct Uniform {
|
|
size : i32;
|
|
numChannels : i32;
|
|
outShapeStrides : vec2<i32>;
|
|
dispatchSize : vec3<u32>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
|
|
`;return[AC,f,r,yC,h,n.getUserCode()].join(`
|
|
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${kn(e[f].shape.length)}; `}),o+=`outShape : ${kn(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
|
|
outShapeStrides: ${kn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${e0(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
[[block]] struct Matrix${1+f} {
|
|
numbers: array<${e0(e[f].dtype,n.isVec4)}>;
|
|
};
|
|
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
|
|
`)}),o!==""&&a.push(`
|
|
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
|
|
`),a.push(r);let[l,c]=ule(t.shape,n.dispatchLayout),u=xC(t.shape),d=[AC,a.join(`
|
|
`),yC,u,l,rle(t.shape.length)];if(n.atomic||d.push(ale(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>ole(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
|
|
`)}var AC=`
|
|
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
|
|
var res: i32 = a / b;
|
|
let mod: i32 = a % b;
|
|
if (sign < 0. && mod != 0) {
|
|
res = res - 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
fn isNanCustom(val : f32) -> bool {
|
|
if (val > 0.0) {
|
|
return false;
|
|
}
|
|
if (val < 0.0) {
|
|
return false;
|
|
}
|
|
if (val == 0.0) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
|
|
var res = vec4<f32> (0.0);
|
|
for (var i = 0u; i < 4u; i = i + 1u) {
|
|
if (isNanCustom(val[i])) {
|
|
res[i] = 1.0;
|
|
} else {
|
|
res[i] = 0.0;
|
|
}
|
|
}
|
|
return res;
|
|
}
|
|
|
|
// Checks whether coordinates lie within the bounds of the shape.
|
|
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
|
|
return all(coord >= vec4<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
|
|
return all(coord >= vec3<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
|
|
return all(coord >= vec2<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
`,yC=`
|
|
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
|
|
return coord;
|
|
}
|
|
|
|
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
|
|
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
|
|
}
|
|
|
|
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
|
|
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
|
|
}
|
|
|
|
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
|
|
return i32(dot(vec4<f32>(coords), vec4<f32>(
|
|
f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0)));
|
|
}
|
|
|
|
// Only used when the y/z dimension of workgroup size is 1.
|
|
fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> i32 {
|
|
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
|
|
return i32(globalId.x);
|
|
}
|
|
|
|
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
|
|
localId.y * workGroupSizeX + localId.x;
|
|
let workGroupID = (globalId - localId)/vec3<u32>(
|
|
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
|
|
|
|
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
|
|
workGroupID.y * numWorkgroups.x + workGroupID.x) *
|
|
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
|
|
localInvocationIndex);
|
|
}
|
|
`;function rle(e){let t="";switch(e){case 0:case 1:t+=`
|
|
fn getOutputFlatIndex(coords : i32) -> i32 {
|
|
return coords;
|
|
}
|
|
`;break;case 2:t+=`
|
|
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
|
|
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
|
|
}
|
|
`;break;case 3:t+=`
|
|
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
|
|
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
|
|
}
|
|
`;break;case 4:t+=`
|
|
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
|
|
return i32(dot(vec4<f32>(coords), vec4<f32>(
|
|
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
|
|
}
|
|
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function ale(e,t,n){let s=e.length,r=e0(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputFlatI32(flatIndex : i32, value : i32) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=kn(s);n?a+=`
|
|
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlat(flatIndex / 4, value);
|
|
}
|
|
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex / 4, value);
|
|
}
|
|
`:a+=`
|
|
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlat(flatIndex, value);
|
|
}
|
|
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function ole(e,t,n,s){let r=ile(e,n);return e.shape.length<=t.length&&(r+=lle(e,t,n,s)),r}function ile(e,t){let n=e.name,s=e.shape.length,r=kn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
|
|
fn ${a}() -> vec4<f32> {
|
|
return vec4<f32>(${n}.numbers[0]);
|
|
}
|
|
`:`
|
|
fn ${a}() ->f32 {
|
|
return f32(${n}.numbers[0]);
|
|
}
|
|
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
|
|
fn ${a}(${i}) -> vec4<f32> {
|
|
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
|
|
${l}) / 4]);
|
|
}
|
|
`:`
|
|
fn ${a}(${i}) -> f32 {
|
|
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
|
|
${l})]);
|
|
}
|
|
`}function lle(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=kn(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
return vec4<f32>(${r}.numbers[globalIndex]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
|
|
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
|
|
return f32(${r}.numbers[globalIndex]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> f32 {
|
|
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
|
|
}
|
|
`;let u=E.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
return get${a}();
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
|
|
return get${a}();
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32{
|
|
return get${a}();
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> f32{
|
|
return get${a}();
|
|
}
|
|
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
|
|
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=kn(i),A=e.shape.map((x,y)=>`coords[${y+d}]`).join(", ");h=`${g}(${A})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
var coords = getCoordsFromFlatIndex(globalIndex);
|
|
${p}
|
|
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
|
|
}
|
|
|
|
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
|
|
var coords = coordsIn;
|
|
${p}
|
|
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
|
|
var coords = getCoordsFromFlatIndex(globalIndex);
|
|
${p}
|
|
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coordsIn : ${c}) -> f32 {
|
|
var coords = coordsIn;
|
|
${p}
|
|
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
|
|
}
|
|
`}function ule(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> ${kn(a)}{
|
|
let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=nle(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=kn(l),d=`fn getOutputCoordsWithNonFlatDispatchLayout(globalId : vec3<u32>) -> ${u} {
|
|
${o}
|
|
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function xC(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=kn(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
|
|
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
|
|
return vec2<i32>(d0, d1);
|
|
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return`
|
|
fn getCoordsFromFlatIndex(index : i32) -> ${s} {
|
|
${a}
|
|
return ${s}(${r.join(",")});
|
|
}
|
|
`}var bC={};Oe(bC,{ArrayBufferToTypedArray:()=>vC,GPUBytesPerElement:()=>Tx,computeDispatch:()=>Fe,computeWorkGroupSizeForConv2d:()=>Sx,computeWorkGroupSizeForMatMul:()=>Ix,computeWorkPerThreadForConv2d:()=>Cx,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>Nx,tilesFitEvenlyIntoShape:()=>ua});var yc=65535,Fl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ua(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Fe(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(Fl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(Fl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(Fl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=yc&&a<=yc&&o<=yc)return[r,a,o];v.assert(r>yc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>yc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=yc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Sx(e,t){let n=Fl(e.x.map(r=>t[r])),s=Fl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Ix(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Cx(e,t){let n=Fl(e.x.map(r=>t[r])),s=Fl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,n)=>n)}}function Tx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function vC(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function Nx(){return!!navigator.gpu}var Gt;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(Gt||(Gt={}));var cle="return a + b;",dle="return areal * breal - aimag * bimag;",ple="return areal * bimag + aimag * breal;",hle="return a / b;",fle="return a * b;",mle="return (a - b) * (a - b);",gle="return a - b;",Ale="return f32(a == b);",yle="return vec4<f32>(a == b);",xle="return f32(a > b);",ble="return vec4<f32>(a > b);",vle="return f32(a >= b);",wle="return vec4<f32>(a >= b);",kle="return f32(a < b);",Sle="return vec4<f32>(a < b);",Ile="return f32(a <= b);",Cle="return vec4<f32>(a <= b);",Tle="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Nle=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,Ele=`
|
|
if (isNanCustom(a)) { return a; }
|
|
if (isNanCustom(b)) { return b; }
|
|
`,wC=`
|
|
if (isNaN.r > 0.) {
|
|
resultTemp.r = uniforms.NAN;
|
|
}
|
|
if (isNaN.g > 0.) {
|
|
resultTemp.g = uniforms.NAN;
|
|
}
|
|
if (isNaN.b > 0.) {
|
|
resultTemp.b = uniforms.NAN;
|
|
}
|
|
if (isNaN.a > 0.) {
|
|
resultTemp.a = uniforms.NAN;
|
|
}
|
|
`,Rle=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,$le=`
|
|
let ia = vec4<i32>(round(a));
|
|
let ib = vec4<i32>(round(b));
|
|
let cond = ib != vec4<i32>(0);
|
|
var resultTemp = vec4<i32>(0);
|
|
let s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4<f32>(resultTemp);
|
|
`,_le="return f32(a != b);",Dle="return vec4<f32>(a != b);",Ple=`
|
|
if(a < 0.0 && floor(b) < b) {
|
|
return uniforms.NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
if (round(abs(b) % 2.0) != 1.0) {
|
|
return pow(abs(a), b);
|
|
}
|
|
return sign(a) * pow(abs(a), b);
|
|
`,Fle=`
|
|
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
|
|
let isModRound1 = vec4<f32>(isModRound1Bool);
|
|
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
|
|
var resultTemp = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
let isExpZero = b == vec4<f32>(0.0);
|
|
if (isExpZero.r) {
|
|
resultTemp.r = 1.0;
|
|
}
|
|
if (isExpZero.g) {
|
|
resultTemp.g = 1.0;
|
|
}
|
|
if (isExpZero.b) {
|
|
resultTemp.b = 1.0;
|
|
}
|
|
if (isExpZero.a) {
|
|
resultTemp.a = 1.0;
|
|
}
|
|
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
|
|
${wC}
|
|
return resultTemp;
|
|
`,Ole="if (a < 0.0) { return b * a; } return a;",Mle=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function kC(e,t){let n=t?wC:Ele;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function kp(e,t){switch(e){case 0:return fle;case 1:return cle;case 2:return gle;case 3:return hle;case 4:return t?yle:Ale;case 5:return t?ble:xle;case 6:return t?wle:vle;case 7:return t?Sle:kle;case 8:return t?Cle:Ile;case 9:return t?Nle:Tle;case 10:return t?Dle:_le;case 11:return mle;case 12:return t?$le:Rle;case 14:return t?Mle:Ole;case 15:return kC("max",t);case 16:return kC("min",t);case 13:return t?Fle:Ple;case 17:return dle;case 18:return ple;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var vt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(vt||(vt={}));var zle="return abs(a);",Lle="return ceil(a);",Ble="return cos(a);",Wle=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Vle="return exp(a) - 1.0;",Ule="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Gle=`
|
|
var resFloat = exp(a) - vec4<f32>(1.0);
|
|
if (a.r >= 0.0) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (a.g >= 0.0) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (a.b >= 0.0) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (a.a >= 0.0) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,Hle="return exp(a);",jle="return floor(a);",qle="return a;",Xle=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,Kle="return f32(!(a >= 1.0));",Zle="return -a;",Yle="return (a < 0.0) ? b * a : a;",Jle="return max(a, 0.0);",Qle="return clamp(a, 0.0, 6.0);",eue="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",tue=`
|
|
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
|
|
let isNaN = isNan(a);
|
|
|
|
if (isNaN.r) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (isNaN.g) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (isNaN.b) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (isNaN.a) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,nue="return 1.0/sqrt(a);",sue="return 1.0 / (1.0 + exp(-1.0 * a));",rue="return sin(a);",aue=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,oue="return sqrt(a);",iue="return a * a;",lue=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,uue="return f32(i32((a)));";function xc(e,t){switch(e){case 0:return zle;case 2:return Ble;case 3:return Wle;case 1:return Lle;case 4:return t?Gle:Ule;case 5:return Hle;case 6:return Vle;case 7:return jle;case 8:return qle;case 9:return Xle;case 10:return Kle;case 11:return Zle;case 12:return Yle;case 13:return t?tue:Jle;case 14:return t?eue:Qle;case 15:return nue;case 18:return sue;case 16:return rue;case 17:return aue;case 19:return oue;case 20:return iue;case 21:return lue;case 22:return uue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ca(e,t=!1){if(e===null)return null;if(e==="linear")return xc(vt.LINEAR);if(e==="relu")return xc(vt.RELU,t);if(e==="elu")return xc(vt.ELU,t);if(e==="relu6")return xc(vt.RELU6,t);if(e==="prelu")return kp(Gt.PRELU,t);if(e==="sigmoid")return xc(vt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function SC(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
|
|
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
|
|
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
|
|
|
|
let RowPerThread = ${n.RowPerThread};
|
|
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
|
|
let TileAOuter = ${n.TileAOuter};
|
|
let TileBOuter = ${n.TileBOuter};
|
|
let TileInner = ${n.TileInner};
|
|
|
|
${Xo()} {
|
|
|
|
let tileRow = i32(localId.y) * RowPerThread;
|
|
let tileCol = i32(localId.x);
|
|
|
|
let globalRow = i32(globalId.y) * RowPerThread;
|
|
let globalCol = i32(globalId.x);
|
|
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
|
|
|
|
var acc: array<vec4<f32>, ${n.RowPerThread}>;
|
|
var ACached : vec4<f32>;
|
|
var BCached : array<vec4<f32>, 4>;
|
|
|
|
// Loop over shared dimension.
|
|
var globalColA = tileCol;
|
|
let RowPerThreadB = TileInner / ${t[1]};
|
|
let tileRowB = i32(localId.y) * RowPerThreadB;
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
|
|
let inputRow = tileRow + innerRow;
|
|
let inputCol = tileCol;
|
|
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
|
|
}
|
|
globalColA = globalColA + TileInner / ColPerThread;
|
|
|
|
// Load one tile of B into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
|
|
let inputRow = tileRowB + innerRow;
|
|
let inputCol = tileCol;
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
|
|
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
|
|
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
|
|
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
|
|
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
|
|
|
|
for (var i = 0; i < RowPerThread; i = i + 1) {
|
|
ACached = mm_Asub[tileRow + i][k];
|
|
acc[i] = BCached[0] * ACached.x + acc[i];
|
|
acc[i] = BCached[1] * ACached.y + acc[i];
|
|
acc[i] = BCached[2] * ACached.z + acc[i];
|
|
acc[i] = BCached[3] * ACached.w + acc[i];
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
|
|
mm_write(globalRow + innerRow,
|
|
globalCol,
|
|
acc[innerRow], globalId);
|
|
}
|
|
}`}function cue(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${Xo()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var due=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Ix(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ua(o,this.aShape.slice(1)),ua(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=ca(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
|
|
${o}
|
|
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
|
|
let batch = i32(globalId.z);
|
|
${e};
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
|
|
let batch = i32(globalId.z);
|
|
${t};
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
|
|
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
|
|
{
|
|
var value = valueIn;
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col * 4);
|
|
${r}
|
|
${s}
|
|
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
|
|
}
|
|
}
|
|
${this.outputShape[1]>1?SC([this.vecSize,this.workPerThread,1],this.workGroupSize):cue(this.workGroupSize)}
|
|
|
|
`}};function Ex(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
|
|
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
|
|
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
|
|
${Xo()} {
|
|
let tileRow = i32(localId.y) * ${e[1]};
|
|
let tileCol = i32(localId.x) * ${e[0]};
|
|
|
|
let globalRow = i32(globalId.y) * ${e[1]};
|
|
let globalCol = i32(globalId.x) * ${e[0]};
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
|
|
|
|
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
|
|
var ACached : f32;
|
|
var BCached : array<f32, ${e[0]}>;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
acc[innerRow][innerCol] = 0.0;
|
|
}
|
|
}
|
|
|
|
let ColPerThreadA = ${r} / ${t[0]};
|
|
let tileColA = i32(localId.x) * ColPerThreadA;
|
|
let RowPerThreadB = ${r} / ${t[1]};
|
|
let tileRowB = i32(localId.y) * RowPerThreadB;
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
|
|
let inputRow = tileRow + innerRow;
|
|
let inputCol = tileColA + innerCol;
|
|
|
|
mm_Asub[inputRow][inputCol] = mm_readA(
|
|
globalRow + innerRow,
|
|
t * ${r} + inputCol, globalId);
|
|
}
|
|
}
|
|
// Load one tile of B into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
let inputRow = tileRowB + innerRow;
|
|
let inputCol = tileCol + innerCol;
|
|
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(
|
|
t * ${r} + inputRow,
|
|
globalCol + innerCol, globalId);
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${r}; k = k + 1) {
|
|
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
|
|
BCached[inner] = mm_Bsub[k][tileCol + inner];
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
ACached = mm_Asub[tileRow + innerRow][k];
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
|
|
}
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
|
|
if ((globalCol + innerCol) < uniforms.dimBOuter &&
|
|
(globalRow + innerRow) < uniforms.dimAOuter) {
|
|
mm_write(globalRow + innerRow,
|
|
globalCol + innerCol,
|
|
acc[innerRow][innerCol], globalId);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`}function pue(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${Xo()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = 0.0;
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * TileSize + tileCol * 4;
|
|
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
|
|
mm_readA(globalRow, colA + 1, globalId),
|
|
mm_readA(globalRow, colA + 2, globalId),
|
|
mm_readA(globalRow, colA + 3, globalId));
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < TileSize / 4; k = k + 1) {
|
|
let rowB = t * TileSize + k * 4;
|
|
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
|
|
mm_readB(rowB + 1, globalCol, globalId),
|
|
mm_readB(rowB + 2, globalCol, globalId),
|
|
mm_readB(rowB + 3, globalCol, globalId));
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + dot(ACached, BCached);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var IC=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=Ix(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ua(r,this.aShape.slice(1)),ua(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
|
|
}
|
|
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
|
|
}
|
|
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
|
|
}
|
|
return 0.0;`;let n="",s="";if(this.activation){let o=ca(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}
|
|
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
let batch = i32(globalId.z);
|
|
${e}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batch = i32(globalId.z);
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
|
|
var value = valueIn;
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
${this.outputShape[1]>1?Ex([this.workPerThread,this.workPerThread,1],this.workGroupSize):pue(this.workGroupSize)}
|
|
`}};function hue(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${Xo()} {
|
|
let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var fue=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=ca(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}
|
|
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
${e}
|
|
}
|
|
|
|
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
|
|
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
|
|
var value = valueIn;
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
${hue()}
|
|
`}};function mue(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
|
|
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
|
|
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
|
|
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
|
|
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
|
|
|
|
// If the output size is small for matrix multiplication, avoid to use vec4
|
|
// and handle some elements per thread to optimally utilize the ALU.
|
|
// Introduces two shared memory buffers, some logical threads could handle
|
|
// arithmetic operations and others handle IO operations between barrier api,
|
|
// makes ALUs and load/store units work simultaneously, could improves
|
|
// the performance.
|
|
${Xo()} {
|
|
let tileRow = i32(localId.y);
|
|
let tileCol = i32(localId.x);
|
|
let globalRow = i32(globalId.y);
|
|
let globalCol = i32(globalId.x);
|
|
|
|
// uniforms.dimInner should be greater than 0.
|
|
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
|
|
var acc = 0.0;
|
|
|
|
var globalColA = tileCol;
|
|
var globalRowB = tileRow;
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
if (t == 0) {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub1[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
}
|
|
} else {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub1[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
} else {
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${s}; k = k + 1) {
|
|
let subRow = tileRow - ${t};
|
|
if (subRow < 0) {
|
|
continue;
|
|
}
|
|
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
|
|
}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
if (t != 0) {
|
|
t = t + 1;
|
|
}
|
|
|
|
if (t < numTiles) {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub2[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
} else {
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${s}; k = k + 1) {
|
|
let subRow = tileRow - ${t};
|
|
if (subRow < 0) {
|
|
continue;
|
|
}
|
|
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
|
|
}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
|
|
if (tileRow >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var gue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
|
|
}
|
|
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;`,n="",s="";if(this.activation){let o=ca(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
let batch = i32(globalId.z);
|
|
${e}
|
|
}
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batch = i32(globalId.z);
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
|
|
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
var value = valueIn;
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
}
|
|
${mue(this.workGroupSize)}
|
|
`}};function je(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Aue={kernelName:Li,backendName:"webgpu",kernelFunc:je};function Rx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],I=je({inputs:{x:e},backend:r,attrs:{shape:w}}),N=je({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,N],O=Math.max(A,x),$=d%4==0&&f%4==0&&!n&&!s&&f>=32,P;h*f<=32?P=new fue([O,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?P=new gue(w,k,[O,h,f],a,l,o):$?P=new due(w,[O,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):P=new IC(w,[O,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[I,N];a&&T.push(a),o&&T.push(o);let F=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=r.runWebGPUProgram(P,T,e.dtype,F),q=je({inputs:{x:U},backend:r,attrs:{shape:b}});R.push(U);for(let z of R)r.disposeData(z.dataId);return q}function yue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Rx({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var xue={kernelName:wo,backendName:"webgpu",kernelFunc:yue},CC=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${kp(this.op,!1)}
|
|
}
|
|
|
|
${st()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},bue=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
|
|
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
|
|
let b = getBAtOutCoordsByCoords(coords);`;return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${kp(this.op,!1)}
|
|
}
|
|
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
|
|
${st()}
|
|
|
|
// Fill in the shared memory buffer. Here we need a loop to make sure
|
|
// that all data in A|B are uploaded when |sharedMemorySize| is larger
|
|
// than work group size.
|
|
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
|
|
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
|
|
}
|
|
workgroupBarrier();
|
|
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},vue=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${kp(this.op,this.isVec4)}
|
|
}
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},TC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${kp(this.op,!1)}
|
|
}
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function NC(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new vue(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new bue(e,t,n,a):new TC(e,t,n)}function sr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var wue={kernelName:Xa,backendName:"webgpu",kernelFunc:sr};function bc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=sr({inputs:{x:s},backend:n}),l=sr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var kue={kernelName:ad,backendName:"webgpu",kernelFunc:bc},n0=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${xc(this.op,!1)}
|
|
}
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function En({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new n0(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Kn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==Gt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[A,x]=g,y={dataId:A.dataId,dtype:A.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=NC(e,o.shape,i.shape);return l.runWebGPUProgram(w,[y,b],Wn(A.dtype,x.dtype))});else{let g=new CC(Gt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new CC(Gt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(A,x,"float32")}let m=bc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Wn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=NC(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Sue,ceilImpl:Iue,concatImpl:Cue,equalImpl:Tue,expImpl:Nue,expm1Impl:Eue,floorImpl:Rue,gatherNdImpl:$ue,gatherV2Impl:_ue,greaterEqualImpl:Due,greaterImpl:Pue,lessEqualImpl:Fue,lessImpl:Oue,logImpl:Mue,maxImpl:zue,maximumImpl:Lue,minimumImpl:Bue,multiplyImpl:Wue,negImpl:Vue,notEqualImpl:Uue,prodImpl:Gue,rangeImpl:Hue,rsqrtImpl:jue,simpleAbsImpl:que,sliceImpl:Xue,stridedSliceImpl:Kue,stringNGramsImpl:Zue,subImpl:Yue,tileImpl:Jue,topKImpl:Que,transposeImpl:ece,uniqueImpl:h1e}=Tm,tce=En({opType:vt.ABS,cpuKernelImpl:que}),nce={kernelName:fi,backendName:"webgpu",kernelFunc:tce},sce=Kn({opSnippet:Gt.ADD,cpuKernelImpl:Sue,supportsComplex:!0}),rce={kernelName:Kr,backendName:"webgpu",kernelFunc:sce},ace=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
|
|
${st()}
|
|
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if (flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
${e.join(`
|
|
`)}
|
|
setOutputFlat(flatIndex, ${t});
|
|
}
|
|
}
|
|
}
|
|
`}};function oce(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return sr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Wn(i,l)),a=s.map(i=>i.shape),o=new ace(a);return n.runWebGPUProgram(o,s,r)}var ice={kernelName:Ra,backendName:"webgpu",kernelFunc:oce},EC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
|
|
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`,n=`
|
|
xBestIndices[localId.x] = bestIndex;
|
|
xBestValues[localId.x] = bestValue;
|
|
|
|
for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) {
|
|
workgroupBarrier();
|
|
|
|
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
|
|
let i = i32(localId.x) * ${this.reductionFactor} + w;
|
|
if (i < currentSize) {
|
|
let candidateIndex = xBestIndices[i];
|
|
let candidate = xBestValues[i];
|
|
if(candidate ${this.op} bestValue && !isNanCustom(candidate)) {
|
|
bestValue = candidate;
|
|
bestIndex = candidateIndex;
|
|
}
|
|
}
|
|
}
|
|
|
|
xBestIndices[localId.x] = bestIndex;
|
|
xBestValues[localId.x] = bestValue;
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
|
|
}
|
|
`,s=(o,i)=>this.outputShape.length===1?o:`${o}[${i}]`,r=o=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${o}]`;return`
|
|
fn DIV_CEIL(a : i32, b : i32) -> i32 {
|
|
return ((a - 1) / b + 1);
|
|
}
|
|
|
|
let WorkGroupSize = ${this.workGroupSize[0]};
|
|
|
|
${e?t:""}
|
|
|
|
// In order to get a flattened index into the input tensor, we need to
|
|
// add back the index along the reduced dimension to |outputCoords|.
|
|
// This function outputs the offset to the first value along
|
|
// |axis| and the stride to get the next value of the input along |axis|.
|
|
fn getInputCoordInfo(globalId : vec3<u32>) -> vec2<i32>{
|
|
let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
var i = ${this.outputShape.length-1};
|
|
|
|
var stride = 1;
|
|
var inputStride = 1;
|
|
var offset = 0;
|
|
|
|
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
|
|
let length = ${r(`${this.inputShape.length} - r`)};
|
|
if (${this.inputShape.length} - r == uniforms.axis) {
|
|
inputStride = stride;
|
|
} else {
|
|
offset = offset + ${s("outputCoords","i")} * stride;
|
|
i = i - 1;
|
|
}
|
|
stride = stride * length;
|
|
}
|
|
|
|
return vec2<i32>(offset, inputStride);
|
|
}
|
|
|
|
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
|
|
return coordInfo[0] + coordInfo[1] * index;
|
|
}
|
|
|
|
${Xo()} {
|
|
let coordInfo = getInputCoordInfo(globalId);
|
|
|
|
var bestIndex = 0;
|
|
var bestValue = f32(x.numbers[getInputIndex(coordInfo, bestIndex)]);
|
|
|
|
let Length = ${r("uniforms.axis")};
|
|
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
|
|
|
|
for (var w = 0; w < WorkPerThread; w = w + 1) {
|
|
let i = i32(globalId.x) * WorkPerThread + w;
|
|
if (i < Length) {
|
|
let candidate = f32(x.numbers[getInputIndex(coordInfo, i)]);
|
|
if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) {
|
|
bestValue = candidate;
|
|
bestIndex = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
let flatOutputIndex = i32(globalId.y);
|
|
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
|
|
}
|
|
`}},lce=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
|
|
let TILE_DIM = ${this.workGroupSize[0]};
|
|
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
|
|
${t0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},uce=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=kn(this.outputShape.length),t=cce(this.newDim);return`
|
|
${st()}
|
|
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(flatIndex);
|
|
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
|
|
${e}(${t}), uniforms.aShape)]);
|
|
}
|
|
}
|
|
}
|
|
`}};function cce(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function Ol(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=ece(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new lce(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new uce(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var dce={kernelName:bo,backendName:"webgpu",kernelFunc:Ol};function pce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ol({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new EC(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var hce={kernelName:$a,backendName:"webgpu",kernelFunc:pce};function fce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ol({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new EC(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var mce={kernelName:du,backendName:"webgpu",kernelFunc:fce},RC=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
|
|
var count = 0.0;
|
|
|
|
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
|
|
let xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= uniforms.convDims.x) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
|
|
let xC = xCCorner + wC;
|
|
if (xC < 0 || xC >= uniforms.convDims.y) {
|
|
continue;
|
|
}
|
|
|
|
let value = getX(batch, xR, xC, coords[3]);
|
|
${e}
|
|
}
|
|
}
|
|
|
|
setOutputFlat(index, ${t});
|
|
}
|
|
}
|
|
`}},$C=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d = coords[3];
|
|
|
|
let xRCCorner = coords.yz * uniforms.stride;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
let value = getX(batch, xRCorner, xCCorner, d);
|
|
setOutputFlat(index, value);
|
|
}
|
|
}
|
|
`}};function gce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return sr({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new $C(u):(d=new RC(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Ace={kernelName:_a,backendName:"webgpu",kernelFunc:gce};function yce(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Rx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var xce={kernelName:Da,backendName:"webgpu",kernelFunc:yce},bce=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${kn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=kn(this.rank),t=vce(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${$x[a]} = uniforms.start[${a}] + coords.${$x[a]};`),`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},$x=["x","y","z","w","u","v"];function vce(e){if(e===1)return"sourceLoc";if(e<=6)return $x.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function vc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ot.parseSliceParams(r,a,o);if(Ot.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=Xue(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new bce(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var wce={kernelName:Gi,backendName:"webgpu",kernelFunc:vc},kce=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=je({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Ol({inputs:{x:f},backend:n,attrs:{perm:c}}),g=je({inputs:{x:m},backend:n,attrs:{shape:u}}),A=vc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),A},Sce={kernelName:mi,backendName:"webgpu",kernelFunc:kce},_C=Kn({opSnippet:Gt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Uue}),Ice={kernelName:_i,backendName:"webgpu",kernelFunc:_C};function Sp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return sr({inputs:{x:r.complexTensorInfos.real},backend:n})}var Cce={kernelName:fd,backendName:"webgpu",kernelFunc:Sp};function Tce(e,t){let n=new n0(e.shape,vt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function _x(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return sr({inputs:{x:r},backend:n});let o=jt(r.shape),i=_x({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=bc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Sp({inputs:{input:r},backend:n}),i=_x({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=sr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Tce(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=_C({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Nce={kernelName:Pa,backendName:"webgpu",kernelFunc:_x},Ece=En({opType:vt.CEIL,cpuKernelImpl:Iue}),Rce={kernelName:Fa,backendName:"webgpu",kernelFunc:Ece},$ce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
|
|
${st()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
var clampedValue : vec4<f32>;
|
|
for (var i = 0; i < 4; i = i + 1) {
|
|
if (isNanCustom(value[i])) {
|
|
clampedValue[i] = value[i];
|
|
} else {
|
|
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
|
|
}
|
|
}
|
|
|
|
setOutputFlat(index, clampedValue);
|
|
}
|
|
}
|
|
`}},_ce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
|
|
${st()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function Dce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new $ce(r.shape):i=new _ce(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Pce={kernelName:Zr,backendName:"webgpu",kernelFunc:Dce},Fce=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
|
|
${st()}
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
let yR = coords.x;
|
|
let yC = coords.y;
|
|
|
|
${t.join(`
|
|
`)}
|
|
}
|
|
}
|
|
}
|
|
`}};function s0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return sr({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Oce={kernelName:cd,backendName:"webgpu",kernelFunc:s0};function Dx(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Sp({inputs:{input:m},backend:n})),d=e.map(m=>s0({inputs:{input:m},backend:n})),p=Dx(u,t,n),h=Dx(d,t,n),f=bc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return je({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=Cue(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeData(A.dataId)),g}let{tensors2D:a,outShape:o}=Mce(e,t,n),i=new Fce(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=je({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Mce(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>je({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function DC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return sr({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),Dx(i,a,n)}var zce={kernelName:gi,backendName:"webgpu",kernelFunc:DC},Lce=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
|
|
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
|
|
${st()}
|
|
|
|
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
|
|
let rc = getCoordsFromFlatIndex(flatIndex);
|
|
|
|
if(flatIndex < uniforms.size) {
|
|
let blockIndex = rc[0];
|
|
let pos = rc[1];
|
|
|
|
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
|
|
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
|
|
var value = 0.0;
|
|
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
|
|
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
|
|
uniforms.pad[0];
|
|
let d1 = offsetX + uniforms.dilation[0] * ((pos %
|
|
uniforms.itemsPerBlockRow) / uniforms.inChannels);
|
|
let ch = pos % uniforms.inChannels;
|
|
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
|
|
value = getA(d0, d1, ch);
|
|
}
|
|
}
|
|
setOutputFlat(flatIndex, value);
|
|
}
|
|
}
|
|
}
|
|
`}};function PC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=je({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=je({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=Rx({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=je({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Bce({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:A,dataFormat:x}=n,y=x==="channelsLast",b=l*c*u,w=m*f,k=[w,b],I=!1,N=!1,R=[],O=je({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),$=je({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(O),R.push($);let P=new Lce(k,y),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,A]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],F=s.runWebGPUProgram(P,[O],O.dtype,T),U=je({inputs:{x:F},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(F),R.push(U);let q=[1,k[0],k[1]],z=new IC(q,[1,w,n.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),I,N),K=q[1],J=q[2],Q=n.outChannels,te=[{type:"int32",data:[K]},{type:"int32",data:[Q]},{type:"int32",data:[J]}],re=s.runWebGPUProgram(z,[U,$],U.dtype,te),G=y?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],se=je({inputs:{x:re},backend:s,attrs:{shape:G}});R.push(re);for(let oe of R)s.disposeData(oe.dataId);return se}var FC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
|
|
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(r,[o,l]),ua(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
|
|
let divBy4Remainder${e} = flatIndex${e} % 4;
|
|
let divBy4Index${e} = flatIndex${e} / 4;
|
|
let curData${e} = x.numbers[divBy4Index${e}];
|
|
if (divBy4Remainder${e} == 0) {
|
|
temp = curData${e};
|
|
} else {
|
|
// TODO: This could end up being a redundant load with another one in
|
|
// the same shader invocation. Perhaps there's an opportunity for
|
|
// optimization
|
|
let nextData${e} = x.numbers[divBy4Index${e} + 1];
|
|
if (divBy4Remainder${e} == 1) {
|
|
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
|
|
} elseif (divBy4Remainder${e} == 2) {
|
|
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
|
|
} elseif (divBy4Remainder${e} == 3) {
|
|
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
|
|
}
|
|
}
|
|
`}getUserCode(){let t=SC([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2];
|
|
let outCol = r % uniforms.outShape[2];
|
|
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let inChCoord = c % uniforms.xShape[3];
|
|
var coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
inChCoord);
|
|
var resData = vec4<f32>(0.0);
|
|
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
|
|
// the 'same' padding type.
|
|
if (coordsInBounds4D(coord, uniforms.xShape)) {
|
|
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
|
|
} else {
|
|
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
|
|
${this.getSampleAWithRemainder(1)}
|
|
resData = temp;
|
|
if (WCol == (uniforms.filterDims[1] - 1)) {
|
|
coord = vec4<i32>(
|
|
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
|
|
${this.getSampleAWithRemainder(2)}
|
|
if (inChCoord == 0) {
|
|
resData = vec4<f32>(resData.xyz, temp.x);
|
|
} elseif (inChCoord == 1) {
|
|
resData = vec4<f32>(resData.xy, temp.xy);
|
|
} else {
|
|
resData = vec4<f32>(resData.x, temp.xyz);
|
|
}
|
|
}
|
|
`}
|
|
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
|
|
${r}
|
|
}
|
|
return vec4<f32>(0.0);
|
|
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return W.numbers[row * uniforms.dimBOuter / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0);
|
|
`,i="",l="";if(this.activation){let d=ca(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${d}
|
|
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
|
|
let b = getLeakyreluAlphaAtOutCoords();
|
|
${d}
|
|
}`,new Error("Leakyrelu is not supported.");i=`
|
|
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
${d}
|
|
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${i}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let r = row;
|
|
let c = col * 4;
|
|
var batch = i32(globalId.z);
|
|
${a}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
${o}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
|
|
{
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col * 4);
|
|
${c}
|
|
${l}
|
|
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
|
|
value);
|
|
}
|
|
}
|
|
${t}
|
|
`}},OC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Sx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Cx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(s,[a,i]),ua(r,[i,o])]}getUserCode(){let e=Ex(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
|
|
${t}
|
|
}
|
|
return 0.0;
|
|
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return W.numbers[row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;
|
|
`,r="",a="";if(this.activation){let l=ca(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${l}
|
|
}`:r=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
${l}
|
|
}
|
|
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${r}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
var batch = i32(globalId.z);
|
|
${n}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
${s}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col);
|
|
${o}
|
|
${a}
|
|
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
|
|
}
|
|
${e}
|
|
`}},MC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=ca(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${r}
|
|
}`:e=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
|
|
${r}
|
|
}
|
|
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${e}
|
|
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return getX(batch, row, col, chan);
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
|
|
let coord = vec4<i32>(row, col, xChannel, outChannel);
|
|
if(coordsInBounds4D(coord, uniforms.wShape)) {
|
|
return getW(row, col, xChannel, outChannel);
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if (coordsInBounds4D(coord, uniforms.outShape)) {
|
|
${n}
|
|
${t}
|
|
setOutput(batch, row, col, chan, value);
|
|
}
|
|
}
|
|
|
|
${kx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
|
|
let batch = coords[0];
|
|
let outChannel = coords[3];
|
|
|
|
var acc = 0.0;
|
|
|
|
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
|
|
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
|
|
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
|
|
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
|
|
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
|
|
let v = readInp(batch, coordRow, coordCol, xChannel);
|
|
let f = readFilt(row, col, xChannel, outChannel);
|
|
acc = acc + v * f;
|
|
}
|
|
}
|
|
}
|
|
|
|
writeResult(batch, coords[1], coords[2], outChannel, acc);
|
|
}
|
|
`}};function Wce(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return PC({x:r,filter:a,convInfo:p,backend:s});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Bce({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=Y().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new MC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new FC(p):h=new OC(p),!g){let A=p.outShape[1]*p.outShape[2],x=p.outShape[3],y=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[A]},{type:"int32",data:[x]},{type:"int32",data:[y]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var Vce={kernelName:Oa,backendName:"webgpu",kernelFunc:Wce},Uce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Sx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Cx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
var batch = i32(globalId.z);
|
|
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
|
|
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
|
|
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
|
|
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
|
|
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
|
|
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
|
|
return 0.0;
|
|
}
|
|
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
|
|
return 0.0;
|
|
}
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
i32(xR),
|
|
i32(xC),
|
|
col % uniforms.outBackprop[3]);
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let coordX = uniforms.filterDims.x - 1 -
|
|
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
|
|
let coordY = uniforms.filterDims.y - 1 -
|
|
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
|
|
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
|
|
coordX >= 0 && coordY >= 0) {
|
|
let coord = vec4<i32>(coordX, coordY, col,
|
|
row % uniforms.outBackprop[3]);
|
|
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col);
|
|
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
|
|
}
|
|
|
|
${Ex(this.elementsPerThread,this.workGroupSize)}
|
|
`}},Gce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
|
|
${st()} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d1 = coords[${n}];
|
|
|
|
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
|
|
let dyRCorner = dyCorner.x;
|
|
let 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.
|
|
var dotProd = 0.0;
|
|
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
|
|
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
|
|
let wRPerm = uniforms.filterDims.x - 1 - wR;
|
|
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
|
|
wRPerm < 0) {
|
|
continue;
|
|
}
|
|
let idyR = dyR;
|
|
|
|
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
|
|
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
|
|
let wCPerm = uniforms.filterDims.y - 1 - wC;
|
|
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
|
|
fract(dyC) > 0.0 || wCPerm < 0) {
|
|
continue;
|
|
}
|
|
let idyC = dyC;
|
|
|
|
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
|
|
if (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function Hce(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Gce(p);else{f=new Uce(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var jce={kernelName:Ma,backendName:"webgpu",kernelFunc:Hce},qce=En({opType:vt.COS}),Xce={kernelName:za,backendName:"webgpu",kernelFunc:qce},Kce=En({opType:vt.COSH}),Zce={kernelName:La,backendName:"webgpu",kernelFunc:Kce},Yce=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let height_ratio = f32(${n});
|
|
let width_ratio = f32(${a});
|
|
let b = coords[0];
|
|
let y = coords[1];
|
|
let x = coords[2];
|
|
let d = coords[3];
|
|
// get box vals
|
|
let y1 = getBoxes(b, 0);
|
|
let x1 = getBoxes(b, 1);
|
|
let y2 = getBoxes(b, 2);
|
|
let x2 = getBoxes(b, 3);
|
|
// get image in batch index
|
|
let bInd = i32(round(getBoxInd(b)));
|
|
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
|
|
return;
|
|
}
|
|
let height_scale = ${s};
|
|
let width_scale = ${o};
|
|
let in_y = ${r};
|
|
if( in_y < 0.0 || in_y > ${e} ) {
|
|
setOutputFlat(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let in_x = ${i};
|
|
if( in_x < 0.0 || in_x > ${t} ) {
|
|
setOutputFlat(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
|
|
if(${this.methodId} == 1) {
|
|
// Compute the four integer indices.
|
|
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
|
|
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
|
|
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
|
|
let top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
let newValue = top + (bottom - top) * fracCR.y;
|
|
setOutputFlat(index, newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
let sourceNearestCR = vec2<i32>(floor(
|
|
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
|
|
let newValue = getImage(
|
|
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
}
|
|
`}},Jce=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new Yce(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Qce={kernelName:yi,backendName:"webgpu",kernelFunc:Jce},ede=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let h = ${this.getHeightCoordString()};
|
|
let w = ${this.getWidthCoordString()};
|
|
let d = ${this.getDepthCoordString()};
|
|
|
|
let in_h = h / uniforms.blockSize;
|
|
let offset_h = h % uniforms.blockSize;
|
|
let in_w = w / uniforms.blockSize;
|
|
let offset_w = w % uniforms.blockSize;
|
|
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
let in_d = d + offset_d;
|
|
|
|
let rlt = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function tde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new ede(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var nde={kernelName:xi,backendName:"webgpu",kernelFunc:tde},zC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=ca(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${r}
|
|
}`:e=`
|
|
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
${r}
|
|
}
|
|
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
|
|
${e}
|
|
|
|
${t0()}
|
|
fn main([[builtin(global_invocation_id)]] globalId: vec3<u32>) {
|
|
let batch = 0;
|
|
let r = i32(globalId.x);
|
|
let c = i32(globalId.y) * 4;
|
|
let d2 = i32(globalId.z) * 4;
|
|
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
|
|
let d1 = d2;
|
|
let q = 0;
|
|
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
var wVals : array<vec4<f32>, 9>;
|
|
wVals[0] = getW(0, 0, d1, q);
|
|
wVals[1] = getW(0, 1, d1, q);
|
|
wVals[2] = getW(0, 2, d1, q);
|
|
wVals[3] = getW(1, 0, d1, q);
|
|
wVals[4] = getW(1, 1, d1, q);
|
|
wVals[5] = getW(1, 2, d1, q);
|
|
wVals[6] = getW(2, 0, d1, q);
|
|
wVals[7] = getW(2, 1, d1, q);
|
|
wVals[8] = getW(2, 2, d1, q);
|
|
|
|
var xVals : array<array<vec4<f32>, 6>, 3>;
|
|
for (var wR = 0; wR < 3; wR = wR + 1) {
|
|
let xR = xRCorner + wR * uniforms.dilation[0];
|
|
for (var wC = 0; wC < 6; wC = wC + 1) {
|
|
let xC = xCCorner + wC * uniforms.dilation[1];
|
|
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
|
|
xVals[wR][wC] = vec4<f32>(0.0);
|
|
} else {
|
|
xVals[wR][wC] = getX(batch, xR, xC, d1);
|
|
}
|
|
}
|
|
}
|
|
|
|
var dotProd : array<vec4<f32>, 4>;
|
|
dotProd[0] = vec4<f32>(0.0);
|
|
dotProd[1] = vec4<f32>(0.0);
|
|
dotProd[2] = vec4<f32>(0.0);
|
|
dotProd[3] = vec4<f32>(0.0);
|
|
|
|
for (var wR = 0; wR < 3; wR = wR + 1) {
|
|
for (var wC = 0; wC < 3; wC = wC + 1) {
|
|
let indexW = wR * 3 + wC;
|
|
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
|
|
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
|
|
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
|
|
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
|
|
}
|
|
}
|
|
|
|
for (var i = 0; i < 4; i = i + 1) {
|
|
let coords = vec4<i32>(batch, r, c + i, d2);
|
|
if (coordsInBounds4D(coords, uniforms.outShape)) {
|
|
${n}
|
|
${t}
|
|
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
`}},LC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=ca(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${a}
|
|
}`:t=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
${a}
|
|
}
|
|
`,n="dotProd = activation(dotProd, coords);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);":"";return`
|
|
${t}
|
|
|
|
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if (coordsInBounds4D(coord, uniforms.outShape)) {
|
|
setOutput(batch, row, col, chan, value);
|
|
}
|
|
}
|
|
|
|
${kx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let d2 = coords[3];
|
|
let d1 = d2 / ${e};
|
|
let q = d2 - d1 * ${e};
|
|
|
|
let inputRowStart = xRCCorner.x;
|
|
let inputColStart = xRCCorner.y;
|
|
let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0];
|
|
let inputColEnd = inputColStart + ${this.convInfo.filterWidth} * uniforms.dilation[1];
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
var dotProd = 0.0;
|
|
|
|
// Extract if checking out of for loop for performance.
|
|
if (inputRowStart >= 0 && inputColStart >= 0 &&
|
|
inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) {
|
|
// Here using a constant value |this.convInfo.filterHeight| instead
|
|
// of uniform value is in order to loop unrolling.
|
|
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
} else {
|
|
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
if (xR < 0 || xR >= uniforms.inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
if (xC < 0 || xC >= uniforms.inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
}
|
|
|
|
${s}
|
|
${n}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function sde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new zC(d):p=new LC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var rde={kernelName:Ba,backendName:"webgpu",kernelFunc:sde},BC=Kn({opSnippet:Gt.MUL,cpuKernelImpl:Wue,supportsComplex:!0}),ade={kernelName:so,backendName:"webgpu",kernelFunc:BC},ode=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=`
|
|
if (isNanCustom(candidate)) {
|
|
bestValue = uniforms.NAN;
|
|
} elseif (candidate ${this.reduceType==="min"?"<":">"}
|
|
bestValue)
|
|
{ bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=`
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`,a=`
|
|
xBestValues[localId.x] = bestValue;
|
|
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "}
|
|
var currentSize = WorkGroupSize;
|
|
for(; currentSize > 1;) {
|
|
workgroupBarrier();
|
|
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
|
|
let i = i32(localId.x) * ${this.reductionFactor} + w;
|
|
if (i < currentSize) {
|
|
let candidate = xBestValues[i];
|
|
${t}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
xBestValues[localId.x] = bestValue;
|
|
currentSize = DIV_CEIL(currentSize, ${this.reductionFactor});
|
|
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""}
|
|
}
|
|
if (localId.x == 0u) {
|
|
${s}
|
|
}
|
|
`;return`
|
|
fn DIV_CEIL(a : i32, b : i32) -> i32 {
|
|
return ((a - 1) / b + 1);
|
|
}
|
|
let WorkGroupSize = ${this.workGroupSize[0]};
|
|
${e?r:""}
|
|
fn getOffset(globalId : vec3<u32>) -> i32 {
|
|
let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
|
|
return offset;
|
|
}
|
|
${Xo()} {
|
|
let offset = getOffset(globalId);
|
|
var bestValue = ${n};
|
|
let Length = uniforms.reduceSize;
|
|
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
|
|
for (var w = 0; w < WorkPerThread; w = w + 1) {
|
|
let i = i32(globalId.x) * WorkPerThread + w;
|
|
if (i < Length) {
|
|
let candidate = f32(x.numbers[offset + i]);
|
|
${t}
|
|
}
|
|
}
|
|
let flatOutputIndex = i32(globalId.y);
|
|
${e?a:s}
|
|
}
|
|
`}};function Ip(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=Ol({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=zue(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:x,outDtype:y}=Gue(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,y,A);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),A=v.sizeFromShape(u.shape)/m,x={windowSize:m,inSize:m,batchSize:A,outSize:1},y=s==="mean"?"float32":Td(e.dtype),b=[{type:"int32",data:[m]}],w=new ode(x,s,y),k=r.runWebGPUProgram(w,[u],y,b);o.push(k),f=je({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Px(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ip(r,a,o,"sum",n)}var ide={kernelName:mo,backendName:"webgpu",kernelFunc:Px};function lde(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=E.getEinsumPermutation(h,l[g]),y;E.isIdentityPermutation(A)?y=a[g]:(y=Ol({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=je({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=BC({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Px({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var ude={kernelName:ud,backendName:"webgpu",kernelFunc:lde},cde=En({opType:vt.ELU}),dde={kernelName:Va,backendName:"webgpu",kernelFunc:cde},pde=Kn({opSnippet:Gt.EQUAL,dtype:"bool",cpuKernelImpl:Tue}),hde={kernelName:bi,backendName:"webgpu",kernelFunc:pde},WC=En({opType:vt.EXP,cpuKernelImpl:Nue,dtype:"float32"}),fde={kernelName:Ua,backendName:"webgpu",kernelFunc:WC};function Fx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),je({inputs:{x:a},backend:s,attrs:{shape:i}})}var mde={kernelName:vi,backendName:"webgpu",kernelFunc:Fx},gde=En({opType:vt.EXPM1,cpuKernelImpl:Eue}),Ade={kernelName:wi,backendName:"webgpu",kernelFunc:gde},yde=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
setOutputFlat(index, uniforms.value);
|
|
}
|
|
}
|
|
`}};function wc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new yde(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var xde={kernelName:yu,backendName:"webgpu",kernelFunc:wc},bde=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let coordX = uniforms.xShape[2] - coords[2] - 1;
|
|
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},vde={kernelName:ki,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bde(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},wde=En({opType:vt.FLOOR,cpuKernelImpl:Rue}),kde={kernelName:Ga,backendName:"webgpu",kernelFunc:wde},Sde=Kn({opSnippet:Gt.INT_DIV,dtype:"int32"}),Ide={kernelName:Ha,backendName:"webgpu",kernelFunc:Sde},Cde=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},VC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=sle(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function UC(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function GC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=UC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>VC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let A=[i,o,...l,...u.dispatch];u.setUniform(n.device,A);let x;if(a){let y={source:t};x=n.device.importExternalTexture(y)}else x=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,x,c.dataId),c}var Tde={kernelName:yd,backendName:"webgpu",kernelFunc:Nde},kc;function Nde(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(Y().getBool("WEBGPU_USE_IMPORT")&&o)return GC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(kc==null&&(kc=document.createElement("canvas").getContext("2d")),kc.canvas.width=u,kc.canvas.height=d,kc.drawImage(r,0,0,u,d),r=kc.canvas),c||l||o||i)return GC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let A=h.length,x=0;for(let y=0;y<A;y++)y%4<a&&(f[x++]=h[y])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Ede=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalIndex(index)"),`
|
|
${st()}
|
|
if (index < uniforms.size)
|
|
{
|
|
let xValue = getXAtOutCoordsByGlobalIndex(index);
|
|
let meanValue = getMeanAtOutCoordsByGlobalIndex(index);
|
|
let varianValue = getVarianceAtOutCoordsByGlobalIndex(index);
|
|
let offsetValue = ${e};
|
|
let scaleValue = ${t};
|
|
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
|
|
setOutputFlat(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
|
|
}
|
|
}
|
|
`}},Rde={kernelName:ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new Ede(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function $de(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A=o!=null,x=i!=null,y;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return PC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],I=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)y=new MC(g,A,h,x);else{w?y=new FC(g,A,h,x):y=new OC(g,A,h,x);let R=g.outShape[1]*g.outShape[2],O=g.outShape[3],$=g.filterHeight*g.filterWidth*g.inShape[3];I.push({type:"int32",data:[R]},{type:"int32",data:[O]},{type:"int32",data:[$]})}let N=[r,a];return A&&N.push(o),x&&N.push(i),n.runWebGPUProgram(y,N,r.dtype,I)}var _de={kernelName:ko,backendName:"webgpu",kernelFunc:$de};function Dde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,A=i!=null;g&&m.push(o),A&&m.push(i);let x;f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?x=new zC(f,g,p,A):x=new LC(f,g,p,A);let y=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}];return n.runWebGPUProgram(x,m,"float32",y)}var Pde={kernelName:So,backendName:"webgpu",kernelFunc:Dde},Fde=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${kn(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
var flattenIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexTemp = i32(round(getIndices(coords[0], j)));
|
|
let strideNum = ${e};
|
|
flattenIndex = flattenIndex + indexTemp * strideNum;
|
|
}
|
|
|
|
setOutputFlat(index, getA(flattenIndex, coords[1]));
|
|
}
|
|
}
|
|
`}};function Ode(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=je({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=je({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),y=n.bufferSync(s),b=$ue(x,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Fde(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),A=je({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),A}var Mde={kernelName:Ii,backendName:"webgpu",kernelFunc:Ode},zde=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Lde(this.aShape,"i32");return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function Lde(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function HC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=je({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=je({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let y=n.tensorMap.get(h.dataId).values,b=ze(h.shape,h.dtype,y),k=n.tensorMap.get(p.dataId).values,I=ze(p.shape,p.dtype,k),N=_ue(I,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,N.dtype,N.values)}let m=new zde(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let A=je({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(x=>n.disposeData(x.dataId)),A}var Bde={kernelName:Si,backendName:"webgpu",kernelFunc:HC},Wde=Kn({opSnippet:Gt.GREATER,cpuKernelImpl:Pue,dtype:"bool"}),Vde={kernelName:Ci,backendName:"webgpu",kernelFunc:Wde},Ude=Kn({opSnippet:Gt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Due}),Gde={kernelName:qa,backendName:"webgpu",kernelFunc:Ude},Hde=Kn({opSnippet:Gt.LESS,dtype:"bool",cpuKernelImpl:Oue}),jde={kernelName:Ni,backendName:"webgpu",kernelFunc:Hde},qde=Kn({opSnippet:Gt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Fue}),Xde={kernelName:Ei,backendName:"webgpu",kernelFunc:qde},Kde=En({opType:vt.LOG,cpuKernelImpl:Mue}),Zde={kernelName:Ka,backendName:"webgpu",kernelFunc:Kde},Yde=Kn({opSnippet:Gt.LOGICAL_AND,dtype:"bool"}),Jde={kernelName:Ri,backendName:"webgpu",kernelFunc:Yde},Qde=En({opType:vt.LOGICAL_NOT}),epe={kernelName:ku,backendName:"webgpu",kernelFunc:Qde};function jC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Ip(r,a,o,"max",n)}var tpe={kernelName:Za,backendName:"webgpu",kernelFunc:jC},npe=Kn({opSnippet:Gt.MAX,cpuKernelImpl:Lue}),spe={kernelName:Ya,backendName:"webgpu",kernelFunc:npe};function rpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return sr({inputs:{x:r},backend:n});d=new $C(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new RC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var ape={kernelName:Ja,backendName:"webgpu",kernelFunc:rpe};function ope(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Ip(r,o,a,"mean",n)}var ipe={kernelName:Qa,backendName:"webgpu",kernelFunc:ope};function lpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ip(r,a,o,"min",n)}var upe={kernelName:eo,backendName:"webgpu",kernelFunc:lpe},cpe=Kn({opSnippet:Gt.MIN,cpuKernelImpl:Bue}),dpe={kernelName:to,backendName:"webgpu",kernelFunc:cpe},ppe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=kn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let start = ${o}(${t});
|
|
let end = ${o}(${n});
|
|
var outC = getCoordsFromFlatIndex(index);
|
|
for (var i = 0; i < ${e}; i = i + 1) {
|
|
if (${a} < ${s}) {
|
|
${a} = ${s} * 2 - ${a} - ${this.offset};
|
|
} elseif(${a} >= ${r}) {
|
|
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
|
|
}
|
|
}
|
|
let coords = outC - start;
|
|
setOutputFlat(index, getX(${i}));
|
|
}
|
|
}
|
|
`}},hpe={kernelName:no,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new ppe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function fpe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Vue(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new n0(s.shape,vt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var mpe={kernelName:$i,backendName:"webgpu",kernelFunc:fpe};function gpe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Qs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ape={kernelName:Di,backendName:"webgpu",kernelFunc:gpe};function ype(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Qs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var xpe={kernelName:Pi,backendName:"webgpu",kernelFunc:ype};function r0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Sp({inputs:{input:s},backend:n}),a=r0({inputs:{x:r},backend:n}),o=s0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=bc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return wc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var bpe={kernelName:Qi,backendName:"webgpu",kernelFunc:r0};function qC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Sp({inputs:{input:s},backend:n}),a=qC({inputs:{x:r},backend:n}),o=s0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=bc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return wc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var vpe={kernelName:Fi,backendName:"webgpu",kernelFunc:qC};function wpe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Fx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Fx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=DC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var kpe={kernelName:Mi,backendName:"webgpu",kernelFunc:wpe},Spe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=kn(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let start = ${r};
|
|
let end = ${a};
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
|
|
if (${o} || ${i}) {
|
|
setOutputFlat(index, uniforms.constantValue);
|
|
} else {
|
|
let coords = outC - start;
|
|
setOutputFlat(index, getX(${l}));
|
|
}
|
|
}
|
|
}
|
|
`}},XC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return sr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return wc({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Spe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Ipe={kernelName:ro,backendName:"webgpu",kernelFunc:XC},Cpe=Kn({opSnippet:Gt.POW}),Tpe={kernelName:ao,backendName:"webgpu",kernelFunc:Cpe};function Npe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new TC(Gt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Epe={kernelName:oo,backendName:"webgpu",kernelFunc:Npe};function Rpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ip(r,a,o,"prod",n)}var $pe={kernelName:zi,backendName:"webgpu",kernelFunc:Rpe},_pe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Hue(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Dpe={kernelName:Cu,backendName:"webgpu",kernelFunc:_pe},KC=Kn({opSnippet:Gt.DIV}),Ppe={kernelName:Wa,backendName:"webgpu",kernelFunc:KC},Fpe=En({opType:vt.RELU}),Ope={kernelName:io,backendName:"webgpu",kernelFunc:Fpe},Mpe=En({opType:vt.RELU6}),zpe={kernelName:uo,backendName:"webgpu",kernelFunc:Mpe},Lpe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
|
|
${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
|
|
${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
|
|
|
|
// Compute the four integer indices.
|
|
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
|
|
let sourceCeilRC = vec2<i32>(
|
|
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
|
|
|
|
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
|
|
|
|
let top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
let newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function Bpe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Lpe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var Wpe={kernelName:lo,backendName:"webgpu",kernelFunc:Bpe},Vpe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
|
|
${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
|
|
${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${t};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
|
|
let sourceNearestRC = vec2<i32>(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e})));
|
|
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function Upe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new Vpe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Gpe={kernelName:Nu,backendName:"webgpu",kernelFunc:Upe},Hpe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
|
|
cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
|
|
${st()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
|
|
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
|
|
uniforms.sinRadians;
|
|
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
|
|
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
|
|
uniforms.cosRadians;
|
|
let coordX = i32(round(coordXFloat + uniforms.centerX));
|
|
let coordY = i32(round(coordYFloat + uniforms.centerY));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},jpe={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Hpe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},qpe=En({opType:vt.RSQRT,cpuKernelImpl:jue}),Xpe={kernelName:co,backendName:"webgpu",kernelFunc:qpe},Kpe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Xe(e),this.dispatch=Fe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=kn(r.length);this.uniforms=`sliceDim : i32; strides: ${i}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
|
|
return index;
|
|
}
|
|
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
|
|
let d0 = index / uniforms.updatesShape[1];
|
|
let d1 = index - d0 * uniforms.updatesShape[1];
|
|
return vec2<i32>(d0, d1);
|
|
}
|
|
`);let o=`getUpdates(${s})`,i=this.type==="int32"?"ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));":`
|
|
var assumed = atomicLoad(&(result.numbers[flatIndex]));
|
|
var success = 0;
|
|
for (; success == 0;) {
|
|
let new = bitcast<f32>(assumed) + updateValue;
|
|
let newI32 = bitcast<i32>(new);
|
|
let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
|
|
assumed = resValue[0];
|
|
success = resValue[1];
|
|
}
|
|
`;return`
|
|
${a}
|
|
|
|
${st()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getUpdatesCoordsFromFlatIndex(index);
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${t}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${n};
|
|
}
|
|
let updateValue = ${o};
|
|
let flatIndex = getOutputFlatIndex(${r});
|
|
|
|
${i}
|
|
}
|
|
}`}};function Zpe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=je({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=je({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=wc({backend:n,attrs:{shape:p,value:0,dtype:m}}),A=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:u},{type:"int32",data:[A]}],y=new Kpe(f.shape,i,h.shape.length,f.shape.length,u,p,m),b=n.runWebGPUProgram(y,[f,h],m,x,g),w=je({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var Ype={kernelName:Vi,backendName:"webgpu",kernelFunc:Zpe},Jpe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
let cVal = getC(${e});
|
|
if (cVal >= 1.0) {
|
|
setOutputFlat(index, getA(${t}));
|
|
} else {
|
|
setOutputFlat(index, getB(${t}));
|
|
}
|
|
}
|
|
}
|
|
`}};function Qpe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Jpe(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Wn(r.dtype,a.dtype))}var ehe={kernelName:Ui,backendName:"webgpu",kernelFunc:Qpe},the=En({opType:vt.SIGMOID}),nhe={kernelName:ho,backendName:"webgpu",kernelFunc:the},she=En({opType:vt.SIN}),rhe={kernelName:po,backendName:"webgpu",kernelFunc:she},ahe=En({opType:vt.SINH}),ohe={kernelName:Hi,backendName:"webgpu",kernelFunc:ahe},ZC=Kn({opSnippet:Gt.SUB,cpuKernelImpl:Yue,supportsComplex:!0}),ihe={kernelName:yo,backendName:"webgpu",kernelFunc:ZC};function lhe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=jC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=je({inputs:{x:i},backend:n,attrs:{shape:l}}),u=ZC({inputs:{a:r,b:c},backend:n}),d=WC({inputs:{x:u},backend:n}),p=Px({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=je({inputs:{x:p},backend:n,attrs:{shape:l}}),f=KC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var uhe={kernelName:go,backendName:"webgpu",kernelFunc:lhe},che=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=XC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=je({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Ol({inputs:{x:f},backend:n,attrs:{perm:p}}),g=je({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeData(A.dataId)),g},dhe={kernelName:ji,backendName:"webgpu",kernelFunc:che},phe=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let l=kn(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
|
|
${st()}
|
|
|
|
let globalIndex = index * ${this.workPerThread};
|
|
if (globalIndex < uniforms.size) {
|
|
var sum = vec4<f32>(0.0);
|
|
var found = vec4<bool>(false);
|
|
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${this.indicesSnippet}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
|
|
}
|
|
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
|
|
let curIndex = globalIndex + innerIndex;
|
|
let coords = getCoordsFromFlatIndex(curIndex);
|
|
if (flattenedIndex == coords[0]) {
|
|
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
|
|
found[innerIndex] = true;
|
|
}
|
|
}
|
|
}
|
|
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
|
|
let curIndex = globalIndex + innerIndex;
|
|
if (curIndex < uniforms.size)
|
|
{
|
|
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
|
|
}
|
|
}
|
|
}
|
|
}`}};function hhe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new phe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=je({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var fhe={kernelName:md,backendName:"webgpu",kernelFunc:hhe};function mhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=vc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var ghe={kernelName:qi,backendName:"webgpu",kernelFunc:mhe},Ahe=En({opType:vt.SQRT}),yhe={kernelName:fo,backendName:"webgpu",kernelFunc:Ahe},xhe={kernelName:_u,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new n0(n.shape,vt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},bhe=Kn({opSnippet:Gt.SQUARED_DIFFERENCE}),vhe={kernelName:Ao,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=kn(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getX(${t}));
|
|
}
|
|
}
|
|
`}};function khe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ot.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=je({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ot.computeOutShape(x,y,b),I=vc({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=je({inputs:{x:I},backend:n,attrs:{shape:f}}),n.disposeData(I.dataId)}else if(n.shouldExecuteOnCPU([r])){let I=n.readSync(r.dataId),N=ze(r.shape,r.dtype,I),R=Kue(h,N,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let I=new whe(h),N=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(I,[r],r.dtype,N);w=je({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var She={kernelName:Xi,backendName:"webgpu",kernelFunc:khe};function Ihe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=Zue(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Che={kernelName:gd,backendName:"webgpu",kernelFunc:Ihe},The=En({opType:vt.TANH}),Nhe={kernelName:xo,backendName:"webgpu",kernelFunc:The},Ehe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Rhe(this.rank,"uniforms.");return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function Rhe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function $he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=ze(r.shape,r.dtype,c),d=Jue(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Ehe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var _he={kernelName:Yr,backendName:"webgpu",kernelFunc:$he},Dhe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
|
|
dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
let batch = outC[0];
|
|
let elemIdx = outC[1];
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced
|
|
// above, Figure5(a) shows that element[1] is in the second half of
|
|
// the group when group size is 2, but it is in the first half of
|
|
// the group when group size is 4.
|
|
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
|
|
var i = 0;
|
|
if (isFirstInPair) {
|
|
i = elemIdx;
|
|
} else {
|
|
i = elemIdx - uniforms.inc;
|
|
}
|
|
|
|
var i0 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i0 = i;
|
|
} else {
|
|
i0 = i32(getIndices(batch, i));
|
|
}
|
|
|
|
var i1 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i1 = i + uniforms.inc;
|
|
} else {
|
|
i1 = i32(getIndices(batch, i + uniforms.inc));
|
|
}
|
|
|
|
var x0 = f32(0.0);
|
|
var x1 = f32(0.0);
|
|
if (i0 < uniforms.inputSize) {
|
|
x0 = getX(batch, i0);
|
|
} else {
|
|
x0 = uniforms.negativeInf;
|
|
}
|
|
if (i1 < uniforms.inputSize) {
|
|
x1 = getX(batch, i1);
|
|
} else {
|
|
x1 = uniforms.negativeInf;
|
|
}
|
|
|
|
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
|
|
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) {
|
|
// Elements in opposite order of direction
|
|
let iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutputFlat(index, f32(i0));
|
|
} else {
|
|
setOutputFlat(index, f32(i1));
|
|
}
|
|
}
|
|
}
|
|
`}},Phe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
let batch = outC[0];
|
|
let elemIdx = outC[1];
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
|
|
// (k=4), we only need to output the indices at positions |, the
|
|
// indices at positions _ can be thrown away, see Figure5(b) After
|
|
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
|
|
// above.
|
|
// For example, the paper shows we only need to output the orange
|
|
// bars. The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back to
|
|
// the previous sequence to find the corresponding value, we need
|
|
// to double the index. When we double the index, we basically
|
|
// interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
|
|
// position of each 2k positions by - elemIdx % k. E.g. for output
|
|
// at index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
var i = 0;
|
|
if (elemIdx < uniforms.k) {
|
|
i = elemIdx;
|
|
} else {
|
|
i = elemIdx * 2 - elemIdx % uniforms.k;
|
|
}
|
|
var i0 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i0 = i;
|
|
} else {
|
|
i0 = i32(getIndices(batch, i));
|
|
}
|
|
var i1 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i1 = i + uniforms.k;
|
|
} else {
|
|
i1 = i32(getIndices(batch, i + uniforms.k));
|
|
}
|
|
|
|
let x0 = getX(batch, i0);
|
|
var x1 = f32(0.0);
|
|
if (i1 < uniforms.inputSize) {
|
|
x1 = getX(batch, i1);
|
|
} else {
|
|
x1 = x0;
|
|
}
|
|
|
|
if (x0 >= x1) {
|
|
setOutputFlat(index, f32(i0));
|
|
} else {
|
|
setOutputFlat(index, f32(i1));
|
|
}
|
|
}
|
|
}
|
|
`}};function Sc(e,t){t!==null&&e.disposeData(t.dataId)}function YC(e){let t=1;for(;t<e;)t*=2;return t}function Fhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,I]=Que(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(I.shape,I.dtype,I.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,wc({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let u=v.sizeFromShape(i)/l,d=je({inputs:{x:r},attrs:{shape:[u,l]},backend:n}),p=YC(a),h=YC(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,I)=>{let N=m(),R=new Dhe(I),$=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram(R,N,"int32",$),Sc(n,P)};for(let w=1;w<p;w*=2){let k=w*2;for(let I=w;I>=1;I/=2)g(k,I,[u,h])}for(let w=h;w>p;w/=2){let k=m(),I=new Phe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],O=f;f=n.runWebGPUProgram(I,k,"int32",R),Sc(n,O);let $=p/2,P=$*2;for(let T=$;T>=1;T/=2)g(P,T,f.shape)}let A=f;f=vc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),Sc(n,A);let x=HC({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Sc(n,d);let y=i.slice(0,-1);y.push(a),A=f,f=je({inputs:{x:f},attrs:{shape:y},backend:n}),Sc(n,A);let b=x;return x=je({inputs:{x},attrs:{shape:y},backend:n}),Sc(n,b),[x,f]}var Ohe={kernelName:Zi,backendName:"webgpu",kernelFunc:Fhe},Mhe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
|
|
fn mapCoord(outCoord : f32, len : f32) -> f32{
|
|
var inCoord = outCoord;
|
|
if(uniforms.fillModeId == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
if (inCoord < -len) {
|
|
inCoord = inCoord + sz2;
|
|
} else {
|
|
inCoord = -inCoord - 1.0;
|
|
}
|
|
}
|
|
} elseif (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz2 = 2.0 * len;
|
|
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} elseif (uniforms.fillModeId == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz = len - 1.0;
|
|
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
|
|
}
|
|
} elseif (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz = len - 1.0;
|
|
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} elseif (uniforms.fillModeId == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
}
|
|
return outCoord;
|
|
}
|
|
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
|
|
channel : i32) -> f32 {
|
|
var outputValue : f32;
|
|
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = uniforms.fillValue;
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
${st()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
var outputValue : f32;
|
|
let batch = coords[0];
|
|
let x = coords[2];
|
|
let y = coords[1];
|
|
let channel = coords[3];
|
|
let xf = f32(x);
|
|
let yf = f32(y);
|
|
let a1 = getTransforms(batch, 0);
|
|
let a2 = getTransforms(batch, 1);
|
|
let a3 = getTransforms(batch, 2);
|
|
let b1 = getTransforms(batch, 3);
|
|
let b2 = getTransforms(batch, 4);
|
|
let b3 = getTransforms(batch, 5);
|
|
let c1 = getTransforms(batch, 6);
|
|
let c2 = getTransforms(batch, 7);
|
|
let projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = uniforms.fillValue;
|
|
} else {
|
|
let inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
let inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
|
|
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
|
|
|
|
if (uniforms.interpolationModeId == 1) {
|
|
let coordY = i32(round(mapY));
|
|
let coordX = i32(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
let yFloor = floor(mapY);
|
|
let xFloor = floor(mapX);
|
|
let yCeil = yFloor + 1.0;
|
|
let xCeil = xFloor + 1.0;
|
|
let valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
|
|
let valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}};function zhe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Mhe(g),x=o==="nearest"?1:2,y;switch(i){case"constant":y=1;break;case"reflect":y=2;break;case"wrap":y=3;break;case"nearest":y=4;break;default:y=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[y]},{type:"float32",data:[l]}];return n.runWebGPUProgram(A,[r,a],"float32",b)}var Lhe={kernelName:Yi,backendName:"webgpu",kernelFunc:zhe};function Bhe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=vc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=je({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Whe={kernelName:Ji,backendName:"webgpu",kernelFunc:Bhe},Vhe=[xue,nce,rce,ice,hce,mce,Ace,xce,Sce,Nce,Rce,Pce,kue,zce,Vce,jce,Xce,Zce,Qce,nde,rde,ude,dde,hde,mde,fde,Ade,xde,vde,Tde,kde,Ide,Rde,_de,Pde,Mde,Bde,Vde,Gde,wue,Oce,jde,Xde,Zde,Jde,epe,tpe,spe,ape,ipe,upe,dpe,hpe,ade,mpe,Ape,xpe,Ice,vpe,kpe,Ipe,Epe,$pe,Tpe,Dpe,Cce,Ppe,Ope,zpe,Aue,Wpe,Gpe,jpe,Xpe,Ype,ehe,nhe,rhe,ohe,wce,She,Che,uhe,dhe,ghe,fhe,yhe,xhe,vhe,ihe,ide,Nhe,_he,Ohe,Lhe,dce,Whe,bpe];for(let e of Vhe)cr(e);var Uhe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=JC(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=JC(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function JC(e,t){return`${e}_${t}`}var QC=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
|
|
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
|
|
|
|
${st()}
|
|
let flatIndexBase = index * uniforms.numChannels;
|
|
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
|
|
let flatIndex = flatIndexBase + i;
|
|
if (flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndexBase);
|
|
let values = ${e};
|
|
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
|
|
}
|
|
}
|
|
}
|
|
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Ghe=class extends QC{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Hhe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),e6=class extends ru{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Nx())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Uhe(this.device),this.tensorMap=new td(this,as()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return e6.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*Tx(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*Tx(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new QC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Ghe),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=vC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),v.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(N=>N.shape),i="int32";o.map(N=>{a.push({type:i,data:N})});let l=v.computeStrides(r.shape);if(a.push({type:i,data:l}),e.size){let N=v.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((N,R)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[R]}}),h=p.map(N=>N.dtype).concat(r.dtype),f=p.map(N=>E.getBroadcastDims(N.shape,r.shape)),m=p.map(N=>v.arraysEqual(N.shape,r.shape)).join("_"),g=f.map(N=>N.join("_")).join(";"),A=UC(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(A,()=>VC(this.device,e,y,p,r)),w=this.activeTimers!=null,k=Cde(this.device,x,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let I=this.getComputePass();if(w&&this.supportTimeQuery&&I.writeTimestamp(this.querySet,0),I.setPipeline(b),I.setBindGroup(0,k),I.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&I.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let N={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(N)}return Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),w&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Hhe){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ox=e6;Ox.nextDataId=0;var t6={};Oe(t6,{WebGPUBackend:()=>Ox,webgpu_util:()=>bC});Fu.isBrowser()&&Nx()&&ul("webgpu",async()=>{Y().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Y().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new Ox(r,s)},3);var Qt;(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"})(Qt||(Qt={}));var Cp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Cp||(Cp={}));var n6;function jhe(e){n6=e.wasm.cwrap(wo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qhe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Cp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],x=c?a.shape[1]:a.shape[2],y=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...y,A,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),I=new Uint8Array(new Int32Array(a.shape).buffer);return n6(p,k,r.shape.length,h,I,a.shape.length,l,c,g,f,m,d||0,w),b}var Xhe={kernelName:wo,backendName:"wasm",setupFunc:jhe,kernelFunc:qhe};function Rn(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,Qt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Khe=Rn(fi);function Zn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=E.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),A=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id,y=()=>s(d,g,c.shape.length,p,A,u.shape.length,Qt[c.dtype],x);if(t&&c.dtype==="float32")return y(),m;let b=E.getBroadcastDims(c.shape,f),w=E.getBroadcastDims(u.shape,f),k=b.every((N,R)=>N===R),I=w.every((N,R)=>N===R);if(k&&I)return y(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Zhe=!0,Yhe=Zn(Kr,Zhe),s6;function Jhe(e){s6=e.wasm.cwrap(Ra,null,["array","number","number","number"])}function Qhe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return s6(a,r.length,Qt[s.dtype],o),s}var efe={kernelName:Ra,backendName:"wasm",setupFunc:Jhe,kernelFunc:Qhe};function a0(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var tfe={kernelName:Xa,backendName:"wasm",kernelFunc:a0},r6;function nfe(e){r6=e.wasm.cwrap(bo,null,["number","array","number","number","number","array","number"])}function Ic(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=rfe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=sfe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=a0({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,l.dtype),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return r6(u,h,l.shape.length,Qt[l.dtype],d,p,a.length),c}function sfe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function rfe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var afe={kernelName:bo,backendName:"wasm",kernelFunc:Ic,setupFunc:nfe};function Ko(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=E.getInnerMostAxes(o.length,r),l=Ic({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var a6;function ofe(e){a6=e.wasm.cwrap(uu,null,["number, number, number"])}function ife(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("all",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;a6(l,A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var lfe={kernelName:uu,backendName:"wasm",setupFunc:ofe,kernelFunc:ife},o6;function ufe(e){o6=e.wasm.cwrap(cu,null,["number, number, number"])}function cfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("any",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;o6(l,A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var dfe={kernelName:cu,backendName:"wasm",setupFunc:ufe,kernelFunc:cfe},i6;function pfe(e){i6=e.wasm.cwrap($a,null,["number","number","number","number","number"])}function hfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=Ko(a,r,t);if(d){let A=t.dataIdMap.get(c.dataId).id;A!==o&&(l=c,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return i6(i,Qt[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var ffe={kernelName:$a,backendName:"wasm",kernelFunc:hfe,setupFunc:pfe},l6;function mfe(e){l6=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gfe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.strideHeight,x=u.strideWidth,y=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return l6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,w),b}var Afe={kernelName:_a,backendName:"wasm",setupFunc:mfe,kernelFunc:gfe};function cs(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var yfe={kernelName:Li,backendName:"wasm",kernelFunc:cs},u6;function xfe(e){u6=e.wasm.cwrap(Da,null,["number","array","number","number","array","number","number","number","number"])}function bfe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[A,h,d]:[A,d,h],k=cs({inputs:{x:r},backend:n,attrs:{shape:b}}),I=cs({inputs:{x:a},backend:n,attrs:{shape:w}}),N=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(I.dataId).id,O=o?k.shape[2]:k.shape[1],$=i?I.shape[1]:I.shape[2],P=Math.max(g,A),T=n.makeOutput([P,O,$],k.dtype),F=n.dataIdMap.get(T.dataId).id,U=new Uint8Array(new Int32Array(k.shape).buffer),q=new Uint8Array(new Int32Array(I.shape).buffer);return u6(N,U,k.shape.length,R,q,I.shape.length,o,i,F),n.disposeData(k.dataId),n.disposeData(I.dataId),T.shape=y,T}var vfe={kernelName:Da,backendName:"wasm",setupFunc:xfe,kernelFunc:bfe};function Tp(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Ot.parseSliceParams(t,n,s),i=Ot.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=Ot.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=Rm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)wfe(l,u[0],p,a,o);else if(h===3)kfe(l,u[0],u[1],p,a,o);else if(h===4)Sfe(l,u[0],u[1],u[2],p,a,o);else{let f=Rm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function wfe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;c<l;c++){let u=c*t+i;n.set(e.subarray(u,u+r[1]),a),a+=r[1]}}function kfe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],c=r[2],u=i+a[0],d=l+a[1];for(let p=i;p<u;p++)for(let h=l;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Sfe(e,t,n,s,r,a,o){let i=0,l=a[0],c=a[1],u=a[2],d=l+o[0],p=c+o[1],h=u+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=c;g<p;g++)for(let A=u;A<h;A++){let x=m*t+g*n+A*s+f;r.set(e.subarray(x,x+o[3]),i),i+=o[3]}}var Ife={kernelName:Gi,backendName:"wasm",kernelFunc:Tp};function Cfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=cs({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ic({inputs:{x:h},backend:n,attrs:{perm:c}}),m=cs({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Tp({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Tfe={kernelName:mi,backendName:"wasm",kernelFunc:Cfe};function Np(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Nfe={kernelName:Pa,backendName:"wasm",kernelFunc:Np},Efe=Rn(Fa),c6;function Rfe(e){c6=e.wasm.cwrap(Zr,null,["number","number","number","number"])}function $fe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return c6(i,a,o,c),l}var _fe={kernelName:Zr,backendName:"wasm",setupFunc:Rfe,kernelFunc:$fe};function d6(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return a0({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(y=>{let b=v.sizeFromShape(y.shape.slice(s));return cs({inputs:{x:y},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(y=>({vals:n.readSync(y.dataId),shape:y.shape}));r=E.computeOutShape(h.map(y=>y.shape),1);let m=h[0].shape[0]===1,g=Hy(f,r,t[0].dtype,m),A=E.computeOutShape(a.map(y=>y.shape),s);o.shape=A;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=E.fromStringArrayToUint8(g),h.forEach(y=>n.disposeData(y.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],A=h*g,x=d[m].subarray(A,A+g);p.set(x,f),f+=g}}return o}var Dfe={kernelName:gi,backendName:"wasm",kernelFunc:d6},p6;function Pfe(e){p6=e.wasm.cwrap(Oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ffe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=E.convertConv2DDataFormat(p),f=E.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,x=f.padInfo.right,y=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,I=f.strideHeight,N=f.strideWidth,R=f.inChannels,O=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let P=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(P.dataId).id;return p6(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,A,x,y,b,$,w,k,I,N,R,O,T),P}var Ofe={kernelName:Oa,backendName:"wasm",setupFunc:Pfe,kernelFunc:Ffe},h6;function Mfe(e){h6=e.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zfe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,inputShape:u}=s,d=1,p=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:A,inHeight:x,inWidth:y,outChannels:b,outHeight:w,outWidth:k,strideHeight:I,strideWidth:N}=h,R=m-1-h.padInfo.top,O=g-1-h.padInfo.left,$=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[F,U,q]=v.computeStrides(a.shape),z=P[0],K=$?P[1]:P[2],J=$?P[2]:1,Q=$?1:P[1],te=T[0],re=$?T[1]:T[2],G=$?T[2]:1,se=$?1:T[1],oe=t.makeOutput(h.inShape,"float32"),pe=t.dataIdMap.get(oe.dataId).id,ye=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return h6(ye,we,f,m,g,x,y,A,w,k,b,I,N,R,O,F,U,q,z,K,J,Q,te,re,G,se,pe),oe}var Lfe={kernelName:Ma,backendName:"wasm",setupFunc:Mfe,kernelFunc:zfe},Bfe=Rn(za),Wfe=Rn(La),Mx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Mx||(Mx={}));var f6;function Vfe(e){f6=e.wasm.cwrap(yi,null,["number","number","number","number","array","number","number","number","number","number"])}function Ufe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:c}=n,u=l.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Np({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,A=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(c.dataId).id,y=t.makeOutput(h,"float32"),b=t.dataIdMap.get(y.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return f6(g,A,x,u,w,d,p,Mx[r],a,b),m!=null&&t.disposeData(m.dataId),y}var Gfe={kernelName:yi,backendName:"wasm",setupFunc:Vfe,kernelFunc:Ufe},m6;function Hfe(e){m6=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number"])}function jfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([a],l),u=r;c!==null&&(u=Ic({inputs:{x:r},attrs:{perm:c},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;m6(f,o?1:0,i?1:0,h,m,Qt[r.dtype]);let g=p;if(c!==null){let A=E.getUndoAxesPermutation(c);g=Ic({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var qfe={kernelName:Ai,backendName:"wasm",setupFunc:Hfe,kernelFunc:jfe},g6;function Xfe(e){g6=e.wasm.cwrap(xi,null,["number","number","number","array","number","array","array","number","number"])}function Kfe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return g6(A,a,o==="NHWC"?1:0,x,r.shape.length-1,y,b,f.length,w),m}var Zfe={kernelName:xi,backendName:"wasm",setupFunc:Xfe,kernelFunc:Kfe},A6;function Yfe(e){A6=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jfe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d}=n,p=c==null?[1,1]:c,h=E.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,x=h.padInfo.bottom,y=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,I=h.strideWidth,N=h.inChannels,R=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(h.outShape,"float32"),P=s.dataIdMap.get($.dataId).id;return A6(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,x,y,O,b,w,k,I,N,R,P),$}var Qfe={kernelName:Ba,backendName:"wasm",setupFunc:Yfe,kernelFunc:Jfe},eme=Rn(Va),tme=!1,nme=Zn(bi,tme,"bool"),sme=Rn(Ua,"float32");function zx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),cs({inputs:{x:r},backend:s,attrs:{shape:i}})}var rme={kernelName:vi,backendName:"wasm",kernelFunc:zx};function y6(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var ame={kernelName:yu,backendName:"wasm",kernelFunc:y6},x6;function ome(e){x6=e.wasm.cwrap(ki,null,["number","number","number","number","number","number"])}function ime(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return x6(a,i,l,c,u,o),r}var lme={kernelName:ki,backendName:"wasm",kernelFunc:ime,setupFunc:ome},ume=Rn(Ga),cme=!1,dme=Zn(Ha,cme),b6;function pme(e){b6=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number"])}function hme(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return b6(u,d,p,h,f,r,g),m}var fme={kernelName:ja,backendName:"wasm",setupFunc:pme,kernelFunc:hme},v6;function mme(e){v6=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let G=s.dataIdMap.get(o.dataId);if(G.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${G.shape.length}.`);if(G.shape[0]!==y)throw new Error(`FusedConv2D bias shape (${G.shape}) does not match the number of output channels (${y})`);b=G.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,O=m.padInfo.left,$=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,q=m.padInfo.type==="SAME"?1:0,z=m.batchSize,K=m.inHeight,J=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let Q=s.makeOutput(m.outShape,"float32"),te=s.dataIdMap.get(Q.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return v6(A,z,K,J,x,w,k,b,I,N,R,O,q,$,P,T,F,U,y,g,re,f||0,te),Q}var Ame={kernelName:ko,backendName:"wasm",setupFunc:mme,kernelFunc:gme},w6;function yme(e){w6=e.wasm.cwrap(So,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 xme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let G=s.dataIdMap.get(o.dataId);if(G.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${G.shape.length}.`);if(G.shape[0]!==y)throw new Error(`FusedDepthwiseConv2D bias shape (${G.shape}) does not match the number of output channels (${y})`);b=G.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,O=m.padInfo.left,$=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,q=m.padInfo.type==="SAME"?1:0,z=m.batchSize,K=m.inHeight,J=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let Q=s.makeOutput(m.outShape,"float32"),te=s.dataIdMap.get(Q.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return w6(A,z,K,J,x,w,k,b,I,N,R,O,q,$,P,T,F,U,y,g,re,f||0,te),Q}var bme={kernelName:So,backendName:"wasm",setupFunc:yme,kernelFunc:xme},k6;function vme(e){k6=e.wasm.cwrap(Ii,null,["number","number","number","number","number","number","array","number"])}function wme(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=B2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(c.dataId).id;return k6(h,Qt[s.dtype],m,o,d,i,g,A),c}var kme={kernelName:Ii,backendName:"wasm",setupFunc:vme,kernelFunc:wme},S6;function Sme(e){S6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Ime(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=t.readSync(a.dataId),u=r.shape[l];for(let R=0;R<c.length;++R){let O=c[R];v.assert(O<=u-1&&O>=0,()=>`GatherV2: the index value ${O} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=cs({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=cs({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let A=p.shape.length-1,y=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),N=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return S6(y,Qt[r.dtype],I,A,w,d.batchSize,N,k),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var Cme={kernelName:Si,backendName:"wasm",setupFunc:Sme,kernelFunc:Ime},Tme=!1,Nme=Zn(Ci,Tme,"bool"),Eme=!1,Rme=Zn(qa,Eme,"bool"),I6;function $me(e){I6=e.wasm.cwrap(Ti,null,["number","number","number","number"])}function _me(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;I6(r,Qt[t.dtype],n,o)}return a}var Dme={kernelName:Ti,backendName:"wasm",setupFunc:$me,kernelFunc:_me},Pme=!1,Fme=Zn(Ni,Pme,"bool"),Ome=!1,Mme=Zn(Ei,Ome,"bool"),zme=Rn(Ka),Lme=!1,Bme=Zn(Ri,Lme,"bool"),C6;function Wme(e){C6=e.wasm.cwrap(Za,null,["number","number","number","number"])}function Vme(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("max",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;C6(l,Qt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var Ume={kernelName:Za,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme},Gme=!1,Hme=Zn(Ya,Gme),T6;function jme(e){T6=e.wasm.cwrap(Ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qme(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.dilationHeight,x=u.dilationWidth,y=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let I=s.makeOutput(u.outShape,"float32"),N=s.dataIdMap.get(I.dataId).id;return T6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,b,w,k,N),I}var Xme={kernelName:Ja,backendName:"wasm",setupFunc:jme,kernelFunc:qme},N6;function Kme(e){N6=e.wasm.cwrap(Qa,null,["number, number, number"])}function Zme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=c;c.dtype!=="float32"&&(x=Np({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let y=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;N6(l,A,b)}if(h&&t.disposeData(u.dataId),a){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return c.dtype!=="float32"&&t.disposeData(x.dataId),y}var Yme={kernelName:Qa,backendName:"wasm",setupFunc:Kme,kernelFunc:Zme},E6;function Jme(e){E6=e.wasm.cwrap(eo,null,["number","number","number","number"])}function Qme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y)}let f=c.shape.length;E.assertAxesAreInnerMostDims("min",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;E6(l,Qt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var e0e={kernelName:eo,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme},t0e=!1,n0e=Zn(to,t0e),Lx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Lx||(Lx={}));var R6;function s0e(e){R6=e.wasm.cwrap(no,null,["number","array","number","number","array","array","number","number"])}function r0e(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return R6(o,c,t.shape.length,Qt[t.dtype],p,h,Lx[r],l),i}var a0e={kernelName:no,backendName:"wasm",kernelFunc:r0e,setupFunc:s0e},o0e=!0,i0e=Zn(so,o0e),l0e=Rn($i);function Bx(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var $6;function u0e(e){$6=e.wasm.cwrap(Di,"number",["number","number","number","number","number"])}function c0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=$6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Bx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var d0e={kernelName:Di,backendName:"wasm",setupFunc:u0e,kernelFunc:c0e},_6;function p0e(e){_6=e.wasm.cwrap(Iu,"number",["number","number","number","number","number","bool"])}function h0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=_6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bx(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[A,x]}var f0e={kernelName:Iu,backendName:"wasm",setupFunc:p0e,kernelFunc:h0e},D6;function m0e(e){D6=e.wasm.cwrap(Pi,"number",["number","number","number","number","number","number"])}function g0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=D6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bx(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[A,x]}var A0e={kernelName:Pi,backendName:"wasm",setupFunc:m0e,kernelFunc:g0e},y0e=!1,x0e=Zn(_i,y0e,"bool"),P6;function b0e(e){P6=e.wasm.cwrap(Oi,null,["number","number","number","number","number"])}function v0e(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return P6(d,a,o,i,c),l}var w0e={kernelName:Oi,backendName:"wasm",setupFunc:b0e,kernelFunc:v0e};function k0e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var S0e={kernelName:Fi,backendName:"wasm",kernelFunc:k0e};function I0e(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return zx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=zx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=d6({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var C0e={kernelName:Mi,backendName:"wasm",kernelFunc:I0e},F6;function T0e(e){F6=e.wasm.cwrap(ro,null,["number","array","number","number","array","array","number","number"])}function N0e(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(v.sizeFromShape(t.shape)===0)return y6({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return F6(o,u,t.shape.length,Qt[t.dtype],h,f,r,c),i}var O6={kernelName:ro,backendName:"wasm",kernelFunc:N0e,setupFunc:T0e},E0e=!1,R0e=Zn(ao,E0e),M6;function $0e(e){M6=e.wasm.cwrap(oo,null,["number","number","number"])}function _0e(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=a,l=s,c=l;l.dtype!=="float32"&&(c=Np({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),i=n.dataIdMap.get(c.dataId).id);let u=n.makeOutput(s.shape,"float32"),d=n.dataIdMap.get(u.dataId).id;return M6(i,o,d),l.dtype!=="float32"&&n.disposeData(c.dataId),u}var D0e={kernelName:oo,backendName:"wasm",setupFunc:$0e,kernelFunc:_0e},z6;function P0e(e){z6=e.wasm.cwrap(zi,null,["number","number","number","number"])}function F0e(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t),f=d;if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;z6(l,A,Qt[x.dtype],y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var O0e={kernelName:zi,backendName:"wasm",setupFunc:P0e,kernelFunc:F0e},M0e=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Xy(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},z0e={kernelName:Cu,backendName:"wasm",kernelFunc:M0e},L0e=!0,B0e=Zn(Wa,L0e),W0e=Rn(io),V0e=Rn(uo),L6;function U0e(e){L6=e.wasm.cwrap(lo,null,["number","number","number","number","number","number","number","number","number","number"])}function G0e(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,[u,d,p,h]=r.shape,f=[u,l,c,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=Np({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let A=m.id,x=t.makeOutput(f,"float32");if(v.sizeFromShape(r.shape)===0)return x;let y=t.dataIdMap.get(x.dataId).id;return L6(A,u,d,p,h,l,c,a?1:0,o?1:0,y),g!=null&&t.disposeData(g.dataId),x}var H0e={kernelName:lo,backendName:"wasm",setupFunc:U0e,kernelFunc:G0e},B6;function j0e(e){B6=e.wasm.cwrap(Bi,null,["number","array","number","array","number","number"])}function q0e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=v.parseAxisParam(a,r.shape);if(r.shape.length===0)return a0({inputs:{x:r},backend:n});let i=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);B6(l,u,o.length,d,r.shape.length,c);let p=cs({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),p}var X0e={kernelName:Bi,backendName:"wasm",kernelFunc:q0e,setupFunc:j0e},W6;function K0e(e){W6=e.wasm.cwrap(el,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Z0e(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:o,center:i}=s,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[d,p,h,f]=r.shape,[m,g]=E.getImageCenter(i,p,h),A=o===0,x=255,y=typeof o=="number"?[o,o,o,A?0:x]:[...o,x],b=new Uint8Array(new Int32Array(y).buffer);return W6(c,d,p,h,f,a,m,g,b,y.length,u),l}var Y0e={kernelName:el,backendName:"wasm",kernelFunc:Z0e,setupFunc:K0e},J0e=Rn(Wi),Q0e=Rn(co),V6;function ege(e){V6=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","array","number","number"])}function tge(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:o}=s,i=t.makeOutput(o,a.dtype);if(v.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=W2.calculateShapes(a,r,o),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,A=new Uint8Array(new Int32Array(d).buffer),x=t.dataIdMap.get(i.dataId).id;return V6(f,g,Qt[a.dtype],l,c,u,A,p,x),i}var nge={kernelName:Vi,backendName:"wasm",setupFunc:ege,kernelFunc:tge},U6;function sge(e){U6=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function rge(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(a.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,d=s.shape.length,p=r.shape.length,h=d===0||d>1||p===1?1:v.sizeFromShape(r.shape.slice(1));return U6(o,i,l,h,u),c}var age={kernelName:Ui,backendName:"wasm",kernelFunc:rge,setupFunc:sge},G6;function oge(e){G6=e.wasm.cwrap(ho,null,["number","number"])}function ige(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return v.sizeFromShape(r.shape)===0||G6(s,a),r}var lge={kernelName:"Sigmoid",backendName:"wasm",setupFunc:oge,kernelFunc:ige},uge=Rn(po),H6;function cge(e){H6=e.wasm.cwrap(go,null,["number","number","number","number"])}function dge(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],l=v.sizeFromShape(n.shape)/i;return v.sizeFromShape(a.shape)===0||H6(r,o,i,l),a}var pge={kernelName:go,backendName:"wasm",setupFunc:cge,kernelFunc:dge};function hge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=O6.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,a,i,!1),d=E.getPermuted(u.length,a.length,!1),p=E.getReshapedPermuted(c.shape,a,i,!1),m=cs({inputs:{x:c},backend:n,attrs:{shape:u}}),x=Ic({inputs:{x:m},backend:n,attrs:{perm:d}}),w=cs({inputs:{x},backend:n,attrs:{shape:p}});return n.disposeData(c.dataId),n.disposeData(m.dataId),n.disposeData(x.dataId),w}var fge={kernelName:ji,backendName:"wasm",kernelFunc:hge};function mge(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let h=Tp({inputs:{x:r},attrs:{begin:c,size:p},backend:s});return c[i]+=d,h})}var gge={kernelName:qi,backendName:"wasm",kernelFunc:mge},Age=Rn(fo),yge=Rn(_u),xge=!0,bge=Zn(Ao,xge),j6;function vge(e){j6=e.wasm.cwrap(vo,null,["number","number","number","number"])}function wge(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return j6(o,r,Qt[a.dtype],l),i}var kge={kernelName:vo,backendName:"wasm",setupFunc:vge,kernelFunc:wge},q6;function Sge(e){q6=e.wasm.cwrap(Xi,null,["number","array","number","array","array","array","array","array","number","number"])}function Ige(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ot.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=cs({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ot.computeOutShape(x,y,b),I=Tp({inputs:{x:r},backend:t,attrs:{begin:x,size:k}});w=cs({inputs:{x:I},backend:t,attrs:{shape:f}}),t.disposeData(I.dataId)}else{let k=t.makeOutput(h,"float32"),I=t.dataIdMap.get(r.dataId).id,N=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),R=new Uint8Array(new Int32Array(x).buffer),O=new Uint8Array(new Int32Array(y).buffer),$=new Uint8Array(new Int32Array(b).buffer),P=new Uint8Array(new Int32Array(h).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(h)).buffer),F=t.dataIdMap.get(k.dataId).id;q6(I,N,r.shape.length,R,O,$,P,T,h.length,F),w=cs({inputs:{x:k},backend:t,attrs:{shape:f}}),t.disposeData(k.dataId)}return w}var Cge={kernelName:Xi,backendName:"wasm",setupFunc:Sge,kernelFunc:Ige},Tge=!0,Nge=Zn(yo,Tge),X6;function Ege(e){X6=e.wasm.cwrap(mo,null,["number","number","number","number"])}function Rge(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ko(o,r,t),f=d;if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;X6(l,A,Qt[x.dtype],y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var $ge={kernelName:mo,backendName:"wasm",setupFunc:Ege,kernelFunc:Rge},_ge=Rn(Ki),Dge=Rn(xo),K6;function Pge(e){K6=e.wasm.cwrap(Yr,null,["number","array","number","array","number","number"])}function Fge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let p=0;p<i.length;p++)i[p]=r.shape[p]*o[p];let l=new Uint8Array(new Int32Array(r.shape).buffer),c=new Uint8Array(new Int32Array(i).buffer),u=n.makeOutput(i,r.dtype),d=n.dataIdMap.get(u.dataId).id;return K6(a,l,r.shape.length,c,i.length,Qt[u.dtype],d),u}var Oge={kernelName:Yr,backendName:"wasm",setupFunc:Pge,kernelFunc:Fge},Z6;function Mge(e){Z6=e.wasm.cwrap(Zi,null,["number","array","number","number","number","bool","number","number"])}var zge=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{k:r,sorted:a}=n,o=t.dataIdMap.get(s.dataId).id,i=new Uint8Array(new Int32Array(s.shape).buffer),l=s.shape.slice();l[l.length-1]=r;let c=t.makeOutput(l,s.dtype),u=t.dataIdMap.get(c.dataId).id,d=t.makeOutput(l,"int32"),p=t.dataIdMap.get(d.dataId).id;return Z6(o,i,s.shape.length,Qt[s.dtype],r,a,u,p),[c,d]},Lge={kernelName:Zi,backendName:"wasm",setupFunc:Mge,kernelFunc:zge},Y6;function Bge(e){Y6=e.wasm.cwrap(Yi,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Wge(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=t.makeOutput(g,r.dtype),y=t.dataIdMap.get(x.dataId).id,w=t.dataIdMap.get(r.dataId).id,I=t.dataIdMap.get(a.dataId).id,N=o==="nearest"?1:2,R;switch(i){case"constant":R=1;break;case"reflect":R=2;break;case"wrap":R=3;break;case"nearest":R=4;break;default:R=1;break}return Y6(w,I,a.shape[0]>1,u,f,m,h,p,d,A,r.shape.length-1,N,R,l,y),x}var Vge={kernelName:Yi,backendName:"wasm",setupFunc:Bge,kernelFunc:Wge};function Uge(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,l=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==a&&(l[c++]=r.shape[h]);let u=new Array(o),d=new Array(i).fill(0),p=r.shape.slice();p[a]=1;for(let h=0;h<u.length;h++)d[a]=h,u[h]=Tp({inputs:{x:r},attrs:{begin:d,size:p},backend:n});return u.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Gge={kernelName:Ji,backendName:"wasm",kernelFunc:Uge};function Hge(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var jge={kernelName:Qi,backendName:"wasm",kernelFunc:Hge},qge=[Khe,Yhe,efe,lfe,dfe,ffe,Afe,vfe,Tfe,Nfe,Efe,_fe,Dfe,Ofe,Lfe,Bfe,Wfe,Gfe,qfe,Zfe,Qfe,eme,nme,sme,rme,ame,lme,ume,dme,Xhe,fme,Ame,bme,kme,Cme,Nme,Rme,tfe,Dme,Fme,Mme,zme,Bme,Ume,Hme,Xme,Yme,e0e,n0e,a0e,i0e,l0e,d0e,f0e,A0e,x0e,w0e,S0e,C0e,O6,R0e,D0e,O0e,z0e,B0e,W0e,V0e,yfe,H0e,X0e,Y0e,Q0e,J0e,nge,age,lge,uge,Ife,pge,fge,gge,Age,yge,bge,kge,Cge,Nge,$ge,_ge,Dge,Oge,Lge,Vge,afe,Gge,jge];for(let e of qge)cr(e);var Wx=Y();Wx.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])));Wx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Wx.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 J6=di(VE()),Xge='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',Kge=di(UE()),Q6=class extends ru{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(n8),Ux=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new td(this,as())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let a=this.wasm.HEAPU8.slice(t,t+v.sizeFromShape(s)*v.bytesPerElement(n));return Jge(a.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function Zge(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function e8(e,t,n){if(o0!=null)return o0;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),Rp!=null&&Rp[s]!=null?Rp[s]:n+s}async function Yge(){let[e,t]=await Promise.all([Y().getAsync("WASM_HAS_SIMD_SUPPORT"),Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=Xge,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?e8(e,t,Ep!=null?Ep:l):l+i},Vx&&(r.instantiateWasm=Zge(e8(e,t,Ep!=null?Ep:"")));let a=!1;r.onAbort=()=>{if(a||$p)return;$p=!0,s({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 o;t&&e&&o0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+J6.default.toString()],{type:"text/javascript"}),o=(0,J6.default)(r)):o=(0,Kge.default)(r),o.then(i=>{a=!0,$p=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})})})}function Jge(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 ${t}`)}}var Qge=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],o0=null,Ep=null,Rp={},$p=!1,Vx=!1;function e2e(e,t=!1){if(X2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),$p)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");o0=e,Vx=t}function t8(e,t=!1){if($p)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Ep=e;else{Rp=e;let n=Qge.filter(s=>Rp[s]==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.`)}Vx=t}var n8=-1,Ux=-1;function t2e(e){n8=e}function n2e(){if(Ux===-1)throw new Error("WASM backend not initialized.");return Ux}var s2e="0.0.0",r2e=2;ul("wasm",async()=>{let{wasm:e}=await Yge();return new Q6(e)},r2e);var Zo="3.11.0-20211110",s8={tfjs:Zo,"tfjs-core":Zo,"tfjs-data":Zo,"tfjs-layers":Zo,"tfjs-converter":Zo,"tfjs-backend-cpu":Zo,"tfjs-backend-webgl":Zo,"tfjs-backend-wasm":Zo},_p=s8["tfjs-core"];var r8=`
|
|
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.);
|
|
}
|
|
`;var a8=`
|
|
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];
|
|
}
|
|
`,o8=`
|
|
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;
|
|
}
|
|
`,i8=`
|
|
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);
|
|
}
|
|
`,l8=`
|
|
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;
|
|
}
|
|
`,u8=`
|
|
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); // top left
|
|
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
|
|
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
|
|
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
|
|
vec4 c22 = texture2D(texture, vUv); // mid center
|
|
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
|
|
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
|
|
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
|
|
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
|
|
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;
|
|
}
|
|
`;var Gx=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},c8=class{constructor(t,n,s){he(this,"uniform",{});he(this,"attribute",{});he(this,"gl");he(this,"id");he(this,"compile",(t,n)=>{let s=this.gl.createShader(n);return s?(this.gl.shaderSource(s,t),this.gl.compileShader(s),this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS)?s:(Z(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`),null)):(Z("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),a=this.compile(s,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!a)){if(!this.id){Z("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,a),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){Z(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),Gx(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);Gx(n,"uniform",this.uniform),Gx(s,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=this.gl.getUniformLocation(this.id,o)}}};function d8(){let e=0,t=null,n=!1,s=-1,r=[null,null],a=[],o=null,i=null,l=Yn(100,100),c={},u={INTERMEDIATE:1},d=l.getContext("webgl");if(this.gl=d,!d){Z("filter: cannot get webgl context");return}function p(x,y){if(!(x===l.width&&y===l.height)){if(l.width=x,l.height=y,!o){let b=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]);o=d.createBuffer(),d.bindBuffer(d.ARRAY_BUFFER,o),d.bufferData(d.ARRAY_BUFFER,b,d.STATIC_DRAW),d.pixelStorei(d.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}d.viewport(0,0,l.width,l.height),r=[null,null]}}function h(x,y){let b=d.createFramebuffer();d.bindFramebuffer(d.FRAMEBUFFER,b);let w=d.createRenderbuffer();d.bindRenderbuffer(d.RENDERBUFFER,w);let k=d.createTexture();return d.bindTexture(d.TEXTURE_2D,k),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,x,y,0,d.RGBA,d.UNSIGNED_BYTE,null),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.framebufferTexture2D(d.FRAMEBUFFER,d.COLOR_ATTACHMENT0,d.TEXTURE_2D,k,0),d.bindTexture(d.TEXTURE_2D,null),d.bindFramebuffer(d.FRAMEBUFFER,null),{fbo:b,texture:k}}function f(x){return r[x]=r[x]||h(l.width,l.height),r[x]}function m(x=0){if(!i)return;let y=null,b=null,w=!1;e===0?y=t:y=f(s).texture||null,e++,n&&!(x&u.INTERMEDIATE)?(b=null,w=e%2==0):(s=(s+1)%2,b=f(s).fbo||null),d.bindTexture(d.TEXTURE_2D,y),d.bindFramebuffer(d.FRAMEBUFFER,b),d.uniform1f(i.uniform.flipY,w?-1:1),d.drawArrays(d.TRIANGLES,0,6)}function g(x){if(c[x])return i=c[x],d.useProgram((i?i.id:null)||null),i;if(i=new c8(d,r8,x),!i)return Z("filter: could not get webgl program"),null;let y=Float32Array.BYTES_PER_ELEMENT,b=4*y;return d.enableVertexAttribArray(i.attribute.pos),d.vertexAttribPointer(i.attribute.pos,2,d.FLOAT,!1,b,0*y),d.enableVertexAttribArray(i.attribute.uv),d.vertexAttribPointer(i.attribute.uv,2,d.FLOAT,!1,b,2*y),c[x]=i,i}let A={colorMatrix:x=>{let y=new Float32Array(x);y[4]/=255,y[9]/=255,y[14]/=255,y[19]/=255;let b=y[18]===1&&y[3]===0&&y[8]===0&&y[13]===0&&y[15]===0&&y[16]===0&&y[17]===0&&y[19]===0?o8:a8,w=g(b);!w||(d.uniform1fv(w.uniform.m,y),m())},brightness:x=>{let y=(x||0)+1;A.colorMatrix([y,0,0,0,0,0,y,0,0,0,0,0,y,0,0,0,0,0,1,0])},saturation:x=>{let y=(x||0)*2/3+1,b=(y-1)*-.5;A.colorMatrix([y,b,b,0,0,b,y,b,0,0,b,b,y,0,0,0,0,0,1,0])},desaturate:()=>{A.saturation(-1)},contrast:x=>{let y=(x||0)+1,b=-128*(y-1);A.colorMatrix([y,0,0,0,b,0,y,0,0,b,0,0,y,0,b,0,0,0,1,0])},negative:()=>{A.contrast(-2)},hue:x=>{x=(x||0)/180*Math.PI;let y=Math.cos(x),b=Math.sin(x),w=.213,k=.715,I=.072;A.colorMatrix([w+y*(1-w)+b*-w,k+y*-k+b*-k,I+y*-I+b*(1-I),0,0,w+y*-w+b*.143,k+y*(1-k)+b*.14,I+y*-I+b*-.283,0,0,w+y*-w+b*-(1-w),k+y*-k+b*k,I+y*(1-I)+b*I,0,0,0,0,0,1,0])},desaturateLuminance:()=>{A.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{A.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{A.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{A.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{A.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])},technicolor:()=>{A.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])},polaroid:()=>{A.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{A.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:x=>{let y=new Float32Array(x),b=1/l.width,w=1/l.height,k=g(u8);!k||(d.uniform1fv(k.uniform.m,y),d.uniform2f(k.uniform.px,b,w),m())},detectEdges:()=>{A.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{A.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{A.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:x=>{let y=x||1;A.convolution.call(this,[0,-1*y,0,-1*y,1+4*y,-1*y,0,-1*y,0])},emboss:x=>{let y=x||1;A.convolution.call(this,[-2*y,-1*y,0,-1*y,1,1*y,0,1*y,2*y])},blur:x=>{let y=x/7/l.width,b=x/7/l.height,w=g(l8);!w||(d.uniform2f(w.uniform.px,0,b),m(u.INTERMEDIATE),d.uniform2f(w.uniform.px,y,0),m())},pixelate:x=>{let y=x/l.width,b=x/l.height,w=g(i8);!w||(d.uniform2f(w.uniform.size,y,b),m())}};this.add=function(x){let y=Array.prototype.slice.call(arguments,1),b=A[x];a.push({func:b,args:y})},this.reset=function(){a=[]},this.get=function(){return a},this.apply=function(x){p(x.width,x.height),e=0,t||(t=d.createTexture()),d.bindTexture(d.TEXTURE_2D,t),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.NEAREST),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.NEAREST),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,d.RGBA,d.UNSIGNED_BYTE,x);for(let y=0;y<a.length;y++){n=y===a.length-1;let b=a[y];b.func.apply(this,b.args||[])}return l},this.draw=function(x){return this.add("brightness",0),this.apply(x)}}async function i0(e){let t=e.shape.length===4?it(e):e,n=rn(t,3,2),s=[Do(n[0]),Do(n[1]),Do(n[2])],r=[yn(n[0]),yn(n[1]),yn(n[2])],a=await Promise.all(r.map(h=>h.data())),o=.99*Math.max(a[0][0],a[1][0],a[2][0]),i=[fe(n[0],s[0]),fe(n[1],s[1]),fe(n[2],s[2])],l=[fe(r[0],s[0]),fe(r[1],s[1]),fe(r[2],s[2])],c=[ge(o,l[0]),ge(o,l[1]),ge(o,l[2])],u=[L(i[0],c[0]),L(i[1],c[1]),L(i[2],c[2])],d=xn([u[0],u[1],u[2]],2),p=H(d,[1,t.shape[0],t.shape[1],3]);return ne([...n,...s,...r,...i,...l,...c,...u,d,t]),p}var l0=2048,lt=null,un=null,Cc=null,Ct,da={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function Yn(e,t){let n;if(de.browser)if(de.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof de.Canvas!="undefined"?n=new de.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Hx(e,t){let n=t||Yn(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function Tc(e,t,n=!0){if(!e)return t.debug&&Z("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof Qe)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof de.Canvas!="undefined"&&e instanceof de.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(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("input error: type is not recognized");if(e instanceof Qe){let s=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)s=Zt(e,0);else if(e.shape[2]===4){let r=fl(e,[0,0,0],[-1,-1,3]);s=Zt(r,0),ne(r)}}else e.shape.length===4&&(e.shape[3]===3?s=Vn(e):e.shape[3]===4&&(s=ml(e,[0,0,0,0],[-1,-1,-1,3])));if(s==null||s.shape.length!==4||s.shape[0]!==1||s.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape}`);if(s.dtype==="int32"){let r=me(s,"float32");ne(s),s=r}return{tensor:s,canvas:t.filter.return?un:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&Z("input stream is not ready"),{tensor:null,canvas:lt};let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return t.debug&&Z("cannot determine input dimensions"),{tensor:null,canvas:lt};let a=s,o=r;if(a>l0&&(a=l0,o=Math.trunc(a*r/s)),o>l0&&(o=l0,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input error: cannot determine dimension");(!lt||(lt==null?void 0:lt.width)!==a||(lt==null?void 0:lt.height)!==o)&&(lt=Yn(a,o));let i=lt.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,lt==null?void 0:lt.width,lt==null?void 0:lt.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,lt==null?void 0:lt.width,lt==null?void 0:lt.height),(!un||lt.width!==un.width||(lt==null?void 0:lt.height)!==(un==null?void 0:un.height))&&(un=Yn(lt.width,lt.height)),t.filter.enabled&&de.webgl.supported){if(Ct||(Ct=de.browser?new d8:null),de.filter=!!Ct,!Ct||!Ct.add)return t.debug&&Z("input process error: cannot initialize filters"),{tensor:null,canvas:lt};Ct.reset(),t.filter.brightness!==0&&Ct.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Ct.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ct.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ct.add("blur",t.filter.blur),t.filter.saturation!==0&&Ct.add("saturation",t.filter.saturation),t.filter.hue!==0&&Ct.add("hue",t.filter.hue),t.filter.negative&&Ct.add("negative"),t.filter.sepia&&Ct.add("sepia"),t.filter.vintage&&Ct.add("brownie"),t.filter.sepia&&Ct.add("sepia"),t.filter.kodachrome&&Ct.add("kodachrome"),t.filter.technicolor&&Ct.add("technicolor"),t.filter.polaroid&&Ct.add("polaroid"),t.filter.pixelate!==0&&Ct.add("pixelate",t.filter.pixelate),Ct.get()>0?un=Ct.apply(lt):un=Ct.draw(lt)}else Hx(lt,un),Ct&&(Ct=null),de.filter=!!Ct;if(!n)return{tensor:null,canvas:un};if(!un)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(de.browser&&Ks)l=Ks?Ks.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=ct(p,[e.height,e.width,c],"int32")}else if((!Cc||un.width!==Cc.width||un.height!==Cc.height)&&(Cc=Yn(un.width,un.height)),Ks&&de.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=Ks.fromPixels(un):(Cc=Hx(un),l=Ks.fromPixels(Cc));else{let f=Hx(un).getContext("2d").getImageData(0,0,a,o);c=f.data.length/a/o;let m=new Uint8Array(f.data.buffer);l=ct(m,[a,o,c])}if(c===4){let p=fl(l,[0,0,0],[-1,-1,3]);ne(l),l=p}if(!l)throw new Error("input error: cannot create tensor");let u=me(l,"float32"),d=t.filter.equalization?await i0(u):Zt(u,0);return ne([l,u]),{tensor:d,canvas:t.filter.return?un:null}}}async function p8(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return n;if(!da.inputTensor)da.inputTensor=Vn(t);else if(da.inputTensor.shape[1]!==t.shape[1]||da.inputTensor.shape[2]!==t.shape[2])ne(da.inputTensor),da.inputTensor=Vn(t);else{let s={};s.diff=fe(t,da.inputTensor),s.squared=L(s.diff,s.diff),s.sum=ke(s.squared);let a=(await s.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;ne([da.inputTensor,s.diff,s.squared,s.sum]),da.inputTensor=Vn(t),n=a<=e.cacheSensitivity}return n}async function h8(e,t,n){let s={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||Z("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||Z("input tensors must be of shape [1, height, width, 3]:",t.shape,n.shape),0;s.input1=Vn(t),s.input2=t.shape[1]!==n.shape[1]||t.shape[2]!==n.shape[2]?Ce.resizeBilinear(n,[t.shape[1],t.shape[2]]):Vn(n),s.diff=fe(s.input1,s.input2),s.squared=L(s.diff,s.diff),s.sum=ke(s.squared);let a=(await s.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return ne([s.input1,s.input2,s.diff,s.squared,s.sum]),a}var f8=class{constructor(){he(this,"browser");he(this,"node");he(this,"worker");he(this,"platform","");he(this,"agent","");he(this,"backends",[]);he(this,"initial");he(this,"filter");he(this,"tfjs");he(this,"offscreen");he(this,"perfadd",!1);he(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});he(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});he(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});he(this,"cpu",{model:void 0,flags:[]});he(this,"kernels",[]);he(this,"Canvas");he(this,"Image");he(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined",this.tfjs={version:_p},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(as().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&Rs()==="wasm"&&(this.wasm.simd=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Yn(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(Rs()==="webgl"||Rs()==="humangl")){let s=Dr().gpgpu!=="undefined"?await Dr().getGPGPUContext().gl:null;s&&(this.webgl.version=s.getParameter(s.VERSION),this.webgl.renderer=s.getParameter(s.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name),this.kernels=Jr(Rs()).map(s=>s.kernelName.toLowerCase())}catch(s){this.webgpu.supported=!1}}async updateCPU(){let t={model:"",flags:[]};if(this.node&&this.platform.startsWith("linux")){let n=Ia("fs");try{let s=n.readFileSync("/proc/cpuinfo").toString();for(let r of s.split(`
|
|
`))r.startsWith("model name")&&(t.model=r.match(/:(.*)/g)[0].replace(":","").trim()),r.startsWith("flags")&&(t.flags=r.match(/:(.*)/g)[0].replace(":","").trim().split(" ").sort())}catch(s){}}this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},de=new f8;var jx="2.5.2";var ds,qx=[],o2e=["white","black","asian","indian","other"],i2e=[15,23,28,35.5,45.5,55.5,65],m8=0,g8=0,Xx=Number.MAX_SAFE_INTEGER;async function A8(e){return de.initial&&(ds=null),ds?e.debug&&Z("cached model:",ds.modelUrl):(ds=await Be(We(e.modelBasePath,e.face.gear.modelPath)),!ds||!ds.modelUrl?Z("load model failed:",e.face.gear.modelPath):e.debug&&Z("load model:",ds.modelUrl)),ds}async function Kx(e,t,n,s){var o,i;if(!ds)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Xx<(((o=t.face.gear)==null?void 0:o.skipFrames)||0),a=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>ie()-g8;return t.skipAllowed&&a&&r&&m8===s&&qx[n]?(Xx++,qx[n]):(Xx=0,new Promise(async l=>{var A,x;if(!(ds==null?void 0:ds.inputs[0].shape))return;let c={},u=[[0,.1,.9,.9]];c.resize=Ce.cropAndResize(e,u,[0],[ds.inputs[0].shape[2],ds.inputs[0].shape[1]]);let d={age:0,gender:"unknown",genderScore:0,race:[]};((A=t.face.gear)==null?void 0:A.enabled)&&([c.age,c.gender,c.race]=ds.execute(c.resize,["age_output","gender_output","race_output"]));let p=await c.gender.data();d.gender=p[0]>p[1]?"male":"female",d.genderScore=Math.round(100*(p[0]>p[1]?p[0]:p[1]))/100;let h=await c.race.data();for(let y=0;y<h.length;y++)h[y]>(((x=t.face.gear)==null?void 0:x.minConfidence)||.2)&&d.race.push({score:Math.round(100*h[y])/100,race:o2e[y]});d.race.sort((y,b)=>b.score-y.score);let m=Array.from(await c.age.data()).map((y,b)=>[i2e[b],y]).sort((y,b)=>b[1]-y[1]),g=m[0][0];for(let y=1;y<m.length;y++)g+=m[y][1]*(m[y][0]-g);d.age=Math.round(10*g)/10,Object.keys(c).forEach(y=>ne(c[y])),qx[n]=d,m8=s,g8=ie(),l(d)}))}var Ze={tf255:255,tf1:1,tf2:2,tf05:.5,tf127:127.5,rgb:[.2989,.587,.114]};function y8(){Ze.tf255=Ie(255,"float32"),Ze.tf1=Ie(1,"float32"),Ze.tf2=Ie(2,"float32"),Ze.tf05=Ie(.5,"float32"),Ze.tf127=Ie(127.5,"float32"),Ze.rgb=Ut([.2989,.587,.114],"float32")}var Fn,u0=[],x8=0,b8=0,Zx=Number.MAX_SAFE_INTEGER;async function v8(e){return de.initial&&(Fn=null),Fn?e.debug&&Z("cached model:",Fn.modelUrl):(Fn=await Be(We(e.modelBasePath,e.face.ssrnet.modelPathAge)),!Fn||!Fn.modelUrl?Z("load model failed:",e.face.ssrnet.modelPathAge):e.debug&&Z("load model:",Fn.modelUrl)),Fn}async function Yx(e,t,n,s){var o,i,l,c;if(!Fn)return{age:0};let r=Zx<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ie()-b8;return t.skipAllowed&&r&&a&&x8===s&&((l=u0[n])==null?void 0:l.age)&&((c=u0[n])==null?void 0:c.age)>0?(Zx++,u0[n]):(Zx=0,new Promise(async u=>{if(!(Fn==null?void 0:Fn.inputs)||!Fn.inputs[0]||!Fn.inputs[0].shape)return;let d={};d.resize=Ce.resizeBilinear(e,[Fn.inputs[0].shape[2],Fn.inputs[0].shape[1]],!1),d.enhance=L(d.resize,Ze.tf255);let p={age:0};if(t.face.ssrnet.enabled&&(d.age=Fn.execute(d.enhance)),d.age){let h=await d.age.data();p.age=Math.trunc(10*h[0])/10}Object.keys(d).forEach(h=>ne(d[h])),u0[n]=p,x8=s,b8=ie(),u(p)}))}var ps,c0=[],w8=0,k8=0,Jx=Number.MAX_SAFE_INTEGER,Qx=[.2989,.587,.114];async function S8(e){return de.initial&&(ps=null),ps?e.debug&&Z("cached model:",ps.modelUrl):(ps=await Be(We(e.modelBasePath,e.face.ssrnet.modelPathGender)),!ps||!ps.modelUrl?Z("load model failed:",e.face.ssrnet.modelPathGender):e.debug&&Z("load model:",ps.modelUrl)),ps}async function eb(e,t,n,s){var o,i,l,c;if(!ps)return{gender:"unknown",genderScore:0};let r=Jx<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ie()-k8;return t.skipAllowed&&r&&a&&w8===s&&((l=c0[n])==null?void 0:l.gender)&&((c=c0[n])==null?void 0:c.genderScore)>0?(Jx++,c0[n]):(Jx=0,new Promise(async u=>{if(!(ps==null?void 0:ps.inputs[0].shape))return;let d={};d.resize=Ce.resizeBilinear(e,[ps.inputs[0].shape[2],ps.inputs[0].shape[1]],!1),d.enhance=X(()=>{let[f,m,g]=rn(d.resize,3,3),A=L(f,Qx[0]),x=L(m,Qx[1]),y=L(g,Qx[2]),b=df([A,x,y]);return L(fe(b,Ze.tf05),2)});let p={gender:"",genderScore:0};t.face.ssrnet.enabled&&(d.gender=ps.execute(d.enhance));let h=await d.gender.data();p.gender=h[0]>h[1]?"female":"male",p.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(d).forEach(f=>ne(d[f])),c0[n]=p,w8=s,k8=ie(),u(p)}))}var cn,d0=[],tb=Number.MAX_SAFE_INTEGER,I8=0,C8=0;async function T8(e){var t,n;return de.initial&&(cn=null),cn?e.debug&&Z("cached model:",cn.modelUrl):(cn=await Be(We(e.modelBasePath,((t=e.face.antispoof)==null?void 0:t.modelPath)||"")),!cn||!cn.modelUrl?Z("load model failed:",(n=e.face.antispoof)==null?void 0:n.modelPath):e.debug&&Z("load model:",cn.modelUrl)),cn}async function nb(e,t,n,s){var o,i;if(!cn)return 0;let r=(((o=t.face.antispoof)==null?void 0:o.skipTime)||0)>ie()-C8,a=tb<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&I8===s&&d0[n]?(tb++,d0[n]):(tb=0,new Promise(async l=>{let c=Ce.resizeBilinear(e,[(cn==null?void 0:cn.inputs[0].shape)?cn.inputs[0].shape[2]:0,(cn==null?void 0:cn.inputs[0].shape)?cn.inputs[0].shape[1]:0],!1),u=cn==null?void 0:cn.execute(c),d=(await u.data())[0];d0[n]=Math.round(100*d)/100,I8=s,C8=ie(),ne([c,u]),l(d0[n])}))}var rr={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]},sb={count:468,mouth:13,symmetryLine:[13,rr.midwayBetweenEyes[0]]},Dp={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},rb=[{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]}],Pp=[[.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]],zl=[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 l2e=[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],u2e=[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],c2e=[33,133,362,263,1,78,308],Y1e=l2e.map(e=>Pp[e]),J1e=u2e.map(e=>Pp[e]),Q1e=c2e.map(e=>Pp[e]);var Fp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],p0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],ab=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],ob=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],N8=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s,landmarks:e.landmarks,confidence:e.confidence}},ib=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=Ce.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n),o=ge(a,Ze.tf255);return ne(a),o},Op=(e,t)=>{let n=p0(e),s=Fp(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},Mp=e=>{let t=p0(e),n=Fp(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks,confidence:e.confidence}},h0=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},lb=[[1,0,0],[0,1,0],[0,0,1]],d2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),p2e=(e,t)=>d2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var E8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ll=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},h2e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},R8=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Ll(e[r],h2e(t,a)))}return n},$8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=E8(t[0],t[1]),o=R8(a,r),i=E8(-t[0],-t[1]);return R8(o,i)},f2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ll(t[0],n),-Ll(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},m2e=(e,t)=>[Ll(e,t[0]),Ll(e,t[1])];function _8(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let c=r*(l+.5);for(let u=0;u<o;u++){let d=r*(u+.5);for(let p=0;p<i;p++)n.push([d,c])}}}return n}function D8(e,t,n,s,r){let a=Fp(t),o=e.map(p=>[a[0]/r*(p[0]-r/2),a[1]/r*(p[1]-r/2),p[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?$8(n,[0,0]):lb,c=i?o.map(p=>[...m2e(p,l),p[2]]):o,u=i?f2e(s):lb,d=[...p0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+Ll(d,u[0])),Math.round(p[1]+Ll(d,u[1])),Math.round(p[2]||0)])}function ub(e,t,n,s){let r=t.landmarks.length>=sb.count?sb.symmetryLine:Dp.symmetryLine,a=0,o=lb,i;if(e&&de.kernels.includes("rotatewithoffset"))if(a=p2e(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=p0({startPoint:t.startPoint,endPoint:t.endPoint}),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=Ce.rotateWithOffset(n,a,0,u);o=$8(-a,c),i=ib(t,d,[s,s]),ne(d)}else i=ib(t,n,[s,s]);else i=ib(t,n,[s,s]);return[a,o,i]}var P8=6,Ws,F8=null,Yo=0,zp=null,f0=()=>Yo;async function O8(e){var t,n;return de.initial&&(Ws=null),Ws?e.debug&&Z("cached model:",Ws.modelUrl):(Ws=await Be(We(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Ws||!Ws.modelUrl?Z("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&Z("load model:",Ws.modelUrl)),Yo=Ws.inputs[0].shape?Ws.inputs[0].shape[2]:0,zp=Ie(Yo,"int32"),F8=fr(_8(Yo)),Ws}function g2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,F8),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=ge(t.boxSizes,zp),t.centersNormalized=ge(t.centers,zp),t.halfBoxSize=ge(t.boxSizesNormalized,Ze.tf2),t.starts=fe(t.centersNormalized,t.halfBoxSize),t.ends=ue(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,zp),t.endNormalized=L(t.ends,zp);let n=Wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ne(t[s])),n}async function M8(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let n={};n.resized=Ce.resizeBilinear(e,[Yo,Yo]),n.div=ge(n.resized,Ze.tf127),n.normalized=fe(n.div,Ze.tf05);let s=Ws==null?void 0:Ws.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=kt([d[0],d[2]],2),n.concat512=kt([d[1],d[3]],2),n.concat=kt([n.concat512,n.concat384],1),n.batch=it(n.concat,0)}else n.batch=it(s);ne(s),n.boxes=g2e(n.batch),n.logits=Pe(n.batch,[0,0],[-1,1]),n.sigmoid=ms(n.logits),n.scores=it(n.sigmoid),n.nms=await Ce.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let d=0;d<r.length;d++){let p=o[r[d]];if(p>(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let h={};h.bbox=Pe(n.boxes,[r[d],0],[1,-1]),h.slice=Pe(n.batch,[r[d],P8-1],[1,-1]),h.squeeze=it(h.slice),h.landmarks=H(h.squeeze,[P8,-1]);let f=await h.bbox.data();a.push({box:{startPoint:[f[0],f[1]],endPoint:[f[2],f[3]]},landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(m=>ne(h[m]))}}return Object.keys(n).forEach(d=>ne(n[d])),{boxes:a,scaleFactor:[e.shape[2]/Yo,e.shape[1]/Yo]}}var pb={};Yc(pb,{connected:()=>db,kpt:()=>cb});var cb=["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","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],db={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var z8={initial:!0},dn=[null,null],Jo=[[0,0],[0,0]],hb=Number.MAX_SAFE_INTEGER,fb,m0=null,Qo=[[0,0],[0,0],[0,0],[0,0]],L8=0;async function B8(e){var t,n,s;if(z8.initial&&(dn[0]=null),!dn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){dn[0]=await Be(We(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(dn[0].modelSignature.inputs);Jo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Jo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0,!dn[0]||!dn[0].modelUrl?Z("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&Z("load model:",dn[0].modelUrl)}else e.debug&&dn[0]&&Z("cached model:",dn[0].modelUrl);return dn[0]}async function W8(e){var t;if(z8.initial&&(dn[1]=null),dn[1])e.debug&&Z("cached model:",dn[1].modelUrl);else{dn[1]=await Be(We(e.modelBasePath,e.body.modelPath||""));let n=Object.values(dn[1].modelSignature.inputs);Jo[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Jo[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?fb=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:fb=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!dn[1]||!dn[1].modelUrl?Z("load model failed:",e.body.modelPath):e.debug&&Z("load model:",dn[1].modelUrl)}return dn[1]}function A2e(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function y2e(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Qo=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],t.pad=Js(e,Qo),t.resize=Ce.resizeBilinear(t.pad,[Jo[1][0],Jo[1][1]]);let n=ge(t.resize,Ze.tf255);return Object.keys(t).forEach(s=>ne(t[s])),n}function x2e(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+Qo[2][0]+Qo[2][1])/t[0]-Qo[2][0],n.position[1]*(t[1]+Qo[1][0]+Qo[1][1])/t[1]-Qo[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}var V8=e=>1-1/(1+Math.exp(e));async function b2e(e,t,n){var h;let s={};s.input=await y2e(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=dn[1])==null?void 0:h.execute(s.input,fb);let r=(await s.poseflag.data())[0],a=Math.max(0,(r-.8)/(1-.8)),o=await s.ld.data(),i=[],l=5;for(let f=0;f<o.length/l;f++){let m=V8(o[l*f+3]),g=V8(o[l*f+4]),A=Math.trunc(100*m*g*a)/100,x=[o[l*f+0]/Jo[1][0],o[l*f+1]/Jo[1][1],o[l*f+2]+0],y=[Math.trunc(n[0]*x[0]),Math.trunc(n[1]*x[1]),x[2]];i.push({part:cb[f],positionRaw:x,position:y,score:A})}if(a<(t.body.minConfidence||0))return null;let c=x2e(i,n),u=A2e(c,[n[0],n[1]]);Object.keys(s).forEach(f=>ne(s[f]));let d={};for(let[f,m]of Object.entries(db)){let g=[];for(let A=0;A<m.length-1;A++){let x=c.find(b=>b.part===m[A]),y=c.find(b=>b.part===m[A+1]);x&&y&&x.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&g.push([x.position,y.position])}d[f]=g}return{id:0,score:Math.trunc(100*a)/100,box:u.keypointsBox,boxRaw:u.keypointsBoxRaw,keypoints:c,annotations:d}}async function mb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ie()-L8,r=hb<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&m0!==null?hb++:(m0=await b2e(e,t,n),L8=ie(),hb=0),m0?[m0]:[]}var Nc=[{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 ar,Bl=0,gb=[],U8=0,Ab=Number.MAX_SAFE_INTEGER;async function G8(e){if(de.initial&&(ar=null),ar)e.debug&&Z("cached model:",ar.modelUrl);else{ar=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(ar.modelSignature.inputs);Bl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!ar||!ar.modelUrl?Z("load model failed:",e.object.modelPath):e.debug&&Z("load model:",ar.modelUrl)}return ar}async function v2e(e,t,n){if(!e)return[];let s={},r=[],a=await e.array();s.squeeze=it(e);let o=rn(s.squeeze,6,1);s.stack=xn([o[1],o[0],o[3],o[2]],1),s.boxes=it(s.stack),s.scores=it(o[4]),s.classes=it(o[5]),ne([e,...o]),s.nms=await Ce.nonMaxSuppressionAsync(s.boxes,s.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);let i=await s.nms.data(),l=0;for(let c of Array.from(i)){let u=Math.trunc(100*a[0][c][4])/100,d=a[0][c][5],p=Nc[d].label,[h,f]=[a[0][c][0]/Bl,a[0][c][1]/Bl],m=[h,f,a[0][c][2]/Bl-h,a[0][c][3]/Bl-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:l++,score:u,class:d,label:p,box:g,boxRaw:m})}return Object.keys(s).forEach(c=>ne(s[c])),r}async function yb(e,t){let n=(t.object.skipTime||0)>ie()-U8,s=Ab<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&gb.length>0?(Ab++,gb):(Ab=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Ce.resizeBilinear(e,[Bl,Bl]),i=t.object.enabled?ar==null?void 0:ar.execute(o,["tower_0/detections"]):null;U8=ie(),ne(o);let l=await v2e(i,a,t);gb=l,r(l)}))}var vb={};Yc(vb,{connected:()=>bb,kpt:()=>xb});var xb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],bb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var pn,H8=0,Jn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},wb=Number.MAX_SAFE_INTEGER;async function j8(e){return de.initial&&(pn=null),pn?e.debug&&Z("cached model:",pn.modelUrl):(pn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!pn||!pn.modelUrl?Z("load model failed:",e.body.modelPath):e.debug&&Z("load model:",pn.modelUrl)),pn}async function w2e(e,t){let[n,s]=e.shape,r=H(e,[s*n]),a=yn(r,0),o=(await a.data())[0];if(ne([r,a]),o>t){let i=Zs(r,0),l=Md(i,n),c=(await l.data())[0],u=ge(i,Ie(n,"int32")),d=(await u.data())[0];return ne([l,u]),[c,d,o]}return[0,0,o]}async function kb(e,t){let n=(t.body.skipTime||0)>ie()-H8,s=wb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(Jn.keypoints).length>0?(wb++,[Jn]):(wb=0,new Promise(async r=>{var d;let a=X(()=>{if(!(pn==null?void 0:pn.inputs[0].shape))return null;let p=Ce.resizeBilinear(e,[pn.inputs[0].shape[2],pn.inputs[0].shape[1]],!1),h=L(p,Ze.tf2);return fe(h,Ze.tf1)}),o;if(t.body.enabled&&(o=pn==null?void 0:pn.execute(a)),H8=ie(),ne(a),o){Jn.keypoints.length=0;let p=o.squeeze();ne(o);let h=p.unstack(2);ne(p);for(let f=0;f<h.length;f++){let[m,g,A]=await w2e(h[f],t.body.minConfidence);A>(((d=t.body)==null?void 0:d.minConfidence)||0)&&Jn.keypoints.push({score:Math.round(100*A)/100,part:xb[f],positionRaw:[m/pn.inputs[0].shape[2],g/pn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/pn.inputs[0].shape[2]),Math.round(e.shape[1]*g/pn.inputs[0].shape[1])]})}h.forEach(f=>ne(f))}Jn.score=Jn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Jn.keypoints.map(p=>p.position[0]),l=Jn.keypoints.map(p=>p.position[1]);Jn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Jn.keypoints.map(p=>p.positionRaw[0]),u=Jn.keypoints.map(p=>p.positionRaw[1]);Jn.boxRaw=[Math.min(...c),Math.min(...u),Math.max(...c)-Math.min(...c),Math.max(...u)-Math.min(...u)];for(let[p,h]of Object.entries(bb)){let f=[];for(let m=0;m<h.length-1;m++){let g=Jn.keypoints.find(x=>x.part===h[m]),A=Jn.keypoints.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}Jn.annotations[p]=f}r([Jn])}))}var k2e=["angry","disgust","fear","happy","sad","surprise","neutral"],Qn,g0=[],q8=0,X8=0,Sb=Number.MAX_SAFE_INTEGER;async function K8(e){var t,n;return de.initial&&(Qn=null),Qn?e.debug&&Z("cached model:",Qn.modelUrl):(Qn=await Be(We(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Qn||!Qn.modelUrl?Z("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&Z("load model:",Qn.modelUrl)),Qn}async function Ib(e,t,n,s){var o,i;if(!Qn)return[];let r=Sb<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ie()-X8;return t.skipAllowed&&a&&r&&q8===s&&g0[n]&&g0[n].length>0?(Sb++,g0[n]):(Sb=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Qn==null?void 0:Qn.inputs[0].shape)?Qn.inputs[0].shape[2]:0;p.resize=Ce.resizeBilinear(e,[h,h],!1),p.channels=L(p.resize,Ze.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=fe(p.grayscale,Ze.tf05),p.grayscaleMul=L(p.grayscaleSub,Ze.tf2),p.emotion=Qn==null?void 0:Qn.execute(p.grayscaleMul),X8=ie();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:k2e[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>ne(p[m]))}g0[n]=c,q8=s,l(c)}))}var ks,Cb=[],Z8=0,Y8=0,J8=Number.MAX_SAFE_INTEGER;async function Q8(e){let t=We(e.modelBasePath,e.face.mobilefacenet.modelPath);return de.initial&&(ks=null),ks?e.debug&&Z("cached model:",t):(ks=await Be(t),ks?e.debug&&Z("load model:",t):Z("load model failed:",e.face.mobilefacenet.modelPath)),ks}async function Tb(e,t,n,s){var o,i;if(!ks)return[];let r=J8<(((o=t.face.embedding)==null?void 0:o.skipFrames)||0),a=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>ie()-Y8;return t.skipAllowed&&a&&r&&Z8===s&&Cb[n]?(J8++,Cb[n]):new Promise(async l=>{var u;let c=[];if(((u=t.face.embedding)==null?void 0:u.enabled)&&(ks==null?void 0:ks.inputs[0].shape)){let d={};d.crop=Ce.resizeBilinear(e,[ks.inputs[0].shape[2],ks.inputs[0].shape[1]],!1),d.data=ks==null?void 0:ks.execute(d.crop);let p=await d.data.data();c=Array.from(p)}Cb[n]=c,Z8=s,Y8=ie(),l(c)})}var or,ei=0,S2e=2.3,Nb=rr.leftEyeLower0,Eb=rr.rightEyeLower0,Ec={leftBounds:[Nb[0],Nb[Nb.length-1]],rightBounds:[Eb[0],Eb[Eb.length-1]]},Rc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function eT(e){var t,n;return de.initial&&(or=null),or?e.debug&&Z("cached model:",or.modelUrl):(or=await Be(We(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!or||!or.modelUrl?Z("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&Z("load model:",or.modelUrl)),ei=or.inputs[0].shape?or.inputs[0].shape[2]:0,ei===-1&&(ei=64),or}function A0(e,t,n,s){for(let r=0;r<rb.length;r++){let{key:a,indices:o}=rb[r],i=rr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var I2e=e=>{let t=e[Ec.leftBounds[0]][2],n=e[Ec.rightBounds[0]][2];return t-n},tT=(e,t,n,s,r,a=!1)=>{let o=Mp(Op(h0([e[n],e[s]]),S2e)),i=Fp(o),l=Ce.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[ei,ei]);if(a&&de.kernels.includes("flipleftright")){let c=Ce.flipLeftRight(l);ne(l),l=c}return{box:o,boxSize:i,crop:l}},nT=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<Rc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/ei:o/ei)*n[0]+t.startPoint[0],i/ei*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Rc.index)}},sT=(e,t,n)=>{let s=e[rr[`${n}EyeUpper0`][Rc.upperCenter]][2],r=e[rr[`${n}EyeLower0`][Rc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function rT(e,t,n,s){if(!or)return n.debug&&Z("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=tT(e,t,Ec.leftBounds[0],Ec.leftBounds[1],s,!0),{box:i,boxSize:l,crop:c}=tT(e,t,Ec.rightBounds[0],Ec.rightBounds[1],s,!0),u=kt([o,c]);ne(o),ne(c);let d=or.execute(u);ne(u);let p=await d.data();ne(d);let h=p.slice(0,Rc.numCoordinates*3),{rawCoords:f,iris:m}=nT(h,r,a,!0),g=p.slice(Rc.numCoordinates*3),{rawCoords:A,iris:x}=nT(g,i,l),y=I2e(e);Math.abs(y)<30?(A0(e,f,"left",null),A0(e,A,"right",null)):y<1?A0(e,f,"left",["EyeUpper0","EyeLower0"]):A0(e,A,"right",["EyeUpper0","EyeLower0"]);let b=sT(e,m,"left"),w=sT(e,x,"right");return e.concat(b).concat(w)}var $c=[],ir=null,Wl=0,Rb=Number.MAX_SAFE_INTEGER,aT=0;async function oT(e,t){var i,l,c,u,d,p,h,f,m,g,A;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ie()-aT,s=Rb<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||$c.length===0){let x=await M8(e,t);aT=ie(),$c=[];for(let y of x.boxes){let b={startPoint:y.box.startPoint,endPoint:y.box.endPoint,landmarks:y.landmarks,confidence:y.confidence},w=N8(b,x.scaleFactor),k=Op(w,Math.sqrt(((c=t.face.detector)==null?void 0:c.cropFactor)||1.6)),I=Mp(k);$c.push(I)}Rb=0}else Rb++;let r=[],a=[],o=0;for(let x=0;x<$c.length;x++){let y=$c[x],b=0,w,k={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,k.tensor]=ub((u=t.face.detector)==null?void 0:u.rotation,y,e,((d=t.face.mesh)==null?void 0:d.enabled)?Wl:f0()),(p=t==null?void 0:t.filter)==null?void 0:p.equalization){let I=await i0(k.tensor);ne(k.tensor),k.tensor=I}if(k.boxScore=Math.round(100*y.confidence)/100,(h=t.face.mesh)==null?void 0:h.enabled)if(!ir)t.debug&&Z("face mesh detection requested, but model is not loaded");else{let[I,N,R]=ir.execute(k.tensor),O=await N.data();k.faceScore=Math.round(100*O[0])/100;let $=H(R,[-1,3]),P=await $.array();if(ne([R,$,N,I]),k.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1))y.confidence=k.faceScore;else{((m=t.face.iris)==null?void 0:m.enabled)&&(P=await rT(P,k.tensor,t,Wl)),k.mesh=D8(P,y,b,w,Wl),k.meshRaw=k.mesh.map(T=>[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/Wl]);for(let T of Object.keys(rr))k.annotations[T]=rr[T].map(F=>k.mesh[F]);y=Mp({...Op(h0(k.mesh),((g=t.face.detector)==null?void 0:g.cropFactor)||1.6),confidence:y.confidence}),k.box=ab(y,e),k.boxRaw=ob(y,e),k.score=k.faceScore,a.push(y),ne(k.tensor),[b,w,k.tensor]=ub((A=t.face.detector)==null?void 0:A.rotation,y,e,Wl)}}else{k.box=ab(y,e),k.boxRaw=ob(y,e),k.score=k.boxScore,k.mesh=y.landmarks.map(I=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*I[0]/f0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*I[1]/f0()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Wl]);for(let I of Object.keys(Dp))k.annotations[I]=[k.mesh[Dp[I]]]}r.push(k)}return $c=[...a],r}async function iT(e){var t,n;return de.initial&&(ir=null),ir?e.debug&&Z("cached model:",ir.modelUrl):(ir=await Be(We(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!ir||!ir.modelUrl?Z("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&Z("load model:",ir.modelUrl)),Wl=ir.inputs[0].shape?ir.inputs[0].shape[2]:0,ir}var lT=zl,uT=Pp;var Ss,y0=[],cT=0,dT=0,$b=Number.MAX_SAFE_INTEGER;async function pT(e){var n,s;let t=We(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return de.initial&&(Ss=null),Ss?e.debug&&Z("cached model:",t):(Ss=await Be(t),Ss?e.debug&&Z("load model:",t):Z("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),Ss}function _b(e){let t=e.image||e.tensor||e;if(!(Ss==null?void 0:Ss.inputs[0].shape))return t;let n=Ce.resizeBilinear(t,[Ss.inputs[0].shape[2],Ss.inputs[0].shape[1]],!1),s=L(n,Ze.tf255);return ne(n),s}async function Db(e,t,n,s){var o,i,l,c;if(!Ss)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let r=$b<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ie()-cT;return t.skipAllowed&&r&&a&&dT===s&&((l=y0[n])==null?void 0:l.age)&&((c=y0[n])==null?void 0:c.age)>0?($b++,y0[n]):($b=0,new Promise(async u=>{var p,h;let d={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=_b(e),m=Ss==null?void 0:Ss.execute(f);cT=ie(),ne(f);let A=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(A[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(d.gender=A[0]<=.5?"female":"male",d.genderScore=Math.min(.99,x));let y=Zs(m.find(R=>R.shape[1]===100),1),b=(await y.data())[0];ne(y);let k=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let I=m.find(R=>R.shape[1]===1024),N=I?await I.data():[];d.descriptor=Array.from(N),m.forEach(R=>ne(R))}y0[n]=d,dT=s,u(d)}))}function x0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Lp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function hT(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return Ce.cropAndResize(t,a,[0],n)}function fT(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function b0(e,t=1.5){let n=Lp(e),s=x0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function v0(e){let t=Lp(e),n=x0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function C2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function mT(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return C2e(n)}var gT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ti(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function T2e(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function AT(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ti(e[r],T2e(t,a)))}return n}function Pb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=gT(t[0],t[1]),o=AT(a,r),i=gT(-t[0],-t[1]);return AT(o,i)}function yT(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ti(t[0],n),-ti(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function Fb(e,t){return[ti(e,t[0]),ti(e,t[1])]}var xT=[{x:.015625,y:.015625},{x:.015625,y:.015625},{x:.046875,y:.015625},{x:.046875,y:.015625},{x:.078125,y:.015625},{x:.078125,y:.015625},{x:.109375,y:.015625},{x:.109375,y:.015625},{x:.140625,y:.015625},{x:.140625,y:.015625},{x:.171875,y:.015625},{x:.171875,y:.015625},{x:.203125,y:.015625},{x:.203125,y:.015625},{x:.234375,y:.015625},{x:.234375,y:.015625},{x:.265625,y:.015625},{x:.265625,y:.015625},{x:.296875,y:.015625},{x:.296875,y:.015625},{x:.328125,y:.015625},{x:.328125,y:.015625},{x:.359375,y:.015625},{x:.359375,y:.015625},{x:.390625,y:.015625},{x:.390625,y:.015625},{x:.421875,y:.015625},{x:.421875,y:.015625},{x:.453125,y:.015625},{x:.453125,y:.015625},{x:.484375,y:.015625},{x:.484375,y:.015625},{x:.515625,y:.015625},{x:.515625,y:.015625},{x:.546875,y:.015625},{x:.546875,y:.015625},{x:.578125,y:.015625},{x:.578125,y:.015625},{x:.609375,y:.015625},{x:.609375,y:.015625},{x:.640625,y:.015625},{x:.640625,y:.015625},{x:.671875,y:.015625},{x:.671875,y:.015625},{x:.703125,y:.015625},{x:.703125,y:.015625},{x:.734375,y:.015625},{x:.734375,y:.015625},{x:.765625,y:.015625},{x:.765625,y:.015625},{x:.796875,y:.015625},{x:.796875,y:.015625},{x:.828125,y:.015625},{x:.828125,y:.015625},{x:.859375,y:.015625},{x:.859375,y:.015625},{x:.890625,y:.015625},{x:.890625,y:.015625},{x:.921875,y:.015625},{x:.921875,y:.015625},{x:.953125,y:.015625},{x:.953125,y:.015625},{x:.984375,y:.015625},{x:.984375,y:.015625},{x:.015625,y:.046875},{x:.015625,y:.046875},{x:.046875,y:.046875},{x:.046875,y:.046875},{x:.078125,y:.046875},{x:.078125,y:.046875},{x:.109375,y:.046875},{x:.109375,y:.046875},{x:.140625,y:.046875},{x:.140625,y:.046875},{x:.171875,y:.046875},{x:.171875,y:.046875},{x:.203125,y:.046875},{x:.203125,y:.046875},{x:.234375,y:.046875},{x:.234375,y:.046875},{x:.265625,y:.046875},{x:.265625,y:.046875},{x:.296875,y:.046875},{x:.296875,y:.046875},{x:.328125,y:.046875},{x:.328125,y:.046875},{x:.359375,y:.046875},{x:.359375,y:.046875},{x:.390625,y:.046875},{x:.390625,y:.046875},{x:.421875,y:.046875},{x:.421875,y:.046875},{x:.453125,y:.046875},{x:.453125,y:.046875},{x:.484375,y:.046875},{x:.484375,y:.046875},{x:.515625,y:.046875},{x:.515625,y:.046875},{x:.546875,y:.046875},{x:.546875,y:.046875},{x:.578125,y:.046875},{x:.578125,y:.046875},{x:.609375,y:.046875},{x:.609375,y:.046875},{x:.640625,y:.046875},{x:.640625,y:.046875},{x:.671875,y:.046875},{x:.671875,y:.046875},{x:.703125,y:.046875},{x:.703125,y:.046875},{x:.734375,y:.046875},{x:.734375,y:.046875},{x:.765625,y:.046875},{x:.765625,y:.046875},{x:.796875,y:.046875},{x:.796875,y:.046875},{x:.828125,y:.046875},{x:.828125,y:.046875},{x:.859375,y:.046875},{x:.859375,y:.046875},{x:.890625,y:.046875},{x:.890625,y:.046875},{x:.921875,y:.046875},{x:.921875,y:.046875},{x:.953125,y:.046875},{x:.953125,y:.046875},{x:.984375,y:.046875},{x:.984375,y:.046875},{x:.015625,y:.078125},{x:.015625,y:.078125},{x:.046875,y:.078125},{x:.046875,y:.078125},{x:.078125,y:.078125},{x:.078125,y:.078125},{x:.109375,y:.078125},{x:.109375,y:.078125},{x:.140625,y:.078125},{x:.140625,y:.078125},{x:.171875,y:.078125},{x:.171875,y:.078125},{x:.203125,y:.078125},{x:.203125,y:.078125},{x:.234375,y:.078125},{x:.234375,y:.078125},{x:.265625,y:.078125},{x:.265625,y:.078125},{x:.296875,y:.078125},{x:.296875,y:.078125},{x:.328125,y:.078125},{x:.328125,y:.078125},{x:.359375,y:.078125},{x:.359375,y:.078125},{x:.390625,y:.078125},{x:.390625,y:.078125},{x:.421875,y:.078125},{x:.421875,y:.078125},{x:.453125,y:.078125},{x:.453125,y:.078125},{x:.484375,y:.078125},{x:.484375,y:.078125},{x:.515625,y:.078125},{x:.515625,y:.078125},{x:.546875,y:.078125},{x:.546875,y:.078125},{x:.578125,y:.078125},{x:.578125,y:.078125},{x:.609375,y:.078125},{x:.609375,y:.078125},{x:.640625,y:.078125},{x:.640625,y:.078125},{x:.671875,y:.078125},{x:.671875,y:.078125},{x:.703125,y:.078125},{x:.703125,y:.078125},{x:.734375,y:.078125},{x:.734375,y:.078125},{x:.765625,y:.078125},{x:.765625,y:.078125},{x:.796875,y:.078125},{x:.796875,y:.078125},{x:.828125,y:.078125},{x:.828125,y:.078125},{x:.859375,y:.078125},{x:.859375,y:.078125},{x:.890625,y:.078125},{x:.890625,y:.078125},{x:.921875,y:.078125},{x:.921875,y:.078125},{x:.953125,y:.078125},{x:.953125,y:.078125},{x:.984375,y:.078125},{x:.984375,y:.078125},{x:.015625,y:.109375},{x:.015625,y:.109375},{x:.046875,y:.109375},{x:.046875,y:.109375},{x:.078125,y:.109375},{x:.078125,y:.109375},{x:.109375,y:.109375},{x:.109375,y:.109375},{x:.140625,y:.109375},{x:.140625,y:.109375},{x:.171875,y:.109375},{x:.171875,y:.109375},{x:.203125,y:.109375},{x:.203125,y:.109375},{x:.234375,y:.109375},{x:.234375,y:.109375},{x:.265625,y:.109375},{x:.265625,y:.109375},{x:.296875,y:.109375},{x:.296875,y:.109375},{x:.328125,y:.109375},{x:.328125,y:.109375},{x:.359375,y:.109375},{x:.359375,y:.109375},{x:.390625,y:.109375},{x:.390625,y:.109375},{x:.421875,y:.109375},{x:.421875,y:.109375},{x:.453125,y:.109375},{x:.453125,y:.109375},{x:.484375,y:.109375},{x:.484375,y:.109375},{x:.515625,y:.109375},{x:.515625,y:.109375},{x:.546875,y:.109375},{x:.546875,y:.109375},{x:.578125,y:.109375},{x:.578125,y:.109375},{x:.609375,y:.109375},{x:.609375,y:.109375},{x:.640625,y:.109375},{x:.640625,y:.109375},{x:.671875,y:.109375},{x:.671875,y:.109375},{x:.703125,y:.109375},{x:.703125,y:.109375},{x:.734375,y:.109375},{x:.734375,y:.109375},{x:.765625,y:.109375},{x:.765625,y:.109375},{x:.796875,y:.109375},{x:.796875,y:.109375},{x:.828125,y:.109375},{x:.828125,y:.109375},{x:.859375,y:.109375},{x:.859375,y:.109375},{x:.890625,y:.109375},{x:.890625,y:.109375},{x:.921875,y:.109375},{x:.921875,y:.109375},{x:.953125,y:.109375},{x:.953125,y:.109375},{x:.984375,y:.109375},{x:.984375,y:.109375},{x:.015625,y:.140625},{x:.015625,y:.140625},{x:.046875,y:.140625},{x:.046875,y:.140625},{x:.078125,y:.140625},{x:.078125,y:.140625},{x:.109375,y:.140625},{x:.109375,y:.140625},{x:.140625,y:.140625},{x:.140625,y:.140625},{x:.171875,y:.140625},{x:.171875,y:.140625},{x:.203125,y:.140625},{x:.203125,y:.140625},{x:.234375,y:.140625},{x:.234375,y:.140625},{x:.265625,y:.140625},{x:.265625,y:.140625},{x:.296875,y:.140625},{x:.296875,y:.140625},{x:.328125,y:.140625},{x:.328125,y:.140625},{x:.359375,y:.140625},{x:.359375,y:.140625},{x:.390625,y:.140625},{x:.390625,y:.140625},{x:.421875,y:.140625},{x:.421875,y:.140625},{x:.453125,y:.140625},{x:.453125,y:.140625},{x:.484375,y:.140625},{x:.484375,y:.140625},{x:.515625,y:.140625},{x:.515625,y:.140625},{x:.546875,y:.140625},{x:.546875,y:.140625},{x:.578125,y:.140625},{x:.578125,y:.140625},{x:.609375,y:.140625},{x:.609375,y:.140625},{x:.640625,y:.140625},{x:.640625,y:.140625},{x:.671875,y:.140625},{x:.671875,y:.140625},{x:.703125,y:.140625},{x:.703125,y:.140625},{x:.734375,y:.140625},{x:.734375,y:.140625},{x:.765625,y:.140625},{x:.765625,y:.140625},{x:.796875,y:.140625},{x:.796875,y:.140625},{x:.828125,y:.140625},{x:.828125,y:.140625},{x:.859375,y:.140625},{x:.859375,y:.140625},{x:.890625,y:.140625},{x:.890625,y:.140625},{x:.921875,y:.140625},{x:.921875,y:.140625},{x:.953125,y:.140625},{x:.953125,y:.140625},{x:.984375,y:.140625},{x:.984375,y:.140625},{x:.015625,y:.171875},{x:.015625,y:.171875},{x:.046875,y:.171875},{x:.046875,y:.171875},{x:.078125,y:.171875},{x:.078125,y:.171875},{x:.109375,y:.171875},{x:.109375,y:.171875},{x:.140625,y:.171875},{x:.140625,y:.171875},{x:.171875,y:.171875},{x:.171875,y:.171875},{x:.203125,y:.171875},{x:.203125,y:.171875},{x:.234375,y:.171875},{x:.234375,y:.171875},{x:.265625,y:.171875},{x:.265625,y:.171875},{x:.296875,y:.171875},{x:.296875,y:.171875},{x:.328125,y:.171875},{x:.328125,y:.171875},{x:.359375,y:.171875},{x:.359375,y:.171875},{x:.390625,y:.171875},{x:.390625,y:.171875},{x:.421875,y:.171875},{x:.421875,y:.171875},{x:.453125,y:.171875},{x:.453125,y:.171875},{x:.484375,y:.171875},{x:.484375,y:.171875},{x:.515625,y:.171875},{x:.515625,y:.171875},{x:.546875,y:.171875},{x:.546875,y:.171875},{x:.578125,y:.171875},{x:.578125,y:.171875},{x:.609375,y:.171875},{x:.609375,y:.171875},{x:.640625,y:.171875},{x:.640625,y:.171875},{x:.671875,y:.171875},{x:.671875,y:.171875},{x:.703125,y:.171875},{x:.703125,y:.171875},{x:.734375,y:.171875},{x:.734375,y:.171875},{x:.765625,y:.171875},{x:.765625,y:.171875},{x:.796875,y:.171875},{x:.796875,y:.171875},{x:.828125,y:.171875},{x:.828125,y:.171875},{x:.859375,y:.171875},{x:.859375,y:.171875},{x:.890625,y:.171875},{x:.890625,y:.171875},{x:.921875,y:.171875},{x:.921875,y:.171875},{x:.953125,y:.171875},{x:.953125,y:.171875},{x:.984375,y:.171875},{x:.984375,y:.171875},{x:.015625,y:.203125},{x:.015625,y:.203125},{x:.046875,y:.203125},{x:.046875,y:.203125},{x:.078125,y:.203125},{x:.078125,y:.203125},{x:.109375,y:.203125},{x:.109375,y:.203125},{x:.140625,y:.203125},{x:.140625,y:.203125},{x:.171875,y:.203125},{x:.171875,y:.203125},{x:.203125,y:.203125},{x:.203125,y:.203125},{x:.234375,y:.203125},{x:.234375,y:.203125},{x:.265625,y:.203125},{x:.265625,y:.203125},{x:.296875,y:.203125},{x:.296875,y:.203125},{x:.328125,y:.203125},{x:.328125,y:.203125},{x:.359375,y:.203125},{x:.359375,y:.203125},{x:.390625,y:.203125},{x:.390625,y:.203125},{x:.421875,y:.203125},{x:.421875,y:.203125},{x:.453125,y:.203125},{x:.453125,y:.203125},{x:.484375,y:.203125},{x:.484375,y:.203125},{x:.515625,y:.203125},{x:.515625,y:.203125},{x:.546875,y:.203125},{x:.546875,y:.203125},{x:.578125,y:.203125},{x:.578125,y:.203125},{x:.609375,y:.203125},{x:.609375,y:.203125},{x:.640625,y:.203125},{x:.640625,y:.203125},{x:.671875,y:.203125},{x:.671875,y:.203125},{x:.703125,y:.203125},{x:.703125,y:.203125},{x:.734375,y:.203125},{x:.734375,y:.203125},{x:.765625,y:.203125},{x:.765625,y:.203125},{x:.796875,y:.203125},{x:.796875,y:.203125},{x:.828125,y:.203125},{x:.828125,y:.203125},{x:.859375,y:.203125},{x:.859375,y:.203125},{x:.890625,y:.203125},{x:.890625,y:.203125},{x:.921875,y:.203125},{x:.921875,y:.203125},{x:.953125,y:.203125},{x:.953125,y:.203125},{x:.984375,y:.203125},{x:.984375,y:.203125},{x:.015625,y:.234375},{x:.015625,y:.234375},{x:.046875,y:.234375},{x:.046875,y:.234375},{x:.078125,y:.234375},{x:.078125,y:.234375},{x:.109375,y:.234375},{x:.109375,y:.234375},{x:.140625,y:.234375},{x:.140625,y:.234375},{x:.171875,y:.234375},{x:.171875,y:.234375},{x:.203125,y:.234375},{x:.203125,y:.234375},{x:.234375,y:.234375},{x:.234375,y:.234375},{x:.265625,y:.234375},{x:.265625,y:.234375},{x:.296875,y:.234375},{x:.296875,y:.234375},{x:.328125,y:.234375},{x:.328125,y:.234375},{x:.359375,y:.234375},{x:.359375,y:.234375},{x:.390625,y:.234375},{x:.390625,y:.234375},{x:.421875,y:.234375},{x:.421875,y:.234375},{x:.453125,y:.234375},{x:.453125,y:.234375},{x:.484375,y:.234375},{x:.484375,y:.234375},{x:.515625,y:.234375},{x:.515625,y:.234375},{x:.546875,y:.234375},{x:.546875,y:.234375},{x:.578125,y:.234375},{x:.578125,y:.234375},{x:.609375,y:.234375},{x:.609375,y:.234375},{x:.640625,y:.234375},{x:.640625,y:.234375},{x:.671875,y:.234375},{x:.671875,y:.234375},{x:.703125,y:.234375},{x:.703125,y:.234375},{x:.734375,y:.234375},{x:.734375,y:.234375},{x:.765625,y:.234375},{x:.765625,y:.234375},{x:.796875,y:.234375},{x:.796875,y:.234375},{x:.828125,y:.234375},{x:.828125,y:.234375},{x:.859375,y:.234375},{x:.859375,y:.234375},{x:.890625,y:.234375},{x:.890625,y:.234375},{x:.921875,y:.234375},{x:.921875,y:.234375},{x:.953125,y:.234375},{x:.953125,y:.234375},{x:.984375,y:.234375},{x:.984375,y:.234375},{x:.015625,y:.265625},{x:.015625,y:.265625},{x:.046875,y:.265625},{x:.046875,y:.265625},{x:.078125,y:.265625},{x:.078125,y:.265625},{x:.109375,y:.265625},{x:.109375,y:.265625},{x:.140625,y:.265625},{x:.140625,y:.265625},{x:.171875,y:.265625},{x:.171875,y:.265625},{x:.203125,y:.265625},{x:.203125,y:.265625},{x:.234375,y:.265625},{x:.234375,y:.265625},{x:.265625,y:.265625},{x:.265625,y:.265625},{x:.296875,y:.265625},{x:.296875,y:.265625},{x:.328125,y:.265625},{x:.328125,y:.265625},{x:.359375,y:.265625},{x:.359375,y:.265625},{x:.390625,y:.265625},{x:.390625,y:.265625},{x:.421875,y:.265625},{x:.421875,y:.265625},{x:.453125,y:.265625},{x:.453125,y:.265625},{x:.484375,y:.265625},{x:.484375,y:.265625},{x:.515625,y:.265625},{x:.515625,y:.265625},{x:.546875,y:.265625},{x:.546875,y:.265625},{x:.578125,y:.265625},{x:.578125,y:.265625},{x:.609375,y:.265625},{x:.609375,y:.265625},{x:.640625,y:.265625},{x:.640625,y:.265625},{x:.671875,y:.265625},{x:.671875,y:.265625},{x:.703125,y:.265625},{x:.703125,y:.265625},{x:.734375,y:.265625},{x:.734375,y:.265625},{x:.765625,y:.265625},{x:.765625,y:.265625},{x:.796875,y:.265625},{x:.796875,y:.265625},{x:.828125,y:.265625},{x:.828125,y:.265625},{x:.859375,y:.265625},{x:.859375,y:.265625},{x:.890625,y:.265625},{x:.890625,y:.265625},{x:.921875,y:.265625},{x:.921875,y:.265625},{x:.953125,y:.265625},{x:.953125,y:.265625},{x:.984375,y:.265625},{x:.984375,y:.265625},{x:.015625,y:.296875},{x:.015625,y:.296875},{x:.046875,y:.296875},{x:.046875,y:.296875},{x:.078125,y:.296875},{x:.078125,y:.296875},{x:.109375,y:.296875},{x:.109375,y:.296875},{x:.140625,y:.296875},{x:.140625,y:.296875},{x:.171875,y:.296875},{x:.171875,y:.296875},{x:.203125,y:.296875},{x:.203125,y:.296875},{x:.234375,y:.296875},{x:.234375,y:.296875},{x:.265625,y:.296875},{x:.265625,y:.296875},{x:.296875,y:.296875},{x:.296875,y:.296875},{x:.328125,y:.296875},{x:.328125,y:.296875},{x:.359375,y:.296875},{x:.359375,y:.296875},{x:.390625,y:.296875},{x:.390625,y:.296875},{x:.421875,y:.296875},{x:.421875,y:.296875},{x:.453125,y:.296875},{x:.453125,y:.296875},{x:.484375,y:.296875},{x:.484375,y:.296875},{x:.515625,y:.296875},{x:.515625,y:.296875},{x:.546875,y:.296875},{x:.546875,y:.296875},{x:.578125,y:.296875},{x:.578125,y:.296875},{x:.609375,y:.296875},{x:.609375,y:.296875},{x:.640625,y:.296875},{x:.640625,y:.296875},{x:.671875,y:.296875},{x:.671875,y:.296875},{x:.703125,y:.296875},{x:.703125,y:.296875},{x:.734375,y:.296875},{x:.734375,y:.296875},{x:.765625,y:.296875},{x:.765625,y:.296875},{x:.796875,y:.296875},{x:.796875,y:.296875},{x:.828125,y:.296875},{x:.828125,y:.296875},{x:.859375,y:.296875},{x:.859375,y:.296875},{x:.890625,y:.296875},{x:.890625,y:.296875},{x:.921875,y:.296875},{x:.921875,y:.296875},{x:.953125,y:.296875},{x:.953125,y:.296875},{x:.984375,y:.296875},{x:.984375,y:.296875},{x:.015625,y:.328125},{x:.015625,y:.328125},{x:.046875,y:.328125},{x:.046875,y:.328125},{x:.078125,y:.328125},{x:.078125,y:.328125},{x:.109375,y:.328125},{x:.109375,y:.328125},{x:.140625,y:.328125},{x:.140625,y:.328125},{x:.171875,y:.328125},{x:.171875,y:.328125},{x:.203125,y:.328125},{x:.203125,y:.328125},{x:.234375,y:.328125},{x:.234375,y:.328125},{x:.265625,y:.328125},{x:.265625,y:.328125},{x:.296875,y:.328125},{x:.296875,y:.328125},{x:.328125,y:.328125},{x:.328125,y:.328125},{x:.359375,y:.328125},{x:.359375,y:.328125},{x:.390625,y:.328125},{x:.390625,y:.328125},{x:.421875,y:.328125},{x:.421875,y:.328125},{x:.453125,y:.328125},{x:.453125,y:.328125},{x:.484375,y:.328125},{x:.484375,y:.328125},{x:.515625,y:.328125},{x:.515625,y:.328125},{x:.546875,y:.328125},{x:.546875,y:.328125},{x:.578125,y:.328125},{x:.578125,y:.328125},{x:.609375,y:.328125},{x:.609375,y:.328125},{x:.640625,y:.328125},{x:.640625,y:.328125},{x:.671875,y:.328125},{x:.671875,y:.328125},{x:.703125,y:.328125},{x:.703125,y:.328125},{x:.734375,y:.328125},{x:.734375,y:.328125},{x:.765625,y:.328125},{x:.765625,y:.328125},{x:.796875,y:.328125},{x:.796875,y:.328125},{x:.828125,y:.328125},{x:.828125,y:.328125},{x:.859375,y:.328125},{x:.859375,y:.328125},{x:.890625,y:.328125},{x:.890625,y:.328125},{x:.921875,y:.328125},{x:.921875,y:.328125},{x:.953125,y:.328125},{x:.953125,y:.328125},{x:.984375,y:.328125},{x:.984375,y:.328125},{x:.015625,y:.359375},{x:.015625,y:.359375},{x:.046875,y:.359375},{x:.046875,y:.359375},{x:.078125,y:.359375},{x:.078125,y:.359375},{x:.109375,y:.359375},{x:.109375,y:.359375},{x:.140625,y:.359375},{x:.140625,y:.359375},{x:.171875,y:.359375},{x:.171875,y:.359375},{x:.203125,y:.359375},{x:.203125,y:.359375},{x:.234375,y:.359375},{x:.234375,y:.359375},{x:.265625,y:.359375},{x:.265625,y:.359375},{x:.296875,y:.359375},{x:.296875,y:.359375},{x:.328125,y:.359375},{x:.328125,y:.359375},{x:.359375,y:.359375},{x:.359375,y:.359375},{x:.390625,y:.359375},{x:.390625,y:.359375},{x:.421875,y:.359375},{x:.421875,y:.359375},{x:.453125,y:.359375},{x:.453125,y:.359375},{x:.484375,y:.359375},{x:.484375,y:.359375},{x:.515625,y:.359375},{x:.515625,y:.359375},{x:.546875,y:.359375},{x:.546875,y:.359375},{x:.578125,y:.359375},{x:.578125,y:.359375},{x:.609375,y:.359375},{x:.609375,y:.359375},{x:.640625,y:.359375},{x:.640625,y:.359375},{x:.671875,y:.359375},{x:.671875,y:.359375},{x:.703125,y:.359375},{x:.703125,y:.359375},{x:.734375,y:.359375},{x:.734375,y:.359375},{x:.765625,y:.359375},{x:.765625,y:.359375},{x:.796875,y:.359375},{x:.796875,y:.359375},{x:.828125,y:.359375},{x:.828125,y:.359375},{x:.859375,y:.359375},{x:.859375,y:.359375},{x:.890625,y:.359375},{x:.890625,y:.359375},{x:.921875,y:.359375},{x:.921875,y:.359375},{x:.953125,y:.359375},{x:.953125,y:.359375},{x:.984375,y:.359375},{x:.984375,y:.359375},{x:.015625,y:.390625},{x:.015625,y:.390625},{x:.046875,y:.390625},{x:.046875,y:.390625},{x:.078125,y:.390625},{x:.078125,y:.390625},{x:.109375,y:.390625},{x:.109375,y:.390625},{x:.140625,y:.390625},{x:.140625,y:.390625},{x:.171875,y:.390625},{x:.171875,y:.390625},{x:.203125,y:.390625},{x:.203125,y:.390625},{x:.234375,y:.390625},{x:.234375,y:.390625},{x:.265625,y:.390625},{x:.265625,y:.390625},{x:.296875,y:.390625},{x:.296875,y:.390625},{x:.328125,y:.390625},{x:.328125,y:.390625},{x:.359375,y:.390625},{x:.359375,y:.390625},{x:.390625,y:.390625},{x:.390625,y:.390625},{x:.421875,y:.390625},{x:.421875,y:.390625},{x:.453125,y:.390625},{x:.453125,y:.390625},{x:.484375,y:.390625},{x:.484375,y:.390625},{x:.515625,y:.390625},{x:.515625,y:.390625},{x:.546875,y:.390625},{x:.546875,y:.390625},{x:.578125,y:.390625},{x:.578125,y:.390625},{x:.609375,y:.390625},{x:.609375,y:.390625},{x:.640625,y:.390625},{x:.640625,y:.390625},{x:.671875,y:.390625},{x:.671875,y:.390625},{x:.703125,y:.390625},{x:.703125,y:.390625},{x:.734375,y:.390625},{x:.734375,y:.390625},{x:.765625,y:.390625},{x:.765625,y:.390625},{x:.796875,y:.390625},{x:.796875,y:.390625},{x:.828125,y:.390625},{x:.828125,y:.390625},{x:.859375,y:.390625},{x:.859375,y:.390625},{x:.890625,y:.390625},{x:.890625,y:.390625},{x:.921875,y:.390625},{x:.921875,y:.390625},{x:.953125,y:.390625},{x:.953125,y:.390625},{x:.984375,y:.390625},{x:.984375,y:.390625},{x:.015625,y:.421875},{x:.015625,y:.421875},{x:.046875,y:.421875},{x:.046875,y:.421875},{x:.078125,y:.421875},{x:.078125,y:.421875},{x:.109375,y:.421875},{x:.109375,y:.421875},{x:.140625,y:.421875},{x:.140625,y:.421875},{x:.171875,y:.421875},{x:.171875,y:.421875},{x:.203125,y:.421875},{x:.203125,y:.421875},{x:.234375,y:.421875},{x:.234375,y:.421875},{x:.265625,y:.421875},{x:.265625,y:.421875},{x:.296875,y:.421875},{x:.296875,y:.421875},{x:.328125,y:.421875},{x:.328125,y:.421875},{x:.359375,y:.421875},{x:.359375,y:.421875},{x:.390625,y:.421875},{x:.390625,y:.421875},{x:.421875,y:.421875},{x:.421875,y:.421875},{x:.453125,y:.421875},{x:.453125,y:.421875},{x:.484375,y:.421875},{x:.484375,y:.421875},{x:.515625,y:.421875},{x:.515625,y:.421875},{x:.546875,y:.421875},{x:.546875,y:.421875},{x:.578125,y:.421875},{x:.578125,y:.421875},{x:.609375,y:.421875},{x:.609375,y:.421875},{x:.640625,y:.421875},{x:.640625,y:.421875},{x:.671875,y:.421875},{x:.671875,y:.421875},{x:.703125,y:.421875},{x:.703125,y:.421875},{x:.734375,y:.421875},{x:.734375,y:.421875},{x:.765625,y:.421875},{x:.765625,y:.421875},{x:.796875,y:.421875},{x:.796875,y:.421875},{x:.828125,y:.421875},{x:.828125,y:.421875},{x:.859375,y:.421875},{x:.859375,y:.421875},{x:.890625,y:.421875},{x:.890625,y:.421875},{x:.921875,y:.421875},{x:.921875,y:.421875},{x:.953125,y:.421875},{x:.953125,y:.421875},{x:.984375,y:.421875},{x:.984375,y:.421875},{x:.015625,y:.453125},{x:.015625,y:.453125},{x:.046875,y:.453125},{x:.046875,y:.453125},{x:.078125,y:.453125},{x:.078125,y:.453125},{x:.109375,y:.453125},{x:.109375,y:.453125},{x:.140625,y:.453125},{x:.140625,y:.453125},{x:.171875,y:.453125},{x:.171875,y:.453125},{x:.203125,y:.453125},{x:.203125,y:.453125},{x:.234375,y:.453125},{x:.234375,y:.453125},{x:.265625,y:.453125},{x:.265625,y:.453125},{x:.296875,y:.453125},{x:.296875,y:.453125},{x:.328125,y:.453125},{x:.328125,y:.453125},{x:.359375,y:.453125},{x:.359375,y:.453125},{x:.390625,y:.453125},{x:.390625,y:.453125},{x:.421875,y:.453125},{x:.421875,y:.453125},{x:.453125,y:.453125},{x:.453125,y:.453125},{x:.484375,y:.453125},{x:.484375,y:.453125},{x:.515625,y:.453125},{x:.515625,y:.453125},{x:.546875,y:.453125},{x:.546875,y:.453125},{x:.578125,y:.453125},{x:.578125,y:.453125},{x:.609375,y:.453125},{x:.609375,y:.453125},{x:.640625,y:.453125},{x:.640625,y:.453125},{x:.671875,y:.453125},{x:.671875,y:.453125},{x:.703125,y:.453125},{x:.703125,y:.453125},{x:.734375,y:.453125},{x:.734375,y:.453125},{x:.765625,y:.453125},{x:.765625,y:.453125},{x:.796875,y:.453125},{x:.796875,y:.453125},{x:.828125,y:.453125},{x:.828125,y:.453125},{x:.859375,y:.453125},{x:.859375,y:.453125},{x:.890625,y:.453125},{x:.890625,y:.453125},{x:.921875,y:.453125},{x:.921875,y:.453125},{x:.953125,y:.453125},{x:.953125,y:.453125},{x:.984375,y:.453125},{x:.984375,y:.453125},{x:.015625,y:.484375},{x:.015625,y:.484375},{x:.046875,y:.484375},{x:.046875,y:.484375},{x:.078125,y:.484375},{x:.078125,y:.484375},{x:.109375,y:.484375},{x:.109375,y:.484375},{x:.140625,y:.484375},{x:.140625,y:.484375},{x:.171875,y:.484375},{x:.171875,y:.484375},{x:.203125,y:.484375},{x:.203125,y:.484375},{x:.234375,y:.484375},{x:.234375,y:.484375},{x:.265625,y:.484375},{x:.265625,y:.484375},{x:.296875,y:.484375},{x:.296875,y:.484375},{x:.328125,y:.484375},{x:.328125,y:.484375},{x:.359375,y:.484375},{x:.359375,y:.484375},{x:.390625,y:.484375},{x:.390625,y:.484375},{x:.421875,y:.484375},{x:.421875,y:.484375},{x:.453125,y:.484375},{x:.453125,y:.484375},{x:.484375,y:.484375},{x:.484375,y:.484375},{x:.515625,y:.484375},{x:.515625,y:.484375},{x:.546875,y:.484375},{x:.546875,y:.484375},{x:.578125,y:.484375},{x:.578125,y:.484375},{x:.609375,y:.484375},{x:.609375,y:.484375},{x:.640625,y:.484375},{x:.640625,y:.484375},{x:.671875,y:.484375},{x:.671875,y:.484375},{x:.703125,y:.484375},{x:.703125,y:.484375},{x:.734375,y:.484375},{x:.734375,y:.484375},{x:.765625,y:.484375},{x:.765625,y:.484375},{x:.796875,y:.484375},{x:.796875,y:.484375},{x:.828125,y:.484375},{x:.828125,y:.484375},{x:.859375,y:.484375},{x:.859375,y:.484375},{x:.890625,y:.484375},{x:.890625,y:.484375},{x:.921875,y:.484375},{x:.921875,y:.484375},{x:.953125,y:.484375},{x:.953125,y:.484375},{x:.984375,y:.484375},{x:.984375,y:.484375},{x:.015625,y:.515625},{x:.015625,y:.515625},{x:.046875,y:.515625},{x:.046875,y:.515625},{x:.078125,y:.515625},{x:.078125,y:.515625},{x:.109375,y:.515625},{x:.109375,y:.515625},{x:.140625,y:.515625},{x:.140625,y:.515625},{x:.171875,y:.515625},{x:.171875,y:.515625},{x:.203125,y:.515625},{x:.203125,y:.515625},{x:.234375,y:.515625},{x:.234375,y:.515625},{x:.265625,y:.515625},{x:.265625,y:.515625},{x:.296875,y:.515625},{x:.296875,y:.515625},{x:.328125,y:.515625},{x:.328125,y:.515625},{x:.359375,y:.515625},{x:.359375,y:.515625},{x:.390625,y:.515625},{x:.390625,y:.515625},{x:.421875,y:.515625},{x:.421875,y:.515625},{x:.453125,y:.515625},{x:.453125,y:.515625},{x:.484375,y:.515625},{x:.484375,y:.515625},{x:.515625,y:.515625},{x:.515625,y:.515625},{x:.546875,y:.515625},{x:.546875,y:.515625},{x:.578125,y:.515625},{x:.578125,y:.515625},{x:.609375,y:.515625},{x:.609375,y:.515625},{x:.640625,y:.515625},{x:.640625,y:.515625},{x:.671875,y:.515625},{x:.671875,y:.515625},{x:.703125,y:.515625},{x:.703125,y:.515625},{x:.734375,y:.515625},{x:.734375,y:.515625},{x:.765625,y:.515625},{x:.765625,y:.515625},{x:.796875,y:.515625},{x:.796875,y:.515625},{x:.828125,y:.515625},{x:.828125,y:.515625},{x:.859375,y:.515625},{x:.859375,y:.515625},{x:.890625,y:.515625},{x:.890625,y:.515625},{x:.921875,y:.515625},{x:.921875,y:.515625},{x:.953125,y:.515625},{x:.953125,y:.515625},{x:.984375,y:.515625},{x:.984375,y:.515625},{x:.015625,y:.546875},{x:.015625,y:.546875},{x:.046875,y:.546875},{x:.046875,y:.546875},{x:.078125,y:.546875},{x:.078125,y:.546875},{x:.109375,y:.546875},{x:.109375,y:.546875},{x:.140625,y:.546875},{x:.140625,y:.546875},{x:.171875,y:.546875},{x:.171875,y:.546875},{x:.203125,y:.546875},{x:.203125,y:.546875},{x:.234375,y:.546875},{x:.234375,y:.546875},{x:.265625,y:.546875},{x:.265625,y:.546875},{x:.296875,y:.546875},{x:.296875,y:.546875},{x:.328125,y:.546875},{x:.328125,y:.546875},{x:.359375,y:.546875},{x:.359375,y:.546875},{x:.390625,y:.546875},{x:.390625,y:.546875},{x:.421875,y:.546875},{x:.421875,y:.546875},{x:.453125,y:.546875},{x:.453125,y:.546875},{x:.484375,y:.546875},{x:.484375,y:.546875},{x:.515625,y:.546875},{x:.515625,y:.546875},{x:.546875,y:.546875},{x:.546875,y:.546875},{x:.578125,y:.546875},{x:.578125,y:.546875},{x:.609375,y:.546875},{x:.609375,y:.546875},{x:.640625,y:.546875},{x:.640625,y:.546875},{x:.671875,y:.546875},{x:.671875,y:.546875},{x:.703125,y:.546875},{x:.703125,y:.546875},{x:.734375,y:.546875},{x:.734375,y:.546875},{x:.765625,y:.546875},{x:.765625,y:.546875},{x:.796875,y:.546875},{x:.796875,y:.546875},{x:.828125,y:.546875},{x:.828125,y:.546875},{x:.859375,y:.546875},{x:.859375,y:.546875},{x:.890625,y:.546875},{x:.890625,y:.546875},{x:.921875,y:.546875},{x:.921875,y:.546875},{x:.953125,y:.546875},{x:.953125,y:.546875},{x:.984375,y:.546875},{x:.984375,y:.546875},{x:.015625,y:.578125},{x:.015625,y:.578125},{x:.046875,y:.578125},{x:.046875,y:.578125},{x:.078125,y:.578125},{x:.078125,y:.578125},{x:.109375,y:.578125},{x:.109375,y:.578125},{x:.140625,y:.578125},{x:.140625,y:.578125},{x:.171875,y:.578125},{x:.171875,y:.578125},{x:.203125,y:.578125},{x:.203125,y:.578125},{x:.234375,y:.578125},{x:.234375,y:.578125},{x:.265625,y:.578125},{x:.265625,y:.578125},{x:.296875,y:.578125},{x:.296875,y:.578125},{x:.328125,y:.578125},{x:.328125,y:.578125},{x:.359375,y:.578125},{x:.359375,y:.578125},{x:.390625,y:.578125},{x:.390625,y:.578125},{x:.421875,y:.578125},{x:.421875,y:.578125},{x:.453125,y:.578125},{x:.453125,y:.578125},{x:.484375,y:.578125},{x:.484375,y:.578125},{x:.515625,y:.578125},{x:.515625,y:.578125},{x:.546875,y:.578125},{x:.546875,y:.578125},{x:.578125,y:.578125},{x:.578125,y:.578125},{x:.609375,y:.578125},{x:.609375,y:.578125},{x:.640625,y:.578125},{x:.640625,y:.578125},{x:.671875,y:.578125},{x:.671875,y:.578125},{x:.703125,y:.578125},{x:.703125,y:.578125},{x:.734375,y:.578125},{x:.734375,y:.578125},{x:.765625,y:.578125},{x:.765625,y:.578125},{x:.796875,y:.578125},{x:.796875,y:.578125},{x:.828125,y:.578125},{x:.828125,y:.578125},{x:.859375,y:.578125},{x:.859375,y:.578125},{x:.890625,y:.578125},{x:.890625,y:.578125},{x:.921875,y:.578125},{x:.921875,y:.578125},{x:.953125,y:.578125},{x:.953125,y:.578125},{x:.984375,y:.578125},{x:.984375,y:.578125},{x:.015625,y:.609375},{x:.015625,y:.609375},{x:.046875,y:.609375},{x:.046875,y:.609375},{x:.078125,y:.609375},{x:.078125,y:.609375},{x:.109375,y:.609375},{x:.109375,y:.609375},{x:.140625,y:.609375},{x:.140625,y:.609375},{x:.171875,y:.609375},{x:.171875,y:.609375},{x:.203125,y:.609375},{x:.203125,y:.609375},{x:.234375,y:.609375},{x:.234375,y:.609375},{x:.265625,y:.609375},{x:.265625,y:.609375},{x:.296875,y:.609375},{x:.296875,y:.609375},{x:.328125,y:.609375},{x:.328125,y:.609375},{x:.359375,y:.609375},{x:.359375,y:.609375},{x:.390625,y:.609375},{x:.390625,y:.609375},{x:.421875,y:.609375},{x:.421875,y:.609375},{x:.453125,y:.609375},{x:.453125,y:.609375},{x:.484375,y:.609375},{x:.484375,y:.609375},{x:.515625,y:.609375},{x:.515625,y:.609375},{x:.546875,y:.609375},{x:.546875,y:.609375},{x:.578125,y:.609375},{x:.578125,y:.609375},{x:.609375,y:.609375},{x:.609375,y:.609375},{x:.640625,y:.609375},{x:.640625,y:.609375},{x:.671875,y:.609375},{x:.671875,y:.609375},{x:.703125,y:.609375},{x:.703125,y:.609375},{x:.734375,y:.609375},{x:.734375,y:.609375},{x:.765625,y:.609375},{x:.765625,y:.609375},{x:.796875,y:.609375},{x:.796875,y:.609375},{x:.828125,y:.609375},{x:.828125,y:.609375},{x:.859375,y:.609375},{x:.859375,y:.609375},{x:.890625,y:.609375},{x:.890625,y:.609375},{x:.921875,y:.609375},{x:.921875,y:.609375},{x:.953125,y:.609375},{x:.953125,y:.609375},{x:.984375,y:.609375},{x:.984375,y:.609375},{x:.015625,y:.640625},{x:.015625,y:.640625},{x:.046875,y:.640625},{x:.046875,y:.640625},{x:.078125,y:.640625},{x:.078125,y:.640625},{x:.109375,y:.640625},{x:.109375,y:.640625},{x:.140625,y:.640625},{x:.140625,y:.640625},{x:.171875,y:.640625},{x:.171875,y:.640625},{x:.203125,y:.640625},{x:.203125,y:.640625},{x:.234375,y:.640625},{x:.234375,y:.640625},{x:.265625,y:.640625},{x:.265625,y:.640625},{x:.296875,y:.640625},{x:.296875,y:.640625},{x:.328125,y:.640625},{x:.328125,y:.640625},{x:.359375,y:.640625},{x:.359375,y:.640625},{x:.390625,y:.640625},{x:.390625,y:.640625},{x:.421875,y:.640625},{x:.421875,y:.640625},{x:.453125,y:.640625},{x:.453125,y:.640625},{x:.484375,y:.640625},{x:.484375,y:.640625},{x:.515625,y:.640625},{x:.515625,y:.640625},{x:.546875,y:.640625},{x:.546875,y:.640625},{x:.578125,y:.640625},{x:.578125,y:.640625},{x:.609375,y:.640625},{x:.609375,y:.640625},{x:.640625,y:.640625},{x:.640625,y:.640625},{x:.671875,y:.640625},{x:.671875,y:.640625},{x:.703125,y:.640625},{x:.703125,y:.640625},{x:.734375,y:.640625},{x:.734375,y:.640625},{x:.765625,y:.640625},{x:.765625,y:.640625},{x:.796875,y:.640625},{x:.796875,y:.640625},{x:.828125,y:.640625},{x:.828125,y:.640625},{x:.859375,y:.640625},{x:.859375,y:.640625},{x:.890625,y:.640625},{x:.890625,y:.640625},{x:.921875,y:.640625},{x:.921875,y:.640625},{x:.953125,y:.640625},{x:.953125,y:.640625},{x:.984375,y:.640625},{x:.984375,y:.640625},{x:.015625,y:.671875},{x:.015625,y:.671875},{x:.046875,y:.671875},{x:.046875,y:.671875},{x:.078125,y:.671875},{x:.078125,y:.671875},{x:.109375,y:.671875},{x:.109375,y:.671875},{x:.140625,y:.671875},{x:.140625,y:.671875},{x:.171875,y:.671875},{x:.171875,y:.671875},{x:.203125,y:.671875},{x:.203125,y:.671875},{x:.234375,y:.671875},{x:.234375,y:.671875},{x:.265625,y:.671875},{x:.265625,y:.671875},{x:.296875,y:.671875},{x:.296875,y:.671875},{x:.328125,y:.671875},{x:.328125,y:.671875},{x:.359375,y:.671875},{x:.359375,y:.671875},{x:.390625,y:.671875},{x:.390625,y:.671875},{x:.421875,y:.671875},{x:.421875,y:.671875},{x:.453125,y:.671875},{x:.453125,y:.671875},{x:.484375,y:.671875},{x:.484375,y:.671875},{x:.515625,y:.671875},{x:.515625,y:.671875},{x:.546875,y:.671875},{x:.546875,y:.671875},{x:.578125,y:.671875},{x:.578125,y:.671875},{x:.609375,y:.671875},{x:.609375,y:.671875},{x:.640625,y:.671875},{x:.640625,y:.671875},{x:.671875,y:.671875},{x:.671875,y:.671875},{x:.703125,y:.671875},{x:.703125,y:.671875},{x:.734375,y:.671875},{x:.734375,y:.671875},{x:.765625,y:.671875},{x:.765625,y:.671875},{x:.796875,y:.671875},{x:.796875,y:.671875},{x:.828125,y:.671875},{x:.828125,y:.671875},{x:.859375,y:.671875},{x:.859375,y:.671875},{x:.890625,y:.671875},{x:.890625,y:.671875},{x:.921875,y:.671875},{x:.921875,y:.671875},{x:.953125,y:.671875},{x:.953125,y:.671875},{x:.984375,y:.671875},{x:.984375,y:.671875},{x:.015625,y:.703125},{x:.015625,y:.703125},{x:.046875,y:.703125},{x:.046875,y:.703125},{x:.078125,y:.703125},{x:.078125,y:.703125},{x:.109375,y:.703125},{x:.109375,y:.703125},{x:.140625,y:.703125},{x:.140625,y:.703125},{x:.171875,y:.703125},{x:.171875,y:.703125},{x:.203125,y:.703125},{x:.203125,y:.703125},{x:.234375,y:.703125},{x:.234375,y:.703125},{x:.265625,y:.703125},{x:.265625,y:.703125},{x:.296875,y:.703125},{x:.296875,y:.703125},{x:.328125,y:.703125},{x:.328125,y:.703125},{x:.359375,y:.703125},{x:.359375,y:.703125},{x:.390625,y:.703125},{x:.390625,y:.703125},{x:.421875,y:.703125},{x:.421875,y:.703125},{x:.453125,y:.703125},{x:.453125,y:.703125},{x:.484375,y:.703125},{x:.484375,y:.703125},{x:.515625,y:.703125},{x:.515625,y:.703125},{x:.546875,y:.703125},{x:.546875,y:.703125},{x:.578125,y:.703125},{x:.578125,y:.703125},{x:.609375,y:.703125},{x:.609375,y:.703125},{x:.640625,y:.703125},{x:.640625,y:.703125},{x:.671875,y:.703125},{x:.671875,y:.703125},{x:.703125,y:.703125},{x:.703125,y:.703125},{x:.734375,y:.703125},{x:.734375,y:.703125},{x:.765625,y:.703125},{x:.765625,y:.703125},{x:.796875,y:.703125},{x:.796875,y:.703125},{x:.828125,y:.703125},{x:.828125,y:.703125},{x:.859375,y:.703125},{x:.859375,y:.703125},{x:.890625,y:.703125},{x:.890625,y:.703125},{x:.921875,y:.703125},{x:.921875,y:.703125},{x:.953125,y:.703125},{x:.953125,y:.703125},{x:.984375,y:.703125},{x:.984375,y:.703125},{x:.015625,y:.734375},{x:.015625,y:.734375},{x:.046875,y:.734375},{x:.046875,y:.734375},{x:.078125,y:.734375},{x:.078125,y:.734375},{x:.109375,y:.734375},{x:.109375,y:.734375},{x:.140625,y:.734375},{x:.140625,y:.734375},{x:.171875,y:.734375},{x:.171875,y:.734375},{x:.203125,y:.734375},{x:.203125,y:.734375},{x:.234375,y:.734375},{x:.234375,y:.734375},{x:.265625,y:.734375},{x:.265625,y:.734375},{x:.296875,y:.734375},{x:.296875,y:.734375},{x:.328125,y:.734375},{x:.328125,y:.734375},{x:.359375,y:.734375},{x:.359375,y:.734375},{x:.390625,y:.734375},{x:.390625,y:.734375},{x:.421875,y:.734375},{x:.421875,y:.734375},{x:.453125,y:.734375},{x:.453125,y:.734375},{x:.484375,y:.734375},{x:.484375,y:.734375},{x:.515625,y:.734375},{x:.515625,y:.734375},{x:.546875,y:.734375},{x:.546875,y:.734375},{x:.578125,y:.734375},{x:.578125,y:.734375},{x:.609375,y:.734375},{x:.609375,y:.734375},{x:.640625,y:.734375},{x:.640625,y:.734375},{x:.671875,y:.734375},{x:.671875,y:.734375},{x:.703125,y:.734375},{x:.703125,y:.734375},{x:.734375,y:.734375},{x:.734375,y:.734375},{x:.765625,y:.734375},{x:.765625,y:.734375},{x:.796875,y:.734375},{x:.796875,y:.734375},{x:.828125,y:.734375},{x:.828125,y:.734375},{x:.859375,y:.734375},{x:.859375,y:.734375},{x:.890625,y:.734375},{x:.890625,y:.734375},{x:.921875,y:.734375},{x:.921875,y:.734375},{x:.953125,y:.734375},{x:.953125,y:.734375},{x:.984375,y:.734375},{x:.984375,y:.734375},{x:.015625,y:.765625},{x:.015625,y:.765625},{x:.046875,y:.765625},{x:.046875,y:.765625},{x:.078125,y:.765625},{x:.078125,y:.765625},{x:.109375,y:.765625},{x:.109375,y:.765625},{x:.140625,y:.765625},{x:.140625,y:.765625},{x:.171875,y:.765625},{x:.171875,y:.765625},{x:.203125,y:.765625},{x:.203125,y:.765625},{x:.234375,y:.765625},{x:.234375,y:.765625},{x:.265625,y:.765625},{x:.265625,y:.765625},{x:.296875,y:.765625},{x:.296875,y:.765625},{x:.328125,y:.765625},{x:.328125,y:.765625},{x:.359375,y:.765625},{x:.359375,y:.765625},{x:.390625,y:.765625},{x:.390625,y:.765625},{x:.421875,y:.765625},{x:.421875,y:.765625},{x:.453125,y:.765625},{x:.453125,y:.765625},{x:.484375,y:.765625},{x:.484375,y:.765625},{x:.515625,y:.765625},{x:.515625,y:.765625},{x:.546875,y:.765625},{x:.546875,y:.765625},{x:.578125,y:.765625},{x:.578125,y:.765625},{x:.609375,y:.765625},{x:.609375,y:.765625},{x:.640625,y:.765625},{x:.640625,y:.765625},{x:.671875,y:.765625},{x:.671875,y:.765625},{x:.703125,y:.765625},{x:.703125,y:.765625},{x:.734375,y:.765625},{x:.734375,y:.765625},{x:.765625,y:.765625},{x:.765625,y:.765625},{x:.796875,y:.765625},{x:.796875,y:.765625},{x:.828125,y:.765625},{x:.828125,y:.765625},{x:.859375,y:.765625},{x:.859375,y:.765625},{x:.890625,y:.765625},{x:.890625,y:.765625},{x:.921875,y:.765625},{x:.921875,y:.765625},{x:.953125,y:.765625},{x:.953125,y:.765625},{x:.984375,y:.765625},{x:.984375,y:.765625},{x:.015625,y:.796875},{x:.015625,y:.796875},{x:.046875,y:.796875},{x:.046875,y:.796875},{x:.078125,y:.796875},{x:.078125,y:.796875},{x:.109375,y:.796875},{x:.109375,y:.796875},{x:.140625,y:.796875},{x:.140625,y:.796875},{x:.171875,y:.796875},{x:.171875,y:.796875},{x:.203125,y:.796875},{x:.203125,y:.796875},{x:.234375,y:.796875},{x:.234375,y:.796875},{x:.265625,y:.796875},{x:.265625,y:.796875},{x:.296875,y:.796875},{x:.296875,y:.796875},{x:.328125,y:.796875},{x:.328125,y:.796875},{x:.359375,y:.796875},{x:.359375,y:.796875},{x:.390625,y:.796875},{x:.390625,y:.796875},{x:.421875,y:.796875},{x:.421875,y:.796875},{x:.453125,y:.796875},{x:.453125,y:.796875},{x:.484375,y:.796875},{x:.484375,y:.796875},{x:.515625,y:.796875},{x:.515625,y:.796875},{x:.546875,y:.796875},{x:.546875,y:.796875},{x:.578125,y:.796875},{x:.578125,y:.796875},{x:.609375,y:.796875},{x:.609375,y:.796875},{x:.640625,y:.796875},{x:.640625,y:.796875},{x:.671875,y:.796875},{x:.671875,y:.796875},{x:.703125,y:.796875},{x:.703125,y:.796875},{x:.734375,y:.796875},{x:.734375,y:.796875},{x:.765625,y:.796875},{x:.765625,y:.796875},{x:.796875,y:.796875},{x:.796875,y:.796875},{x:.828125,y:.796875},{x:.828125,y:.796875},{x:.859375,y:.796875},{x:.859375,y:.796875},{x:.890625,y:.796875},{x:.890625,y:.796875},{x:.921875,y:.796875},{x:.921875,y:.796875},{x:.953125,y:.796875},{x:.953125,y:.796875},{x:.984375,y:.796875},{x:.984375,y:.796875},{x:.015625,y:.828125},{x:.015625,y:.828125},{x:.046875,y:.828125},{x:.046875,y:.828125},{x:.078125,y:.828125},{x:.078125,y:.828125},{x:.109375,y:.828125},{x:.109375,y:.828125},{x:.140625,y:.828125},{x:.140625,y:.828125},{x:.171875,y:.828125},{x:.171875,y:.828125},{x:.203125,y:.828125},{x:.203125,y:.828125},{x:.234375,y:.828125},{x:.234375,y:.828125},{x:.265625,y:.828125},{x:.265625,y:.828125},{x:.296875,y:.828125},{x:.296875,y:.828125},{x:.328125,y:.828125},{x:.328125,y:.828125},{x:.359375,y:.828125},{x:.359375,y:.828125},{x:.390625,y:.828125},{x:.390625,y:.828125},{x:.421875,y:.828125},{x:.421875,y:.828125},{x:.453125,y:.828125},{x:.453125,y:.828125},{x:.484375,y:.828125},{x:.484375,y:.828125},{x:.515625,y:.828125},{x:.515625,y:.828125},{x:.546875,y:.828125},{x:.546875,y:.828125},{x:.578125,y:.828125},{x:.578125,y:.828125},{x:.609375,y:.828125},{x:.609375,y:.828125},{x:.640625,y:.828125},{x:.640625,y:.828125},{x:.671875,y:.828125},{x:.671875,y:.828125},{x:.703125,y:.828125},{x:.703125,y:.828125},{x:.734375,y:.828125},{x:.734375,y:.828125},{x:.765625,y:.828125},{x:.765625,y:.828125},{x:.796875,y:.828125},{x:.796875,y:.828125},{x:.828125,y:.828125},{x:.828125,y:.828125},{x:.859375,y:.828125},{x:.859375,y:.828125},{x:.890625,y:.828125},{x:.890625,y:.828125},{x:.921875,y:.828125},{x:.921875,y:.828125},{x:.953125,y:.828125},{x:.953125,y:.828125},{x:.984375,y:.828125},{x:.984375,y:.828125},{x:.015625,y:.859375},{x:.015625,y:.859375},{x:.046875,y:.859375},{x:.046875,y:.859375},{x:.078125,y:.859375},{x:.078125,y:.859375},{x:.109375,y:.859375},{x:.109375,y:.859375},{x:.140625,y:.859375},{x:.140625,y:.859375},{x:.171875,y:.859375},{x:.171875,y:.859375},{x:.203125,y:.859375},{x:.203125,y:.859375},{x:.234375,y:.859375},{x:.234375,y:.859375},{x:.265625,y:.859375},{x:.265625,y:.859375},{x:.296875,y:.859375},{x:.296875,y:.859375},{x:.328125,y:.859375},{x:.328125,y:.859375},{x:.359375,y:.859375},{x:.359375,y:.859375},{x:.390625,y:.859375},{x:.390625,y:.859375},{x:.421875,y:.859375},{x:.421875,y:.859375},{x:.453125,y:.859375},{x:.453125,y:.859375},{x:.484375,y:.859375},{x:.484375,y:.859375},{x:.515625,y:.859375},{x:.515625,y:.859375},{x:.546875,y:.859375},{x:.546875,y:.859375},{x:.578125,y:.859375},{x:.578125,y:.859375},{x:.609375,y:.859375},{x:.609375,y:.859375},{x:.640625,y:.859375},{x:.640625,y:.859375},{x:.671875,y:.859375},{x:.671875,y:.859375},{x:.703125,y:.859375},{x:.703125,y:.859375},{x:.734375,y:.859375},{x:.734375,y:.859375},{x:.765625,y:.859375},{x:.765625,y:.859375},{x:.796875,y:.859375},{x:.796875,y:.859375},{x:.828125,y:.859375},{x:.828125,y:.859375},{x:.859375,y:.859375},{x:.859375,y:.859375},{x:.890625,y:.859375},{x:.890625,y:.859375},{x:.921875,y:.859375},{x:.921875,y:.859375},{x:.953125,y:.859375},{x:.953125,y:.859375},{x:.984375,y:.859375},{x:.984375,y:.859375},{x:.015625,y:.890625},{x:.015625,y:.890625},{x:.046875,y:.890625},{x:.046875,y:.890625},{x:.078125,y:.890625},{x:.078125,y:.890625},{x:.109375,y:.890625},{x:.109375,y:.890625},{x:.140625,y:.890625},{x:.140625,y:.890625},{x:.171875,y:.890625},{x:.171875,y:.890625},{x:.203125,y:.890625},{x:.203125,y:.890625},{x:.234375,y:.890625},{x:.234375,y:.890625},{x:.265625,y:.890625},{x:.265625,y:.890625},{x:.296875,y:.890625},{x:.296875,y:.890625},{x:.328125,y:.890625},{x:.328125,y:.890625},{x:.359375,y:.890625},{x:.359375,y:.890625},{x:.390625,y:.890625},{x:.390625,y:.890625},{x:.421875,y:.890625},{x:.421875,y:.890625},{x:.453125,y:.890625},{x:.453125,y:.890625},{x:.484375,y:.890625},{x:.484375,y:.890625},{x:.515625,y:.890625},{x:.515625,y:.890625},{x:.546875,y:.890625},{x:.546875,y:.890625},{x:.578125,y:.890625},{x:.578125,y:.890625},{x:.609375,y:.890625},{x:.609375,y:.890625},{x:.640625,y:.890625},{x:.640625,y:.890625},{x:.671875,y:.890625},{x:.671875,y:.890625},{x:.703125,y:.890625},{x:.703125,y:.890625},{x:.734375,y:.890625},{x:.734375,y:.890625},{x:.765625,y:.890625},{x:.765625,y:.890625},{x:.796875,y:.890625},{x:.796875,y:.890625},{x:.828125,y:.890625},{x:.828125,y:.890625},{x:.859375,y:.890625},{x:.859375,y:.890625},{x:.890625,y:.890625},{x:.890625,y:.890625},{x:.921875,y:.890625},{x:.921875,y:.890625},{x:.953125,y:.890625},{x:.953125,y:.890625},{x:.984375,y:.890625},{x:.984375,y:.890625},{x:.015625,y:.921875},{x:.015625,y:.921875},{x:.046875,y:.921875},{x:.046875,y:.921875},{x:.078125,y:.921875},{x:.078125,y:.921875},{x:.109375,y:.921875},{x:.109375,y:.921875},{x:.140625,y:.921875},{x:.140625,y:.921875},{x:.171875,y:.921875},{x:.171875,y:.921875},{x:.203125,y:.921875},{x:.203125,y:.921875},{x:.234375,y:.921875},{x:.234375,y:.921875},{x:.265625,y:.921875},{x:.265625,y:.921875},{x:.296875,y:.921875},{x:.296875,y:.921875},{x:.328125,y:.921875},{x:.328125,y:.921875},{x:.359375,y:.921875},{x:.359375,y:.921875},{x:.390625,y:.921875},{x:.390625,y:.921875},{x:.421875,y:.921875},{x:.421875,y:.921875},{x:.453125,y:.921875},{x:.453125,y:.921875},{x:.484375,y:.921875},{x:.484375,y:.921875},{x:.515625,y:.921875},{x:.515625,y:.921875},{x:.546875,y:.921875},{x:.546875,y:.921875},{x:.578125,y:.921875},{x:.578125,y:.921875},{x:.609375,y:.921875},{x:.609375,y:.921875},{x:.640625,y:.921875},{x:.640625,y:.921875},{x:.671875,y:.921875},{x:.671875,y:.921875},{x:.703125,y:.921875},{x:.703125,y:.921875},{x:.734375,y:.921875},{x:.734375,y:.921875},{x:.765625,y:.921875},{x:.765625,y:.921875},{x:.796875,y:.921875},{x:.796875,y:.921875},{x:.828125,y:.921875},{x:.828125,y:.921875},{x:.859375,y:.921875},{x:.859375,y:.921875},{x:.890625,y:.921875},{x:.890625,y:.921875},{x:.921875,y:.921875},{x:.921875,y:.921875},{x:.953125,y:.921875},{x:.953125,y:.921875},{x:.984375,y:.921875},{x:.984375,y:.921875},{x:.015625,y:.953125},{x:.015625,y:.953125},{x:.046875,y:.953125},{x:.046875,y:.953125},{x:.078125,y:.953125},{x:.078125,y:.953125},{x:.109375,y:.953125},{x:.109375,y:.953125},{x:.140625,y:.953125},{x:.140625,y:.953125},{x:.171875,y:.953125},{x:.171875,y:.953125},{x:.203125,y:.953125},{x:.203125,y:.953125},{x:.234375,y:.953125},{x:.234375,y:.953125},{x:.265625,y:.953125},{x:.265625,y:.953125},{x:.296875,y:.953125},{x:.296875,y:.953125},{x:.328125,y:.953125},{x:.328125,y:.953125},{x:.359375,y:.953125},{x:.359375,y:.953125},{x:.390625,y:.953125},{x:.390625,y:.953125},{x:.421875,y:.953125},{x:.421875,y:.953125},{x:.453125,y:.953125},{x:.453125,y:.953125},{x:.484375,y:.953125},{x:.484375,y:.953125},{x:.515625,y:.953125},{x:.515625,y:.953125},{x:.546875,y:.953125},{x:.546875,y:.953125},{x:.578125,y:.953125},{x:.578125,y:.953125},{x:.609375,y:.953125},{x:.609375,y:.953125},{x:.640625,y:.953125},{x:.640625,y:.953125},{x:.671875,y:.953125},{x:.671875,y:.953125},{x:.703125,y:.953125},{x:.703125,y:.953125},{x:.734375,y:.953125},{x:.734375,y:.953125},{x:.765625,y:.953125},{x:.765625,y:.953125},{x:.796875,y:.953125},{x:.796875,y:.953125},{x:.828125,y:.953125},{x:.828125,y:.953125},{x:.859375,y:.953125},{x:.859375,y:.953125},{x:.890625,y:.953125},{x:.890625,y:.953125},{x:.921875,y:.953125},{x:.921875,y:.953125},{x:.953125,y:.953125},{x:.953125,y:.953125},{x:.984375,y:.953125},{x:.984375,y:.953125},{x:.015625,y:.984375},{x:.015625,y:.984375},{x:.046875,y:.984375},{x:.046875,y:.984375},{x:.078125,y:.984375},{x:.078125,y:.984375},{x:.109375,y:.984375},{x:.109375,y:.984375},{x:.140625,y:.984375},{x:.140625,y:.984375},{x:.171875,y:.984375},{x:.171875,y:.984375},{x:.203125,y:.984375},{x:.203125,y:.984375},{x:.234375,y:.984375},{x:.234375,y:.984375},{x:.265625,y:.984375},{x:.265625,y:.984375},{x:.296875,y:.984375},{x:.296875,y:.984375},{x:.328125,y:.984375},{x:.328125,y:.984375},{x:.359375,y:.984375},{x:.359375,y:.984375},{x:.390625,y:.984375},{x:.390625,y:.984375},{x:.421875,y:.984375},{x:.421875,y:.984375},{x:.453125,y:.984375},{x:.453125,y:.984375},{x:.484375,y:.984375},{x:.484375,y:.984375},{x:.515625,y:.984375},{x:.515625,y:.984375},{x:.546875,y:.984375},{x:.546875,y:.984375},{x:.578125,y:.984375},{x:.578125,y:.984375},{x:.609375,y:.984375},{x:.609375,y:.984375},{x:.640625,y:.984375},{x:.640625,y:.984375},{x:.671875,y:.984375},{x:.671875,y:.984375},{x:.703125,y:.984375},{x:.703125,y:.984375},{x:.734375,y:.984375},{x:.734375,y:.984375},{x:.765625,y:.984375},{x:.765625,y:.984375},{x:.796875,y:.984375},{x:.796875,y:.984375},{x:.828125,y:.984375},{x:.828125,y:.984375},{x:.859375,y:.984375},{x:.859375,y:.984375},{x:.890625,y:.984375},{x:.890625,y:.984375},{x:.921875,y:.984375},{x:.921875,y:.984375},{x:.953125,y:.984375},{x:.953125,y:.984375},{x:.984375,y:.984375},{x:.984375,y:.984375},{x:.03125,y:.03125},{x:.03125,y:.03125},{x:.09375,y:.03125},{x:.09375,y:.03125},{x:.15625,y:.03125},{x:.15625,y:.03125},{x:.21875,y:.03125},{x:.21875,y:.03125},{x:.28125,y:.03125},{x:.28125,y:.03125},{x:.34375,y:.03125},{x:.34375,y:.03125},{x:.40625,y:.03125},{x:.40625,y:.03125},{x:.46875,y:.03125},{x:.46875,y:.03125},{x:.53125,y:.03125},{x:.53125,y:.03125},{x:.59375,y:.03125},{x:.59375,y:.03125},{x:.65625,y:.03125},{x:.65625,y:.03125},{x:.71875,y:.03125},{x:.71875,y:.03125},{x:.78125,y:.03125},{x:.78125,y:.03125},{x:.84375,y:.03125},{x:.84375,y:.03125},{x:.90625,y:.03125},{x:.90625,y:.03125},{x:.96875,y:.03125},{x:.96875,y:.03125},{x:.03125,y:.09375},{x:.03125,y:.09375},{x:.09375,y:.09375},{x:.09375,y:.09375},{x:.15625,y:.09375},{x:.15625,y:.09375},{x:.21875,y:.09375},{x:.21875,y:.09375},{x:.28125,y:.09375},{x:.28125,y:.09375},{x:.34375,y:.09375},{x:.34375,y:.09375},{x:.40625,y:.09375},{x:.40625,y:.09375},{x:.46875,y:.09375},{x:.46875,y:.09375},{x:.53125,y:.09375},{x:.53125,y:.09375},{x:.59375,y:.09375},{x:.59375,y:.09375},{x:.65625,y:.09375},{x:.65625,y:.09375},{x:.71875,y:.09375},{x:.71875,y:.09375},{x:.78125,y:.09375},{x:.78125,y:.09375},{x:.84375,y:.09375},{x:.84375,y:.09375},{x:.90625,y:.09375},{x:.90625,y:.09375},{x:.96875,y:.09375},{x:.96875,y:.09375},{x:.03125,y:.15625},{x:.03125,y:.15625},{x:.09375,y:.15625},{x:.09375,y:.15625},{x:.15625,y:.15625},{x:.15625,y:.15625},{x:.21875,y:.15625},{x:.21875,y:.15625},{x:.28125,y:.15625},{x:.28125,y:.15625},{x:.34375,y:.15625},{x:.34375,y:.15625},{x:.40625,y:.15625},{x:.40625,y:.15625},{x:.46875,y:.15625},{x:.46875,y:.15625},{x:.53125,y:.15625},{x:.53125,y:.15625},{x:.59375,y:.15625},{x:.59375,y:.15625},{x:.65625,y:.15625},{x:.65625,y:.15625},{x:.71875,y:.15625},{x:.71875,y:.15625},{x:.78125,y:.15625},{x:.78125,y:.15625},{x:.84375,y:.15625},{x:.84375,y:.15625},{x:.90625,y:.15625},{x:.90625,y:.15625},{x:.96875,y:.15625},{x:.96875,y:.15625},{x:.03125,y:.21875},{x:.03125,y:.21875},{x:.09375,y:.21875},{x:.09375,y:.21875},{x:.15625,y:.21875},{x:.15625,y:.21875},{x:.21875,y:.21875},{x:.21875,y:.21875},{x:.28125,y:.21875},{x:.28125,y:.21875},{x:.34375,y:.21875},{x:.34375,y:.21875},{x:.40625,y:.21875},{x:.40625,y:.21875},{x:.46875,y:.21875},{x:.46875,y:.21875},{x:.53125,y:.21875},{x:.53125,y:.21875},{x:.59375,y:.21875},{x:.59375,y:.21875},{x:.65625,y:.21875},{x:.65625,y:.21875},{x:.71875,y:.21875},{x:.71875,y:.21875},{x:.78125,y:.21875},{x:.78125,y:.21875},{x:.84375,y:.21875},{x:.84375,y:.21875},{x:.90625,y:.21875},{x:.90625,y:.21875},{x:.96875,y:.21875},{x:.96875,y:.21875},{x:.03125,y:.28125},{x:.03125,y:.28125},{x:.09375,y:.28125},{x:.09375,y:.28125},{x:.15625,y:.28125},{x:.15625,y:.28125},{x:.21875,y:.28125},{x:.21875,y:.28125},{x:.28125,y:.28125},{x:.28125,y:.28125},{x:.34375,y:.28125},{x:.34375,y:.28125},{x:.40625,y:.28125},{x:.40625,y:.28125},{x:.46875,y:.28125},{x:.46875,y:.28125},{x:.53125,y:.28125},{x:.53125,y:.28125},{x:.59375,y:.28125},{x:.59375,y:.28125},{x:.65625,y:.28125},{x:.65625,y:.28125},{x:.71875,y:.28125},{x:.71875,y:.28125},{x:.78125,y:.28125},{x:.78125,y:.28125},{x:.84375,y:.28125},{x:.84375,y:.28125},{x:.90625,y:.28125},{x:.90625,y:.28125},{x:.96875,y:.28125},{x:.96875,y:.28125},{x:.03125,y:.34375},{x:.03125,y:.34375},{x:.09375,y:.34375},{x:.09375,y:.34375},{x:.15625,y:.34375},{x:.15625,y:.34375},{x:.21875,y:.34375},{x:.21875,y:.34375},{x:.28125,y:.34375},{x:.28125,y:.34375},{x:.34375,y:.34375},{x:.34375,y:.34375},{x:.40625,y:.34375},{x:.40625,y:.34375},{x:.46875,y:.34375},{x:.46875,y:.34375},{x:.53125,y:.34375},{x:.53125,y:.34375},{x:.59375,y:.34375},{x:.59375,y:.34375},{x:.65625,y:.34375},{x:.65625,y:.34375},{x:.71875,y:.34375},{x:.71875,y:.34375},{x:.78125,y:.34375},{x:.78125,y:.34375},{x:.84375,y:.34375},{x:.84375,y:.34375},{x:.90625,y:.34375},{x:.90625,y:.34375},{x:.96875,y:.34375},{x:.96875,y:.34375},{x:.03125,y:.40625},{x:.03125,y:.40625},{x:.09375,y:.40625},{x:.09375,y:.40625},{x:.15625,y:.40625},{x:.15625,y:.40625},{x:.21875,y:.40625},{x:.21875,y:.40625},{x:.28125,y:.40625},{x:.28125,y:.40625},{x:.34375,y:.40625},{x:.34375,y:.40625},{x:.40625,y:.40625},{x:.40625,y:.40625},{x:.46875,y:.40625},{x:.46875,y:.40625},{x:.53125,y:.40625},{x:.53125,y:.40625},{x:.59375,y:.40625},{x:.59375,y:.40625},{x:.65625,y:.40625},{x:.65625,y:.40625},{x:.71875,y:.40625},{x:.71875,y:.40625},{x:.78125,y:.40625},{x:.78125,y:.40625},{x:.84375,y:.40625},{x:.84375,y:.40625},{x:.90625,y:.40625},{x:.90625,y:.40625},{x:.96875,y:.40625},{x:.96875,y:.40625},{x:.03125,y:.46875},{x:.03125,y:.46875},{x:.09375,y:.46875},{x:.09375,y:.46875},{x:.15625,y:.46875},{x:.15625,y:.46875},{x:.21875,y:.46875},{x:.21875,y:.46875},{x:.28125,y:.46875},{x:.28125,y:.46875},{x:.34375,y:.46875},{x:.34375,y:.46875},{x:.40625,y:.46875},{x:.40625,y:.46875},{x:.46875,y:.46875},{x:.46875,y:.46875},{x:.53125,y:.46875},{x:.53125,y:.46875},{x:.59375,y:.46875},{x:.59375,y:.46875},{x:.65625,y:.46875},{x:.65625,y:.46875},{x:.71875,y:.46875},{x:.71875,y:.46875},{x:.78125,y:.46875},{x:.78125,y:.46875},{x:.84375,y:.46875},{x:.84375,y:.46875},{x:.90625,y:.46875},{x:.90625,y:.46875},{x:.96875,y:.46875},{x:.96875,y:.46875},{x:.03125,y:.53125},{x:.03125,y:.53125},{x:.09375,y:.53125},{x:.09375,y:.53125},{x:.15625,y:.53125},{x:.15625,y:.53125},{x:.21875,y:.53125},{x:.21875,y:.53125},{x:.28125,y:.53125},{x:.28125,y:.53125},{x:.34375,y:.53125},{x:.34375,y:.53125},{x:.40625,y:.53125},{x:.40625,y:.53125},{x:.46875,y:.53125},{x:.46875,y:.53125},{x:.53125,y:.53125},{x:.53125,y:.53125},{x:.59375,y:.53125},{x:.59375,y:.53125},{x:.65625,y:.53125},{x:.65625,y:.53125},{x:.71875,y:.53125},{x:.71875,y:.53125},{x:.78125,y:.53125},{x:.78125,y:.53125},{x:.84375,y:.53125},{x:.84375,y:.53125},{x:.90625,y:.53125},{x:.90625,y:.53125},{x:.96875,y:.53125},{x:.96875,y:.53125},{x:.03125,y:.59375},{x:.03125,y:.59375},{x:.09375,y:.59375},{x:.09375,y:.59375},{x:.15625,y:.59375},{x:.15625,y:.59375},{x:.21875,y:.59375},{x:.21875,y:.59375},{x:.28125,y:.59375},{x:.28125,y:.59375},{x:.34375,y:.59375},{x:.34375,y:.59375},{x:.40625,y:.59375},{x:.40625,y:.59375},{x:.46875,y:.59375},{x:.46875,y:.59375},{x:.53125,y:.59375},{x:.53125,y:.59375},{x:.59375,y:.59375},{x:.59375,y:.59375},{x:.65625,y:.59375},{x:.65625,y:.59375},{x:.71875,y:.59375},{x:.71875,y:.59375},{x:.78125,y:.59375},{x:.78125,y:.59375},{x:.84375,y:.59375},{x:.84375,y:.59375},{x:.90625,y:.59375},{x:.90625,y:.59375},{x:.96875,y:.59375},{x:.96875,y:.59375},{x:.03125,y:.65625},{x:.03125,y:.65625},{x:.09375,y:.65625},{x:.09375,y:.65625},{x:.15625,y:.65625},{x:.15625,y:.65625},{x:.21875,y:.65625},{x:.21875,y:.65625},{x:.28125,y:.65625},{x:.28125,y:.65625},{x:.34375,y:.65625},{x:.34375,y:.65625},{x:.40625,y:.65625},{x:.40625,y:.65625},{x:.46875,y:.65625},{x:.46875,y:.65625},{x:.53125,y:.65625},{x:.53125,y:.65625},{x:.59375,y:.65625},{x:.59375,y:.65625},{x:.65625,y:.65625},{x:.65625,y:.65625},{x:.71875,y:.65625},{x:.71875,y:.65625},{x:.78125,y:.65625},{x:.78125,y:.65625},{x:.84375,y:.65625},{x:.84375,y:.65625},{x:.90625,y:.65625},{x:.90625,y:.65625},{x:.96875,y:.65625},{x:.96875,y:.65625},{x:.03125,y:.71875},{x:.03125,y:.71875},{x:.09375,y:.71875},{x:.09375,y:.71875},{x:.15625,y:.71875},{x:.15625,y:.71875},{x:.21875,y:.71875},{x:.21875,y:.71875},{x:.28125,y:.71875},{x:.28125,y:.71875},{x:.34375,y:.71875},{x:.34375,y:.71875},{x:.40625,y:.71875},{x:.40625,y:.71875},{x:.46875,y:.71875},{x:.46875,y:.71875},{x:.53125,y:.71875},{x:.53125,y:.71875},{x:.59375,y:.71875},{x:.59375,y:.71875},{x:.65625,y:.71875},{x:.65625,y:.71875},{x:.71875,y:.71875},{x:.71875,y:.71875},{x:.78125,y:.71875},{x:.78125,y:.71875},{x:.84375,y:.71875},{x:.84375,y:.71875},{x:.90625,y:.71875},{x:.90625,y:.71875},{x:.96875,y:.71875},{x:.96875,y:.71875},{x:.03125,y:.78125},{x:.03125,y:.78125},{x:.09375,y:.78125},{x:.09375,y:.78125},{x:.15625,y:.78125},{x:.15625,y:.78125},{x:.21875,y:.78125},{x:.21875,y:.78125},{x:.28125,y:.78125},{x:.28125,y:.78125},{x:.34375,y:.78125},{x:.34375,y:.78125},{x:.40625,y:.78125},{x:.40625,y:.78125},{x:.46875,y:.78125},{x:.46875,y:.78125},{x:.53125,y:.78125},{x:.53125,y:.78125},{x:.59375,y:.78125},{x:.59375,y:.78125},{x:.65625,y:.78125},{x:.65625,y:.78125},{x:.71875,y:.78125},{x:.71875,y:.78125},{x:.78125,y:.78125},{x:.78125,y:.78125},{x:.84375,y:.78125},{x:.84375,y:.78125},{x:.90625,y:.78125},{x:.90625,y:.78125},{x:.96875,y:.78125},{x:.96875,y:.78125},{x:.03125,y:.84375},{x:.03125,y:.84375},{x:.09375,y:.84375},{x:.09375,y:.84375},{x:.15625,y:.84375},{x:.15625,y:.84375},{x:.21875,y:.84375},{x:.21875,y:.84375},{x:.28125,y:.84375},{x:.28125,y:.84375},{x:.34375,y:.84375},{x:.34375,y:.84375},{x:.40625,y:.84375},{x:.40625,y:.84375},{x:.46875,y:.84375},{x:.46875,y:.84375},{x:.53125,y:.84375},{x:.53125,y:.84375},{x:.59375,y:.84375},{x:.59375,y:.84375},{x:.65625,y:.84375},{x:.65625,y:.84375},{x:.71875,y:.84375},{x:.71875,y:.84375},{x:.78125,y:.84375},{x:.78125,y:.84375},{x:.84375,y:.84375},{x:.84375,y:.84375},{x:.90625,y:.84375},{x:.90625,y:.84375},{x:.96875,y:.84375},{x:.96875,y:.84375},{x:.03125,y:.90625},{x:.03125,y:.90625},{x:.09375,y:.90625},{x:.09375,y:.90625},{x:.15625,y:.90625},{x:.15625,y:.90625},{x:.21875,y:.90625},{x:.21875,y:.90625},{x:.28125,y:.90625},{x:.28125,y:.90625},{x:.34375,y:.90625},{x:.34375,y:.90625},{x:.40625,y:.90625},{x:.40625,y:.90625},{x:.46875,y:.90625},{x:.46875,y:.90625},{x:.53125,y:.90625},{x:.53125,y:.90625},{x:.59375,y:.90625},{x:.59375,y:.90625},{x:.65625,y:.90625},{x:.65625,y:.90625},{x:.71875,y:.90625},{x:.71875,y:.90625},{x:.78125,y:.90625},{x:.78125,y:.90625},{x:.84375,y:.90625},{x:.84375,y:.90625},{x:.90625,y:.90625},{x:.90625,y:.90625},{x:.96875,y:.90625},{x:.96875,y:.90625},{x:.03125,y:.96875},{x:.03125,y:.96875},{x:.09375,y:.96875},{x:.09375,y:.96875},{x:.15625,y:.96875},{x:.15625,y:.96875},{x:.21875,y:.96875},{x:.21875,y:.96875},{x:.28125,y:.96875},{x:.28125,y:.96875},{x:.34375,y:.96875},{x:.34375,y:.96875},{x:.40625,y:.96875},{x:.40625,y:.96875},{x:.46875,y:.96875},{x:.46875,y:.96875},{x:.53125,y:.96875},{x:.53125,y:.96875},{x:.59375,y:.96875},{x:.59375,y:.96875},{x:.65625,y:.96875},{x:.65625,y:.96875},{x:.71875,y:.96875},{x:.71875,y:.96875},{x:.78125,y:.96875},{x:.78125,y:.96875},{x:.84375,y:.96875},{x:.84375,y:.96875},{x:.90625,y:.96875},{x:.90625,y:.96875},{x:.96875,y:.96875},{x:.96875,y:.96875},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375}];var Ob=class{constructor(t){he(this,"model");he(this,"anchors");he(this,"anchorsTensor");he(this,"inputSize");he(this,"inputSizeTensor");he(this,"doubleInputSizeTensor");this.model=t,this.anchors=xT.map(n=>[n.x,n.y]),this.anchorsTensor=fr(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=Ut([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Ut([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=Pe(t,[0,0],[-1,2]),n.boxSizes=Pe(t,[0,2],[-1,2]),n.div=ge(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=ue(n.div,this.anchorsTensor),n.halfBoxSizes=ge(n.boxSizes,this.doubleInputSizeTensor),n.sub=fe(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=L(n.sub,this.inputSizeTensor),n.add=ue(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=L(n.add,this.inputSizeTensor);let s=Wu([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>ne(n[r])),s}normalizeLandmarks(t,n){let s={};s.reshape=H(t,[-1,7,2]),s.div=ge(s.reshape,this.inputSizeTensor),s.landmarks=ue(s.div,this.anchors[n]);let r=L(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ne(s[a])),r}async predict(t,n){let s={};s.resize=Ce.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=ge(s.resize,Ze.tf127),s.image=fe(s.div,Ze.tf1),s.batched=this.model.execute(s.image),s.predictions=it(s.batched),s.slice=Pe(s.predictions,[0,0],[-1,1]),s.sigmoid=ms(s.slice),s.scores=it(s.sigmoid);let r=await s.scores.data();s.boxes=Pe(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Ce.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l={};l.box=Pe(s.norm,[i,0],[1,-1]),l.slice=Pe(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=H(l.norm,[-1,2]);let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array(),h={startPoint:u,endPoint:d,palmLandmarks:p,confidence:r[i]},f=fT(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ne(l[m]))}return Object.keys(s).forEach(i=>ne(s[i])),o}};var N2e=5,bT=1.65,vT=[0,5,9,13,17,1,2],E2e=0,R2e=2,wT=0,Mb=class{constructor(t,n){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>Fb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return b0(v0(r),N2e)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=b0(v0(n),bT);s.palmLandmarks=[];for(let r=0;r<vT.length;r++)s.palmLandmarks.push(t[vT[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=x0(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=Pb(s,[0,0]),c=i.map(h=>[...Fb(h,l),h[2]]),u=yT(r),d=[...Lp(n),1],p=[ti(d,u[0]),ti(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ie()-wT,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(!!c)if(n.hand.landmarks){let u=n.hand.rotation?mT(c.palmLandmarks[E2e],c.palmLandmarks[R2e]):0,d=Lp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&de.kernels.includes("rotatewithoffset")?Ce.rotateWithOffset(t,u,0,p):t.clone(),f=Pb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=hT(m,h,[this.inputSize,this.inputSize]),A=ge(g,Ze.tf255);ne(g),ne(h);let[x,y]=this.handPoseModel.execute(A);wT=ie(),ne(A);let b=(await x.data())[0];if(ne(x),b>=n.hand.minConfidence/4){let w=H(y,[-1,3]),k=await w.array();ne(y),ne(w);let I=this.transformRawCoords(k,m,u,f),N=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...N,confidence:b};let R={landmarks:I,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ne(y)}else{let u=b0(v0(c),bT),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var es={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>es.nameMapping[e],getPoints:e=>es.pointsMapping[e]},ni={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>ni.nameMapping[e]},Lt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Lt.nameMapping[e]},Vl=class{constructor(t){he(this,"name");he(this,"curls");he(this,"directions");he(this,"weights");he(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}direction(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}weight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var{thumb:Sr,index:pa,middle:ha,ring:Ul,pinky:Gl}=es,{none:Ir,half:$2e,full:Cr}=ni,{verticalUp:_c,verticalDown:bye,horizontalLeft:zb,horizontalRight:_2e,diagonalUpRight:D2e,diagonalUpLeft:Dc,diagonalDownRight:vye,diagonalDownLeft:wye}=Lt,si=new Vl("thumbs up");si.curl(Sr,Ir,1);si.direction(Sr,_c,1);si.direction(Sr,Dc,.25);si.direction(Sr,D2e,.25);for(let e of[es.index,es.middle,es.ring,es.pinky])si.curl(e,Cr,1),si.direction(e,zb,1),si.direction(e,_2e,1);var en=new Vl("victory");en.curl(Sr,$2e,.5);en.curl(Sr,Ir,.5);en.direction(Sr,_c,1);en.direction(Sr,Dc,1);en.curl(pa,Ir,1);en.direction(pa,_c,.75);en.direction(pa,Dc,1);en.curl(ha,Ir,1);en.direction(ha,_c,1);en.direction(ha,Dc,.75);en.curl(Ul,Cr,1);en.direction(Ul,_c,.2);en.direction(Ul,Dc,1);en.direction(Ul,zb,.2);en.curl(Gl,Cr,1);en.direction(Gl,_c,.2);en.direction(Gl,Dc,1);en.direction(Gl,zb,.2);en.weight(pa,2);en.weight(ha,2);var ri=new Vl("point");ri.curl(Sr,Cr,1);ri.curl(pa,Ir,.5);ri.curl(ha,Cr,.5);ri.curl(Ul,Cr,.5);ri.curl(Gl,Cr,.5);ri.weight(pa,2);ri.weight(ha,2);var ai=new Vl("middle finger");ai.curl(Sr,Ir,1);ai.curl(pa,Cr,.5);ai.curl(ha,Cr,.5);ai.curl(Ul,Cr,.5);ai.curl(Gl,Cr,.5);ai.weight(pa,2);ai.weight(ha,2);var Pc=new Vl("open palm");Pc.curl(Sr,Ir,.75);Pc.curl(pa,Ir,.75);Pc.curl(ha,Ir,.75);Pc.curl(Ul,Ir,.75);Pc.curl(Gl,Ir,.75);var kT=[si,en,ri,ai,Pc];var P2e=.7,Hl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function ST(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function IT(e,t){if(!e||!t)return[0,0];let n=ST(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=ST(e[1],e[2],t[1],t[2]);return[n,s]}function CT(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function F2e(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let A;return g>Hl.NO_CURL_START_LIMIT?A=ni.none:g>Hl.HALF_CURL_START_LIMIT?A=ni.half:A=ni.full,A}function TT(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Lt.horizontalLeft:r=Lt.horizontalRight:s===Math.abs(t)?t>0?r=Lt.horizontalLeft:r=Lt.horizontalRight:n>0?r=Lt.horizontalLeft:r=Lt.horizontalRight,r}function NT(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Lt.verticalDown:r=Lt.verticalUp:s===Math.abs(t)?t<0?r=Lt.verticalDown:r=Lt.verticalUp:n<0?r=Lt.verticalDown:r=Lt.verticalUp,r}function O2e(e,t,n,s,r,a,o,i){let l,c=NT(e,t,n,s),u=TT(r,a,o,i);return c===Lt.verticalUp?u===Lt.horizontalLeft?l=Lt.diagonalUpLeft:l=Lt.diagonalUpRight:u===Lt.horizontalLeft?l=Lt.diagonalDownLeft:l=Lt.diagonalDownRight,l}function M2e(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=Hl.DISTANCE_VOTE_POWER:m>.66?h+=Hl.DISTANCE_VOTE_POWER:f+=Hl.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+c*c),y=Math.max(g,A,x),b=e[0],w=e[1],k=n[0],I=n[1];y===g?(k=n[0],I=n[1]):y===x&&(b=t[0],w=t[1]);let O=IT([b,w],[k,I]),$=CT(O,Hl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],h+=$[1],f+=$[2];for(let T of s){let F=CT(T,Hl.SINGLE_ANGLE_VOTE_POWER);p+=F[0],h+=F[1],f+=F[2]}let P;return p===Math.max(p,h,f)?P=NT(l,i,c,d):f===Math.max(h,f)?P=TT(a,r,o,u):P=O2e(l,i,c,d,a,r,o,u),P}function ET(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of es.all){let o=es.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=IT(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of es.all){let o=a===es.thumb?1:0,i=es.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=F2e(l,c,u),p=M2e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function w0(e){if(!e||e.length===0)return null;let t=ET(e),n={};for(let s of es.all)n[es.getName(s)]={curl:ni.getName(t.curls[s]),direction:Lt.getName(t.directions[s])};return n}function RT(e){let t=[];if(!e||e.length===0)return t;let n=ET(e);for(let s of kT){let r=s.matchAgainst(n.curls,n.directions);r>=P2e&&t.push({name:s.name,confidence:r})}return t}var $T={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},fa,ma,_T;async function Lb(e,t){let n=await _T.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys($T))a[u]=$T[u].map(d=>n[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=w0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function Bb(e){var n,s,r,a,o,i;de.initial&&(fa=null,ma=null),!fa||!ma?([fa,ma]=await Promise.all([e.hand.enabled?Be(We(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?Be(We(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!fa||!fa.modelUrl?Z("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&Z("load model:",fa.modelUrl),!ma||!ma.modelUrl?Z("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&Z("load model:",ma.modelUrl))):(e.debug&&Z("cached model:",fa.modelUrl),e.debug&&Z("cached model:",ma.modelUrl));let t=new Ob(fa);return _T=new Mb(t,ma),[fa,ma]}function jl(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function DT(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function k0(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function Wb(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var Tt=[null,null],z2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oi=[[0,0],[0,0]],L2e=["hand","fist","pinch","point","face","tip","pinchtip"],PT=4,FT=1.6,B2e=512,W2e=1.4,S0=Number.MAX_SAFE_INTEGER,Vb=0,ga=[0,0],Xt={boxes:[],hands:[]},OT={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function MT(e){var t,n;if(de.initial&&(Tt[0]=null),Tt[0])e.debug&&Z("cached model:",Tt[0].modelUrl);else{I0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Tt[0]=await Be(We(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(Tt[0].modelSignature.inputs);oi[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,oi[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Tt[0]||!Tt[0].modelUrl?Z("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&Z("load model:",Tt[0].modelUrl)}return Tt[0]}async function zT(e){var t,n;if(de.initial&&(Tt[1]=null),Tt[1])e.debug&&Z("cached model:",Tt[1].modelUrl);else{Tt[1]=await Be(We(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(Tt[1].modelSignature.inputs);oi[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,oi[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Tt[1]||!Tt[1].modelUrl?Z("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&Z("load model:",Tt[1].modelUrl)}return Tt[1]}async function V2e(e,t){let n=[];if(!e||!Tt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,B2e),o=Math.round(a*r/8)*8;s.resize=Ce.resizeBilinear(e,[a,o]),s.cast=me(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Tt[0].executeAsync(s.cast,z2e),s.boxes=it(s.rawBoxes,[0,2]),s.scores=it(s.rawScores,[0]);let i=os(s.scores,1);ne(i[PT]),i.splice(PT,1),s.filtered=xn(i,1),ne(i),s.max=yn(s.filtered,1),s.argmax=Zs(s.filtered,1);let l=0;s.nms=await Ce.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=Pe(s.boxes,p,1),f=await h.data();ne(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=k0(m,W2e),A=Wb(g),x=[Math.trunc(m[0]*ga[0]),Math.trunc(m[1]*ga[1]),Math.trunc(m[2]*ga[0]),Math.trunc(m[3]*ga[1])],y=u[p],b=L2e[d[p]],w={id:l++,score:y,box:x,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>ne(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Ub(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Tt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=Ce.cropAndResize(e,[t.boxCrop],[0],[oi[1][0],oi[1][1]],"bilinear"),r.div=ge(r.crop,Ze.tf255),[r.score,r.keypoints]=Tt[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=H(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/oi[1][1],u[1]/oi[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[ga[0]*(u[0]+t.boxRaw[0]),ga[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=w0(s.keypoints);for(let u of Object.keys(OT))s.annotations[u]=OT[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ne(r[i]))}return s}async function Gb(e,t){var r,a;if(!Tt[0]||!Tt[1]||!((r=Tt[0])==null?void 0:r.inputs[0].shape)||!((a=Tt[1])==null?void 0:a.inputs[0].shape))return[];ga=[e.shape[2]||0,e.shape[1]||0],S0++;let n=(t.hand.skipTime||0)>ie()-Vb,s=S0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?Xt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ie()-Vb,l=S0<3*(t.hand.skipFrames||0);t.skipAllowed&&Xt.hands.length===t.hand.maxDetected?Xt.hands=await Promise.all(Xt.boxes.map(u=>Ub(e,u,t))):t.skipAllowed&&i&&l&&Xt.hands.length>0?Xt.hands=await Promise.all(Xt.boxes.map(u=>Ub(e,u,t))):(Xt.boxes=await V2e(e,t),Vb=ie(),Xt.hands=await Promise.all(Xt.boxes.map(u=>Ub(e,u,t))),S0=0);let c=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<Xt.hands.length;u++){let d=DT(Xt.hands[u].keypoints,ga);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&Xt.hands[u].fingerScore&&Xt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=k0(d.box,FT),h=k0(d.boxRaw,FT),f=Wb(h);Xt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<Xt.hands.length;u++){let d=jl(Xt.hands[u].keypoints,ga);Xt.hands[u].box=d.box,Xt.hands[u].boxRaw=d.boxRaw}o(Xt.hands)})}var hn,C0=[],Hb=Number.MAX_SAFE_INTEGER,LT=0,BT=0;async function WT(e){var t,n;return de.initial&&(hn=null),hn?e.debug&&Z("cached model:",hn.modelUrl):(hn=await Be(We(e.modelBasePath,((t=e.face.liveness)==null?void 0:t.modelPath)||"")),!hn||!hn.modelUrl?Z("load model failed:",(n=e.face.liveness)==null?void 0:n.modelPath):e.debug&&Z("load model:",hn.modelUrl)),hn}async function jb(e,t,n,s){var o,i;if(!hn)return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ie()-BT,a=Hb<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&<===s&&C0[n]?(Hb++,C0[n]):(Hb=0,new Promise(async l=>{let c=Ce.resizeBilinear(e,[(hn==null?void 0:hn.inputs[0].shape)?hn.inputs[0].shape[2]:0,(hn==null?void 0:hn.inputs[0].shape)?hn.inputs[0].shape[1]:0],!1),u=hn==null?void 0:hn.execute(c),d=(await u.data())[0];C0[n]=Math.round(100*d)/100,LT=s,BT=ie(),ne([c,u]),l(C0[n])}))}var Zb={};Yc(Zb,{connected:()=>N0,horizontal:()=>qb,kpt:()=>T0,relative:()=>Kb,vertical:()=>Xb});var T0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],qb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Xb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Kb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],N0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var VT=.005,Is={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Yb(e){for(let t of qb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of Xb){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Kb){let s=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function UT(e){for(let t=0;t<e.length;t++)if(e[t]&&Is.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-Is.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-Is.keypoints[t].positionRaw[1])];n[0]<VT&&n[1]<VT?e[t]=Is.keypoints[t]:Is.keypoints[t]=e[t]}else Is.keypoints[t]=e[t];return e}function GT(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Is.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Js(e,Is.padding),n.resize=Ce.resizeBilinear(n.pad,[t,t]);let s=me(n.resize,"int32");return Object.keys(n).forEach(r=>ne(n[r])),s}function HT(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+Is.padding[2][0]+Is.padding[2][1])/t[0]-Is.padding[2][0],s.position[1]*(t[1]+Is.padding[1][0]+Is.padding[1][1])/t[1]-Is.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=jl(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var On,jT=0,Jb=Number.MAX_SAFE_INTEGER,ql={boxes:[],bodies:[],last:0};async function qT(e){return de.initial&&(On=null),On?e.debug&&Z("cached model:",On.modelUrl):(I0(["size"],e),On=await Be(We(e.modelBasePath,e.body.modelPath||"")),!On||!On.modelUrl?Z("load model failed:",e.body.modelPath):e.debug&&Z("load model:",On.modelUrl)),jT=On.inputs[0].shape?On.inputs[0].shape[2]:0,On}async function U2e(e,t,n){let s=e[0][0],r=[],a=0;for(let u=0;u<s.length;u++)if(a=s[u][2],a>t.body.minConfidence){let d=[s[u][1],s[u][0]];r.push({score:Math.round(100*a)/100,part:T0[u],positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}a=r.reduce((u,d)=>d.score>u?d.score:u,0);let o=[],i=jl(r.map(u=>u.position),[n.shape[2],n.shape[1]]),l={};for(let[u,d]of Object.entries(N0)){let p=[];for(let h=0;h<d.length-1;h++){let f=r.find(g=>g.part===d[h]),m=r.find(g=>g.part===d[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[u]=p}let c={id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:r,annotations:l};return Yb(c),o.push(c),o}async function G2e(e,t,n){let s=[];for(let r=0;r<e[0].length;r++){let a=e[0][r],o=Math.round(100*a[51+4])/100;if(o>t.body.minConfidence){let i=[];for(let d=0;d<17;d++){let p=a[3*d+2];if(p>t.body.minConfidence){let h=[a[3*d+1],a[3*d+0]];i.push({part:T0[d],score:Math.round(100*p)/100,positionRaw:h,position:[Math.round((n.shape[2]||0)*h[0]),Math.round((n.shape[1]||0)*h[1])]})}}let l=jl(i.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(N0)){let h=[];for(let f=0;f<p.length-1;f++){let m=i.find(A=>A.part===p[f]),g=i.find(A=>A.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:r,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:c};Yb(u),s.push(u)}}return s.sort((r,a)=>a.score-r.score),s.length>t.body.maxDetected&&(s.length=t.body.maxDetected),s}async function Qb(e,t){if(!On||!(On==null?void 0:On.inputs[0].shape))return[];t.skipAllowed||(ql.boxes.length=0),Jb++;let n=(t.body.skipTime||0)>ie()-ql.last,s=Jb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?ql.bodies:new Promise(async r=>{let a={};Jb=0,a.input=GT(e,jT),a.res=On==null?void 0:On.execute(a.input),ql.last=ie();let o=await a.res.array();ql.bodies=a.res.shape[2]===17?await U2e(o,t,e):await G2e(o,t,e);for(let i of ql.bodies)HT(i,[e.shape[2]||1,e.shape[1]||1]),UT(i.keypoints);Object.keys(a).forEach(i=>ne(a[i])),r(ql.bodies)})}var Vs,E0=[],XT=0,e5=Number.MAX_SAFE_INTEGER,R0=2.5;async function KT(e){if(!Vs||de.initial){Vs=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Vs.modelSignature.inputs);Vs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Vs||!Vs.modelUrl?Z("load model failed:",e.object.modelPath):e.debug&&Z("load model:",Vs.modelUrl)}else e.debug&&Z("cached model:",Vs.modelUrl);return Vs}async function H2e(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])X(async()=>{var g,A;let u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===Nc.length))==null?void 0:g.squeeze(),p=(A=e.find(x=>x.shape[1]===u**2&&x.shape[2]<Nc.length))==null?void 0:A.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let x=0;x<d.shape[0];x++)for(let y=0;y<d.shape[1];y++){let b=m[x][y];if(b>s.object.minConfidence&&y!==61){let w=(.5+Math.trunc(x%u))/u,k=(.5+Math.trunc(x/u))/u,I=f[x].map(U=>U*(u/c/t)),[N,R]=[w-R0/c*I[0],k-R0/c*I[1]],[O,$]=[w+R0/c*I[2]-N,k+R0/c*I[3]-R],P=[N,R,O,$];P=P.map(U=>Math.max(0,Math.min(U,1)));let T=[P[0]*n[0],P[1]*n[1],P[2]*n[0],P[3]*n[1]],F={id:r++,score:Math.round(100*b)/100,class:y+1,label:Nc[y].label,box:T.map(U=>Math.trunc(U)),boxRaw:P};a.push(F)}}});e.forEach(c=>ne(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await Ce.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ne(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function t5(e,t){let n=(t.object.skipTime||0)>ie()-XT,s=e5<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&E0.length>0?(e5++,E0):(e5=0,!de.kernels.includes("mod")||!de.kernels.includes("sparsetodense")?E0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Ce.resizeBilinear(e,[Vs.inputSize,Vs.inputSize],!1),i=ge(o,Ze.tf255),l=i.transpose([0,3,1,2]);ne(i),ne(o);let c;t.object.enabled&&(c=Vs.execute(l)),XT=ie(),ne(l);let u=await H2e(c,Vs.inputSize,a,t);E0=u,r(u)}))}var Bp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],j2e=Bp.length,Wp=Bp.reduce((e,t,n)=>(e[t]=n,e),{}),q2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],dxe=q2e.map(([e,t])=>[Wp[e],Wp[t]]),ZT=[["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 YT(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function JT(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((c,u)=>i(c,u))}var n5=class{constructor(t,n){he(this,"priorityQueue");he(this,"numberOfElements");he(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function s5(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+j2e)}}function r5(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=s5(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function a5(e,t,n){return e<t?t:e>n?n:e}function QT(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function o5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Cs,X2e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],$0=1,Fc=16,K2e=50**2;function eN(e,t,n,s,r,a,o=2){let i=A=>({y:a.get(A.y,A.x,e),x:a.get(A.y,A.x,a.shape[2]/2+e)}),l=(A,x,y)=>({y:a5(Math.round(A.y/Fc),0,x-1),x:a5(Math.round(A.x/Fc),0,y-1)}),[c,u]=s.shape,d=l(t.position,c,u),p=i(d),f=o5(t.position,p);for(let A=0;A<o;A++){let x=l(f,c,u),y=s5(x.y,x.x,n,r);f=o5({x:x.x*Fc,y:x.y*Fc},{x:y.x,y:y.y})}let m=l(f,c,u),g=s.get(m.y,m.x,n);return{position:f,part:Bp[n],score:g}}function Z2e(e,t,n,s,r){let a=ZT.map(([p,h])=>[Wp[p],Wp[h]]),o=a.map(([,p])=>p),i=a.map(([p])=>p),l=t.shape[2],c=o.length,u=new Array(l),d=r5(e.part,Fc,n);u[e.part.id]={score:e.score,part:Bp[e.part.id],position:d};for(let p=c-1;p>=0;--p){let h=o[p],f=i[p];u[h]&&!u[f]&&(u[f]=eN(p,u[h],f,t,n,r))}for(let p=0;p<c;++p){let h=i[p],f=o[p];u[h]&&!u[f]&&(u[f]=eN(p,u[h],f,t,n,s))}return u}function Y2e(e,t,n,s,r){let[a,o]=r.shape,i=!0,l=Math.max(n-$0,0),c=Math.min(n+$0+1,a);for(let u=l;u<c;++u){let d=Math.max(s-$0,0),p=Math.min(s+$0+1,o);for(let h=d;h<p;++h)if(r.get(u,h,e)>t){i=!1;break}if(!i)break}return i}function J2e(e,t){let[n,s,r]=t.shape,a=new n5(n*s*r,({score:o})=>o);for(let o=0;o<n;++o)for(let i=0;i<s;++i)for(let l=0;l<r;++l){let c=t.get(o,i,l);c<e||Y2e(l,c,o,i,t)&&a.enqueue({score:c,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function tN(e,{x:t,y:n},s){return e.some(({keypoints:r})=>{var o;let a=(o=r[s])==null?void 0:o.position;return a?QT(n,t,a.y,a.x)<=K2e:!1})}function Q2e(e,t){return t.reduce((s,{position:r,score:a},o)=>(tN(e,r,o)||(s+=a),s),0)/t.length}function e1e(e,t,n,s,r,a){let o=[],i=J2e(a,t);for(;o.length<r&&!i.empty();){let l=i.dequeue(),c=r5(l.part,Fc,e);if(tN(o,c,l.part.id))continue;let u=Z2e(l,t,e,n,s);u=u.filter(h=>h.score>a);let d=Q2e(o,u),p=YT(u);d>a&&o.push({keypoints:u,box:p,score:Math.round(100*d)/100})}return o}async function i5(e,t){let n=X(()=>{if(!Cs.inputs[0].shape)return[];let o=Ce.resizeBilinear(e,[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]),i=fe(ge(me(o,"float32"),127.5),1),c=Cs.execute(i,X2e).map(u=>it(u,[0]));return c[1]=c[1].sigmoid(),c}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)ne(o);let r=await e1e(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Cs.inputs[0].shape?JT(r,[e.shape[1],e.shape[2]],[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]):[]}async function nN(e){return!Cs||de.initial?(Cs=await Be(We(e.modelBasePath,e.body.modelPath||"")),!Cs||!Cs.modelUrl?Z("load model failed:",e.body.modelPath):e.debug&&Z("load model:",Cs.modelUrl)):e.debug&&Z("cached model:",Cs.modelUrl),Cs}var Us,l5=!1;async function u5(e){return!Us||de.initial?(Us=await Be(We(e.modelBasePath,e.segmentation.modelPath||"")),!Us||!Us.modelUrl?Z("load model failed:",e.segmentation.modelPath):e.debug&&Z("load model:",Us.modelUrl)):e.debug&&Z("cached model:",Us.modelUrl),Us}async function sN(e,t,n){var m,g;if(l5)return{data:[],canvas:null,alpha:null};l5=!0,Us||await u5(n);let s=await Tc(e,n),r=((m=s.tensor)==null?void 0:m.shape[2])||0,a=((g=s.tensor)==null?void 0:g.shape[1])||0;if(!s.tensor)return{data:[],canvas:null,alpha:null};let o={};o.resize=Ce.resizeBilinear(s.tensor,[Us.inputs[0].shape?Us.inputs[0].shape[1]:0,Us.inputs[0].shape?Us.inputs[0].shape[2]:0],!1),ne(s.tensor),o.norm=ge(o.resize,Ze.tf255),o.res=Us.execute(o.norm),o.squeeze=it(o.res,0),o.squeeze.shape[2]===2?(o.softmax=Xu(o.squeeze),[o.bg,o.fg]=os(o.softmax,2),o.expand=Zt(o.fg,2),o.pad=Zt(o.expand,0),o.crop=Ce.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=it(o.crop,0)):o.data=Ce.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(de.node&&!de.Canvas&&typeof ImageData=="undefined")return n.debug&&Z("canvas support missing"),Object.keys(o).forEach(A=>ne(o[A])),{data:i,canvas:null,alpha:null};let l=Yn(r,a);await Ks.toPixels(o.data,l);let c=l.getContext("2d");n.segmentation.blur&&n.segmentation.blur>0&&(c.filter=`blur(${n.segmentation.blur}px)`);let u=c.getImageData(0,0,r,a),d=Yn(r,a),p=d.getContext("2d");s.canvas&&p.drawImage(s.canvas,0,0),p.globalCompositeOperation="darken",n.segmentation.blur&&n.segmentation.blur>0&&(p.filter=`blur(${n.segmentation.blur}px)`),p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none";let h=p.getImageData(0,0,r,a);for(let A=0;A<r*a;A++)h.data[4*A+3]=u.data[4*A+0];p.putImageData(h,0,0);let f=null;if(t&&d){f=Yn(r,a);let A=await Tc(t,n);ne(A.tensor);let x=f.getContext("2d");x.drawImage(A.canvas,0,0,f.width,f.height),x.drawImage(d,0,0)}return Object.keys(o).forEach(A=>ne(o[A])),l5=!1,{data:i,canvas:d,alpha:l}}var c5=class{constructor(){he(this,"ssrnetage",null);he(this,"gear",null);he(this,"blazeposedetect",null);he(this,"blazepose",null);he(this,"centernet",null);he(this,"efficientpose",null);he(this,"mobilefacenet",null);he(this,"emotion",null);he(this,"facedetect",null);he(this,"faceiris",null);he(this,"facemesh",null);he(this,"faceres",null);he(this,"ssrnetgender",null);he(this,"handpose",null);he(this,"handskeleton",null);he(this,"handtrack",null);he(this,"liveness",null);he(this,"movenet",null);he(this,"nanodet",null);he(this,"posenet",null);he(this,"segmentation",null);he(this,"antispoof",null)}};function d5(e){for(let t of Object.keys(e.models))e.models[t]=null}async function rN(e){var t,n,s,r,a,o,i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k,I,N,R,O,$,P,T,F,U,q,z;de.initial&&d5(e),e.config.hand.enabled&&(!e.models.handpose&&((n=(t=e.config.hand.detector)==null?void 0:t.modelPath)==null?void 0:n.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await Bb(e.config)),!e.models.handskeleton&&e.config.hand.landmarks&&((r=(s=e.config.hand.detector)==null?void 0:s.modelPath)==null?void 0:r.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await Bb(e.config))),e.config.body.enabled&&!e.models.blazepose&&((o=(a=e.config.body)==null?void 0:a.modelPath)==null?void 0:o.includes("blazepose"))&&(e.models.blazepose=W8(e.config)),e.config.body.enabled&&!e.models.blazeposedetect&&((i=e.config.body.detector)==null?void 0:i.modelPath)&&((c=(l=e.config.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))&&(e.models.blazeposedetect=B8(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((d=(u=e.config.body)==null?void 0:u.modelPath)==null?void 0:d.includes("efficientpose"))&&(e.models.efficientpose=j8(e.config)),e.config.body.enabled&&!e.models.movenet&&((h=(p=e.config.body)==null?void 0:p.modelPath)==null?void 0:h.includes("movenet"))&&(e.models.movenet=qT(e.config)),e.config.body.enabled&&!e.models.posenet&&((m=(f=e.config.body)==null?void 0:f.modelPath)==null?void 0:m.includes("posenet"))&&(e.models.posenet=nN(e.config)),e.config.face.enabled&&!e.models.facedetect&&(e.models.facedetect=O8(e.config)),e.config.face.enabled&&((g=e.config.face.antispoof)==null?void 0:g.enabled)&&!e.models.antispoof&&(e.models.antispoof=T8(e.config)),e.config.face.enabled&&((A=e.config.face.liveness)==null?void 0:A.enabled)&&!e.models.liveness&&(e.models.liveness=WT(e.config)),e.config.face.enabled&&((x=e.config.face.description)==null?void 0:x.enabled)&&!e.models.faceres&&(e.models.faceres=pT(e.config)),e.config.face.enabled&&((y=e.config.face.emotion)==null?void 0:y.enabled)&&!e.models.emotion&&(e.models.emotion=K8(e.config)),e.config.face.enabled&&((b=e.config.face.iris)==null?void 0:b.enabled)&&!e.models.faceiris&&(e.models.faceiris=eT(e.config)),e.config.face.enabled&&((w=e.config.face.mesh)==null?void 0:w.enabled)&&!e.models.facemesh&&(e.models.facemesh=iT(e.config)),e.config.face.enabled&&((k=e.config.face.gear)==null?void 0:k.enabled)&&!e.models.gear&&(e.models.gear=A8(e.config)),e.config.face.enabled&&((I=e.config.face.ssrnet)==null?void 0:I.enabled)&&!e.models.ssrnetage&&(e.models.ssrnetage=v8(e.config)),e.config.face.enabled&&((N=e.config.face.ssrnet)==null?void 0:N.enabled)&&!e.models.ssrnetgender&&(e.models.ssrnetgender=S8(e.config)),e.config.face.enabled&&((R=e.config.face.mobilefacenet)==null?void 0:R.enabled)&&!e.models.mobilefacenet&&(e.models.mobilefacenet=Q8(e.config)),e.config.hand.enabled&&!e.models.handtrack&&(($=(O=e.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:$.includes("handtrack"))&&(e.models.handtrack=MT(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((T=(P=e.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handtrack"))&&(e.models.handskeleton=zT(e.config)),e.config.object.enabled&&!e.models.centernet&&((U=(F=e.config.object)==null?void 0:F.modelPath)==null?void 0:U.includes("centernet"))&&(e.models.centernet=G8(e.config)),e.config.object.enabled&&!e.models.nanodet&&((z=(q=e.config.object)==null?void 0:q.modelPath)==null?void 0:z.includes("nanodet"))&&(e.models.nanodet=KT(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=u5(e.config));for await(let K of Object.keys(e.models))e.models[K]&&typeof e.models[K]!="undefined"&&(e.models[K]=await e.models[K])}async function aN(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let n of Object.keys(e.models))if(e.models[n]){let s=[];Array.isArray(e.models[n])?s=e.models[n].filter(r=>r!==null).map(r=>r&&r.executor?r:r.model):s=[e.models[n]];for(let r of s){if(!r){e.config.debug&&Z("model marked as loaded but not defined:",n);continue}let a=[],o=r==null?void 0:r.executor;if(o&&o.graph.nodes)for(let l of Object.values(o.graph.nodes)){let c=l.op.toLowerCase();a.includes(c)||a.push(c)}else!o&&e.config.debug&&Z("model signature not determined:",n);let i=[];for(let l of a)!t.includes(l)&&!e.env.kernels.includes(l)&&!e.env.kernels.includes(l.replace("_",""))&&!e.env.kernels.includes(l.replace("native",""))&&!e.env.kernels.includes(l.replace("v2",""))&&i.push(l);i.length>0&&e.config.debug&&Z("model validation:",n,i)}}}var Nt={name:"humangl",priority:999,canvas:null,gl:null,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function t1e(){let e=Nt.gl;!e||(Nt.extensions=e.getSupportedExtensions())}async function oN(e){var t;if(e.config.backend==="humangl"&&(Nt.name in as().registry&&(!Nt.gl||!Nt.gl.getParameter(Nt.gl.VERSION))&&(Z("error: humangl backend invalid context"),d5(e)),!K2(Nt.name))){try{Nt.canvas=await Yn(100,100)}catch(s){Z("error: cannot create canvas:",s);return}try{if(Nt.gl=(t=Nt.canvas)==null?void 0:t.getContext("webgl2",Nt.webGLattr),!Nt.gl.getParameter(Nt.gl.VERSION).includes("2.0")){Z("override: using fallback webgl backend as webgl 2.0 is not detected"),e.config.backend="webgl";return}Nt.canvas&&(Nt.canvas.addEventListener("webglcontextlost",async r=>{throw Z("error: humangl:",r.type),Z("possible browser memory leak using webgl or conflict with multiple backend registrations"),e.emit("error"),new Error("backend error: webgl context lost")}),Nt.canvas.addEventListener("webglcontextrestored",r=>{Z("error: humangl context restored:",r)}),Nt.canvas.addEventListener("webglcontextcreationerror",r=>{Z("error: humangl context create:",r)}))}catch(s){Z("error: cannot get WebGL context:",s);return}try{Dm(2,Nt.gl)}catch(s){Z("error: cannot set WebGL context:",s);return}try{let s=new Vm(Nt.gl);ul(Nt.name,()=>new yp(s),Nt.priority)}catch(s){Z("error: cannot register WebGL backend:",s);return}try{Jr("webgl").forEach(r=>{let a={...r,backendName:Nt.name};cr(a)})}catch(s){Z("error: cannot update WebGL backend registration:",s);return}let n=Dr().getGPGPUContext?Dr().getGPGPUContext().gl:null;if(n)Z(`humangl webgl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`);else{Z("error: no current gl context:",n,Nt.gl);return}try{Rr.set("WEBGL_VERSION",2)}catch(s){Z("error: cannot set WebGL backend flags:",s);return}t1e(),Z("backend registered:",Nt.name)}}function n1e(){if(!de.kernels.includes("mod")){let e={kernelName:"Mod",backendName:Rs(),kernelFunc:t=>X(()=>fe(t.inputs.a,L(ge(t.inputs.a,t.inputs.b),t.inputs.b)))};cr(e),de.kernels.push("mod")}if(!de.kernels.includes("floormod")){let e={kernelName:"FloorMod",backendName:Rs(),kernelFunc:t=>X(()=>cf(t.inputs.a/t.inputs.b)*t.inputs.b+Md(t.inputs.a,t.inputs.b))};cr(e),de.kernels.push("floormod")}}async function _0(e,t=!1){if(e.state="backend",t||de.initial||e.config.backend&&e.config.backend.length>0&&Rs()!==e.config.backend){let n=ie();if(e.config.backend&&e.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&e.config.debug&&e.config.debug&&Z("running inside web worker"),de.browser&&e.config.backend==="tensorflow"&&(e.config.debug&&Z("override: backend set to tensorflow while running in browser"),e.config.backend="humangl"),de.node&&(e.config.backend==="webgl"||e.config.backend==="humangl")&&(e.config.debug&&Z(`override: backend set to ${e.config.backend} while running in nodejs`),e.config.backend="tensorflow"),de.browser&&e.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")Z("override: backend set to webgpu but browser does not support webgpu"),e.config.backend="humangl";else{let r=await navigator.gpu.requestAdapter();e.config.debug&&Z("enumerated webgpu adapter:",r)}e.config.backend==="humangl"&&await oN(e);let s=Object.keys(as().registryFactory);if(e.config.debug&&Z("available backends:",s),s.includes(e.config.backend)||(Z(`error: backend ${e.config.backend} not found in registry`),e.config.backend=de.node?"tensorflow":"webgl",e.config.debug&&Z(`override: setting backend ${e.config.backend}`)),e.config.debug&&Z("setting backend:",e.config.backend),e.config.backend==="wasm"){if(e.config.debug&&Z("wasm path:",e.config.wasmPath),typeof(Ml==null?void 0:Ml.setWasmPaths)!="undefined")await t8(e.config.wasmPath);else throw new Error("backend error: attempting to use wasm backend but wasm path is not set");let r=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&Z(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),e.config.debug&&!r&&Z("warning: wasm simd support is not enabled")}try{await J3(e.config.backend),await uf(),y8()}catch(r){return Z("error: cannot set backend:",e.config.backend,r),!1}}if(Rs()==="humangl"&&(Rr.set("CHECK_COMPUTATION_FOR_ERRORS",!1),Rr.set("WEBGL_CPU_FORWARD",!0),Rr.set("WEBGL_USE_SHAPES_UNIFORMS",!0),Rr.set("CPU_HANDOFF_SIZE_THRESHOLD",256),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(Z("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),Rr.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),Dr().getGPGPUContext)){let s=await Dr().getGPGPUContext().gl;e.config.debug&&Z(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}Rs()==="webgpu",Y3(),await uf(),e.performance.initBackend=Math.trunc(ie()-n),e.config.backend=Rs(),await de.updateBackend(),n1e()}return!0}function I0(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&Z("kernelFunc",n,t.backend)}};cr(s)}de.kernels=Jr(Rs()).map(n=>n.kernelName.toLowerCase())}var Aa={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1},p5=0,Xl=e=>{if(!e)Z("draw error: invalid canvas");else if(!e.getContext)Z("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)Z("draw error: cannot get canvas context");else return t}return null},Oc=e=>Math.round(e*180/Math.PI);function h5(e,t,n,s,r){s=s||0,e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Vp(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function iN(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function s1e(e,t,n){if(!(t.length<2)){if(!n.useCurves||t.length<=2){iN(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function lN(e,t,n,s=5){let r,a,o;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.moveTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),e.closePath(),e.stroke(),e.fill()}async function f5(e,t,n){let s=_n(Aa,n);if(!(!t||!e)&&s.drawGestures){let r=Xl(e);if(!r)return;r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let c=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${c}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(u,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(u,6,0+a*s.lineHeight),a+=1}}}}async function m5(e,t,n){var a,o,i,l,c;let s=_n(Aa,n);if(!t||!e)return;let r=Xl(e);if(!!r)for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Vp(r,u.box[0],u.box[1],u.box[2],u.box[3],s),s.drawLabels){let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.real&&d.push(`real: ${Math.trunc(100*u.real)}%`),u.live&&d.push(`live: ${Math.trunc(100*u.live)}%`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${Oc(u.rotation.angle.roll)}\xB0 yaw:${Oc(u.rotation.angle.yaw)}\xB0 pitch:${Oc(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${Oc(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=s.color;for(let p=d.length-1;p>=0;p--){let h=Math.max(u.box[0],0),f=p*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(d[p],h+5,f+16)),r.fillStyle=s.labelColor,r.fillText(d[p],h+4,f+15)}}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)h5(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let d=0;d<zl.length/3;d++){let p=[zl[d*3+0],zl[d*3+1],zl[d*3+2]].map(h=>u.mesh[h]);iN(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)&&typeof Path2D!="undefined"){r.strokeStyle="pink";let d=u.box[0]+u.box[2]/2-u.box[3]*Oc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*Oc(u.rotation.angle.pitch)/90,h=new Path2D(`
|
|
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
|
|
C
|
|
${d} ${u.box[1]},
|
|
${d} ${u.box[1]+u.box[3]},
|
|
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
|
|
`),f=new Path2D(`
|
|
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
|
|
C
|
|
${u.box[0]} ${p},
|
|
${u.box[0]+u.box[2]} ${p},
|
|
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
|
|
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];lN(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];lN(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function g5(e,t,n){var a;let s=_n(Aa,n);if(!t||!e)return;let r=Xl(e);if(!!r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(Vp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,h5(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)s1e(r,l,s)}}}async function A5(e,t,n){let s=_n(Aa,n);if(!t||!e)return;let r=Xl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Vp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,h5(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+l*i[l][2]}, ${127.5-l*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function y5(e,t,n){let s=_n(Aa,n);if(!t||!e)return;let r=Xl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Vp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}}async function uN(e,t,n){let s=_n(Aa,n);if(!t||!e)return;let r=Xl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Vp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}}async function cN(e,t){if(!e||!t)return;let n=Xl(t);!n||n.drawImage(e,0,0)}async function dN(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ie(),r=_n(Aa,n),a=Promise.all([m5(e,t.face,r),g5(e,t.body,r),A5(e,t.hand,r),y5(e,t.object,r),f5(e,t.gesture,r)]);return p5=de.perfadd?p5+Math.round(ie()-s):Math.round(ie()-s),t.performance.draw=p5,a}var Mc=.1,x5=.5;function r1e(e,t,n){let s=!1,r=n.length-1;for(let a=0;a<n.length;r=a++)n[a].y>t!=n[r].y>t&&e<(n[r].x-n[a].x)*(t-n[a].y)/(n[r].y-n[a].y)+n[a].x&&(s=!s);return s}async function pN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,s=await e.tensor.buffer(),r=[];for(let o of rr.silhouette)r.push({x:(e.mesh[o][0]-e.box[0])/e.box[2],y:(e.mesh[o][1]-e.box[1])/e.box[3]});Mc&&Mc>0&&(r=r.map(o=>({x:o.x>.5?o.x+Mc:o.x-Mc,y:o.y>.5?o.y+Mc:o.y-Mc})));for(let o=0;o<t;o++)for(let i=0;i<n;i++)r1e(o/t,i/t,r)||(s.set(x5*s.get(0,i,o,0),0,i,o,0),s.set(x5*s.get(0,i,o,1),0,i,o,1),s.set(x5*s.get(0,i,o,2),0,i,o,2));let a=s.toTensor();return ne(s),a}var a1e=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},hN=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},s=(g,A)=>{let x=g[0]-A[0],y=g[1]-A[1],b=g[2]-A[2];return[x,y,b]},r=(g,A)=>{let x=g[1]*A[2]-g[2]*A[1],y=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[x,y,b]},a=g=>{let[A,x,y,b,w,k,I,N,R]=g,O,$,P;return b<1?b>-1?(P=Math.asin(b),$=Math.atan2(-I,A),O=Math.atan2(-k,w)):(P=-Math.PI/2,$=-Math.atan2(N,R),O=0):(P=Math.PI/2,$=Math.atan2(N,R),O=0),isNaN(O)&&(O=0),isNaN($)&&($=0),isNaN(P)&&(P=0),{pitch:2*-O,yaw:2*-$,roll:2*-P}},o=g=>{let A=(y,b,w,k)=>Math.atan2(k-b,w-y);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?a1e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var b5=async(e,t)=>{var h,f,m,g,A,x,y,b,w,k,I,N,R,O,$,P,T,F,U,q,z,K,J,Q,te,re;let n,s,r,a,o,i,l,c,u,d=[];e.state="run:face",n=ie();let p=await oT(t,e.config);if(e.performance.face=de.perfadd?(e.performance.face||0)+Math.trunc(ie()-n):Math.trunc(ie()-n),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let G=0;G<p.length;G++){if(e.analyze("Get Face"),!p[G].tensor||p[G].tensor.isDisposedInternal){Z("Face object is disposed:",p[G].tensor);continue}if((h=e.config.face.detector)==null?void 0:h.mask){let we=await pN(p[G]);ne(p[G].tensor),p[G].tensor=we}let se=p[G].mesh&&p[G].mesh.length>200?hN(p[G],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=((f=e.config.face.emotion)==null?void 0:f.enabled)?Ib(p[G].tensor||ct([]),e.config,G,p.length):null:(e.state="run:emotion",n=ie(),o=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Ib(p[G].tensor||ct([]),e.config,G,p.length):null,e.performance.emotion=de.perfadd?(e.performance.emotion||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=((g=e.config.face.antispoof)==null?void 0:g.enabled)?nb(p[G].tensor||ct([]),e.config,G,p.length):null:(e.state="run:antispoof",n=ie(),l=((A=e.config.face.antispoof)==null?void 0:A.enabled)?await nb(p[G].tensor||ct([]),e.config,G,p.length):null,e.performance.antispoof=de.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=((x=e.config.face.liveness)==null?void 0:x.enabled)?jb(p[G].tensor||ct([]),e.config,G,p.length):null:(e.state="run:liveness",n=ie(),c=((y=e.config.face.liveness)==null?void 0:y.enabled)?await jb(p[G].tensor||ct([]),e.config,G,p.length):null,e.performance.liveness=de.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=((b=e.config.face.gear)==null?void 0:b.enabled)?Kx(p[G].tensor||ct([]),e.config,G,p.length):{}:(e.state="run:gear",n=ie(),r=((w=e.config.face.gear)==null?void 0:w.enabled)?await Kx(p[G].tensor||ct([]),e.config,G,p.length):{},e.performance.gear=Math.trunc(ie()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=((k=e.config.face.ssrnet)==null?void 0:k.enabled)?Yx(p[G].tensor||ct([]),e.config,G,p.length):{},a=((I=e.config.face.ssrnet)==null?void 0:I.enabled)?eb(p[G].tensor||ct([]),e.config,G,p.length):{}):(e.state="run:ssrnet",n=ie(),s=((N=e.config.face.ssrnet)==null?void 0:N.enabled)?await Yx(p[G].tensor||ct([]),e.config,G,p.length):{},a=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await eb(p[G].tensor||ct([]),e.config,G,p.length):{},e.performance.ssrnet=Math.trunc(ie()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=((O=e.config.face.mobilefacenet)==null?void 0:O.enabled)?Tb(p[G].tensor||ct([]),e.config,G,p.length):{}:(e.state="run:mobilefacenet",n=ie(),i=(($=e.config.face.mobilefacenet)==null?void 0:$.enabled)?await Tb(p[G].tensor||ct([]),e.config,G,p.length):{},e.performance.mobilefacenet=Math.trunc(ie()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?u=((P=e.config.face.description)==null?void 0:P.enabled)?Db(p[G].tensor||ct([]),e.config,G,p.length):null:(e.state="run:description",n=ie(),u=((T=e.config.face.description)==null?void 0:T.enabled)?await Db(p[G].tensor||ct([]),e.config,G,p.length):null,e.performance.description=de.perfadd?(e.performance.description||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,u,r,l,c]=await Promise.all([s,a,o,i,u,r,l,c])),e.analyze("Finish Face:"),((F=e.config.face.ssrnet)==null?void 0:F.enabled)&&s&&a&&(u={age:s.age,gender:a.gender,genderScore:a.genderScore}),((U=e.config.face.gear)==null?void 0:U.enabled)&&r&&(u={age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((q=e.config.face.mobilefacenet)==null?void 0:q.enabled)&&i&&(u.descriptor=i),!((z=e.config.face.iris)==null?void 0:z.enabled)&&((J=(K=p[G])==null?void 0:K.annotations)==null?void 0:J.leftEyeIris)&&((te=(Q=p[G])==null?void 0:Q.annotations)==null?void 0:te.rightEyeIris)&&(delete p[G].annotations.leftEyeIris,delete p[G].annotations.rightEyeIris);let oe=p[G].annotations&&p[G].annotations.leftEyeIris&&p[G].annotations.leftEyeIris[0]&&p[G].annotations.rightEyeIris&&p[G].annotations.rightEyeIris[0]&&p[G].annotations.leftEyeIris.length>0&&p[G].annotations.rightEyeIris.length>0&&p[G].annotations.leftEyeIris[0]!==null&&p[G].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[G].annotations.leftEyeIris[3][0]-p[G].annotations.leftEyeIris[1][0]),Math.abs(p[G].annotations.rightEyeIris[4][1]-p[G].annotations.rightEyeIris[2][1]))/t.shape[2]:0,pe=((re=e.config.face.detector)==null?void 0:re.return)?it(p[G].tensor):null;ne(p[G].tensor),p[G].tensor&&delete p[G].tensor;let ye={...p[G],id:G};(u==null?void 0:u.age)&&(ye.age=u.age),(u==null?void 0:u.gender)&&(ye.gender=u.gender),(u==null?void 0:u.genderScore)&&(ye.genderScore=u==null?void 0:u.genderScore),(u==null?void 0:u.descriptor)&&(ye.embedding=u==null?void 0:u.descriptor),(u==null?void 0:u.race)&&(ye.race=u==null?void 0:u.race),o&&(ye.emotion=o),l&&(ye.real=l),c&&(ye.live=c),oe&&oe!==0&&(ye.iris=Math.trunc(500/oe/11.7)/100),se&&(ye.rotation=se),pe&&(ye.tensor=pe),d.push(ye),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),d};var fN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},mN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=e[n].mesh[33][2]-e[n].mesh[263][2],r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(s/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2];Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},gN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},AN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=RT(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var De={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},v5=0;function yN(e,t){var o,i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k,I,N,R,O,$,P,T,F,U,q;let n=ie();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(e.canvas&&(De.canvas=e.canvas),e.error&&(De.error=e.error),!De.body||e.body.length!==De.body.length)De.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let K=e.body[z].box.map((G,se)=>((r-1)*De.body[z].box[se]+G)/r),J=e.body[z].boxRaw.map((G,se)=>((r-1)*De.body[z].boxRaw[se]+G)/r),Q=e.body[z].keypoints.map((G,se)=>({score:G.score,part:G.part,position:[De.body[z].keypoints[se]?((r-1)*De.body[z].keypoints[se].position[0]+G.position[0])/r:G.position[0],De.body[z].keypoints[se]?((r-1)*De.body[z].keypoints[se].position[1]+G.position[1])/r:G.position[1]],positionRaw:[De.body[z].keypoints[se]?((r-1)*De.body[z].keypoints[se].positionRaw[0]+G.positionRaw[0])/r:G.position[0],De.body[z].keypoints[se]?((r-1)*De.body[z].keypoints[se].positionRaw[1]+G.positionRaw[1])/r:G.position[1]]})),te={},re={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?re=vb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?re=pb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(re=Zb);for(let[G,se]of Object.entries(re.connected)){let oe=[];for(let pe=0;pe<se.length-1;pe++){let ye=Q.find(Ne=>Ne.part===se[pe]),we=Q.find(Ne=>Ne.part===se[pe+1]);ye&&we&&ye.score>(t.body.minConfidence||0)&&we.score>(t.body.minConfidence||0)&&oe.push([ye.position,we.position])}te[G]=oe}De.body[z]={...e.body[z],box:K,boxRaw:J,keypoints:Q,annotations:te}}if(!De.hand||e.hand.length!==De.hand.length)De.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let K=e.hand[z].box.map((re,G)=>((r-1)*De.hand[z].box[G]+re)/r),J=e.hand[z].boxRaw.map((re,G)=>((r-1)*De.hand[z].boxRaw[G]+re)/r);De.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(De.hand[z].keypoints=e.hand[z].keypoints);let Q=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((re,G)=>re.map((se,oe)=>((r-1)*(De.hand[z].keypoints[G][oe]||1)+(se||0))/r)):[],te={};if(Object.keys(De.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)De.hand[z].annotations=e.hand[z].annotations,te=De.hand[z].annotations;else if(e.hand[z].annotations)for(let re of Object.keys(e.hand[z].annotations))te[re]=e.hand[z].annotations[re]&&e.hand[z].annotations[re][0]?e.hand[z].annotations[re].map((G,se)=>G.map((oe,pe)=>((r-1)*De.hand[z].annotations[re][se][pe]+oe)/r)):null;De.hand[z]={...e.hand[z],box:K,boxRaw:J,keypoints:Q,annotations:te}}if(!De.face||e.face.length!==De.face.length)De.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let K=e.face[z].box.map((Q,te)=>((r-1)*De.face[z].box[te]+Q)/r),J=e.face[z].boxRaw.map((Q,te)=>((r-1)*De.face[z].boxRaw[te]+Q)/r);if(e.face[z].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,Q.angle={roll:((r-1)*(((f=(h=De.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(A=De.face[z].rotation)==null?void 0:A.angle)==null?void 0:x.yaw)||0)+(((b=(y=e.face[z].rotation)==null?void 0:y.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=De.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((N=(I=e.face[z].rotation)==null?void 0:I.angle)==null?void 0:N.pitch)||0))/r},Q.gaze={bearing:((r-1)*(((O=(R=De.face[z].rotation)==null?void 0:R.gaze)==null?void 0:O.bearing)||0)+(((P=($=e.face[z].rotation)==null?void 0:$.gaze)==null?void 0:P.bearing)||0))/r,strength:((r-1)*(((F=(T=De.face[z].rotation)==null?void 0:T.gaze)==null?void 0:F.strength)||0)+(((q=(U=e.face[z].rotation)==null?void 0:U.gaze)==null?void 0:q.strength)||0))/r},De.face[z]={...e.face[z],rotation:Q,box:K,boxRaw:J}}De.face[z]={...e.face[z],box:K,boxRaw:J}}if(!De.object||e.object.length!==De.object.length)De.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let K=e.object[z].box.map((Q,te)=>((r-1)*De.object[z].box[te]+Q)/r),J=e.object[z].boxRaw.map((Q,te)=>((r-1)*De.object[z].boxRaw[te]+Q)/r);De.object[z]={...e.object[z],box:K,boxRaw:J}}if(e.persons){let z=e.persons;if(!De.persons||z.length!==De.persons.length)De.persons=JSON.parse(JSON.stringify(z));else for(let K=0;K<z.length;K++)De.persons[K].box=z[K].box.map((J,Q)=>((r-1)*De.persons[K].box[Q]+J)/r)}e.gesture&&(De.gesture=e.gesture);let a=ie();return v5=de.perfadd?v5+Math.round(a-n):Math.round(a-n),e.performance&&(De.performance={...e.performance,interpolate:v5}),De}function D0(e,t,n={order:2,multiplier:25}){let s=0;for(let r=0;r<e.length;r++){let a=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);s+=!n.order||n.order===2?a*a:a**n.order}return(n.multiplier||20)*s}var xN=(e,t,n,s)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),a=(1-r/100-n)/(s-n);return Math.max(Math.min(a,1),0)};function bN(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=D0(e,t,n);return xN(s,n.order||2,n.min||0,n.max||1)}function vN(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let s=Number.MAX_SAFE_INTEGER,r=-1;for(let o=0;o<t.length;o++){let i=D0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=xN(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function wN(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k;let a=0,o=[];for(let I of e){let N={id:a++,face:I,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let F of t)I.box[0]>F.box[0]&&I.box[0]<F.box[0]+F.box[2]&&I.box[1]+I.box[3]>F.box[1]&&I.box[1]+I.box[3]<F.box[1]+F.box[3]&&(N.body=F);if(N.body)for(let F of n)F.box[0]+F.box[2]>N.body.box[0]&&F.box[0]+F.box[2]<N.body.box[0]+N.body.box[2]&&F.box[1]+F.box[3]>N.body.box[1]&&F.box[1]+F.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.left=F),F.box[0]<N.body.box[0]+N.body.box[2]&&F.box[0]>N.body.box[0]&&F.box[1]+F.box[3]>N.body.box[1]&&F.box[1]+F.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.right=F);for(let F of s)F.face!==void 0&&F.face===I.id?(i=N.gestures)==null||i.push(F):F.iris!==void 0&&F.iris===I.id?(l=N.gestures)==null||l.push(F):F.body!==void 0&&F.body===((c=N.body)==null?void 0:c.id)?(u=N.gestures)==null||u.push(F):F.hand!==void 0&&F.hand===((p=(d=N.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=N.gestures)==null||h.push(F):F.hand!==void 0&&F.hand===((m=(f=N.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=N.gestures)==null||g.push(F));let R=[],O=[],$=F=>{F&&F.length===4&&(R.push(F[0],F[0]+F[2]),O.push(F[1],F[1]+F[3]))};$((A=N.face)==null?void 0:A.box),$((x=N.body)==null?void 0:x.box),$((b=(y=N.hands)==null?void 0:y.left)==null?void 0:b.box),$((k=(w=N.hands)==null?void 0:w.right)==null?void 0:k.box);let P=Math.min(...R),T=Math.min(...O);N.box=[P,T,Math.max(...R)-P,Math.max(...O)-T],r&&r[1]&&r[2]&&(N.boxRaw=[N.box[0]/r[2],N.box[1]/r[1],N.box[2]/r[2],N.box[3]/r[1]]),o.push(N)}return o}var P0=`
|
|
/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==`,F0=`
|
|
/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==`;async function o1e(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(P0);break;case"body":case"full":n=await t(F0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function i1e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+P0;break;case"full":case"body":n="data:image/jpeg;base64,"+F0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:de.Image&&(s=new de.Image),s.onload=async()=>{let r=Yn(s.naturalWidth,s.naturalHeight);if(!r)Z("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function l1e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(P0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(F0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&Z("Warmup tfjs-node not loaded");return s}async function kN(e,t){let n=ie();if(e.state="warmup",t&&(e.config=_n(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await o1e(e):typeof Image!="undefined"||de.Canvas!==void 0?s=await i1e(e):s=await l1e(e);let a=ie();e.config.debug&&Z("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var zc,Up,Gp,O0,IN=class{constructor(t){he(this,"version");he(this,"config");he(this,"result");he(this,"state");he(this,"process");he(this,"tf");he(this,"env");he(this,"draw");he(this,"models");he(this,"events");he(this,"faceTriangulation");he(this,"faceUVMap");he(this,"performance");Qc(this,zc,void 0);Qc(this,Up,void 0);Qc(this,Gp,void 0);he(this,"gl");he(this,"analyze",(...t)=>{if(!Jc(this,Up))return;let n=this.tf.engine().state.numTensors,s=Jc(this,zc);ed(this,zc,n);let r=n-s;r!==0&&Z(...t,r)});Qc(this,O0,t=>{if(!Jc(this,Gp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Qe))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});he(this,"similarity",bN);he(this,"distance",D0);he(this,"match",vN);he(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=de,Ca.wasmPath=_p.includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${_p}/dist/`,Ca.modelBasePath=de.browser?"../models/":"file://models/",Ca.backend=de.browser?"humangl":"tensorflow",this.version=jx,Object.defineProperty(this,"version",{value:jx}),this.config=JSON.parse(JSON.stringify(Ca)),Object.seal(this.config),t&&(this.config=_n(this.config,t)),this.tf=Ml,this.state="idle",ed(this,zc,0),ed(this,Up,!1),ed(this,Gp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new c5,this.draw={options:Aa,canvas:(n,s)=>cN(n,s),face:(n,s,r)=>m5(n,s,r),body:(n,s,r)=>g5(n,s,r),hand:(n,s,r)=>A5(n,s,r),gesture:(n,s,r)=>f5(n,s,r),object:(n,s,r)=>y5(n,s,r),person:(n,s,r)=>uN(n,s,r),all:(n,s,r)=>dN(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=lT,this.faceUVMap=uT,this.gl=Nt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ca)),this.config.backend=t}validate(t){return o2(Ca,t||this.config)}now(){return ie()}image(t,n=!0){return Tc(t,this.config,n)}async segmentation(t,n){return sN(t,n,this.config)}enhance(t){return _b(t)}compare(t,n){return h8(this.config,t,n)}async init(){await _0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=ie(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=_n(this.config,t)),this.env.initial&&(this.config.debug&&Z(`version: ${this.version}`),this.config.debug&&Z(`tfjs version: ${this.tf.version_core}`),await _0(this)||Z("error: backend check failed"),await uf(),this.env.browser&&(this.config.debug&&Z("configuration:",this.config),this.config.debug&&Z("environment:",this.env),this.config.debug&&Z("tf flags:",this.tf.ENV.flags))),await rN(this),this.env.initial&&this.config.debug&&Z("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await aN(this),this.emit("load"));let a=Math.trunc(ie()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return yN(t,this.config)}async warmup(t){let n=ie(),s=await kN(this,t),r=ie();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,x,y,b,w,k,I,N,R,O,$,P,T,F,U,q,z,K,J,Q,te;this.state="config";let r;this.config=_n(this.config,n),this.state="check";let a=Jc(this,O0).call(this,t);a&&(Z(a,t),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ie(),persons:[],error:a}));let o=ie();await _0(this),await this.load(),r=ie(),this.state="image";let i=await Tc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&Z("could not convert input to tensor"),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ie(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=ie(),this.config.skipAllowed=await p8(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?b5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ie(),l=this.config.face.enabled?await b5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?_n(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?i5(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?c=this.config.body.enabled?mb(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?kb(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("movenet"))&&(c=this.config.body.enabled?Qb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=ie(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await i5(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await mb(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await kb(i.tensor,p):[]:((I=this.config.body.modelPath)==null?void 0:I.includes("movenet"))&&(c=this.config.body.enabled?await Qb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?_n(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(N=this.config.hand.detector)==null?void 0:N.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?Lb(i.tensor,h):[]:(($=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:$.includes("handtrack"))&&(u=this.config.hand.enabled?Gb(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ie(),((T=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await Lb(i.tensor,h):[]:((U=(F=this.config.hand.detector)==null?void 0:F.modelPath)==null?void 0:U.includes("handtrack"))&&(u=this.config.hand.enabled?await Gb(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((q=this.config.object.modelPath)==null?void 0:q.includes("nanodet"))?d=this.config.object.enabled?t5(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?yb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ie(),((K=this.config.object.modelPath)==null?void 0:K.includes("nanodet"))?d=this.config.object.enabled?await t5(i.tensor,this.config):[]:((J=this.config.object.modelPath)==null?void 0:J.includes("centernet"))&&(d=this.config.object.enabled?await yb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,u,d]=await Promise.all([l,c,u,d])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ie(),f=[...mN(l),...fN(c),...AN(u),...gN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ie()-o):Math.trunc(ie()-o);let m=((te=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:te.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return wN(l,c,u,f,m)}},ne(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};zc=new WeakMap,Up=new WeakMap,Gp=new WeakMap,O0=new WeakMap;return u1e;})();
|
|
/**
|
|
* @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 backend 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 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
|
|
* 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
|
|
*
|
|
* https://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.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* Human main module
|
|
* @default Human Library
|
|
* @summary <https://github.com/vladmandic/human>
|
|
* @author <https://github.com/vladmandic>
|
|
* @copyright <https://github.com/vladmandic>
|
|
* @license 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 See the LICENSE file. */
|