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 r2=Object.defineProperty;var wE=(e,t,n)=>t in e?r2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var kE=e=>r2(e,"__esModule",{value:!0});var Ca=(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)=>{kE(e);for(var n in t)r2(e,n,{get:t[n],enumerable:!0})};var he=(e,t,n)=>(wE(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 l1e={};Yc(l1e,{Human:()=>SN,default:()=>SN,defaults:()=>Ta,env:()=>de});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: ${r} expecting json file`);return r}function ee(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var ie=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function a2(e,t,n="config",s=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")a2(e[r],t[r],r,s);else{let a=e&&typeof e[r]!="undefined";a||s.push({reason:"unknown property",where:`${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&&ee("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 Ta={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:()=>ou,Acosh:()=>iu,AdadeltaOptimizer:()=>Df,AdagradOptimizer:()=>Pf,AdamOptimizer:()=>Ff,AdamaxOptimizer:()=>Of,Add:()=>Kr,AddN:()=>$a,All:()=>lu,Any:()=>uu,ArgMax:()=>_a,ArgMin:()=>cu,Asin:()=>du,Asinh:()=>pu,Atan:()=>hu,Atan2:()=>mu,Atanh:()=>fu,AvgPool:()=>Da,AvgPool3D:()=>rd,AvgPool3DGrad:()=>wh,AvgPoolGrad:()=>vh,BackendWasm:()=>Q6,BatchMatMul:()=>Pa,BatchToSpaceND:()=>mi,Bincount:()=>kh,BroadcastArgs:()=>Sh,BroadcastTo:()=>Y5,Callback:()=>l7,CallbackList:()=>Zw,Cast:()=>Fa,Ceil:()=>Oa,ClipByValue:()=>Zr,Complex:()=>ad,ComplexAbs:()=>od,Concat:()=>gi,Conv2D:()=>Ma,Conv2DBackpropFilter:()=>Ih,Conv2DBackpropInput:()=>za,Conv3D:()=>id,Conv3DBackpropFilterV2:()=>Ch,Conv3DBackpropInputV2:()=>Th,Cos:()=>La,Cosh:()=>Ba,CropAndResize:()=>yi,Cumsum:()=>Ai,CustomCallback:()=>Jw,DataStorage:()=>td,DenseBincount:()=>Nh,DepthToSpace:()=>xi,DepthwiseConv2dNative:()=>Wa,DepthwiseConv2dNativeBackpropFilter:()=>Eh,DepthwiseConv2dNativeBackpropInput:()=>Rh,Diag:()=>$h,Dilation2D:()=>ld,Dilation2DBackpropFilter:()=>Dh,Dilation2DBackpropInput:()=>_h,ENV:()=>Rr,EarlyStopping:()=>c7,Einsum:()=>ud,Elu:()=>Ua,EluGrad:()=>Ph,Environment:()=>K5,Equal:()=>bi,Erf:()=>gu,Exp:()=>Ga,ExpandDims:()=>vi,Expm1:()=>wi,FFT:()=>Fh,Fill:()=>Au,FlipLeftRight:()=>ki,Floor:()=>Ha,FloorDiv:()=>ja,FromPixels:()=>yd,FusedBatchNorm:()=>qa,FusedConv2D:()=>So,FusedDepthwiseConv2D:()=>Io,GPGPUContext:()=>Bm,GatherNd:()=>Ii,GatherV2:()=>Si,GraphModel:()=>V7,Greater:()=>Ci,GreaterEqual:()=>Xa,History:()=>Yw,IFFT:()=>Oh,Identity:()=>Ka,Imag:()=>cd,InputSpec:()=>Yt,IsFinite:()=>yu,IsInf:()=>xu,IsNan:()=>bu,KernelBackend:()=>su,LRN:()=>pd,LRNGrad:()=>zh,LayerVariable:()=>Hw,LayersModel:()=>aa,LeakyRelu:()=>Ti,Less:()=>Ni,LessEqual:()=>Ei,LinSpace:()=>Mh,Log:()=>Za,Log1p:()=>vu,LogSoftmax:()=>J5,LogicalAnd:()=>Ri,LogicalNot:()=>wu,LogicalOr:()=>dd,MathBackendCPU:()=>Wy,MathBackendWebGL:()=>yp,Max:()=>Ya,MaxPool:()=>Qa,MaxPool3D:()=>hd,MaxPool3DGrad:()=>Bh,MaxPoolGrad:()=>Lh,MaxPoolWithArgmax:()=>Wh,Maximum:()=>Ja,Mean:()=>eo,Min:()=>to,Minimum:()=>no,MirrorPad:()=>so,Mod:()=>ku,MomentumOptimizer:()=>Mf,Multinomial:()=>Vh,Multiply:()=>ro,Neg:()=>$i,NonMaxSuppressionV3:()=>Di,NonMaxSuppressionV4:()=>Su,NonMaxSuppressionV5:()=>Pi,NotEqual:()=>_i,OP_SCOPE_SUFFIX:()=>h3,OneHot:()=>Oi,OnesLike:()=>Fi,Optimizer:()=>na,OptimizerConstructors:()=>Mo,Pack:()=>Mi,PadV2:()=>ao,Pool:()=>d9,Pow:()=>oo,Prelu:()=>io,Prod:()=>zi,RMSPropOptimizer:()=>zf,RNN:()=>oa,Range:()=>Iu,Rank:()=>g2,Real:()=>fd,RealDiv:()=>Va,Reciprocal:()=>Cu,Reduction:()=>Un,Relu:()=>lo,Relu6:()=>co,Reshape:()=>Li,ResizeBilinear:()=>uo,ResizeBilinearGrad:()=>Gh,ResizeNearestNeighbor:()=>Tu,ResizeNearestNeighborGrad:()=>Uh,Reverse:()=>Bi,RotateWithOffset:()=>el,Round:()=>Wi,Rsqrt:()=>po,SGDOptimizer:()=>Vd,ScatterNd:()=>Vi,Select:()=>Ui,Selu:()=>Nu,Sequential:()=>um,Sigmoid:()=>fo,Sign:()=>Eu,Sin:()=>ho,Sinh:()=>Hi,Slice:()=>Gi,Softmax:()=>Ao,Softplus:()=>Ru,SpaceToBatchND:()=>ji,SparseFillEmptyRows:()=>Hh,SparseReshape:()=>jh,SparseSegmentMean:()=>qh,SparseSegmentSum:()=>Xh,SparseToDense:()=>md,SplitV:()=>qi,Sqrt:()=>mo,Square:()=>$u,SquaredDifference:()=>yo,Step:()=>wo,StridedSlice:()=>Xi,StringNGrams:()=>gd,StringSplit:()=>Kh,StringToHashBucketFast:()=>Zh,Sub:()=>xo,Sum:()=>go,SymbolicTensor:()=>xr,Tan:()=>Ki,Tanh:()=>bo,Tensor:()=>Je,TensorBuffer:()=>nn,Tile:()=>Yr,TopK:()=>Zi,Transform:()=>Yi,Transpose:()=>vo,Unique:()=>Yh,Unpack:()=>Ji,UnsortedSegmentSum:()=>Ad,Variable:()=>Cd,ZerosLike:()=>Qi,_FusedMatMul:()=>ko,abs:()=>sn,acos:()=>Q3,acosh:()=>ev,add:()=>ue,addN:()=>Mu,all:()=>K2,any:()=>cf,argMax:()=>Zs,argMin:()=>tv,asin:()=>nv,asinh:()=>sv,atan:()=>rv,atan2:()=>av,atanh:()=>ov,avgPool:()=>pf,avgPool3d:()=>J2,backend:()=>Dr,backend_util:()=>E,basicLSTMCell:()=>J$,batchNorm:()=>Bu,batchNorm2d:()=>cv,batchNorm3d:()=>dv,batchNorm4d:()=>pv,batchToSpaceND:()=>hf,bincount:()=>Q2,booleanMaskAsync:()=>cF,broadcastArgs:()=>hv,broadcastTo:()=>_d,broadcast_util:()=>ol,browser:()=>Ks,buffer:()=>ze,callbacks:()=>IU,cast:()=>fe,ceil:()=>fv,clipByValue:()=>ms,clone:()=>Wn,complex:()=>No,concat:()=>wt,concat1d:()=>mv,concat2d:()=>Wu,concat3d:()=>gv,concat4d:()=>Av,constraints:()=>Sw,conv1d:()=>e1,conv2d:()=>Do,conv2dTranspose:()=>n1,conv3d:()=>s1,conv3dTranspose:()=>xv,copyRegisteredKernels:()=>m9,cos:()=>ff,cosh:()=>r1,cosineWindow:()=>E1,cumsum:()=>a1,customGrad:()=>Fr,data:()=>U7,denseBincount:()=>bv,deprecationWarn:()=>q2,depthToSpace:()=>vv,depthwiseConv2d:()=>Dd,deregisterOp:()=>NU,device_util:()=>Pu,diag:()=>N_,dilation2d:()=>wv,disableDeprecationWarnings:()=>f$,dispose:()=>te,disposeVariables:()=>m$,div:()=>me,divNoNan:()=>kv,dot:()=>F_,dropout:()=>Jv,einsum:()=>Sv,elu:()=>Pd,enableDebugMode:()=>h$,enableProdMode:()=>Y3,enclosingPowerOfTwo:()=>Qv,engine:()=>rs,env:()=>Z,equal:()=>$s,erf:()=>Iv,exp:()=>_s,expandDims:()=>Kt,expm1:()=>Cv,eye:()=>o1,fft:()=>If,fill:()=>Vu,findBackend:()=>X2,findBackendFactory:()=>x$,floor:()=>Fd,floorDiv:()=>uf,forceHalfFloat:()=>x4,fused:()=>Oo,gather:()=>Uu,gatherND:()=>Yv,gather_util:()=>L2,getBackend:()=>Rs,getGradient:()=>p2,getKernel:()=>Jh,getKernelsForBackend:()=>Jr,getThreadsCount:()=>t2e,gpgpu_util:()=>jI,grad:()=>oD,grads:()=>iD,greater:()=>gs,greaterEqual:()=>dl,ifft:()=>Ld,imag:()=>mf,image:()=>Ie,inTopKAsync:()=>vF,initializers:()=>$w,input:()=>vk,io:()=>ss,irfft:()=>k1,isFinite:()=>Z_,isInf:()=>J_,isNaN:()=>Tv,keep:()=>gn,kernel_impls:()=>Qs,layers:()=>Vw,leakyRelu:()=>gf,less:()=>i1,lessEqual:()=>pl,linalg:()=>uw,linspace:()=>Nv,loadGraphModel:()=>Be,loadLayersModel:()=>OW,localResponseNormalization:()=>Ev,log:()=>Ds,log1p:()=>Af,logSigmoid:()=>hD,logSoftmax:()=>l1,logSumExp:()=>Pv,logicalAnd:()=>hr,logicalNot:()=>xf,logicalOr:()=>d1,logicalXor:()=>ID,losses:()=>aM,matMul:()=>He,math:()=>$3,max:()=>An,maxPool:()=>bf,maxPool3d:()=>p1,maxPoolWithArgmax:()=>Fv,maximum:()=>ea,mean:()=>Wt,memory:()=>of,meshgrid:()=>$D,metrics:()=>a7,min:()=>Po,minimum:()=>Od,mirrorPad:()=>Ov,mod:()=>Md,model:()=>PW,models:()=>o7,moments:()=>vf,movingAverage:()=>hF,mul:()=>L,multiRNNCell:()=>LD,multinomial:()=>Mv,neg:()=>Ot,nextFrame:()=>cw,norm:()=>T1,notEqual:()=>Hu,oneHot:()=>Rd,ones:()=>As,onesLike:()=>Ps,op:()=>V,outerProduct:()=>GD,pad:()=>Js,pad1d:()=>qD,pad2d:()=>KD,pad3d:()=>YD,pad4d:()=>QD,pool:()=>rP,pow:()=>Fo,prelu:()=>kf,print:()=>I3,prod:()=>h1,profile:()=>g$,rand:()=>uP,randomGamma:()=>hP,randomNormal:()=>zv,randomUniform:()=>ju,range:()=>qu,ready:()=>lf,real:()=>zd,reciprocal:()=>Lv,registerBackend:()=>ul,registerCallbackConstructor:()=>MW,registerGradient:()=>Q5,registerKernel:()=>cr,registerOp:()=>TU,regularizers:()=>i7,relu:()=>Or,relu6:()=>g1,removeBackend:()=>y$,reshape:()=>H,reverse:()=>Fs,reverse1d:()=>wP,reverse2d:()=>SP,reverse3d:()=>CP,reverse4d:()=>NP,rfft:()=>Cf,round:()=>A1,rsqrt:()=>y1,scalar:()=>$e,scatterND:()=>Zv,scatter_util:()=>B2,selu:()=>x1,separableConv2d:()=>Bv,sequential:()=>FW,serialization:()=>ce,setBackend:()=>J3,setPlatform:()=>b$,setThreadsCount:()=>e2e,setWasmPath:()=>Qge,setWasmPaths:()=>t8,setWebGLContext:()=>$m,setdiff1dAsync:()=>Wv,shared:()=>Im,sigmoid:()=>fs,sign:()=>Vv,signal:()=>rM,sin:()=>b1,sinh:()=>v1,slice:()=>De,slice1d:()=>Sf,slice2d:()=>w1,slice3d:()=>fl,slice4d:()=>ml,slice_util:()=>Ft,softmax:()=>Xu,softplus:()=>Gu,spaceToBatchND:()=>wf,sparse:()=>Wd,sparseToDense:()=>N1,spectral:()=>sM,split:()=>Ht,sqrt:()=>Dn,square:()=>At,squaredDifference:()=>S1,squeeze:()=>ot,stack:()=>yn,step:()=>Bd,stridedSlice:()=>Uv,string:()=>_f,sub:()=>ge,sum:()=>Se,sumOutType:()=>Td,tan:()=>Gv,tanh:()=>Lu,tensor:()=>ut,tensor1d:()=>Zt,tensor2d:()=>fr,tensor3d:()=>D3,tensor4d:()=>eF,tensor5d:()=>tF,tensor6d:()=>nF,tensor_util:()=>dr,test_util:()=>X3,tidy:()=>X,tile:()=>Ys,time:()=>A$,topk:()=>Hv,train:()=>gl,transpose:()=>Qe,truncatedNormal:()=>Tf,unique:()=>I1,unregisterGradient:()=>f9,unregisterKernel:()=>h9,unsortedSegmentSum:()=>jv,unstack:()=>as,upcastType:()=>Bn,util:()=>v,valueAndGrad:()=>lD,valueAndGrads:()=>uD,variable:()=>qv,variableGrads:()=>Rv,version:()=>s8,version_converter:()=>$G,version_core:()=>_p,version_cpu:()=>Aj,version_layers:()=>dA,version_wasm:()=>n2e,version_webgl:()=>GQ,webgl:()=>HQ,webgl_util:()=>gI,webgpu:()=>t6,where:()=>Vn,whereAsync:()=>C1,zeros:()=>Gt,zerosLike:()=>et});var nu=(e=>typeof Ca!="undefined"?Ca:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof Ca!="undefined"?Ca:t)[n]}):e)(function(e){if(typeof Ca!="undefined")return Ca.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),SE=Object.create,mh=Object.defineProperty,IE=Object.getOwnPropertyDescriptor,CE=Object.getOwnPropertyNames,TE=Object.getPrototypeOf,NE=Object.prototype.hasOwnProperty,O5=e=>mh(e,"__esModule",{value:!0}),js=(e=>typeof nu!="undefined"?nu:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof nu!="undefined"?nu:t)[n]}):e)(function(e){if(typeof nu!="undefined")return nu.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),ns=(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)mh(e,n,{get:t[n],enumerable:!0})},EE=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of CE(t))!NE.call(e,s)&&s!=="default"&&mh(e,s,{get:()=>t[s],enumerable:!(n=IE(t,s))||n.enumerable});return e},di=e=>EE(O5(mh(e!=null?SE(TE(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),RE=ns({"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 Y=Math.min(8,P.length-K),J=parseInt(P.substring(K,K+Y),F);if(Y<8){var ne=l(u(F,Y));z=z.mul(ne).add(l(J))}else z=z.mul(q),z=z.add(l(J))}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,Y="";;){var J=K.div(z),ne=K.sub(J.mul(z)).toInt()>>>0,re=ne.toString(T);if(K=J,K.isZero())return re+Y;for(;re.length<6;)re="0"+re;Y=""+re+Y}},_.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,Y=T.high&65535,J=T.low>>>16,ne=T.low&65535,re=0,G=0,se=0,oe=0;return oe+=z+ne,se+=oe>>>16,oe&=65535,se+=q+J,G+=se>>>16,se&=65535,G+=U+Y,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,Y=T.high>>>16,J=T.high&65535,ne=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*ne,se+=oe>>>16,oe&=65535,se+=q*re,G+=se>>>16,se&=65535,se+=z*ne,G+=se>>>16,se&=65535,se+=K*J,G+=se>>>16,se&=65535,G+=U*re+q*ne+z*J+K*Y,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 Y=Math.ceil(Math.log(U)/Math.LN2),J=Y<=48?1:u(2,Y-48),ne=l(U),re=ne.mul(T);re.isNegative()||re.gt(q);)U-=J,ne=l(U,this.unsigned),re=ne.mul(T);ne.isZero()&&(ne=w),z=z.add(ne),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=ns({"(disabled):node-fetch"(){}}),_E=ns({"(disabled):util"(){}}),DE=ns({"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)}}),PE=ns({"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)}}),FE=ns({"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)}}),OE=ns({"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)}}),ME=ns({"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)}}),zE=ns({"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)}}),LE=ns({"(disabled):crypto"(){}}),BE=ns({"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=LE()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):r["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),gh=ns({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=DE(),s=PE(),r=FE(),a=OE(),o=ME(),i=zE(),l=BE();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),M5=ns({"(disabled):src/node_modules/string_decoder/index.js"(){}}),WE=ns({"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!=Tt&&Sn(se.buffer),Dt}function o(){return se.buffer!=Tt&&Sn(se.buffer),Ts}function i(){return se.buffer!=Tt&&Sn(se.buffer),On}function l(){return se.buffer!=Tt&&Sn(se.buffer),ps}function c(){return se.buffer!=Tt&&Sn(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 $},x=!1,y=!1,b=!1,w=!1;x=typeof window=="object",y=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!x&&!b&&!y;var k=u.ENVIRONMENT_IS_PTHREAD||!1;k&&(Tt=u.buffer);var I="";function N(C){return u.locateFile?u.locateFile(C,I):I+C}var R,O,_,P,T,F;if(b){y?I=js("path").dirname(I)+"/":I=__dirname+"/",R=function($,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,$,B){var Q=new XMLHttpRequest;Q.open("GET",C,!0),Q.responseType="arraybuffer",Q.onload=function(){if(Q.status==200||Q.status==0&&Q.response){$(Q.response);return}B()},Q.onerror=B,Q.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 Y=Atomics.load,J=Atomics.store,ne=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 Te(C){var $=u["_"+C];return we($,"Cannot call unknown function "+C+", make sure it is exported"),$}function Me(C,$,B,Q,be){var Ae={string:function(Mn){var tu=0;if(Mn!=null&&Mn!==0){var P5=(Mn.length<<2)+1;tu=Jl(P5),pt(Mn,tu,P5)}return tu},array:function(Mn){var tu=Jl(Mn.length);return kt(Mn,tu),tu}};function xe(Mn){return $==="string"?Ke(Mn):$==="boolean"?Boolean(Mn):Mn}var Ne=Te(C),ct=[],hn=0;if(Q)for(var tn=0;tn<Q.length;tn++){var Ia=Ae[B[tn]];Ia?(hn===0&&(hn=Kc()),ct[tn]=Ia(Q[tn])):ct[tn]=Q[tn]}var eu=Ne.apply(null,ct);return eu=xe(eu),hn!==0&&Yl(hn),eu}function Ue(C,$,B,Q){B=B||[];var be=B.every(function(xe){return xe==="number"}),Ae=$!=="string";return Ae&&be&&!Q?Te(C):function(){return Me(C,$,B,arguments,Q)}}function qe(C,$,B){for(var Q=$+B,be="";!($>=Q);){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 Ne=C[$++]&63;if((Ae&240)==224?Ae=(Ae&15)<<12|xe<<6|Ne:Ae=(Ae&7)<<18|xe<<12|Ne<<6|C[$++]&63,Ae<65536)be+=String.fromCharCode(Ae);else{var ct=Ae-65536;be+=String.fromCharCode(55296|ct>>10,56320|ct&1023)}}return be}function Ke(C,$){return C?qe(o(),C,$):""}function dt(C,$,B,Q){if(!(Q>0))return 0;for(var be=B,Ae=B+Q-1,xe=0;xe<C.length;++xe){var Ne=C.charCodeAt(xe);if(Ne>=55296&&Ne<=57343){var ct=C.charCodeAt(++xe);Ne=65536+((Ne&1023)<<10)|ct&1023}if(Ne<=127){if(B>=Ae)break;$[B++]=Ne}else if(Ne<=2047){if(B+1>=Ae)break;$[B++]=192|Ne>>6,$[B++]=128|Ne&63}else if(Ne<=65535){if(B+2>=Ae)break;$[B++]=224|Ne>>12,$[B++]=128|Ne>>6&63,$[B++]=128|Ne&63}else{if(B+3>=Ae)break;$[B++]=240|Ne>>18,$[B++]=128|Ne>>12&63,$[B++]=128|Ne>>6&63,$[B++]=128|Ne&63}}return $[B]=0,B-be}function pt(C,$,B){return dt(C,o(),$,B)}function rt(C){for(var $=0,B=0;B<C.length;++B){var Q=C.charCodeAt(B);Q>=55296&&Q<=57343&&(Q=65536+((Q&1023)<<10)|C.charCodeAt(++B)&1023),Q<=127?++$:Q<=2047?$+=2:Q<=65535?$+=3:$+=4}return $}function kt(C,$){a().set(C,$)}function mt(C,$){return C%$>0&&(C+=$-C%$),C}var Tt,Dt,Ts,kn,lr,On,ps,Gs,Ns;function Sn(C){Tt=C,u.HEAP8=Dt=new Int8Array(C),u.HEAP16=kn=new Int16Array(C),u.HEAP32=On=new Int32Array(C),u.HEAPU8=Ts=new Uint8Array(C),u.HEAPU16=lr=new Uint16Array(C),u.HEAPU32=ps=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,Tt=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&&(Tt=se.buffer),Tr=Tt.byteLength,Sn(Tt);var Rn,Nr=[],Er=[],xa=[],Lc=[],ur=[],Gp=!1,O0=!1;k||Er.push({func:function(){lh()}});function Hp(){if(!k){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)M0(u.preRun.shift());Xl(Nr)}}function jp(){Gp=!0,!k&&Xl(Er)}function qp(){k||Xl(xa)}function es(){k||(O0=!0)}function Xp(){if(!k){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)z0(u.postRun.shift());Xl(ur)}}function M0(C){Nr.unshift(C)}function z0(C){ur.unshift(C)}var Hs=0,Bc=null,ii=null;function L0(C){we(!k,"addRunDependency cannot be used in a pthread worker"),Hs++,u.monitorRunDependencies&&u.monitorRunDependencies(Hs)}function B0(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 W0="data:application/octet-stream;base64,";function Kp(C){return li(C,W0)}var V0="file://";function Zp(C){return li(C,V0)}var ts="tfjs-backend-wasm-threaded-simd.wasm";Kp(ts)||(ts=N(ts));function U0(C){try{if(C==ts&&re)return new Uint8Array(re);if(_)return _(C);throw"both async and sync fetching of the wasm failed"}catch($){jr($)}}function Yp(){if(!re&&(x||y)){if(typeof fetch=="function"&&!Zp(ts))return fetch(ts,{credentials:"same-origin"}).then(function(C){if(!C.ok)throw"failed to load wasm binary file at '"+ts+"'";return C.arrayBuffer()}).catch(function(){return U0(ts)});if(O)return new Promise(function(C,$){O(ts,function(B){C(new Uint8Array(B))},$)})}return Promise.resolve().then(function(){return U0(ts)})}function G0(){var C={a:Og};function $(xe,Ne){var ct=xe.exports;if(u.asm=ct,Rn=u.asm.kb,oe=Ne,!k){var hn=Re.unusedWorkers.length;Re.unusedWorkers.forEach(function(tn){Re.loadWasmModuleToWorker(tn,function(){--hn||B0("wasm-instantiate")})})}}k||L0("wasm-instantiate");function B(xe){$(xe.instance,xe.module)}function Q(xe){return Yp().then(function(Ne){return WebAssembly.instantiate(Ne,C)}).then(xe,function(Ne){z("failed to asynchronously prepare wasm: "+Ne),jr(Ne)})}function be(){return!re&&typeof WebAssembly.instantiateStreaming=="function"&&!Kp(ts)&&!Zp(ts)&&typeof fetch=="function"?fetch(ts,{credentials:"same-origin"}).then(function(xe){var Ne=WebAssembly.instantiateStreaming(xe,C);return Ne.then(B,function(ct){return z("wasm streaming compile failed: "+ct),z("falling back to ArrayBuffer instantiation"),Q(B)})}):Q(B)}if(u.instantiateWasm)try{var Ae=u.instantiateWasm(C,$);return Ae}catch(xe){return z("Module.instantiateWasm callback failed with error: "+xe),!1}return be().catch(p),{}}var Jp={10072:function(){throw"Canceled!"},10090:function(C,$){setTimeout(function(){N5(C,$)},0)}};function H0(){Re.initRuntime()}function Xl(C){for(;C.length>0;){var $=C.shift();if(typeof $=="function"){$(u);continue}var B=$.func;typeof B=="number"?$.arg===void 0?Rn.get(B)():Rn.get(B)($.arg):B($.arg===void 0?null:$.arg)}}var ba={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};function Wc(C,$){if(C<=0||C>a().length||C&!0||$<0)return-28;if($==0)return 0;$>=2147483647&&($=1/0);var B=Atomics.load(i(),Ql>>2),Q=0;if(B==C){var be=Atomics.compareExchange(i(),Ql>>2,B,0);if(be==B&&(--$,Q=1,$<=0))return 1}var Ae=Atomics.notify(i(),C>>2,$);if(Ae>=0)return Ae+Q;throw"Atomics.notify returned an unexpected value "+Ae}u._emscripten_futex_wake=Wc;function j0(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 $=Re.pthreads[C];$.worker.terminate(),Re.freeThreadData($),Re.runningWorkers.splice(Re.runningWorkers.indexOf($.worker),1),$.worker.pthread=void 0}function q0(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 $=Re.pthreads[C];$.worker.postMessage({cmd:"cancel"})}function Qp(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 $=Re.pthreads[C];if($){i()[C+12>>2]=0;var B=$.worker;Re.returnWorkerToPool(B)}}var Re={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var C=8,$=0;$<C;++$)Re.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 Q=ci(512),$=0;$<128;++$)l()[Q/4+$]=0;Atomics.store(l(),C+100>>2,Q),Atomics.store(l(),C+40>>2,C),n2(C,!y,1),C5(C)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Re.threadExitHandlers.length>0;)Re.threadExitHandlers.pop()();k&&Sa()&&I5()},runExitHandlersAndDeinitThread:function(C,$){Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),Re.runExitHandlers(),Atomics.store(l(),C+4>>2,$),Atomics.store(l(),C+0>>2,1),Wc(C+0,2147483647),n2(0,0,0)},threadExit:function(C){var $=Sa();$&&(Re.runExitHandlersAndDeinitThread($,C),k&&postMessage({cmd:"exit"}))},threadCancel:function(){Re.runExitHandlersAndDeinitThread(Sa(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var C in Re.pthreads){var $=Re.pthreads[C];$&&$.worker&&Re.returnWorkerToPool($.worker)}Re.pthreads={};for(var B=0;B<Re.unusedWorkers.length;++B){var Q=Re.unusedWorkers[B];Q.terminate()}Re.unusedWorkers=[];for(var B=0;B<Re.runningWorkers.length;++B){var Q=Re.runningWorkers[B],$=Q.pthread;Re.freeThreadData($),Q.terminate()}Re.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){Re.runWithoutMainThreadQueuedCalls(function(){delete Re.pthreads[C.pthread.threadInfoStruct],Re.unusedWorkers.push(C),Re.runningWorkers.splice(Re.runningWorkers.indexOf(C),1),Re.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,$){C.onmessage=function(B){var Q=B.data,be=Q.cmd;if(C.pthread&&(Re.currentProxiedOperationCallerThread=C.pthread.threadInfoStruct),Q.targetThread&&Q.targetThread!=Sa()){var Ae=Re.pthreads[Q.targetThread];Ae?Ae.worker.postMessage(B.data,Q.transferList):console.error('Internal error! Worker sent a message "'+be+'" to target pthread '+Q.targetThread+", but that thread no longer exists!"),Re.currentProxiedOperationCallerThread=void 0;return}if(be==="processQueuedMainThreadWork")hh();else if(be==="spawnThread")oh(B.data);else if(be==="cleanupThread")Qp(Q.thread);else if(be==="killThread")j0(Q.thread);else if(be==="cancelThread")q0(Q.thread);else if(be==="loaded")C.loaded=!0,$&&$(C),C.runPthread&&(C.runPthread(),delete C.runPthread);else if(be==="print")q("Thread "+Q.threadId+": "+Q.text);else if(be==="printErr")z("Thread "+Q.threadId+": "+Q.text);else if(be==="alert")alert("Thread "+Q.threadId+": "+Q.text);else if(be==="exit"){var xe=C.pthread&&Atomics.load(l(),C.pthread.threadInfoStruct+64>>2);xe&&Re.returnWorkerToPool(C)}else if(be==="exitProcess")try{vE(Q.returnCode)}catch(Ne){if(Ne instanceof Zc)return;throw Ne}else be==="cancelDone"?Re.returnWorkerToPool(C):be==="objectTransfer"?Re.receiveObjectTransfer(B.data):B.data.target==="setimmediate"?C.postMessage(B.data):z("worker sent an unknown command "+be);Re.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");Re.unusedWorkers.push(new Worker(C))},getNewWorker:function(){return Re.unusedWorkers.length==0&&(Re.allocateUnusedWorker(),Re.loadWasmModuleToWorker(Re.unusedWorkers[0])),Re.unusedWorkers.length>0?Re.unusedWorkers.pop():null},busySpinWait:function(C){for(var $=performance.now()+C;performance.now()<$;);}};function X0(C,$){$5(C,$),Yl(C)}u.establishStackSpace=X0;function K0(){return G}u.getNoExitRuntime=K0;function Z0(C,$){return Rn.get(C)($)}u.invokeEntryPoint=Z0;function Y0(C,$,B,Q){jr("Assertion failed: "+Ke(C)+", at: "+[$?Ke($):"unknown filename",B,Q?Ke(Q):"unknown function"])}function J0(C,$){var B=_main(C,$)}var ui;b?ui=function(){var C=process.hrtime();return C[0]*1e3+C[1]/1e6}:k?ui=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?ui=dateNow:ui=function(){return performance.now()};function Q0(C){return i()[k5()>>2]=C,C}function eg(C,$){if(k)return va(1,1,C,$)}function tg(C,$){if(C==$)postMessage({cmd:"processQueuedMainThreadWork"});else if(k)postMessage({targetThread:C,cmd:"processThreadQueue"});else{var B=Re.pthreads[C],Q=B&&B.worker;if(!Q)return;Q.postMessage({cmd:"processThreadQueue"})}return 1}function ng(){jr()}function sg(C,$,B){var Q=ig($,B);return Jp[C].apply(null,Q)}function rg(C,$){}function eh(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(),Ql>>2,C);;){if(be=performance.now(),be>Ae)return xe=Atomics.exchange(i(),Ql>>2,0),-73;if(xe=Atomics.exchange(i(),Ql>>2,0),xe==0)break;if(hh(),Atomics.load(i(),C>>2)!=$)return-6;xe=Atomics.exchange(i(),Ql>>2,C)}return 0}else{var Q=Atomics.wait(i(),C>>2,$,B);if(Q==="timed-out")return-73;if(Q==="not-equal")return-6;if(Q==="ok")return 0;throw"Atomics.wait returned an unexpected value "+Q}}function ag(C,$,B){o().copyWithin(C,$,$+B)}function og(){return b?js("os").cpus().length:navigator.hardwareConcurrency}function va(C,$){for(var B=arguments.length-2,Q=Kc(),be=B,Ae=Jl(be*8),xe=Ae>>3,Ne=0;Ne<B;Ne++){var ct=arguments[2+Ne];c()[xe+Ne]=ct}var hn=R5(C,be,Ae,$);return Yl(Q),hn}var Vc=[],Uc=[];function ig(C,$){Uc.length=0;var B;for($>>=2;B=o()[C++];){var Q=B<105;Q&&$&1&&$++,Uc.push(Q?c()[$++>>1]:i()[$]),++$}return Uc}function lg(C,$,B){Vc.length=$;for(var Q=B>>3,be=0;be<$;be++)Vc[be]=c()[Q+be];var Ae=C<0,xe=Ae?Jp[-C-1]:Fg[C];return xe.apply(null,Vc)}function ug(){return o().length}function cg(C){try{return se.grow(C-Tt.byteLength+65535>>>16),Sn(se.buffer),1}catch($){}}function dg(C){var $=ug();if(C<=$)return!1;var B=2147483648;if(C>B)return!1;for(var Q=1;Q<=4;Q*=2){var be=$*(1+.2/Q);be=Math.min(be,C+100663296);var Ae=Math.min(B,mt(Math.max(C,be),65536)),xe=cg(Ae);if(xe)return!0}return!1}var Ge={inEventHandler:0,removeAllEventListeners:function(){for(var C=Ge.eventHandlers.length-1;C>=0;--C)Ge._removeHandler(C);Ge.eventHandlers=[],Ge.deferredCalls=[]},registerRemoveEventListeners:function(){Ge.removeEventListenersRegistered||(Lc.push(Ge.removeAllEventListeners),Ge.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(C,$,B){function Q(xe,Ne){if(xe.length!=Ne.length)return!1;for(var ct in xe)if(xe[ct]!=Ne[ct])return!1;return!0}for(var be in Ge.deferredCalls){var Ae=Ge.deferredCalls[be];if(Ae.targetFunction==C&&Q(Ae.argsList,B))return}Ge.deferredCalls.push({targetFunction:C,precedence:$,argsList:B}),Ge.deferredCalls.sort(function(xe,Ne){return xe.precedence<Ne.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,Q,be){var Ae=Kc(),xe=Jl(12);i()[xe>>2]=B,i()[xe+4>>2]=Q,i()[xe+8>>2]=be,t2(0,C,637534208,$,Q,xe),Yl(Ae)},getTargetThreadForEventCallback:function(C){switch(C){case 1:return 0;case 2:return Re.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 pg(C){var $=rt(C)+1,B=ci($);return pt(C,B,$),B}function hg(C,$,B,Q){var be=Kc(),Ae=Jl(12),xe=0;$&&(xe=pg($)),i()[Ae>>2]=xe,i()[Ae+4>>2]=B,i()[Ae+8>>2]=Q,t2(0,C,657457152,0,xe,Ae),Yl(be)}function fg(C,$,B,Q){$=$?Ke($):"",hg(C,$,B,Q)}function mg(C){return C>2?Ke(C):C}var gg=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function Ag(C){C=mg(C);var $=gg[C]||(typeof document!="undefined"?document.querySelector(C):void 0);return $}function Gc(C){return Ag(C)}function th(C,$,B){var Q=Gc(C);if(!Q)return-4;if(Q.canvasSharedPtr&&(i()[Q.canvasSharedPtr>>2]=$,i()[Q.canvasSharedPtr+4>>2]=B),Q.offscreenCanvas||!Q.controlTransferredOffscreen){Q.offscreenCanvas&&(Q=Q.offscreenCanvas);var be=!1;if(Q.GLctxObject&&Q.GLctxObject.GLctx){var Ae=Q.GLctxObject.GLctx.getParameter(2978);be=Ae[0]===0&&Ae[1]===0&&Ae[2]===Q.width&&Ae[3]===Q.height}Q.width=$,Q.height=B,be&&Q.GLctxObject.GLctx.viewport(0,0,$,B)}else if(Q.canvasSharedPtr){var xe=i()[Q.canvasSharedPtr+8>>2];return fg(xe,C,$,B),1}else return-4;return 0}function nh(C,$,B){return k?va(2,1,C,$,B):th(C,$,B)}function yg(C,$,B){var Q=Gc(C);return Q?th(C,$,B):nh(C,$,B)}function xg(C){}function bg(C,$){}function vg(C){var $=C.getExtension("ANGLE_instanced_arrays");if($)return C.vertexAttribDivisor=function(B,Q){$.vertexAttribDivisorANGLE(B,Q)},C.drawArraysInstanced=function(B,Q,be,Ae){$.drawArraysInstancedANGLE(B,Q,be,Ae)},C.drawElementsInstanced=function(B,Q,be,Ae,xe){$.drawElementsInstancedANGLE(B,Q,be,Ae,xe)},1}function wg(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 kg(C){var $=C.getExtension("WEBGL_draw_buffers");if($)return C.drawBuffers=function(B,Q){$.drawBuffersWEBGL(B,Q)},1}function Sg(C){return!!(C.multiDrawWebgl=C.getExtension("WEBGL_multi_draw"))}var lt={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function($){lt.lastError||(lt.lastError=$)},getNewId:function(C){for(var $=lt.counter++,B=C.length;B<$;B++)C[B]=null;return $},getSource:function(C,$,B,Q){for(var be="",Ae=0;Ae<$;++Ae){var xe=Q?i()[Q+Ae*4>>2]:-1;be+=Ke(i()[B+Ae*4>>2],xe<0?void 0:xe)}return be},createContext:function(C,$){var B=C.getContext("webgl",$);if(!B)return 0;var Q=lt.registerContext(B,$);return Q},registerContext:function(C,$){var B=ci(8);i()[B+4>>2]=Sa();var Q={handle:B,attributes:$,version:$.majorVersion,GLctx:C};return C.canvas&&(C.canvas.GLctxObject=Q),lt.contexts[B]=Q,(typeof $.enableExtensionsByDefault=="undefined"||$.enableExtensionsByDefault)&<.initExtensions(Q),B},makeContextCurrent:function(C){return lt.currentContext=lt.contexts[C],u.ctx=wa=lt.currentContext&<.currentContext.GLctx,!(C&&!wa)},getContext:function(C){return lt.contexts[C]},deleteContext:function(C){lt.currentContext===lt.contexts[C]&&(lt.currentContext=null),typeof Ge=="object"&&Ge.removeAllHandlersOnTarget(lt.contexts[C].GLctx.canvas),lt.contexts[C]&<.contexts[C].GLctx.canvas&&(lt.contexts[C].GLctx.canvas.GLctxObject=void 0),Xc(lt.contexts[C].handle),lt.contexts[C]=null},initExtensions:function(C){if(C||(C=lt.currentContext),!C.initExtensionsDone){C.initExtensionsDone=!0;var $=C.GLctx;vg($),wg($),kg($),$.disjointTimerQueryExt=$.getExtension("EXT_disjoint_timer_query"),Sg($);var B=$.getSupportedExtensions()||[];B.forEach(function(Q){Q.indexOf("lose_context")<0&&Q.indexOf("debug")<0&&$.getExtension(Q)})}},populateUniformTable:function(C){for(var $=lt.programs[C],B=lt.programInfos[C]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},Q=B.uniforms,be=wa.getProgramParameter($,35718),Ae=0;Ae<be;++Ae){var xe=wa.getActiveUniform($,Ae),Ne=xe.name;B.maxUniformLength=Math.max(B.maxUniformLength,Ne.length+1),Ne.slice(-1)=="]"&&(Ne=Ne.slice(0,Ne.lastIndexOf("[")));var ct=wa.getUniformLocation($,Ne);if(ct){var hn=lt.getNewId(lt.uniforms);Q[Ne]=[xe.size,hn],lt.uniforms[hn]=ct;for(var tn=1;tn<xe.size;++tn){var Ia=Ne+"["+tn+"]";ct=wa.getUniformLocation($,Ia),hn=lt.getNewId(lt.uniforms),lt.uniforms[hn]=ct}}}}},Ig=["default","low-power","high-performance"];function Cg(C,$){var B=$>>2,Q=i()[B+(24>>2)],be={alpha:!!i()[B+(0>>2)],depth:!!i()[B+(4>>2)],stencil:!!i()[B+(8>>2)],antialias:!!i()[B+(12>>2)],premultipliedAlpha:!!i()[B+(16>>2)],preserveDrawingBuffer:!!i()[B+(20>>2)],powerPreference:Ig[Q],failIfMajorPerformanceCaveat:!!i()[B+(28>>2)],majorVersion:i()[B+(32>>2)],minorVersion:i()[B+(36>>2)],enableExtensionsByDefault:i()[B+(40>>2)],explicitSwapControl:i()[B+(44>>2)],proxyContextToMainThread:i()[B+(48>>2)],renderViaOffscreenBackBuffer:i()[B+(52>>2)]},Ae=Gc(C);if(!Ae||be.explicitSwapControl)return 0;var xe=lt.createContext(Ae,be);return xe}function Tg(C,$){return Cg(C,$)}var Kl={mappings:{},buffers:[null,[],[]],printChar:function(C,$){var B=Kl.buffers[C];$===0||$===10?((C===1?q:z)(qe(B,0)),B.length=0):B.push($)},varargs:void 0,get:function(){Kl.varargs+=4;var C=i()[Kl.varargs-4>>2];return C},getStr:function(C){var $=Ke(C);return $},get64:function(C,$){return C}};function sh(C){return k?va(3,1,C):0}function rh(C,$,B,Q,be){if(k)return va(4,1,C,$,B,Q,be)}function ah(C,$,B,Q){if(k)return va(5,1,C,$,B,Q);for(var be=0,Ae=0;Ae<B;Ae++){for(var xe=i()[$+Ae*8>>2],Ne=i()[$+(Ae*8+4)>>2],ct=0;ct<Ne;ct++)Kl.printChar(C,o()[xe+ct]);be+=Ne}return i()[Q>>2]=be,0}function Ng(C){var $=Re.threadExitHandlers.pop();C&&$()}function Eg(C,$){Re.threadExitHandlers.push(function(){Rn.get(C)($)})}function oh(C){if(k)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var $=Re.getNewWorker();if($.pthread!==void 0)throw"Internal error!";if(!C.pthread_ptr)throw"Internal error, no pthread ptr!";Re.runningWorkers.push($);for(var B=ci(128*4),Q=0;Q<128;++Q)i()[B+Q*4>>2]=0;var be=C.stackBase+C.stackSize,Ae=Re.pthreads[C.pthread_ptr]={worker:$,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 Ne=S5(),ct=Ne+40;Atomics.store(l(),xe+(172>>2),ct),$.pthread=Ae;var hn={cmd:"run",start_routine:C.startRoutine,arg:C.arg,threadInfoStruct:C.pthread_ptr,stackBase:C.stackBase,stackSize:C.stackSize};$.runPthread=function(){hn.time=performance.now(),$.postMessage(hn,C.transferList)},$.loaded&&($.runPthread(),delete $.runPthread)}function Rg(C,$,B,Q){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,$,B,Q);if(Ae)return Ae;var xe=0,Ne=0,ct=0;$&&$!=-1?(xe=i()[$>>2],xe+=81920,Ne=i()[$+8>>2],ct=i()[$+12>>2]!==0):xe=2097152;var hn=Ne==0;hn?Ne=_5(16,xe):(Ne-=xe,we(Ne>0));for(var tn=ci(228),Ia=0;Ia<228>>2;++Ia)l()[(tn>>2)+Ia]=0;i()[C>>2]=tn,i()[tn+12>>2]=tn;var eu=tn+152;i()[eu>>2]=eu;var Mn={stackBase:Ne,stackSize:xe,allocatedOwnStack:hn,detached:ct,startRoutine:B,pthread_ptr:tn,arg:Q,transferList:be};return k?(Mn.cmd="spawnThread",postMessage(Mn,be)):oh(Mn),0}function $g(){if(!!k){var C=Sa();if(!!C){var $=Atomics.load(l(),C+56>>2);if(!$){var B=Atomics.load(l(),C+0>>2);if(B==2)throw"Canceled!"}}}}function _g(){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 Dg(C,$,B){if(!C)return z("pthread_join attempted on a null thread pointer!"),ba.ESRCH;if(k&&Sa()==C)return z("PThread "+C+" is attempting to join to itself!"),ba.EDEADLK;if(!k&&T5()==C)return z("Main thread "+C+" is attempting to join to itself!"),ba.EDEADLK;var Q=i()[C+12>>2];if(Q!==C)return z("pthread_join attempted on thread "+C+", which does not point to a valid thread, or does not exist anymore!"),ba.ESRCH;var be=Atomics.load(l(),C+64>>2);if(be)return z("Attempted to join thread "+C+", which was already detached!"),ba.EINVAL;for(B&&_g();;){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}):Qp(C),0}if(!B)return ba.EBUSY;$g(),k||hh(),eh(C+0,Ae,k?100:1)}}function Pg(C,$){return Dg(C,$,!0)}function ih(C){if(k)return va(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 Q0(28),-1}k||Re.initMainThreadBlock();var wa,Fg=[null,eg,nh,sh,rh,ah,ih],Og={e:Y0,r:J0,x:tg,b:ng,y:sg,j:rg,d:eh,c:Wc,f:ui,p:ag,A:og,u:lg,q:dg,v:yg,i:xg,s:bg,w:Tg,l:sh,n:rh,g:ah,o:H0,a:se||u.wasmMemory,z:Ng,k:Eg,h:Rg,m:Pg,t:ih},w5=G0(),lh=u.___wasm_call_ctors=function(){return(lh=u.___wasm_call_ctors=u.asm.B).apply(null,arguments)},Mg=u._init=function(){return(Mg=u._init=u.asm.C).apply(null,arguments)},zg=u._init_with_threads_count=function(){return(zg=u._init_with_threads_count=u.asm.D).apply(null,arguments)},Lg=u._get_threads_count=function(){return(Lg=u._get_threads_count=u.asm.E).apply(null,arguments)},Bg=u._register_tensor=function(){return(Bg=u._register_tensor=u.asm.F).apply(null,arguments)},Wg=u._dispose_data=function(){return(Wg=u._dispose_data=u.asm.G).apply(null,arguments)},Vg=u._dispose=function(){return(Vg=u._dispose=u.asm.H).apply(null,arguments)},Ug=u._Abs=function(){return(Ug=u._Abs=u.asm.I).apply(null,arguments)},Gg=u._Add=function(){return(Gg=u._Add=u.asm.J).apply(null,arguments)},Hg=u._AddN=function(){return(Hg=u._AddN=u.asm.K).apply(null,arguments)},jg=u._All=function(){return(jg=u._All=u.asm.L).apply(null,arguments)},qg=u._Any=function(){return(qg=u._Any=u.asm.M).apply(null,arguments)},Xg=u._ArgMax=function(){return(Xg=u._ArgMax=u.asm.N).apply(null,arguments)},Kg=u._AvgPool=function(){return(Kg=u._AvgPool=u.asm.O).apply(null,arguments)},Zg=u._BatchMatMul=function(){return(Zg=u._BatchMatMul=u.asm.P).apply(null,arguments)},Yg=u._Ceil=function(){return(Yg=u._Ceil=u.asm.Q).apply(null,arguments)},Jg=u._ClipByValue=function(){return(Jg=u._ClipByValue=u.asm.R).apply(null,arguments)},Qg=u._Conv2D=function(){return(Qg=u._Conv2D=u.asm.S).apply(null,arguments)},uh=u._Conv2DBackpropInput=function(){return(uh=u._Conv2DBackpropInput=u.asm.T).apply(null,arguments)},ch=u._Cos=function(){return(ch=u._Cos=u.asm.U).apply(null,arguments)},Hc=u._Cosh=function(){return(Hc=u._Cosh=u.asm.V).apply(null,arguments)},Zl=u._CropAndResize=function(){return(Zl=u._CropAndResize=u.asm.W).apply(null,arguments)},e2=u._Cumsum=function(){return(e2=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)},ke=u._Equal=function(){return(ke=u._Equal=u.asm.$).apply(null,arguments)},at=u._Exp=function(){return(at=u._Exp=u.asm.aa).apply(null,arguments)},Lt=u._FlipLeftRight=function(){return(Lt=u._FlipLeftRight=u.asm.ba).apply(null,arguments)},Nt=u._Floor=function(){return(Nt=u._Floor=u.asm.ca).apply(null,arguments)},Ze=u._FloorDiv=function(){return(Ze=u._FloorDiv=u.asm.da).apply(null,arguments)},Ye=u._FusedBatchNorm=function(){return(Ye=u._FusedBatchNorm=u.asm.ea).apply(null,arguments)},In=u._FusedConv2D=function(){return(In=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)},dh=u._GatherNd=function(){return(dh=u._GatherNd=u.asm.ia).apply(null,arguments)},qc=u._Greater=function(){return(qc=u._Greater=u.asm.ja).apply(null,arguments)},hs=u._GreaterEqual=function(){return(hs=u._GreaterEqual=u.asm.ka).apply(null,arguments)},ka=u._LeakyRelu=function(){return(ka=u._LeakyRelu=u.asm.la).apply(null,arguments)},ph=u._Less=function(){return(ph=u._Less=u.asm.ma).apply(null,arguments)},IN=u._LessEqual=function(){return(IN=u._LessEqual=u.asm.na).apply(null,arguments)},CN=u._Log=function(){return(CN=u._Log=u.asm.oa).apply(null,arguments)},TN=u._LogicalAnd=function(){return(TN=u._LogicalAnd=u.asm.pa).apply(null,arguments)},NN=u._Max=function(){return(NN=u._Max=u.asm.qa).apply(null,arguments)},EN=u._MaxPool=function(){return(EN=u._MaxPool=u.asm.ra).apply(null,arguments)},RN=u._Maximum=function(){return(RN=u._Maximum=u.asm.sa).apply(null,arguments)},$N=u._Mean=function(){return($N=u._Mean=u.asm.ta).apply(null,arguments)},_N=u._Min=function(){return(_N=u._Min=u.asm.ua).apply(null,arguments)},DN=u._Minimum=function(){return(DN=u._Minimum=u.asm.va).apply(null,arguments)},PN=u._MirrorPad=function(){return(PN=u._MirrorPad=u.asm.wa).apply(null,arguments)},FN=u._Multiply=function(){return(FN=u._Multiply=u.asm.xa).apply(null,arguments)},ON=u._Neg=function(){return(ON=u._Neg=u.asm.ya).apply(null,arguments)},MN=u._NonMaxSuppressionV3=function(){return(MN=u._NonMaxSuppressionV3=u.asm.za).apply(null,arguments)},zN=u._NonMaxSuppressionV4=function(){return(zN=u._NonMaxSuppressionV4=u.asm.Aa).apply(null,arguments)},LN=u._NonMaxSuppressionV5=function(){return(LN=u._NonMaxSuppressionV5=u.asm.Ba).apply(null,arguments)},BN=u._NotEqual=function(){return(BN=u._NotEqual=u.asm.Ca).apply(null,arguments)},WN=u._OneHot=function(){return(WN=u._OneHot=u.asm.Da).apply(null,arguments)},VN=u._PadV2=function(){return(VN=u._PadV2=u.asm.Ea).apply(null,arguments)},UN=u._Pow=function(){return(UN=u._Pow=u.asm.Fa).apply(null,arguments)},GN=u._Prelu=function(){return(GN=u._Prelu=u.asm.Ga).apply(null,arguments)},HN=u._Prod=function(){return(HN=u._Prod=u.asm.Ha).apply(null,arguments)},jN=u._RealDiv=function(){return(jN=u._RealDiv=u.asm.Ia).apply(null,arguments)},qN=u._Relu=function(){return(qN=u._Relu=u.asm.Ja).apply(null,arguments)},XN=u._Relu6=function(){return(XN=u._Relu6=u.asm.Ka).apply(null,arguments)},KN=u._ResizeBilinear=function(){return(KN=u._ResizeBilinear=u.asm.La).apply(null,arguments)},ZN=u._Reverse=function(){return(ZN=u._Reverse=u.asm.Ma).apply(null,arguments)},YN=u._RotateWithOffset=function(){return(YN=u._RotateWithOffset=u.asm.Na).apply(null,arguments)},JN=u._Round=function(){return(JN=u._Round=u.asm.Oa).apply(null,arguments)},QN=u._Rsqrt=function(){return(QN=u._Rsqrt=u.asm.Pa).apply(null,arguments)},eE=u._ScatterNd=function(){return(eE=u._ScatterNd=u.asm.Qa).apply(null,arguments)},tE=u._SelectV2=function(){return(tE=u._SelectV2=u.asm.Ra).apply(null,arguments)},nE=u._Sigmoid=function(){return(nE=u._Sigmoid=u.asm.Sa).apply(null,arguments)},sE=u._Sin=function(){return(sE=u._Sin=u.asm.Ta).apply(null,arguments)},rE=u._Softmax=function(){return(rE=u._Softmax=u.asm.Ua).apply(null,arguments)},aE=u._Sqrt=function(){return(aE=u._Sqrt=u.asm.Va).apply(null,arguments)},oE=u._Square=function(){return(oE=u._Square=u.asm.Wa).apply(null,arguments)},iE=u._SquaredDifference=function(){return(iE=u._SquaredDifference=u.asm.Xa).apply(null,arguments)},lE=u._Step=function(){return(lE=u._Step=u.asm.Ya).apply(null,arguments)},uE=u._StridedSlice=function(){return(uE=u._StridedSlice=u.asm.Za).apply(null,arguments)},cE=u._Sub=function(){return(cE=u._Sub=u.asm._a).apply(null,arguments)},dE=u._Sum=function(){return(dE=u._Sum=u.asm.$a).apply(null,arguments)},pE=u._Tan=function(){return(pE=u._Tan=u.asm.ab).apply(null,arguments)},hE=u._Tanh=function(){return(hE=u._Tanh=u.asm.bb).apply(null,arguments)},fE=u._Tile=function(){return(fE=u._Tile=u.asm.cb).apply(null,arguments)},mE=u._TopK=function(){return(mE=u._TopK=u.asm.db).apply(null,arguments)},gE=u._Transform=function(){return(gE=u._Transform=u.asm.eb).apply(null,arguments)},AE=u._Transpose=function(){return(AE=u._Transpose=u.asm.fb).apply(null,arguments)},yE=u.__FusedMatMul=function(){return(yE=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)},Sa=u._pthread_self=function(){return(Sa=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)},hh=u._emscripten_main_thread_process_queued_calls=function(){return(hh=u._emscripten_main_thread_process_queued_calls=u.asm.ob).apply(null,arguments)},xE=u._emscripten_current_thread_process_queued_calls=function(){return(xE=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)},t2=u.__emscripten_call_on_thread=function(){return(t2=u.__emscripten_call_on_thread=u.asm.vb).apply(null,arguments)},bE=u._emscripten_tls_init=function(){return(bE=u._emscripten_tls_init=u.asm.wb).apply(null,arguments)},n2=u.__emscripten_thread_init=function(){return(n2=u.__emscripten_thread_init=u.asm.xb).apply(null,arguments)},Kc=u.stackSave=function(){return(Kc=u.stackSave=u.asm.yb).apply(null,arguments)},Yl=u.stackRestore=function(){return(Yl=u.stackRestore=u.asm.zb).apply(null,arguments)},Jl=u.stackAlloc=function(){return(Jl=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,Ql=u.__emscripten_main_thread_futex=10268;u.cwrap=Ue,u.PThread=Re,u.PThread=Re,u.wasmMemory=se,u.ExitStatus=Zc;var fh;function Zc(C){this.name="ExitStatus",this.message="Program terminated with exit("+C+")",this.status=C}ii=function C(){fh||s2(),fh||(ii=C)};function s2(C){if(C=C||m,Hs>0)return;if(k){d(u),jp(),postMessage({cmd:"loaded"});return}if(Hp(),Hs>0)return;function $(){fh||(fh=!0,u.calledRun=!0,!pe&&(jp(),qp(),d(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),Xp()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),$()},1)):$()}u.run=s2;function vE(C,$){if(!($&&G&&C===0)){if(!$&&k)throw postMessage({cmd:"exitProcess",returnCode:C}),new Zc(C);G||(Re.terminateAllThreads(),ye=C,es(),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,Re.initWorker()),s2(),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)}}),VE=ns({"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,ke){return I||(I=js("fs")),N||(N=js("path")),le=N.normalize(le),I.readFileSync(le,ke?null:"utf8")},w=function(le){var ke=y(le,!0);return ke.buffer||(ke=new Uint8Array(ke)),q(ke.buffer),ke},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(ae){if(!(ae instanceof e2))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 ke;return typeof readbuffer=="function"?new Uint8Array(readbuffer(le)):(ke=read(le,"binary"),q(typeof ke=="object"),ke)},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,ke){var at=new XMLHttpRequest;at.open("GET",ae,!0),at.responseType="arraybuffer",at.onload=function(){if(at.status==200||at.status==0&&at.response){le(at.response);return}ke()},at.onerror=ke,at.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,ke,at,Lt){var Nt={string:function(hs){var ka=0;if(hs!=null&&hs!==0){var ph=(hs.length<<2)+1;ka=Hc(ph),se(hs,ka,ph)}return ka},array:function(hs){var ka=Hc(hs.length);return oe(hs,ka),ka}};function Ze(hs){return le==="string"?re(hs):le==="boolean"?Boolean(hs):hs}var Ye=z(ae),In=[],qr=0;if(at)for(var Xr=0;Xr<at.length;Xr++){var dh=Nt[ke[Xr]];dh?(qr===0&&(qr=uh()),In[Xr]=dh(at[Xr])):In[Xr]=at[Xr]}var qc=Ye.apply(null,In);return qc=Ze(qc),qr!==0&&ch(qr),qc}function Y(ae,le,ke,at){ke=ke||[];var Lt=ke.every(function(Ze){return Ze==="number"}),Nt=le!=="string";return Nt&&Lt&&!at?z(ae):function(){return K(ae,le,ke,arguments,at)}}var J=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function ne(ae,le,ke){for(var at=le+ke,Lt=le;ae[Lt]&&!(Lt>=at);)++Lt;if(Lt-le>16&&ae.subarray&&J)return J.decode(ae.subarray(le,Lt));for(var Nt="";le<Lt;){var Ze=ae[le++];if(!(Ze&128)){Nt+=String.fromCharCode(Ze);continue}var Ye=ae[le++]&63;if((Ze&224)==192){Nt+=String.fromCharCode((Ze&31)<<6|Ye);continue}var In=ae[le++]&63;if((Ze&240)==224?Ze=(Ze&15)<<12|Ye<<6|In:Ze=(Ze&7)<<18|Ye<<12|In<<6|ae[le++]&63,Ze<65536)Nt+=String.fromCharCode(Ze);else{var qr=Ze-65536;Nt+=String.fromCharCode(55296|qr>>10,56320|qr&1023)}}return Nt}function re(ae,le){return ae?ne(Te,ae,le):""}function G(ae,le,ke,at){if(!(at>0))return 0;for(var Lt=ke,Nt=ke+at-1,Ze=0;Ze<ae.length;++Ze){var Ye=ae.charCodeAt(Ze);if(Ye>=55296&&Ye<=57343){var In=ae.charCodeAt(++Ze);Ye=65536+((Ye&1023)<<10)|In&1023}if(Ye<=127){if(ke>=Nt)break;le[ke++]=Ye}else if(Ye<=2047){if(ke+1>=Nt)break;le[ke++]=192|Ye>>6,le[ke++]=128|Ye&63}else if(Ye<=65535){if(ke+2>=Nt)break;le[ke++]=224|Ye>>12,le[ke++]=128|Ye>>6&63,le[ke++]=128|Ye&63}else{if(ke+3>=Nt)break;le[ke++]=240|Ye>>18,le[ke++]=128|Ye>>12&63,le[ke++]=128|Ye>>6&63,le[ke++]=128|Ye&63}}return le[ke]=0,ke-Lt}function se(ae,le,ke){return G(ae,Te,le,ke)}function oe(ae,le){we.set(ae,le)}function pe(ae,le){return ae%le>0&&(ae+=le-ae%le),ae}var ye,we,Te,Me,Ue,qe,Ke,dt,pt;function rt(ae){ye=ae,a.HEAP8=we=new Int8Array(ae),a.HEAP16=Me=new Int16Array(ae),a.HEAP32=qe=new Int32Array(ae),a.HEAPU8=Te=new Uint8Array(ae),a.HEAPU16=Ue=new Uint16Array(ae),a.HEAPU32=Ke=new Uint32Array(ae),a.HEAPF32=dt=new Float32Array(ae),a.HEAPF64=pt=new Float64Array(ae)}var kt=a.INITIAL_MEMORY||16777216,mt,Tt=[],Dt=[],Ts=[],kn=[],lr=!1;Dt.push({func:function(){Yp()}});function On(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)Sn(a.preRun.shift());Hs(Tt)}function ps(){lr=!0,Hs(Dt)}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(kn)}function Sn(ae){Tt.unshift(ae)}function Tr(ae){kn.unshift(ae)}var Rn=0,Nr=null,Er=null;function xa(ae){Rn++,a.monitorRunDependencies&&a.monitorRunDependencies(Rn)}function Lc(ae){if(Rn--,a.monitorRunDependencies&&a.monitorRunDependencies(Rn),Rn==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 Gp(ae,le){return String.prototype.startsWith?ae.startsWith(le):ae.indexOf(le)===0}var O0="data:application/octet-stream;base64,";function Hp(ae){return Gp(ae,O0)}var jp="file://";function qp(ae){return Gp(ae,jp)}var es="tfjs-backend-wasm.wasm";Hp(es)||(es=x(es));function Xp(ae){try{if(ae==es&&_)return new Uint8Array(_);if(w)return w(ae);throw"both async and sync fetching of the wasm failed"}catch(le){ur(le)}}function M0(){if(!_&&(h||f)){if(typeof fetch=="function"&&!qp(es))return fetch(es,{credentials:"same-origin"}).then(function(ae){if(!ae.ok)throw"failed to load wasm binary file at '"+es+"'";return ae.arrayBuffer()}).catch(function(){return Xp(es)});if(b)return new Promise(function(ae,le){b(es,function(ke){ae(new Uint8Array(ke))},le)})}return Promise.resolve().then(function(){return Xp(es)})}function z0(){var ae={a:ts};function le(Ze,Ye){var In=Ze.exports;a.asm=In,T=a.asm.h,rt(T.buffer),mt=a.asm.Sa,Lc("wasm-instantiate")}xa("wasm-instantiate");function ke(Ze){le(Ze.instance)}function at(Ze){return M0().then(function(Ye){return WebAssembly.instantiate(Ye,ae)}).then(Ze,function(Ye){O("failed to asynchronously prepare wasm: "+Ye),ur(Ye)})}function Lt(){return!_&&typeof WebAssembly.instantiateStreaming=="function"&&!Hp(es)&&!qp(es)&&typeof fetch=="function"?fetch(es,{credentials:"same-origin"}).then(function(Ze){var Ye=WebAssembly.instantiateStreaming(Ze,ae);return Ye.then(ke,function(In){return O("wasm streaming compile failed: "+In),O("falling back to ArrayBuffer instantiation"),at(ke)})}):at(ke)}if(a.instantiateWasm)try{var Nt=a.instantiateWasm(ae,le);return Nt}catch(Ze){return O("Module.instantiateWasm callback failed with error: "+Ze),!1}return Lt().catch(i),{}}function Hs(ae){for(;ae.length>0;){var le=ae.shift();if(typeof le=="function"){le(a);continue}var ke=le.func;typeof ke=="number"?le.arg===void 0?mt.get(ke)():mt.get(ke)(le.arg):ke(le.arg===void 0?null:le.arg)}}function Bc(){ur()}function ii(ae,le,ke){Te.copyWithin(ae,le,le+ke)}function L0(){return Te.length}function B0(ae){try{return T.grow(ae-ye.byteLength+65535>>>16),rt(T.buffer),1}catch(le){}}function jr(ae){var le=L0(),ke=2147483648;if(ae>ke)return!1;for(var at=1;at<=4;at*=2){var Lt=le*(1+.2/at);Lt=Math.min(Lt,ae+100663296);var Nt=Math.min(ke,pe(Math.max(ae,Lt),65536)),Ze=B0(Nt);if(Ze)return!0}return!1}var li={mappings:{},buffers:[null,[],[]],printChar:function(ae,le){var ke=li.buffers[ae];le===0||le===10?((ae===1?R:O)(ne(ke,0)),ke.length=0):ke.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 W0(ae){return 0}function Kp(ae,le,ke,at,Lt){}function V0(ae,le,ke,at){for(var Lt=0,Nt=0;Nt<ke;Nt++){for(var Ze=qe[le+Nt*8>>2],Ye=qe[le+(Nt*8+4)>>2],In=0;In<Ye;In++)li.printChar(ae,Te[Ze+In]);Lt+=Ye}return qe[at>>2]=Lt,0}function Zp(){return 28}var ts={a:Bc,d:ii,e:jr,f:W0,c:Kp,b:V0,g:Zp},U0=z0(),Yp=a.___wasm_call_ctors=function(){return(Yp=a.___wasm_call_ctors=a.asm.i).apply(null,arguments)},G0=a._init=function(){return(G0=a._init=a.asm.j).apply(null,arguments)},Jp=a._init_with_threads_count=function(){return(Jp=a._init_with_threads_count=a.asm.k).apply(null,arguments)},H0=a._get_threads_count=function(){return(H0=a._get_threads_count=a.asm.l).apply(null,arguments)},Xl=a._register_tensor=function(){return(Xl=a._register_tensor=a.asm.m).apply(null,arguments)},ba=a._dispose_data=function(){return(ba=a._dispose_data=a.asm.n).apply(null,arguments)},Wc=a._dispose=function(){return(Wc=a._dispose=a.asm.o).apply(null,arguments)},j0=a._Abs=function(){return(j0=a._Abs=a.asm.p).apply(null,arguments)},q0=a._Add=function(){return(q0=a._Add=a.asm.q).apply(null,arguments)},Qp=a._AddN=function(){return(Qp=a._AddN=a.asm.r).apply(null,arguments)},Re=a._All=function(){return(Re=a._All=a.asm.s).apply(null,arguments)},X0=a._Any=function(){return(X0=a._Any=a.asm.t).apply(null,arguments)},K0=a._ArgMax=function(){return(K0=a._ArgMax=a.asm.u).apply(null,arguments)},Z0=a._AvgPool=function(){return(Z0=a._AvgPool=a.asm.v).apply(null,arguments)},Y0=a._BatchMatMul=function(){return(Y0=a._BatchMatMul=a.asm.w).apply(null,arguments)},J0=a._Ceil=function(){return(J0=a._Ceil=a.asm.x).apply(null,arguments)},ui=a._ClipByValue=function(){return(ui=a._ClipByValue=a.asm.y).apply(null,arguments)},Q0=a._Conv2D=function(){return(Q0=a._Conv2D=a.asm.z).apply(null,arguments)},eg=a._Conv2DBackpropInput=function(){return(eg=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},tg=a._Cos=function(){return(tg=a._Cos=a.asm.B).apply(null,arguments)},ng=a._Cosh=function(){return(ng=a._Cosh=a.asm.C).apply(null,arguments)},sg=a._CropAndResize=function(){return(sg=a._CropAndResize=a.asm.D).apply(null,arguments)},rg=a._Cumsum=function(){return(rg=a._Cumsum=a.asm.E).apply(null,arguments)},eh=a._DepthToSpace=function(){return(eh=a._DepthToSpace=a.asm.F).apply(null,arguments)},ag=a._DepthwiseConv2dNative=function(){return(ag=a._DepthwiseConv2dNative=a.asm.G).apply(null,arguments)},og=a._Elu=function(){return(og=a._Elu=a.asm.H).apply(null,arguments)},va=a._Equal=function(){return(va=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)},ig=a._Floor=function(){return(ig=a._Floor=a.asm.L).apply(null,arguments)},lg=a._FloorDiv=function(){return(lg=a._FloorDiv=a.asm.M).apply(null,arguments)},ug=a._FusedBatchNorm=function(){return(ug=a._FusedBatchNorm=a.asm.N).apply(null,arguments)},cg=a._FusedConv2D=function(){return(cg=a._FusedConv2D=a.asm.O).apply(null,arguments)},dg=a._FusedDepthwiseConv2D=function(){return(dg=a._FusedDepthwiseConv2D=a.asm.P).apply(null,arguments)},Ge=a._Gather=function(){return(Ge=a._Gather=a.asm.Q).apply(null,arguments)},pg=a._GatherNd=function(){return(pg=a._GatherNd=a.asm.R).apply(null,arguments)},hg=a._Greater=function(){return(hg=a._Greater=a.asm.S).apply(null,arguments)},fg=a._GreaterEqual=function(){return(fg=a._GreaterEqual=a.asm.T).apply(null,arguments)},mg=a._LeakyRelu=function(){return(mg=a._LeakyRelu=a.asm.U).apply(null,arguments)},gg=a._Less=function(){return(gg=a._Less=a.asm.V).apply(null,arguments)},Ag=a._LessEqual=function(){return(Ag=a._LessEqual=a.asm.W).apply(null,arguments)},Gc=a._Log=function(){return(Gc=a._Log=a.asm.X).apply(null,arguments)},th=a._LogicalAnd=function(){return(th=a._LogicalAnd=a.asm.Y).apply(null,arguments)},nh=a._Max=function(){return(nh=a._Max=a.asm.Z).apply(null,arguments)},yg=a._MaxPool=function(){return(yg=a._MaxPool=a.asm._).apply(null,arguments)},xg=a._Maximum=function(){return(xg=a._Maximum=a.asm.$).apply(null,arguments)},bg=a._Mean=function(){return(bg=a._Mean=a.asm.aa).apply(null,arguments)},vg=a._Min=function(){return(vg=a._Min=a.asm.ba).apply(null,arguments)},wg=a._Minimum=function(){return(wg=a._Minimum=a.asm.ca).apply(null,arguments)},kg=a._MirrorPad=function(){return(kg=a._MirrorPad=a.asm.da).apply(null,arguments)},Sg=a._Multiply=function(){return(Sg=a._Multiply=a.asm.ea).apply(null,arguments)},lt=a._Neg=function(){return(lt=a._Neg=a.asm.fa).apply(null,arguments)},Ig=a._NonMaxSuppressionV3=function(){return(Ig=a._NonMaxSuppressionV3=a.asm.ga).apply(null,arguments)},Cg=a._NonMaxSuppressionV4=function(){return(Cg=a._NonMaxSuppressionV4=a.asm.ha).apply(null,arguments)},Tg=a._NonMaxSuppressionV5=function(){return(Tg=a._NonMaxSuppressionV5=a.asm.ia).apply(null,arguments)},Kl=a._NotEqual=function(){return(Kl=a._NotEqual=a.asm.ja).apply(null,arguments)},sh=a._OneHot=function(){return(sh=a._OneHot=a.asm.ka).apply(null,arguments)},rh=a._PadV2=function(){return(rh=a._PadV2=a.asm.la).apply(null,arguments)},ah=a._Pow=function(){return(ah=a._Pow=a.asm.ma).apply(null,arguments)},Ng=a._Prelu=function(){return(Ng=a._Prelu=a.asm.na).apply(null,arguments)},Eg=a._Prod=function(){return(Eg=a._Prod=a.asm.oa).apply(null,arguments)},oh=a._RealDiv=function(){return(oh=a._RealDiv=a.asm.pa).apply(null,arguments)},Rg=a._Relu=function(){return(Rg=a._Relu=a.asm.qa).apply(null,arguments)},$g=a._Relu6=function(){return($g=a._Relu6=a.asm.ra).apply(null,arguments)},_g=a._ResizeBilinear=function(){return(_g=a._ResizeBilinear=a.asm.sa).apply(null,arguments)},Dg=a._Reverse=function(){return(Dg=a._Reverse=a.asm.ta).apply(null,arguments)},Pg=a._RotateWithOffset=function(){return(Pg=a._RotateWithOffset=a.asm.ua).apply(null,arguments)},ih=a._Round=function(){return(ih=a._Round=a.asm.va).apply(null,arguments)},wa=a._Rsqrt=function(){return(wa=a._Rsqrt=a.asm.wa).apply(null,arguments)},Fg=a._ScatterNd=function(){return(Fg=a._ScatterNd=a.asm.xa).apply(null,arguments)},Og=a._SelectV2=function(){return(Og=a._SelectV2=a.asm.ya).apply(null,arguments)},w5=a._Sigmoid=function(){return(w5=a._Sigmoid=a.asm.za).apply(null,arguments)},lh=a._Sin=function(){return(lh=a._Sin=a.asm.Aa).apply(null,arguments)},Mg=a._Softmax=function(){return(Mg=a._Softmax=a.asm.Ba).apply(null,arguments)},zg=a._Sqrt=function(){return(zg=a._Sqrt=a.asm.Ca).apply(null,arguments)},Lg=a._Square=function(){return(Lg=a._Square=a.asm.Da).apply(null,arguments)},Bg=a._SquaredDifference=function(){return(Bg=a._SquaredDifference=a.asm.Ea).apply(null,arguments)},Wg=a._Step=function(){return(Wg=a._Step=a.asm.Fa).apply(null,arguments)},Vg=a._StridedSlice=function(){return(Vg=a._StridedSlice=a.asm.Ga).apply(null,arguments)},Ug=a._Sub=function(){return(Ug=a._Sub=a.asm.Ha).apply(null,arguments)},Gg=a._Sum=function(){return(Gg=a._Sum=a.asm.Ia).apply(null,arguments)},Hg=a._Tan=function(){return(Hg=a._Tan=a.asm.Ja).apply(null,arguments)},jg=a._Tanh=function(){return(jg=a._Tanh=a.asm.Ka).apply(null,arguments)},qg=a._Tile=function(){return(qg=a._Tile=a.asm.La).apply(null,arguments)},Xg=a._TopK=function(){return(Xg=a._TopK=a.asm.Ma).apply(null,arguments)},Kg=a._Transform=function(){return(Kg=a._Transform=a.asm.Na).apply(null,arguments)},Zg=a._Transpose=function(){return(Zg=a._Transpose=a.asm.Oa).apply(null,arguments)},Yg=a.__FusedMatMul=function(){return(Yg=a.__FusedMatMul=a.asm.Pa).apply(null,arguments)},Jg=a._malloc=function(){return(Jg=a._malloc=a.asm.Qa).apply(null,arguments)},Qg=a._free=function(){return(Qg=a._free=a.asm.Ra).apply(null,arguments)},uh=a.stackSave=function(){return(uh=a.stackSave=a.asm.Ta).apply(null,arguments)},ch=a.stackRestore=function(){return(ch=a.stackRestore=a.asm.Ua).apply(null,arguments)},Hc=a.stackAlloc=function(){return(Hc=a.stackAlloc=a.asm.Va).apply(null,arguments)};a.cwrap=Y;var Zl;function e2(ae){this.name="ExitStatus",this.message="Program terminated with exit("+ae+")",this.status=ae}Er=function ae(){Zl||jc(),Zl||(Er=ae)};function jc(ae){if(ae=ae||u,Rn>0||(On(),Rn>0))return;function le(){Zl||(Zl=!0,a.calledRun=!0,!F&&(ps(),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)}}),UE=1e-7,GE=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}},su=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?UE:GE}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--,Ah(e,t,n)}function HE(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--,Ah(e,n,s),Ah(t,n,s)}function nd(e,t,n){return Math.max(e,Math.min(t,n))}function jE(e){return e%2==0?e:e+1}function Ah(e,t,n){let s=e[t];e[t]=e[n],e[n]=s}function qE(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function XE(e,t){let n=Math.random();return t*n+(1-n)*e}function KE(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 zn(e,t,n=""){M(Na(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)||_n(e)&&!n)for(let s=0;s<e.length;++s)hi(e[s],t,n);else t.push(e);return t}function Ut(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 ZE(e){return e.length===0}function Na(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function fn(e){return e%1==0}function YE(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 JE(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function QE(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 e9(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 t9(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=>fn(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 n9(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function _n(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray}function o2(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function G5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Ea(e){return typeof e=="string"||e instanceof String}function H5(e){return typeof e=="boolean"}function j5(e){return typeof e=="number"}function yh(e){return Array.isArray(e)?yh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray?"int32":j5(e)?"float32":Ea(e)?"string":H5(e)?"bool":"float32"}function Ra(e){return!!(e&&e.constructor&&e.call&&e.apply)}function xh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function ru(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let s=t-3;s>=0;--s)n[s]=n[s+1]*e[s+1];return n}function q5(e,t,n,s=!1){let r=new Array;if(t.length===1){let a=t[0]*(s?2:1);for(let o=0;o<a;o++)r[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,c)=>l*c)*(s?2:1);for(let l=0;l<a;l++)r[l]=q5(e+l*i,o,n,s)}return r}function au(e,t,n=!1){if(e.length===0)return t[0];let s=e.reduce((r,a)=>r*a)*(n?2:1);if(s===0)return[];if(s!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return q5(0,e,t,n)}function i2(e,t){let n=bh(e,t);for(let s=0;s<n.length;s++)n[s]=1;return n}function bh(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function s9(e,t){let n=e.reduce((s,r)=>s*r,1);if(t==null||t==="float32")return au(e,new Float32Array(n));if(t==="int32")return au(e,new Int32Array(n));if(t==="bool")return au(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function l2(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function r9(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 a9(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 u2(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=o9,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&(Z().getBool("IS_TEST")||Z().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];Z().getBool("IS_TEST")||Z().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(u2(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]=l9(s,r)})}};function o9(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...s)=>(i9(t,s[0],s[1]),s.join("="))),t}function i9(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function l9(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 ${e}.`)}function Z(){return Rr}var Rr=null;function u9(e){Rr=e}var c2;function Z5(){if(c2==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");c2=e}return c2}function c9(){let e=Z5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function d2(e,t){let n=c9();if(n.has(e))return n.get(e);{let s=t();return n.set(e,s),n.get(e)}}var fi="Abs",ou="Acos",iu="Acosh",Kr="Add",$a="AddN",lu="All",uu="Any",_a="ArgMax",cu="ArgMin",du="Asin",pu="Asinh",hu="Atan",fu="Atanh",mu="Atan2",Da="AvgPool",vh="AvgPoolGrad",rd="AvgPool3D",wh="AvgPool3DGrad",Pa="BatchMatMul",mi="BatchToSpaceND",kh="Bincount",Y5="BroadcastTo",Sh="BroadcastArgs",Fa="Cast",Oa="Ceil",Zr="ClipByValue",ad="Complex",od="ComplexAbs",gi="Concat",Ma="Conv2D",Ih="Conv2DBackpropFilter",za="Conv2DBackpropInput",id="Conv3D",Ch="Conv3DBackpropFilterV2",Th="Conv3DBackpropInputV2",La="Cos",Ba="Cosh",Ai="Cumsum",yi="CropAndResize",Nh="DenseBincount",xi="DepthToSpace",Wa="DepthwiseConv2dNative",Eh="DepthwiseConv2dNativeBackpropFilter",Rh="DepthwiseConv2dNativeBackpropInput",$h="Diag",ld="Dilation2D",_h="Dilation2DBackpropInput",Dh="Dilation2DBackpropFilter",Va="RealDiv",ud="Einsum",Ua="Elu",Ph="EluGrad",gu="Erf",bi="Equal",Ga="Exp",vi="ExpandDims",wi="Expm1",Fh="FFT",Au="Fill",ki="FlipLeftRight",Ha="Floor",ja="FloorDiv",qa="FusedBatchNorm",Si="GatherV2",Ii="GatherNd",Ci="Greater",Xa="GreaterEqual",Ka="Identity",Oh="IFFT",cd="Imag",yu="IsFinite",xu="IsInf",bu="IsNan",Ti="LeakyRelu",Ni="Less",Ei="LessEqual",Mh="LinSpace",Za="Log",vu="Log1p",Ri="LogicalAnd",wu="LogicalNot",dd="LogicalOr",J5="LogSoftmax",pd="LRN",zh="LRNGrad",Ya="Max",Ja="Maximum",Qa="MaxPool",Lh="MaxPoolGrad",hd="MaxPool3D",Bh="MaxPool3DGrad",Wh="MaxPoolWithArgmax",eo="Mean",to="Min",no="Minimum",so="MirrorPad",ku="Mod",Vh="Multinomial",ro="Multiply",$i="Neg",_i="NotEqual",Di="NonMaxSuppressionV3",Su="NonMaxSuppressionV4",Pi="NonMaxSuppressionV5",Fi="OnesLike",Oi="OneHot",Mi="Pack",ao="PadV2",d9="Pool",oo="Pow",io="Prelu",zi="Prod",Iu="Range",fd="Real",Cu="Reciprocal",lo="Relu",Li="Reshape",Tu="ResizeNearestNeighbor",Uh="ResizeNearestNeighborGrad",uo="ResizeBilinear",Gh="ResizeBilinearGrad",co="Relu6",Bi="Reverse",Wi="Round",po="Rsqrt",Vi="ScatterNd",Ui="Select",Nu="Selu",Gi="Slice",ho="Sin",Hi="Sinh",Eu="Sign",fo="Sigmoid",Ru="Softplus",mo="Sqrt",go="Sum",ji="SpaceToBatchND",qi="SplitV",Ao="Softmax",Hh="SparseFillEmptyRows",jh="SparseReshape",qh="SparseSegmentMean",Xh="SparseSegmentSum",md="SparseToDense",yo="SquaredDifference",$u="Square",Xi="StridedSlice",gd="StringNGrams",Kh="StringSplit",Zh="StringToHashBucketFast",xo="Sub",Ki="Tan",bo="Tanh",Yr="Tile",Zi="TopK",Yi="Transform",vo="Transpose",Yh="Unique",Ji="Unpack",Ad="UnsortedSegmentSum",Qi="ZerosLike",wo="Step",yd="FromPixels",el="RotateWithOffset",ko="_FusedMatMul",So="FusedConv2D",Io="FusedDepthwiseConv2D";function Co(...e){Z().getBool("IS_TEST")||Z().getBool("PROD")||console.warn(...e)}function p9(...e){Z().getBool("IS_TEST")||Z().getBool("PROD")||console.log(...e)}var _u=d2("kernelRegistry",()=>new Map),xd=d2("gradRegistry",()=>new Map);function Jh(e,t){let n=h2(e,t);return _u.get(n)}function p2(e){return xd.get(e)}function Jr(e){let t=_u.entries(),n=[];for(;;){let{done:s,value:r}=t.next();if(s)break;let[a,o]=r,[i]=a.split("_");i===e&&n.push(o)}return n}function cr(e){let{kernelName:t,backendName:n}=e,s=h2(t,n);_u.has(s)&&Co(`The kernel '${t}' for backend '${n}' is already registered`),_u.set(s,e)}function Q5(e){let{kernelName:t}=e;xd.has(t)&&Z().getBool("DEBUG")&&Co(`Overriding the gradient for '${t}'`),xd.set(t,e)}function h9(e,t){let n=h2(e,t);if(!_u.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);_u.delete(n)}function f9(e){if(!xd.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);xd.delete(e)}function m9(e,t){Jr(e).forEach(s=>{let r=Object.assign({},s,{backendName:t});cr(r)})}function h2(e,t){return`${t}_${e}`}var v={};Oe(v,{arraysEqual:()=>Na,assert:()=>M,assertNonNegativeIntegerDimensions:()=>l2,assertNonNull:()=>pi,assertShapesMatch:()=>zn,bytesFromStringArray:()=>G5,bytesPerElement:()=>o2,checkConversionForErrors:()=>V5,clamp:()=>nd,computeStrides:()=>ru,createScalarValue:()=>v9,createShuffledIndices:()=>QE,decodeString:()=>tf,distSquared:()=>KE,encodeString:()=>wd,fetch:()=>k9,fingerPrint64:()=>b9,flatten:()=>hi,getArrayFromDType:()=>W5,getTypedArrayFromDType:()=>B5,hasEncodingLoss:()=>n9,hexToLong:()=>bd,indexToLoc:()=>a9,inferDtype:()=>yh,inferFromImplicitShape:()=>t9,isBoolean:()=>H5,isFunction:()=>Ra,isInt:()=>fn,isNumber:()=>j5,isPromise:()=>u2,isScalarShape:()=>ZE,isString:()=>Ea,isTypedArray:()=>_n,isValidDtype:()=>U5,locToIndex:()=>r9,makeOnesTypedArray:()=>i2,makeZerosNestedTypedArray:()=>s9,makeZerosTypedArray:()=>bh,nearestDivisor:()=>xh,nearestLargerEven:()=>jE,now:()=>vd,parseAxisParam:()=>Xs,randUniform:()=>XE,repeatedTry:()=>e9,rightPad:()=>sd,shuffle:()=>z5,shuffleCombo:()=>HE,sizeFromShape:()=>Ut,sizeToSquarishShape:()=>JE,squeezeShape:()=>L5,sum:()=>qE,swap:()=>Ah,tanh:()=>YE,toNestedArray:()=>au,toTypedArray:()=>ef});var e3=di(RE()),tl=e3.default||e3;function bd(e){return tl.fromString(e,!0,16)}var t3=bd("c3a5c85c97cb3127"),nl=bd("b492b66fbe98f273"),Ln=bd("9ae16a3b2f90404f");function f2(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 vt(e,t){return n3(e,t,8)}function s3(e,t){return n3(e,t,4)}function mn(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function To(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 g9(e,t,n,s,r,a){r=r.add(e),a=mn(a.add(r).add(s),21);let o=r;return r=r.add(t),r=r.add(n),a=a.add(mn(r,44)),[r.add(s),a.add(o)]}function Qh(e,t,n,s){return g9(vt(e,t),vt(e,t+8),vt(e,t+16),vt(e,t+24),n,s)}function A9(e,t=e.length){if(t>=8){let n=Ln.add(t*2),s=vt(e,0).add(Ln),r=vt(e,t-8),a=mn(r,37).mul(n).add(s),o=mn(s,25).add(r).mul(n);return To(a,o,n)}if(t>=4){let n=Ln.add(t*2),s=s3(e,0);return To(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 f2(Ln.mul(a).xor(t3.mul(o))).mul(Ln)}return Ln}function y9(e,t=e.length){let n=Ln.add(t*2),s=vt(e,0).mul(nl),r=vt(e,8),a=vt(e,t-8).mul(n),o=vt(e,t-16).mul(Ln);return To(mn(s.add(r),43).add(mn(a,30)).add(o),s.add(mn(r.add(Ln),18)).add(a),n)}function x9(e,t=e.length){let n=Ln.add(t*2),s=vt(e,0).mul(Ln),r=vt(e,8),a=vt(e,t-8).mul(n),o=vt(e,t-16).mul(Ln),i=mn(s.add(r),43).add(mn(a,30)).add(o),l=To(i,s.add(mn(r.add(Ln),18)).add(a),n),c=vt(e,16).mul(n),u=vt(e,24),d=i.add(vt(e,t-32)).mul(n),p=l.add(vt(e,t-24)).mul(n);return To(mn(c.add(u),43).add(mn(d,30)).add(p),c.add(mn(u.add(s),18)).add(d),n)}function b9(e,t=e.length){let n=tl.fromNumber(81,!0);if(t<=32)return t<=16?A9(e,t):y9(e,t);if(t<=64)return x9(e,t);let s=n,r=n.mul(nl).add(113),a=f2(r.mul(Ln).add(113)).mul(Ln),o=[tl.UZERO,tl.UZERO],i=[tl.UZERO,tl.UZERO];s=s.mul(Ln).add(vt(e,0));let l=0,c=(t-1>>6)*64,u=c+(t-1&63)-63;do s=mn(s.add(r).add(o[0]).add(vt(e,l+8)),37).mul(nl),r=mn(r.add(o[1]).add(vt(e,l+48)),42).mul(nl),s=s.xor(i[1]),r=r.add(o[0]).add(vt(e,l+40)),a=mn(a.add(i[0]),33).mul(nl),o=Qh(e,l,o[1].mul(nl),s.add(i[0])),i=Qh(e,l+32,a.add(i[1]),r.add(vt(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=mn(s.add(r).add(o[0]).add(vt(e,l+8)),37).mul(d),r=mn(r.add(o[1]).add(vt(e,l+48)),42).mul(d),s=s.xor(i[1].mul(9)),r=r.add(o[0].mul(9).add(vt(e,l+40))),a=mn(a.add(i[0]),33).mul(d),o=Qh(e,l,o[1].mul(d),s.add(i[0])),i=Qh(e,l+32,a.add(i[1]),r.add(vt(e,l+16))),[a,s]=[s,a],To(To(o[0],i[0],d).add(f2(r).mul(t3)).add(a),To(o[1],i[1],d).add(s),d)}function v9(e,t){return t==="string"?wd(e):ef([e],t)}function w9(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function ef(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=hi(e)),Z().getBool("DEBUG")&&V5(e,t),w9(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let 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 Z().platform.now()}function k9(e,t){return Z().platform.fetch(e,t)}function wd(e,t="utf-8"){return t=t||"utf-8",Z().platform.encode(e,t)}function tf(e,t="utf-8"){return t=t||"utf-8",Z().platform.decode(e,t)}var S9=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new C9)}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(Z().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<s.length;l++){let c=s[l];c.data().then(u=>{I9(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 I9(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 C9=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 T9(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 N9(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(!Na(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,m2=7;function E9(e,t,n,s){let r=ru(t),a=R9(e,t,n,r),o=t.length,i=nf(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 R9(e,t,n,s){let r=Ut(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(m2))} + ${parseFloat(e[1].toFixed(m2))}j`:Ea(e)?s=`'${e}'`:n==="bool"?s=a3(e):s=parseFloat(e.toFixed(m2)).toString(),sd(s,t)}function a3(e){return e===0?"false":"true"}function nf(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(...nf(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(...nf(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(...nf(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=Ut(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=ru(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,Du=null,$9=null;function _9(e){$r=e}function D9(e){Du=e}function P9(e){$9=e}var Je=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ut(e),this.strides=ru(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Du.buffer(this.shape,this.dtype,e)}bufferSync(){return Du.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return au(this.shape,e,this.dtype==="complex64")}arraySync(){return au(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=$r().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>tf(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=>tf(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 Du.print(this,e)}clone(){return this.throwIfDisposed(),Du.clone(this)}toString(e=!1){let t=this.dataSync();return E9(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Du.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),$r().makeVariable(this,e,t,n)}};Object.defineProperty(Je,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function F9(){return d2("Tensor",()=>Je)}F9();var Cd=class extends Je{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Na(e.shape,this.shape))throw new Error(`shape of the new value (${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 Je&&e.assign!=null&&e.assign instanceof Function});var dr={};Oe(dr,{assertTypesMatch:()=>o3,getTensorsInContainer:()=>v2,isTensorInList:()=>M9,makeTypesMatch:()=>Pt});var g2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(g2||(g2={}));var A2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(A2||(A2={}));var y2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(y2||(y2={}));var x2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(x2||(x2={}));var b2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(b2||(b2={}));var O9={float32:x2,int32:A2,bool:y2,complex64:b2};function Bn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return O9[e][t]}function Td(e){return Bn(e,"int32")}function Pt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Bn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function o3(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function M9(e,t){return t.some(n=>n.id===e.id)}function v2(e){let t=[],n=new Set;return i3(e,t,n),t}function i3(e,t,n){if(e==null)return;if(e instanceof Je){t.push(e);return}if(!z9(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),i3(a,t,n))}}function z9(e){return Array.isArray(e)||typeof e=="object"}function w2(e){return e.kernelName!=null}var l3=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},k2=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new l3}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Co(`${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 S9(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 su)&&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,Co(`Initialization of backend ${e} failed`),Co(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 Co(`Initialization of backend ${e} failed`),Co(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 k2.nextTensorId++}nextVariableId(){return k2.nextVariableId++}clone(e){let t=W.runKernel(Ka,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return W.runKernel(Fa,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,!(Jh(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 '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=w2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(w2(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Jh(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${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=w2(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=p2(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"&&Ea(e[0])&&(r=e.map(i=>wd(i)));let a=s.write(r,t,n),o=new Je(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 Je(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*o2(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*o2(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=p2(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=bh(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=v2(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 Je,()=>"The result y returned by f() must be a tensor.");let a=T9(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?L9(r.shape):n,N9(o,a,l=>this.tidy(l),B9);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(Ra(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(o=>o instanceof Je),()=>"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 Je,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(Ra(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 Je),()=>"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}},S2=k2;S2.nextTensorId=0;S2.nextVariableId=0;function L9(e){let t=i2(Ut(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 S2(t)}return u9(e._tfengine.ENV),_9(()=>e._tfengine),e._tfengine}var W=u3();function B9(e,t){let n={a:e,b:t};return W.runKernel(Kr,n)}var Pu={};Oe(Pu,{isBrowser:()=>c3,isMobile:()=>U9,mockIsMobile:()=>V9});function W9(){return typeof navigator!="undefined"&&navigator!=null}var I2;function V9(e){I2=e}function U9(e){if(I2!==void 0)return I2;if(e||W9()){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=Z();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(_n(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||_n(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&Z().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&d3(e,s,[]),s}function d3(e,t,n){if(n=n||[],!Array.isArray(e)&&!_n(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 ${t} tensor`)}}function D(e,t,n,s="numeric"){if(e instanceof Je)return p3(s,e.dtype,t,n),e;let r=yh(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),p3(s,r,t,n),e==null||!_n(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=_r(e,r);!_n(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?ef(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 u2(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 G9(e,t){let n=D(e,"real","complex"),s=D(t,"imag","complex");zn(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);let r={real:n,imag:s};return W.runKernel(ad,r)}var No=V({complex_:G9});function Eo(e,t,n,s){if(s==null&&(s=yh(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!_n(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){l2(t);let r=Ut(t),a=Ut(n);M(r===a,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==Ut(t.slice(o)):!0;M(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!_n(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?ef(e,s):hi(e,[],!0),W.makeTensor(e,t,s)}function ut(e,t,n){let s=_r(e,n);return Eo(e,t,s,n)}var C2={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},sf=4;async function H9(e,t){let n=[],s=[],r=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<r.length;++o){let i=r[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${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)+sf*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+=sf,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:j9(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=Ut(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=C2[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=J9()),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}': ${i}`);r+=c*p}else if(i==="string"){let d=Ut(a.shape);u=[];for(let p=0;p<d;p++){let h=new Uint32Array(e.slice(r,r+sf))[0];r+=sf;let f=new Uint8Array(e.slice(r,r+h));u.push(f),r+=h}}else{let d=C2[i],p=e.slice(r,r+c*d);if(i==="float32")u=new Float32Array(p);else if(i==="int32")u=new Int32Array(p);else if(i==="bool")u=new Uint8Array(p);else if(i==="complex64"){u=new Float32Array(p);let h=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let A=0;A<h.length;A++)h[A]=u[A*2],f[A]=u[A*2+1];let m=ut(h,l,"float32"),g=ut(f,l,"float32");n[o]=No(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=c*d}i!=="complex64"&&(n[o]=ut(u,l,i))}return n}function j9(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 T2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function m3(e){return T2?Buffer.byteLength(e):new Blob([e]).size}function q9(e){if(T2)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 X9(e){if(T2){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 N2(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 E2(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 K9(){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 Z9(){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 Y9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function J9(){let e=K9(),t=Z9(),n=Y9();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 Bt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Bt.instance==null&&(Bt.instance=new Bt),Bt.instance}static registerSaveRouter(e){Bt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Bt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Bt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Bt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let s=[];return(t==="load"?Bt.getInstance().loadRouters:Bt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&s.push(o)}),s}},Q9=e=>Bt.registerSaveRouter(e),eR=e=>Bt.registerLoadRouter(e),tR=e=>Bt.getSaveHandlers(e),nR=(e,t)=>Bt.getLoadHandlers(e,t),R2="tensorflowjs",$2=1,sl="models_store",Ro="model_info_store";function y3(){if(!Z().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 _2(e){let t=e.result;t.createObjectStore(sl,{keyPath:"modelPath"}),t.createObjectStore(Ro,{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(R2,$2);r.onupgradeneeded=()=>_2(r),r.onsuccess=()=>{let a=r.result;if(t==null){let o=a.transaction(sl,"readonly"),l=o.objectStore(sl).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),s(new Error(`Cannot find model with path '${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(Ro,"readwrite"),l=i.objectStore(Ro),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(Ro);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=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(rl.URL_SCHEME)?sR(e.slice(rl.URL_SCHEME.length)):null;Bt.registerSaveRouter(x3);Bt.registerLoadRouter(x3);function sR(e){return new rl(e)}function rR(e){return e.startsWith(rl.URL_SCHEME)?e.slice(rl.URL_SCHEME.length):e}var aR=class{constructor(){this.indexedDB=y3()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(R2,$2);n.onupgradeneeded=()=>_2(n),n.onsuccess=()=>{let s=n.result,r=s.transaction(Ro,"readonly"),o=r.objectStore(Ro).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=rR(e),new Promise((t,n)=>{let s=this.indexedDB.open(R2,$2);s.onupgradeneeded=()=>_2(s),s.onsuccess=()=>{let r=s.result,a=r.transaction(Ro,"readwrite"),o=a.objectStore(Ro),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return r.close(),n(new Error(`Cannot find model with path '${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="/",Fu="tensorflowjs_models",b3="info",oR="model_topology",iR="weight_specs",lR="weight_data",uR="model_metadata";function v3(e){return{info:[Fu,e,b3].join(Qr),topology:[Fu,e,oR].join(Qr),weightSpecs:[Fu,e,iR].join(Qr),weightData:[Fu,e,lR].join(Qr),modelMetadata:[Fu,e,uR].join(Qr)}}function w3(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function cR(e){let t=e.split(Qr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Qr)}function dR(e){return e.startsWith(al.URL_SCHEME)?e.slice(al.URL_SCHEME.length):e}var al=class{constructor(e){if(!Z().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=v3(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Ed(e);try{this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,q9(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:s}}catch(r){throw w3(this.keys),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=X9(a),t}};al.URL_SCHEME="localstorage://";var k3=e=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(al.URL_SCHEME)?pR(e.slice(al.URL_SCHEME.length)):null;Bt.registerSaveRouter(k3);Bt.registerLoadRouter(k3);function pR(e){return new al(e)}var hR=class{constructor(){M(Z().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Fu+Qr,n=Qr+b3;for(let s=0;s<this.LS.length;++s){let r=this.LS.key(s);if(r.startsWith(t)&&r.endsWith(n)){let a=cR(r);e[a]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=dR(e);let t=v3(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return w3(t),n}},Ou="://",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(Ou)&&(e=e.slice(0,e.indexOf(Ou))),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 rf(e){if(e.indexOf(Ou)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Es.getSchemes().join(",")}`);return{scheme:e.split(Ou)[0],path:e.split(Ou)[1]}}async function S3(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let s=Bt.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=Bt.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=rf(e).scheme,l=rf(e).path,c=i===rf(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 fR(){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+Ou+r;t[a]=s[r]}}return t}async function mR(e){let t=rf(e);return Es.getManager(t.scheme).removeModel(t.path)}async function gR(e,t){return S3(e,t,!1)}async function AR(e,t){return S3(e,t,!0)}var yR=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 ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(Z().get("IS_BROWSER")){Z().setPlatform("browser",new yR);try{Es.registerManager(al.URL_SCHEME,new hR)}catch(e){}try{Es.registerManager(rl.URL_SCHEME,new aR)}catch(e){}}var xR={importFetch:()=>$E()},D2,bR=class{constructor(){this.util=_E(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Z().global.fetch!=null?Z().global.fetch(e,t):(D2==null&&(D2=xR.importFetch()),D2(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)}};Z().get("IS_NODE")&&Z().setPlatform("node",new bR);function ze(e,t="float32",n){return t=t||"float32",l2(e),new nn(e,t,n)}function vR(e,t){let n=D(e,"x","cast");if(!U5(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let s={x:n},r={dtype:t};return W.runKernel(Fa,s,r)}var fe=V({cast_:vR});function wR(e){let n={x:D(e,"x","clone","string_or_numeric")};return W.runKernel(Ka,n)}var Wn=V({clone_:wR});function I3(e,t=!1){console.log(e.toString(t))}u3();var kR={buffer:ze,cast:fe,clone:Wn,print:I3};D9(kR);var ss={};Oe(ss,{browserFiles:()=>RR,browserHTTPRequest:()=>FR,concatenateArrayBuffers:()=>N2,copyModel:()=>gR,decodeWeights:()=>f3,encodeWeights:()=>H9,fromMemory:()=>MR,getLoadHandlers:()=>nR,getModelArtifactsForJSON:()=>E2,getModelArtifactsInfoForJSON:()=>Ed,getSaveHandlers:()=>tR,http:()=>M2,isHTTPScheme:()=>O2,listModels:()=>fR,loadWeights:()=>$R,moveModel:()=>AR,registerLoadRouter:()=>eR,registerSaveRouter:()=>Q9,removeModel:()=>mR,weightsLoaderFactory:()=>E3,withSaveHandler:()=>zR});var SR="model",IR=".json",CR=".weights.bin";function C3(e){return new Promise(t=>setTimeout(t)).then(e)}var P2=class{constructor(e){if(!Z().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(P2.URL_SCHEME)&&(e=e.slice(P2.URL_SCHEME.length)),(e==null||e.length===0)&&(e=SR),this.modelJsonFileName=e+IR,this.weightDataFileName=e+CR}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)}}}},af=P2;af.URL_SCHEME="downloads://";var TR=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 ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let i=E2(r,l=>this.loadWeights(l));e(i)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${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,N2(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}},NR=e=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(af.URL_SCHEME)?ER(e.slice(af.URL_SCHEME.length)):null;Bt.registerSaveRouter(NR);function ER(e="model"){return new af(e)}function RR(e){return new TR(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?Z().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=C2[A]*Ut(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 _R="application/octet-stream",DR="application/json",F2=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=Z().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:DR}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:_R}),"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 E2(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,s]=PR(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,N2(l)]}};F2.URL_SCHEME_REGEX=/^https?:\/\//;function PR(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),s=e.substring(0,t),r=n>t?e.substring(n):"";return[s+"/",r]}function O2(e){return e.match(F2.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=>O2(s)):n=O2(e),n)return M2(e,t)}return null};Bt.registerSaveRouter(R3);Bt.registerLoadRouter(R3);function M2(e,t){return new F2(e,t)}function FR(e,t){return M2(e,t)}var z2=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},OR=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function MR(e,t,n,s){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new z2(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 z2({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 z2({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:s}))}function zR(e){return new OR(e)}var $3={};Oe($3,{confusionMatrix:()=>UR});function LR(e,t,n=!1,s=!1){let r=D(e,"a","matMul"),a=D(t,"b","matMul");[r,a]=Pt(r,a);let o={a:r,b:a},i={transposeA:n,transposeB:s};return W.runKernel(Pa,o,i)}var He=V({matMul_:LR});function BR(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_:BR});function WR(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(vo,s,r)}var Qe=V({transpose_:WR});function VR(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(fe(s,"int32"),n),o=Rd(fe(r,"int32"),n),i=Qe(a),l=He(i,o);return fe(l,"int32")}var UR=V({confusionMatrix_:VR}),ol={};Oe(ol,{assertAndGetBroadcastShape:()=>gt,getBroadcastDims:()=>_3,getReductionAxes:()=>Xt});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 Xt(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 gt(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:()=>ZR,fromPixelsAsync:()=>XR,toPixels:()=>KR});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 Eo(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(Jh(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 GR(e){return e!=null&&e.data instanceof Uint8Array}function HR(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function jR(e){return e!=null&&e.width!==0&&e.height!==0}function qR(e){return HR()&&!(e instanceof ImageBitmap)&&jR(e)&&!GR(e)}async function XR(e,t=3){let n=null;if(Z().getBool("WRAP_TO_IMAGEBITMAP")&&qR(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 KR(e,t){let n=D(e,"img","toPixels");if(!(e instanceof Je)){let c=n;n=fe(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 ZR=V({fromPixels_:P3}),L2={};Oe(L2,{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(Ut(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=[...ru(e.shape).map(d=>d/c),1].slice(0,a);return[l,o,c,u]}var B2={};Oe(B2,{calculateShapes:()=>O3,validateInput:()=>V2,validateUpdateShape:()=>W2});function W2(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 V2(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: ${e.shape}`)}W2(n,t,e)}function O3(e,t,n){let s=t.shape.length,r=s>1?t.shape[s-1]:1,a=n.length,o=1;for(let d=r;d<a;++d)o*=n[d];let i=r<1?1:r,l=Ut(t.shape)/i,c=[...ru(n.slice(0,r)),1],u=Ut(n);return{sliceRank:r,numUpdates:l,sliceSize:o,strides:c,outputSize:u}}var Ft={};Oe(Ft,{assertParamsValid:()=>JR,computeFlatOffset:()=>s$,computeOutShape:()=>e$,getNormalizedAxes:()=>t$,isSliceContinous:()=>n$,maskToAxes:()=>QR,parseSliceParams:()=>H3,sliceInfo:()=>r$,startForAxis:()=>U3,startIndicesWithElidedDims:()=>B3,stopForAxis:()=>G3,stopIndicesWithElidedDims:()=>W3,stridesForAxis:()=>V3,stridesWithElidedDims:()=>M3});var U2=-2,YR=-1;function JR(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 QR(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function e$(e,t,n){let s=[];for(let r=0;r<e.length;r++)s[r]=Math.ceil((t[r]-e[r])/n[r]);return s}function M3(e,t,n,s){let r=[...e];for(let a=r.length;a<s.length;a++)r.push(1);for(let a=0;a<n;a++)a===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function z3(e,t,n){return n<=e?n:n-(t-1)}function L3(e,t){let n=[];for(let s=0;s<e;s++)n.push(t+s);return n}function t$(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 n$(e,t,n){let s=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){s=r;break}for(let r=s+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function s$(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 ${i}.`),e.shape[i]-s[i])),[s,a]}function r$(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};a$(d,p);let h=!0,f=!0,m=!0,g=[],A=[];for(let y=0;y<e.length;++y){if(p.strides[y]===0)throw Error(`strides[${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 ${y} out of bounds.`)}else p.begin[y]=j3(p.begin[y],0,p.strides[y],w,k,I),p.end[y]=j3(p.end[y],1,p.strides[y],w,k,I);let _=p.strides[y]===1&&p.begin[y]===0&&p.end[y]===w;h=h&&_,f=f&&(y===0&&p.strides[y]===1||_)}else h=h&&p.strides[y]===1&&N,f=f&&(y===0&&p.strides[y]===1||N);let R,O=!1;if(p.beginValid&&p.endValid?(R=p.end[y]-p.begin[y],O=!0):b?(R=1,O=!0):N&&w>=0&&(p.strides[y]<0?R=-w:R=w,O=!0),O){let _;R===0||R<0!=p.strides[y]<0?_=0:_=Math.trunc(R/p.strides[y])+(R%p.strides[y]!=0?1:0),g.push(_)}else g.push(-1)}for(let y=0;y<p.finalShapeGatherIndices.length;++y){let b=p.finalShapeGatherIndices[y];b>=0?A.push(g[b]):b===U2&&A.push(1)}return{finalShapeSparse:A.filter((y,b)=>p.finalShapeGatherIndices[b]!==U2),finalShape:A,isIdentity:h,sliceDim0:f,isSimpleSlice:m,begin:p.begin,end:p.end,strides:p.strides}}function a$(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(U2),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, ${t.begin.length}.`);e.begin!=null&&(t.begin[n]=e.begin[s]),e.end!=null&&(t.end[n]=e.end[s]),t.strides[n]=e.strides[s],e.beginMask&1<<s&&(t.beginMask|=1<<n),e.endMask&1<<s&&(t.endMask|=1<<n),e.shrinkAxisMask&1<<s?(t.finalShapeGatherIndices.push(YR),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<n):(t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(s)),t.inputShapeGatherIndicesSparse[n]=s,n++}}function j3(e,t,n,s,r,a){if(r[t])return n>0?a[t]:a[t+1&1];{let o=e<0?s+e:e;return o<a[0]?a[0]:o>a[1]?a[1]:o}}var ce={};Oe(ce,{Serializable:()=>q3,SerializationMap:()=>ll,registerClass:()=>$o});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 $o(e){M(e.className!=null,()=>"Class being registered does not have the static className property defined."),M(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),M(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ll.register(e)}var X3={};Oe(X3,{TEST_EPSILON_FLOAT16:()=>K3,encodeStrings:()=>Z3,expectArrayBuffersEqual:()=>p$,expectArraysClose:()=>i$,expectArraysEqual:()=>u$,expectNumbersClose:()=>c$,expectPromiseToFail:()=>l$,expectValuesInRange:()=>d$,testEpsilon:()=>G2});var o$=.001,K3=.1;function i$(e,t,n){return n==null&&(n=G2()),H2(e,t,(s,r)=>j2(s,r,n))}function G2(){return W.backend.floatPrecision()===32?o$:K3}function H2(e,t,n){let s=!0;if((_n(e)||_n(t))&&(s=!1),_n(e)&&_n(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: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=_r(e),i=_r(t);if(!Na(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let r=_n(e)?e:hi(e),a=_n(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: ${a}.`)}}function l$(e,t){e().then(()=>t.fail(),()=>t())}function u$(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Ea(e)||Ea(e[0])||Ea(t)||Ea(t[0])?H2(e,n,(s,r)=>s==r):H2(e,t,(s,r)=>j2(s,r,0))}function c$(e,t,n){if(n==null&&(n=G2()),!j2(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function j2(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function d$(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: ${n}`)}function p$(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(){Z().set("PROD",!0)}function h$(){Z().set("DEBUG",!0)}function f$(){Z().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function q2(e){Z().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}P9(q2);function m$(){W.disposeVariables()}function rs(){return W}function of(){return W.memory()}function g$(e){return W.profile(e)}function X(e,t){return W.tidy(e,t)}function te(e){v2(e).forEach(n=>n.dispose())}function gn(e){return W.keep(e)}function A$(e){return W.time(e)}function J3(e){return W.setBackend(e)}function lf(){return W.ready()}function Rs(){return W.backendName}function y$(e){W.removeBackend(e)}function X2(e){return W.findBackend(e)}function x$(e){return W.findBackendFactory(e)}function ul(e,t,n=1){return W.registerBackend(e,t,n)}function Dr(){return W.backend}function b$(e,t){Z().setPlatform(e,t)}function v$(e,t){let n=D(e,"a","add"),s=D(t,"b","add");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(Kr,r)}var ue=V({add_:v$});function w$(e,t){let n=D(e,"a","floorDiv"),s=D(t,"b","floorDiv");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(ja,r)}var uf=V({floorDiv_:w$});function k$(e,t){let n=D(e,"a","div"),s=D(t,"b","div");if([n,s]=Pt(n,s),n.dtype==="int32"&&s.dtype==="int32")return uf(n,s);let r={a:n,b:s},a={};return W.runKernel(Va,r,a)}var me=V({div_:k$});function S$(e,t){let n=D(e,"a","mul"),s=D(t,"b","mul");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(ro,r)}var L=V({mul_:S$});function I$(e){let t=D(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return W.runKernel(od,n)}else{let n={x:t};return W.runKernel(fi,n)}}var sn=V({abs_:I$});function C$(e){let n={x:D(e,"x","acos")};return W.runKernel(ou,n)}var Q3=V({acos_:C$});function T$(e){let n={x:D(e,"x","acosh")};return W.runKernel(iu,n)}var ev=V({acosh_:T$});function N$(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(!Na(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let s=t;return W.runKernel($a,s)}var Mu=V({addN_:N$});function E$(e,t=null,n=!1){let r={x:D(e,"x","all","bool")},a={axis:t,keepDims:n};return W.runKernel(lu,r,a)}var K2=V({all_:E$});function R$(e,t=null,n=!1){let r={x:D(e,"x","any","bool")},a={axis:t,keepDims:n};return W.runKernel(uu,r,a)}var cf=V({any_:R$});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 _$(e,t=0){let s={x:D(e,"x","argMin")},r={axis:t};return W.runKernel(cu,s,r)}var tv=V({argMin_:_$});function D$(e){let n={x:D(e,"x","asin")};return W.runKernel(du,n)}var nv=V({asin_:D$});function P$(e){let n={x:D(e,"x","asinh")};return W.runKernel(pu,n)}var sv=V({asinh_:P$});function F$(e){let n={x:D(e,"x","atan")};return W.runKernel(hu,n)}var rv=V({atan_:F$});function O$(e,t){let n=D(e,"a","atan2"),s=D(t,"b","atan2");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(mu,r)}var av=V({atan2_:O$});function M$(e){let n={x:D(e,"x","atanh")};return W.runKernel(fu,n)}var ov=V({atanh_:M$});function z$(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]=df(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 $d(e,c,n,s,r,a,!1,o)}function L$(e,t,n,s,r,a,o="NDHWC"){let[i,l,c]=Y2(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 ${i}`);let[p,h,,f]=t,[m,g]=df(n),[A,x]=df(s),y=zu(p,A),b=zu(h,x),{padInfo:w,outHeight:k,outWidth:I}=V$(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 ${o}`);let[h,f,m,,g]=t,[A,x,y]=Y2(n),[b,w,k]=Y2(s),I=zu(h,b),N=zu(f,w),R=zu(m,k),{padInfo:O,outDepth:_,outHeight:P,outWidth:T}=U$(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 B$(e,t,n,s,r){s==null&&(s=Z2(e,t,n));let a=e[0],o=e[1],i=cl((a-t+2*s)/n+1,r),l=cl((o-t+2*s)/n+1,r);return[i,l]}function W$(e,t,n,s,r,a){r==null&&(r=Z2(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 Z2(e,t,n,s=1){let r=zu(t,s);return Math.floor((e[0]*(n-1)-n+r)/2)}function df(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Y2(e){return typeof e=="number"?[e,e,e]:e}function zu(e,t){return t<=1?e:e+(e-1)*(t-1)}function V$(e,t,n,s,r,a,o,i,l){let c,u,d;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let h=B$([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: ${e}`);return{padInfo:c,outHeight:u,outWidth:d}}function U$(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=W$([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]=df(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 ${e}`)}function G$(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_:G$});function H$(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(fn(s),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=W.runKernel(Da,c,u);return d=fe(d,a.dtype),l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var pf=V({avgPool_:H$});function j$(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(fn(s),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=W.runKernel(rd,c,u);return d=fe(d,i.dtype),l?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var J2=V({avgPool3d_:j$});function q$(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 ${a.dtype}. `)}),n.length===1)return Wn(n[0]);let s=n,r={axis:t};return W.runKernel(gi,s,r)}var wt=V({concat_:q$});function X$(e){let n={x:D(e,"x","sigmoid","float32")};return W.runKernel(fo,n)}var fs=V({sigmoid_:X$});function K$(e,t,n){let s=D(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return W.runKernel(Gi,r,a)}var De=V({slice_:K$});function Z$(e){let n={x:D(e,"x","tanh","float32")};return W.runKernel(bo,n)}var Lu=V({tanh_:Z$});function Y$(e,t,n,s,r,a){let o=D(e,"forgetBias","basicLSTMCell"),i=D(t,"lstmKernel","basicLSTMCell"),l=D(n,"lstmBias","basicLSTMCell"),c=D(s,"data","basicLSTMCell"),u=D(r,"c","basicLSTMCell"),d=D(a,"h","basicLSTMCell"),p=wt([c,d],1),h=He(p,i),f=ue(h,l),m=f.shape[0],g=f.shape[1]/4,A=[m,g],x=De(f,[0,0],A),y=De(f,[0,g],A),b=De(f,[0,g*2],A),w=De(f,[0,g*3],A),k=ue(L(fs(x),Lu(y)),L(u,fs(ue(o,b)))),I=L(Lu(k),fs(w));return[k,I]}var J$=V({basicLSTMCell_:Y$});function Q$(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(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return W.runKernel(mi,a,o)}var hf=V({batchToSpaceND_:Q$});function e_(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 t_(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:e_(o),scale:c,offset:u,mean:i,variance:l},h={varianceEpsilon:a},f=W.runKernel(qa,p,h);return H(f,o.shape)}var Bu=V({batchNorm_:t_});function n_(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_: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===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_: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===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_:r_});function a_(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(kh,a,o)}var Q2=V({bincount_:a_});function o_(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 ${s.rank}`);let r={s0:n,s1:s};return W.runKernel(Sh,r)}var hv=V({broadcastArgs_:o_});function i_(e,t){let n=D(e,"broadcastTo","x"),s=n.shape;if(t.some(c=>!(c>0)||c%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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 Wn(n);let i={x:n},l={reps:a};return W.runKernel(Yr,i,l)}var _d=V({broadcastTo_:i_});function l_(e){let n={x:D(e,"x","ceil","float32")};return W.runKernel(Oa,n)}var fv=V({ceil_:l_});function u_(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 ms=V({clipByValue_:u_});function c_(e){return wt(e,0)}var mv=V({concat1d_:c_});function d_(e,t){return wt(e,t)}var Wu=V({concat2d_:d_});function p_(e,t){return wt(e,t)}var gv=V({concat3d_:p_});function h_(e,t){return wt(e,t)}var Av=V({concat4d_:h_});function f_(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(fn(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(Ma,p,h);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Do=V({conv2d_:f_});function m_(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(fn(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 ${r} but only NWC is currently supported.`);let d=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),g=Do(p,d,[1,n],s,"NHWC",[1,a],o);return u?H(g,[g.shape[2],g.shape[3]]):H(g,[g.shape[0],g.shape[2],g.shape[3]])}var e1=V({conv1d_:m_});function g_(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${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(fn(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(za,p,h);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var t1=V({conv2DBackpropInput_:g_});function A_(e,t,n,s,r,a){let o=D(e,"x","conv2dTranspose"),i=D(t,"filter","conv2dTranspose");return t1(n,o,i,s,r,"NHWC",a)}var n1=V({conv2dTranspose_:A_});function y_(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 ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=W.runKernel(id,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var s1=V({conv3d_:y_});function x_(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${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(Th,u,d);return i?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var yv=V({conv3DBackpropInput_:x_});function b_(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_:b_});function v_(e){let n={x:D(e,"x","cos","float32")};return W.runKernel(La,n)}var ff=V({cos_:v_});function w_(e){let n={x:D(e,"x","cosh","float32")};return W.runKernel(Ba,n)}var r1=V({cosh_:w_});function k_(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 a1=V({cumsum_:k_});function S_(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(Nh,o,i)}var bv=V({denseBincount_:S_});function I_(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_:I_});function C_(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(fn(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=W.runKernel(Wa,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Dd=V({depthwiseConv2d_:C_});function T_(e){let n={x:D(e,"x","diag")};return W.runKernel($h,n)}var N_=V({diag_:T_});function E_(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_:E_});function R_(e,t){let n=D(e,"a","equal","string_or_numeric"),s=D(t,"b","equal","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(bi,r)}var $s=V({equal_:R_});function $_(e,t,n){let s=D(t,"a","where"),r=D(n,"b","where"),a=D(e,"condition","where","bool"),o=gt(gt(a.shape,s.shape),r.shape),i=_d(a,o),l=_d(s,o),c=_d(r,o),u={condition:i,t:l,e:c};return W.runKernel(Ui,u)}var Vn=V({where_:$_});function __(e){let n={x:D(e,"x","zerosLike")};return W.runKernel(Qi,n)}var et=V({zerosLike_:__});function D_(e,t){let n=D(e,"a","div"),s=D(t,"b","div");[n,s]=Pt(n,s);let r=me(n,s),a=et(r),o=$s(s,a);return Vn(o,a,r)}var kv=V({divNoNan_:D_});function P_(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 ${a}.`),n.rank===1&&s.rank===1){let o=H(n,[1,-1]),i=H(s,[-1,1]),l=He(o,i);return H(l,[])}else if(n.rank===1&&s.rank===2){let o=H(n,[1,-1]),i=H(s,[s.shape[0],s.shape[1]]),l=He(o,i);return H(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=H(s,[-1,1]),i=He(n,o);return H(i,[i.size])}else{let o=H(s,[s.shape[0],s.shape[1]]);return He(n,o)}}var F_=V({dot_:P_});function O_(e,...t){let n=t.map((r,a)=>D(r,`tensors${a}`,"einsum")),s={equation:e};return W.runKernel(ud,n,s)}var Sv=V({einsum_:O_});function M_(e){let n={x:D(e,"x","elu","float32")};return W.runKernel(Ua,n)}var Pd=V({elu_:M_});function z_(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=fe(t,"float32"));let n={x:t};return W.runKernel(gu,n)}var Iv=V({erf_:z_});function L_(e){let n={x:D(e,"x","exp")};return W.runKernel(Ga,n)}var _s=V({exp_:L_});function B_(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 Kt=V({expandDims_:B_});function W_(e){let n={x:D(e,"x","expm1")};return W.runKernel(wi,n)}var Cv=V({expm1_:W_});function V_(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_:V_});function U_(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(Kt(o,0),[n[0],1,1]);if(n.length===2)return Ys(Kt(Kt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Ys(Kt(Kt(Kt(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 o1=V({eye_:U_});function Vu(e,t,n){let s={shape:e,value:t,dtype:n};return W.runKernel(Au,{},s)}function G_(e){let n={x:D(e,"x","floor","float32")};return W.runKernel(Ha,n)}var Fd=V({floor_:G_});function H_(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_:H_});function j_(e,t){let n=D(e,"a","greater","string_or_numeric"),s=D(t,"b","greater","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ci,r)}var gs=V({greater_:j_});function q_(e,t){let n=D(e,"a","greaterEqual","string_or_numeric"),s=D(t,"b","greaterEqual","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Xa,r)}var dl=V({greaterEqual_:q_});function X_(e){let n={input:D(e,"input","imag")};return W.runKernel(cd,n)}var mf=V({imag_:X_});function K_(e){let n={x:D(e,"x","isFinite")};return W.runKernel(yu,n)}var Z_=V({isFinite_:K_});function Y_(e){let n={x:D(e,"x","isInf")};return W.runKernel(xu,n)}var J_=V({isInf_:Y_});function Q_(e){let n={x:D(e,"x","isNaN")};return W.runKernel(bu,n)}var Tv=V({isNaN_:Q_});function eD(e,t=.2){let s={x:D(e,"x","leakyRelu")},r={alpha:t};return W.runKernel(Ti,s,r)}var gf=V({leakyRelu_:eD});function tD(e,t){let n=D(e,"a","less","string_or_numeric"),s=D(t,"b","less","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ni,r)}var i1=V({less_:tD});function nD(e,t){let n=D(e,"a","lessEqual","string_or_numeric"),s=D(t,"b","lessEqual","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ei,r)}var pl=V({lessEqual_:nD});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(Mh,{},s)}function sD(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(fn(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_:sD});function rD(e){let n={x:D(e,"x","log","float32")};return W.runKernel(Za,n)}var Ds=V({log_:rD});function aD(e){let n={x:D(e,"x","log1p")};return W.runKernel(vu,n)}var Af=V({log1p_:aD});function oD(e){return M(Ra(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&&zn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),yf(o),o[0]})}}function iD(e){return M(Ra(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&&zn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),yf(o),o})}}function lD(e){return M(Ra(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Je,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Je,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=W.gradients(()=>e(t),[t],n);return yf(s),{grad:s[0],value:r}}}function uD(e){return M(Ra(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof Je),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Je,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=W.gradients(()=>e(...t),t,n);return n!=null&&zn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),yf(s.grads),s}}function Rv(e,t){M(Ra(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 yf(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 cD(e){let n={x:D(e,"x","neg")};return W.runKernel($i,n)}var Ot=V({neg_:cD});function dD(e){let n={x:D(e,"x","softplus")};return W.runKernel(Ru,n)}var Gu=V({softplus_:dD});function pD(e){let t=D(e,"x","logSigmoid");return Fr(s=>({value:Ot(Gu(Ot(s))),gradFunc:o=>L(o,fs(Ot(s)))}))(t)}var hD=V({logSigmoid_:pD});function fD(e,t=null,n=!1){let r={x:D(e,"x","max")},a={reductionIndices:t,keepDims:n};return W.runKernel(Ya,r,a)}var An=V({max_:fD});function mD(e,t){let n=D(e,"a","sub"),s=D(t,"b","sub");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(xo,r)}var ge=V({sub_:mD});function gD(e,t=null,n=!1){let s=D(e,"x","sum");s.dtype==="bool"&&(s=fe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(go,r,a)}var Se=V({sum_:gD});function AD(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=An(r,t,!0),l=ge(r,i),c=ge(fe(l,"float32"),Ds(Se(_s(l),t,o)));return a([c]),{value:c,gradFunc:(d,p)=>{let[h]=p,f=!0,m=_s(h);return ge(d,L(Se(d,t,f),m))}}})(n)}var l1=V({logSoftmax_:AD});function u1(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 $v(e,n,t)}function yD(e,t,n){M(u1(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Dv(e,t){if(u1(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 c1(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function xD(e,t){let n=[];for(let s=t-e;s<t;++s)n.push(s);return n}function bD(e,t=null,n=!1){let s=D(e,"x","logSumExp"),r=Xs(t,s.shape),a=An(s,r,!0),o=ge(s,a),i=_s(o),l=Se(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_:bD});function vD(e,t){let n=D(e,"a","logicalAnd","bool"),s=D(t,"b","logicalAnd","bool");gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ri,r)}var hr=V({logicalAnd_:vD});function wD(e){let n={x:D(e,"x","logicalNot","bool")};return W.runKernel(wu,n)}var xf=V({logicalNot_:wD});function kD(e,t){let n=D(e,"a","logicalOr","bool"),s=D(t,"b","logicalOr","bool");gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(dd,r)}var d1=V({logicalOr_:kD});function SD(e,t){let n=D(e,"a","logicalXor","bool"),s=D(t,"b","logicalXor","bool");return gt(n.shape,s.shape),hr(d1(e,t),xf(hr(e,t)))}var ID=V({logicalXor_:SD});function CD(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(fn(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(Qa,c,u);return l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var bf=V({maxPool_:CD});function TD(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(fn(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 p1=V({maxPool3d_:TD});function ND(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(Wh,o,i);return{result:l[0],indexes:l[1]}}var Fv=V({maxPoolWithArgmax_:ND});function ED(e,t){let n=D(e,"a","maximum"),s=D(t,"b","maximum");[n,s]=Pt(n,s),n.dtype==="bool"&&(n=fe(n,"int32"),s=fe(s,"int32")),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ja,r)}var ea=V({maximum_:ED});function RD(e,t=null,n=!1){let r={x:D(e,"x","mean")},a={axis:t,keepDims:n};return W.runKernel(eo,r,a)}var Wt=V({mean_:RD});function Gt(e,t="float32"){if(t==="complex64"){let s=Gt(e,"float32"),r=Gt(e,"float32");return No(s,r)}let n=bh(Ut(e),t);return W.makeTensor(n,e,t)}function As(e,t="float32"){if(t==="complex64"){let s=As(e,"float32"),r=Gt(e,"float32");return No(s,r)}let n=i2(Ut(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 Je?e.dtype:"float32");if(t===void 0)return[s];let r=D(t,"y","meshgrid",t instanceof Je?t.dtype:"float32"),a=Ut(s.shape),o=Ut(r.shape);return n==="xy"?(s=H(s,[1,-1]),r=H(r,[-1,1]),[He(As([o,1],s.dtype),s),He(r,As([1,a],r.dtype))]):(s=H(s,[-1,1]),r=H(r,[1,-1]),[He(s,As([1,o],s.dtype)),He(As([a,1],r.dtype),r)])}function _D(e,t=null,n=!1){let r={x:D(e,"x","min")},a={axis:t,keepDims:n};return W.runKernel(to,r,a)}var Po=V({min_:_D});function DD(e,t){let n=D(e,"a","minimum"),s=D(t,"b","minimum");[n,s]=Pt(n,s),n.dtype==="bool"&&(n=fe(n,"int32"),s=fe(s,"int32")),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(no,r)}var Od=V({minimum_:DD});function PD(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(so,o,a)}var Ov=V({mirrorPad_:PD});function FD(e,t){let n=D(e,"a","mod"),s=D(t,"b","mod");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(ku,r)}var Md=V({mod_:FD});function OD(e){let t=D(e,"x","square"),n={};return W.runKernel("Square",{x:t},n)}var At=V({square_:OD});function MD(e,t=null,n=!1){e=D(e,"x","moments");let s=Xs(t,e.shape),r=Wt(e,s,n),a=r.shape;n||(a=hl(r.shape,s));let o=At(ge(fe(e,"float32"),H(r,a))),i=Wt(o,s,n);return{mean:r,variance:i}}var vf=V({moments_:MD});function zD(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 LD=V({multiRNNCell_:zD});function BD(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(Vh,l,c);return o===1?H(u,[u.size]):u}var Mv=V({multinomial_:BD});function WD(e,t){let n=D(e,"a","notEqual","string_or_numeric"),s=D(t,"b","notEqual","string_or_numeric");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(_i,r)}var Hu=V({notEqual_:WD});function VD(e){let n={x:D(e,"x","onesLike")};return W.runKernel(Fi,n)}var Ps=V({onesLike_:VD});function UD(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 GD=V({outerProduct_:UD});function HD(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(ao,a,r)}var Js=V({pad_:HD});function jD(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Js(e,[t],n)}var qD=V({pad1d_:jD});function XD(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 KD=V({pad2d_:XD});function ZD(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 YD=V({pad3d_:ZD});function JD(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 QD=V({pad4d_:JD});function eP(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 wf=V({spaceToBatchND_:eP});function tP(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=sP([c.filterHeight,c.filterWidth],u):d=[[0,0],[0,0]];let p=u[0]===1&&u[1]===1,[h,f]=nP([c.inHeight,c.inWidth],u,d),m=p?s:"valid",g=p?i:wf(i,u,h),x=(n==="avg"?()=>pf(g,t,a,m):()=>bf(g,t,a,m))(),y=p?x:hf(x,u,f);return l?H(y,[y.shape[1],y.shape[2],y.shape[3]]):y}function nP(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 sP(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 rP=V({pool_:tP});function aP(e,t){let n=D(e,"base","pow"),s=D(t,"exp","pow");[n,s]=Pt(n,s);let r={a:n,b:s};return W.runKernel(oo,r)}var Fo=V({pow_:aP});function oP(e,t){let n=D(e,"x","prelu"),s=D(t,"alpha","prelu"),r={x:n,alpha:s};return W.runKernel(io,r)}var kf=V({prelu_:oP});function iP(e,t=null,n=!1){let s=D(e,"x","prod");s.dtype==="bool"&&(s=fe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(zi,r,a)}var h1=V({prod_:iP});function lP(e,t,n){let s=Ut(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 uP=V({rand_:lP}),f1=di(gh()),m1=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=f1.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}},cP=class{constructor(e,t,n,s){this.alpha=e,this.beta=1/t,this.dtype=n;let r=s||Math.random();this.randu=f1.alea(r.toString()),this.randn=new m1(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)}},dP=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=f1.alea(s)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function pP(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 cP(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 hP=V({randomGamma_:pP});function fP(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error(`Unsupported data type ${s}`);let a=new m1(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_:fP});function mP(e,t=0,n=1,s="float32",r){let a=ze(e,s),o=new dP(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_:mP});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(Iu,{},r)}function gP(e){let n={input:D(e,"input","real")};return W.runKernel(fd,n)}var zd=V({real_:gP});function AP(e){let n={x:D(e,"x","reciprocal")};return W.runKernel(Cu,n)}var Lv=V({reciprocal_:AP});function yP(e){let n={x:D(e,"x","relu")};return W.runKernel(lo,n)}var Or=V({relu_:yP});function xP(e){let n={x:D(e,"x","relu6")};return W.runKernel(co,n)}var g1=V({relu6_:xP});function bP(e,t){let s={x:D(e,"x","reverse")},r={dims:t};return W.runKernel(Bi,s,r)}var Fs=V({reverse_:bP});function vP(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 wP=V({reverse1d_:vP});function kP(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 SP=V({reverse2d_:kP});function IP(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 CP=V({reverse3d_:IP});function TP(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 NP=V({reverse4d_:TP});function EP(e){let n={x:D(e,"x","round")};return W.runKernel(Wi,n)}var A1=V({round_:EP});function RP(e){let n={x:D(e,"x","rsqrt","float32")};return W.runKernel(po,n)}var y1=V({rsqrt_:RP});function $e(e,t){if((_n(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"&&_n(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Eo(e,[],[],t)}function $P(e){let n={x:D(e,"x","selu")};return W.runKernel(Nu,n)}var x1=V({selu_:$P});function _P(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 ${c.shape[2]}.`);let f=Dd(u,l,s,r,o,a),g=Do(f,c,1,"valid",o);return d?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var Bv=V({separableConv2d_:_P});async function DP(e,t){let n=D(e,"x","setdiff1d"),s=D(t,"y","setdiff1d");M(n.dtype===s.dtype,()=>`x and y should have the same dtype, but got x (${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 (${s.shape}).`);let r=await n.data(),a=await s.data(),o=new Set(a),i=0;for(let u=0;u<r.length;u++)o.has(r[u])||i++;let l=new nn([i],n.dtype),c=new nn([i],"int32");for(let u=0,d=0;u<r.length;u++)o.has(r[u])||(l.values[d]=r[u],c.values[d]=u,d++);return[l.toTensor(),c.toTensor()]}var Wv=DP;function PP(e){let n={x:D(e,"x","sign")};return W.runKernel(Eu,n)}var Vv=V({sign_:PP});function FP(e){let n={x:D(e,"x","sin","float32")};return W.runKernel(ho,n)}var b1=V({sin_:FP});function OP(e){let n={x:D(e,"x","sinh")};return W.runKernel(Hi,n)}var v1=V({sinh_:OP});function MP(e,t,n){let s=D(e,"x","slice1d");return M(s.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`),De(s,[t],[n])}var Sf=V({slice1d_:MP});function zP(e,t,n){let s=D(e,"x","slice2d");return M(s.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`),De(s,t,n)}var w1=V({slice2d_:zP});function LP(e,t,n){let s=D(e,"x","slice3d");return M(s.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`),De(s,t,n)}var fl=V({slice3d_:LP});function BP(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`),De(s,t,n)}var ml=V({slice4d_:BP});function WP(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(Ao,s,r)}var Xu=V({softmax_:WP});function VP(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(Fh,t)}var If=V({fft_:VP});function UP(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return W.runKernel(Oh,t)}var Ld=V({ifft_:UP});function GP(e){let t=e.shape[e.shape.length-1],n=e.size/t,s;if(t<=2){let r=H(e,[n,t]);s=Ld(r)}else{let r=[n,2*(t-1)],a=H(zd(e),[n,t]),o=H(mf(e),[n,t]),i=Fs(De(a,[0,1],[n,t-2]),1),l=L(Fs(De(o,[0,1],[n,t-2]),1),$e(-1)),c=wt([a,i],1),u=wt([o,l],1),d=H(No(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 k1=V({irfft_:GP});function HP(e,t,n=0){let r={x:D(e,"x","split")},a={numOrSizeSplits:t,axis:n};return W.runKernel(qi,r,a)}var Ht=V({split_:HP});function jP(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=De(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=wt([e,Gt(f)],e.shape.length-1),n=t}else r=e;let a=et(r),o=H(No(r,a),[s,n]),i=If(o),l=Math.floor(n/2)+1,c=zd(i),u=mf(i),d=Ht(c,[l,n-l],c.shape.length-1),p=Ht(u,[l,n-l],u.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,H(No(d[0],p[0]),h)}var Cf=V({rfft_:jP});function qP(e){let n={x:D(e,"x","sqrt","float32")};return W.runKernel(mo,n)}var Dn=V({sqrt_:qP});function XP(e,t){let n=D(e,"a","squaredDifference"),s=D(t,"b","squaredDifference");[n,s]=Pt(n,s),gt(n.shape,s.shape);let r={a:n,b:s},a={};return W.runKernel(yo,r,a)}var S1=V({squaredDifference_:XP});function KP(e,t){let n=D(e,"x","squeeze");return H(n,L5(n.shape,t).newShape)}var ot=V({squeeze_:KP});function ZP(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 yn=V({stack_:ZP});function YP(e,t=0){let s={x:D(e,"x","step")},r={alpha:t};return W.runKernel(wo,s,r)}var Bd=V({step_:YP});function JP(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_:JP});function QP(e){let n={x:D(e,"x","tan","float32")};return W.runKernel(Ki,n)}var Gv=V({tan_:QP});function Zt(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 Eo(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 Eo(e,t,s,n)}function eF(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 Eo(e,t,s,n)}function tF(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 Eo(e,t,s,n)}function nF(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,Eo(e,t,s,n)}function sF(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_:sF});function rF(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new m1(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 Tf=V({truncatedNormal_:rF});function aF(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(Yh,s,r);return{values:a,indices:o}}var I1=V({unique_:aF});function oF(e,t,n){let s=D(e,"x","unsortedSegmentSum"),r=D(t,"segmentIds","unsortedSegmentSum","int32");M(fn(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_:oF});function iF(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}, ${n.shape.length})`);let s={value:n},r={axis:t};return W.runKernel(Ji,s,r)}var as=V({unstack_:iF});function qv(e,t=!0,n,s){return W.makeVariable(e,t,n,s)}function Xv(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let s=ze(e,"int32"),r=ze([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=s.indexToLoc(n[a]),i=a*e.length;r.values.set(o,i)}return r.toTensor()}async function lF(e){let t=D(e,"condition","whereAsync","bool"),n=await t.data(),s=Xv(t.shape,n);return e!==t&&t.dispose(),s}var C1=lF;async function uF(e,t,n){let s=D(e,"tensor","boolMask"),r=D(t,"mask","boolMask","bool"),a=n==null?0:n,o=r.rank,i=s.shape;M(o>0,()=>"mask cannot be scalar"),zn(i.slice(a,a+o),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=a;m<a+o;m++)l*=i[m];let c=i.slice(0,a).concat([l],i.slice(a+o)),u=H(s,c),d=H(r,[-1]),p=await C1(d),h=ot(p,[1]),f=Uu(u,h,a);return e!==s&&s.dispose(),t!==r&&r.dispose(),h.dispose(),u.dispose(),d.dispose(),p.dispose(),f}var cF=uF;function dF(e,t="euclidean",n=null,s=!1){e=D(e,"x","norm");let r=Kv(e,t,n),a=r.shape;if(s){let o=Xs(n,e.shape);a=hl(r.shape,o)}return H(r,a)}function Kv(e,t,n=null){if(e.rank===0)return sn(e);if(e.rank!==1&&n===null)return Kv(H(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Se(sn(e),n);if(t===1/0)return An(sn(e),n);if(t===-1/0)return Po(sn(e),n);if(t==="euclidean"||t===2)return Dn(Se(Fo(sn(e),$e(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return An(Se(sn(e),n[0]),n[1]-1);if(t===1/0)return An(Se(sn(e),n[1]),n[0]);if(t===-1/0)return Po(Se(sn(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Dn(Se(At(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var T1=V({norm_:dF});function pF(e,t,n,s,r=!0){let a=D(e,"v","movingAverage"),o=D(t,"x","movingAverage"),i=D(n,"decay","movingAverage");o3(a,o),M(Na(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=$e(1),c=ge(l,i),u=L(ge(o,a),c);if(r){M(s!=null,()=>"When using zeroDebias: true, step is required.");let d=D(s,"step","movingAverage");u=me(u,ge(l,Fo(i,d)))}return ue(a,u)}var hF=V({movingAverage_:pF});function fF(e,t,n){let s=D(e,"indices","scatterND","int32"),r=D(t,"updates","scatterND");V2(r,s,n);let a={indices:s,updates:r},o={shape:n};return W.runKernel(Vi,a,o)}var Zv=V({scatterND_:fF});function mF(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 gF(e,t,n,s=0){let r=D(e,"sparseIndices","sparseToDense","int32"),a=D(t,"sparseValues","sparseToDense"),o=D(s,"defaultValue","sparseToDense",a.dtype);mF(r,a,n,o);let i={sparseIndices:r,sparseValues:a,defaultValue:o},l={outputShape:n};return W.runKernel(md,i,l)}var N1=V({sparseToDense_:gF});function AF(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_:AF});function yF(e,t){if(t==null)return e.shape.slice();if(Na(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let 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 xF(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 Je?r.clone():r;let a=yF(r,n),o=1-t,i=me(Fd(ue(ju(a,0,1,"float32",s),o)),o);return L(r,i)}var Jv=V({dropout_:xF});function Qv(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function E1(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 Zt(r,"float32")}async function bF(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 ${r.rank}`),zn(s.shape.slice(0,s.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=s.shape[s.shape.length-1];M(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${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(),ut(u,r.shape,"bool")}var vF=bF,Oo={};Oe(Oo,{conv2d:()=>SF,depthwiseConv2d:()=>NF,matMul:()=>RF});function wF(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(fn(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(Ih,d,p)}var R1=V({conv2DBackpropFilter_:wF});function Nf(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 Ef(e,t){let n=t,s=Xt(e.shape,t.shape);return s.length>0&&(n=Se(n,s)),H(n,e.shape)}function Rf(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 g1(e);if(t==="prelu")return kf(e,n);if(t==="leakyrelu")return gf(e,s);if(t==="sigmoid")return fs(e);throw new Error(`Unknown fused activation ${t}.`)}var $f=(e,t)=>!(e>0)||t==="linear";function kF({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",$f(W.state.gradientDepth,l)===!1){let w=Do(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),Rf(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(fn(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]=Pt(g,d),gt(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,_=Nf(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 '${a}'`);let P=t1(N.shape,_,I,n,s),T=R1(N,_,I.shape,n,s),F=[P,T];if(O!=null){let U=Ef(O,_);F.push(U)}return F},y={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Fr((k,I,N)=>{let R=W.runKernel(So,y,b);return N([I,k,R]),f&&(R=H(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,p):Fr((k,I,N,R)=>{let O=W.runKernel(So,y,b);return R([I,k,O,N]),f&&(O=H(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:x}})(h,p,g)}var SF=V({fusedConv2d_:kF});function IF(e,t,n,s,r,a=[1,1],o){let i=e;e.rank===3&&(i=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:i,dy:l},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,filterShape:n};return W.runKernel(Eh,c,u)}var ew=V({depthwiseConv2dNativeBackpropFilter_:IF});function CF(e,t,n,s,r,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:i,filter:n},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,inputShape:e},d=W.runKernel(Rh,c,u);return l?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var tw=V({depthwiseConv2dNativeBackpropInput_:CF});function TF({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if($f(W.state.gradientDepth,l)===!1){let w=Dd(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),Rf(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(fn(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]=Pt(g,d),gt(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 '${a}'`);let[I,N,R,O]=k,_=Nf(w,R,l),P=tw(N.shape,_,I,n,s,a,o),T=ew(N,_,I.shape,n,s,a,o);if(O!=null){let F=Ef(g,_);return[P,T,F]}return[P,T]},y={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Fr((k,I,N)=>{let R=W.runKernel(Io,y,b);return N([I,k,R]),f&&(R=H(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,p):Fr((k,I,N,R)=>{let O=W.runKernel(Io,y,b);return R([I,k,O,N]),f&&(O=H(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:x}})(h,p,g)}var NF=V({fusedDepthwiseConv2d_:TF});function EF({a:e,b:t,transposeA:n=!1,transposeB:s=!1,bias:r,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if($f(W.state.gradientDepth,a)===!1){let _=He(e,t,n,s);return r!=null&&(_=ue(_,r)),Rf(_,a,o,i)}let l=D(e,"a","fused matMul"),c=D(t,"b","fused matMul");[l,c]=Pt(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=Ut(f),A=Ut(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=gt(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]=Pt(k,l),gt(y,k.shape));let I;o!=null&&(I=D(o,"prelu weights","fused matMul"));let N=(_,P)=>{let[T,F,U,q]=P,z=Nf(H(_,U.shape),U,a),K,Y;if(!n&&!s?(K=He(z,F,!1,!0),Y=He(T,z,!0,!1)):!n&&s?(K=He(z,F,!1,!1),Y=He(z,T,!0,!1)):n&&!s?(K=He(F,z,!1,!0),Y=He(T,z,!1,!1)):(K=He(F,z,!0,!0),Y=He(z,T,!0,!0)),r!=null){let J=Ef(q,z);return[K,Y,J]}else return[K,Y]},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(ko,R,O);return F([P,T,U]),{value:H(U,y),gradFunc:N}})(b,w):Fr((P,T,F,U)=>{let q=W.runKernel(ko,R,O);return U([P,T,q,F]),{value:H(q,y),gradFunc:N}})(b,w,k)}var RF=V({fusedMatMul_:EF});function $F(e){return E1(e,.54,.46)}var _F=V({hammingWindow_:$F});function DF(e){return E1(e,.5,.5)}var nw=V({hannWindow_:DF});function PF(e,t,n,s=!1,r=0){let a=0,o=[];for(;a+t<=e.size;)o.push(De(e,a,t)),a+=n;if(s)for(;a<e.size;){let i=a+t-e.size,l=wt([De(e,a,t-i),Vu([i],r)]);o.push(l),a+=n}return o.length===0?fr([],[0,t]):H(wt(o),[o.length,t])}var sw=V({frame_:PF});function FF(e,t,n,s,r=nw){s==null&&(s=Qv(t));let a=sw(e,t,n),o=L(a,r(t));return Cf(o,s)}var OF=V({stft_:FF});function MF(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 zF=V({cropAndResize_:MF});function LF(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 BF=V({flipLeftRight_:LF});function WF(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 VF=V({grayscaleToRGB_:WF});function UF(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 ${r.rank}.`);let a={image:r},o={radians:t,fillValue:n,center:s};return W.runKernel(el,a,o)}var GF=V({rotateWithOffset_:UF});function Ku(e,t,n,s,r,a){s==null&&(s=.5),r==null&&(r=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),M(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${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 HF(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 jF=V({nonMaxSuppression_:HF});function qF(e,t,n){let s=XF(e,t,n),r=s<0?-(s+1):s;e.splice(r,0,t)}function XF(e,t,n){return ZF(e,t,n||KF)}function KF(e,t){return e>t?1:e<t?-1:0}function ZF(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=YF(e,x,d[w]);if(k>=s){b=!0;break}if(g.score=g.score*JF(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&&qF(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 YF(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 JF(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 QF(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(),Zt(d,"int32")}var eO=QF;function tO(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 nO=V({nonMaxSuppressionWithScore_:tO});async function sO(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:Zt(p,"int32"),selectedScores:Zt(h)}}var rO=sO;function aO(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(Su,p,h);return{selectedIndices:f[0],validOutputs:f[1]}}var oO=V({nonMaxSuppressionPadded_:aO});async function iO(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:Zt(f,"int32"),validOutputs:$e(m,"int32")}}var lO=iO;function uO(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(uo,i,l);return o?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var cO=V({resizeBilinear_:uO});function dO(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(Tu,i,l);return o?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var pO=V({resizeNearestNeighbor_:dO});function hO(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(Zt([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]=Ht(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=Q2(fe(A1(h),"int32"),ut([]),256);c=fO(g,l)}let f=n?pl(h,c):gs(h,c);return fe(L(f,255),"int32")}function fO(e,t){let n=Zt([-1]),s=Zt([0]),r=Zt([0]),a,o,i,l,c,u;for(let d=0;d<e.size-1;d++){a=De(e,0,d+1),o=De(e,d+1),c=me(Se(a),t),u=me(Se(o),t);let p=Se(L(a,qu(0,a.size)));i=me(p,Se(a));let h=Vu(o.shape,a.size),f=ue(qu(0,o.size),h),m=L(o,f);l=me(Se(m),Se(o));let g=ge(i,l),A=ge(i,l),x=L(c,u);r=L(L(x,g),A);let y=gs(r,s);s=Vn(y,r,s),n=Vn(y,Zt([d]),n)}return n}var mO=V({threshold_:hO});function gO(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 AO=V({transform_:gO});function yO(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 (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=H(qu(0,a,1,"int32"),[-1,1]),l=qu(0,o,1,"int32"),c=ge(i,l),u=hr(pl(c,$e(+t,"int32")),dl(c,$e(-n,"int32"))),d=Gt([a,o],s.dtype);return H(yn(as(H(s,[-1,a,o])).map(p=>Vn(u,p,d))),r)}var xO=V({bandPart_:yO});function bO(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=Ht(e,e.shape[0],0).map(r=>ot(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${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(Se(L(n[o],a)),n[o]);a=ge(a,i)}return me(a,T1(a,"euclidean"))}));return t?yn(n,0):n}var vO=V({gramSchmidt_:bO});function wO(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=as(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(yn(r,0),e.shape),i=H(yn(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 ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=o1(n),a=Wn(e),o=fr([[1]],[1,1]),i=Wn(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=W.tidy(()=>{let h=De(a,[c,c],[n-c,1]),f=T1(h),m=De(a,[c,c],[1,1]),g=Vn(gs(m,0),fr([[-1]]),fr([[1]])),A=ge(m,L(g,f)),x=me(h,A);x.shape[0]===1?i=Wn(o):i=wt([o,De(x,[1,0],[x.shape[0]-1,x.shape[1]])],0);let y=Ot(me(He(g,A),f)),b=De(a,[c,0],[n-c,s]),w=L(y,i),k=Qe(i);if(c===0)a=ge(b,He(w,He(k,b)));else{let R=ge(b,He(w,He(k,b)));a=wt([De(a,[0,0],[c,s]),R],0)}let I=Qe(w),N=De(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=ge(N,He(He(N,i),I));else{let R=ge(N,He(He(N,i),I));r=wt([De(r,[0,0],[n,c]),R],1)}return[i,a,r]}),te([u,d,p])}return!t&&n>s&&(r=De(r,[0,0],[n,s]),a=De(a,[0,0],[s,s])),[r,a]})}var kO=V({qr_:wO}),Un;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Un||(Un={}));function SO(e,t,n=Un.SUM_BY_NONZERO_WEIGHTS){let s=D(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=D(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===Un.NONE)return a;if(n===Un.SUM)return Se(a);if(n===Un.MEAN){if(r==null)return Wt(a);{let o=s.size/r.size,i=me(Se(a),Se(r));return o>1?me(i,$e(o)):i}}if(n===Un.SUM_BY_NONZERO_WEIGHTS){if(r==null)return me(Se(a),$e(s.size));{let o=L(r,As(s.shape)),i=fe(Se(Hu(o,$e(0))),"float32");return me(Se(a),i)}}throw Error(`Unknown reduction: ${n}`)}var ta=V({computeWeightedLoss_:SO});function IO(e,t,n,s=Un.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","absoluteDifference"),a=D(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=D(n,"weights","absoluteDifference")),zn(r.shape,a.shape,"Error in absoluteDifference: ");let i=sn(ge(r,a));return ta(i,o,s)}var CO=V({absoluteDifference_:IO});function TO(e,t,n,s,r=Un.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","cosineDistance"),o=D(t,"predictions","cosineDistance"),i=null;s!=null&&(i=D(s,"weights","cosineDistance")),zn(a.shape,o.shape,"Error in cosineDistance: ");let l=$e(1),c=ge(l,Se(L(a,o),n,!0));return ta(c,i,r)}var NO=V({cosineDistance_:TO});function EO(e,t,n,s=Un.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","hingeLoss"),a=D(t,"predictions","hingeLoss"),o=null;n!=null&&(o=D(n,"weights","hingeLoss")),zn(r.shape,a.shape,"Error in hingeLoss: ");let i=$e(1);r=ge(L($e(2),r),i);let l=Or(ge(i,L(r,a)));return ta(l,o,s)}var RO=V({hingeLoss_:EO});function $O(e,t,n,s=1,r=Un.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","huberLoss"),o=D(t,"predictions","huberLoss"),i=null;n!=null&&(i=D(n,"weights","huberLoss")),zn(a.shape,o.shape,"Error in huberLoss: ");let l=$e(s),c=sn(ge(o,a)),u=Od(c,l),d=ge(c,u),p=ue(L($e(.5),At(u)),L(l,d));return ta(p,i,r)}var _O=V({huberLoss_:$O});function DO(e,t,n,s=1e-7,r=Un.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","logLoss"),o=D(t,"predictions","logLoss"),i=null;n!=null&&(i=D(n,"weights","logLoss")),zn(a.shape,o.shape,"Error in logLoss: ");let l=$e(1),c=$e(s),u=Ot(L(a,Ds(ue(o,c)))),d=L(ge(l,a),Ds(ue(ge(l,o),c))),p=ge(u,d);return ta(p,i,r)}var PO=V({logLoss_:DO});function FO(e,t,n,s=Un.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","meanSquaredError"),a=D(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=D(n,"weights","meanSquaredError")),zn(r.shape,a.shape,"Error in meanSquaredError: ");let i=S1(r,a);return ta(i,o,s)}var OO=V({meanSquaredError_:FO});function MO(e,t){let n=D(e,"labels","sigmoidCrossEntropyWithLogits"),s=D(t,"logits","sigmoidCrossEntropyWithLogits");zn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Or(s),a=L(s,n),o=Af(_s(Ot(sn(s))));return ue(ge(r,a),o)}function zO(e,t,n,s=0,r=Un.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")),zn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=$e(s),u=$e(1),d=$e(.5);a=ue(L(a,ge(u,c)),L(d,c))}let l=MO(a,o);return ta(l,i,r)}var LO=V({sigmoidCrossEntropy_:zO});function BO(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 ${n}`);return Fr((r,a,o)=>{let l=Pv(a,[n],!0),c=ge(fe(a,"float32"),l);o([r,c]);let u=Ot(L(c,r));return{value:Se(u,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=hl(h.shape,[n]);return[L(H(h,A),ge(fe(m,"float32"),_s(g))),L(H(h,A),ge(_s(g),fe(m,"float32")))]}}})(e,t)}function WO(e,t,n,s=0,r=Un.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"onehotLabels","softmaxCrossEntropy"),o=D(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","softmaxCrossEntropy")),zn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let c=$e(s),u=$e(1),d=$e(a.shape[1]);a=ue(L(a,ge(u,c)),me(c,d))}let l=BO(a,o);return ta(l,i,r)}var VO=V({softmaxCrossEntropy_:WO});function UO(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(Hh,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var GO=V({sparseFillEmptyRows_:UO});function HO(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(jh,o);return{outputIndices:i[0],outputShape:i[1]}}var jO=V({sparseReshape_:HO});function qO(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(qh,o)}var XO=V({sparseSegmentMean_:qO});function KO(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(Xh,o)}var ZO=V({sparseSegmentSum_:KO});function YO(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 JO=V({stringNGrams_:YO});function QO(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 ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=W.runKernel(Kh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var eM=V({stringSplit_:QO});function tM(e,t){let n=D(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return W.runKernel(Zh,r,s)}var nM=V({stringToHashBucketFast_:tM}),sM={fft:If,ifft:Ld,rfft:Cf,irfft:k1},rM={hammingWindow:_F,hannWindow:nw,frame:sw,stft:OF},Ie={flipLeftRight:BF,grayscaleToRGB:VF,resizeNearestNeighbor:pO,resizeBilinear:cO,rotateWithOffset:GF,cropAndResize:zF,nonMaxSuppression:jF,nonMaxSuppressionAsync:eO,nonMaxSuppressionWithScore:nO,nonMaxSuppressionWithScoreAsync:rO,nonMaxSuppressionPadded:oO,nonMaxSuppressionPaddedAsync:lO,threshold:mO,transform:AO},uw={bandPart:xO,gramSchmidt:vO,qr:kO},aM={absoluteDifference:CO,computeWeightedLoss:ta,cosineDistance:NO,hingeLoss:RO,huberLoss:_O,logLoss:PO,meanSquaredError:OO,sigmoidCrossEntropy:LO,softmaxCrossEntropy:VO},Wd={sparseFillEmptyRows:GO,sparseReshape:jO,sparseSegmentMean:XO,sparseSegmentSum:ZO},_f={stringNGrams:JO,stringSplit:eM,stringToHashBucketFast:nM},na=class extends q3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return te(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Rv(e,t)}dispose(){this.iterations_!=null&&te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:$e(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(na,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Df=class extends na{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:X(()=>et(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:X(()=>et(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;X(()=>{let c=ue(L(i,this.rho),L(At(o),1-this.rho)),u=L(me(Dn(ue(l,this.epsilon)),Dn(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(At(u),1-this.rho));i.assign(c),l.assign(d);let p=ue(L(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(te(this.accumulatedGrads.map(e=>e.variable)),te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Df.className="Adadelta";$o(Df);var Pf=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:`${n}/accumulator`,variable:X(()=>Vu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;X(()=>{let i=ue(o,At(a));o.assign(i);let l=ue(L(me(a,Dn(ue(i,W.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Pf.className="Adagrad";$o(Pf);var Ff=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=$e(t).variable(),this.accBeta2=$e(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=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:X(()=>et(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:X(()=>et(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(At(l),1-this.beta2)),h=me(d,n),f=me(p,s);c.assign(d),u.assign(p);let m=ue(L(me(h,ue(Dn(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&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),X(()=>{this.accBeta1.assign(Fo(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fo(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)}};Ff.className="Adam";$o(Ff);var Of=class extends na{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],X(()=>{this.iteration=$e(0).variable(),this.accBeta1=$e(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);X(()=>{let n=ge(1,this.accBeta1),s=me(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:et(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:et(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),h=sn(l),f=ea(p,h);c.assign(d),u.assign(f);let m=ue(L(me(s,n),me(d,ue(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ue(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&te(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Of.className="Adamax";$o(Of);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=gn($e(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Vd.className="SGD";$o(Vd);var Mf=class extends Vd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=$e(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:X(()=>et(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&&te(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Mf.className="Momentum";$o(Mf);var zf=class extends na{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:X(()=>et(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:X(()=>et(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:X(()=>et(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(At(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=me(L(o,this.learningRate),Dn(ge(c,ue(At(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=ge(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(At(o),1-this.decay)),d=ue(L(l,this.momentum),me(L(o,this.learningRate),Dn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=ge(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&te(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(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)}};zf.className="RMSProp";$o(zf);var Mo=class{static sgd(e){return new Vd(e)}static momentum(e,t,n=!1){return new Mf(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new zf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Ff(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Df(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new Of(e,t,n,s,r)}static adagrad(e,t=.1){return new Pf(e,t)}},gl={sgd:Mo.sgd,momentum:Mo.momentum,adadelta:Mo.adadelta,adagrad:Mo.adagrad,rmsprop:Mo.rmsprop,adamax:Mo.adamax,adam:Mo.adam},oM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function cw(){return new Promise(e=>oM(()=>e()))}var E={};Oe(E,{ERF_A1:()=>AM,ERF_A2:()=>yM,ERF_A3:()=>xM,ERF_A4:()=>bM,ERF_A5:()=>vM,ERF_P:()=>gM,PARALLELIZE_THRESHOLD:()=>_1,SELU_SCALE:()=>pw,SELU_SCALEALPHA:()=>dw,applyActivation:()=>Rf,assertAndGetBroadcastShape:()=>gt,assertAxesAreInnerMostDims:()=>yD,assertParamsConsistent:()=>iM,assignToTypedArray:()=>TM,axesAreInnerMostDims:()=>u1,calculateShapes:()=>O3,checkEinsumDimSizes:()=>DM,combineLocations:()=>$v,complexWithEvenIndex:()=>SM,complexWithOddIndex:()=>IM,computeConv2DInfo:()=>$d,computeConv3DInfo:()=>lv,computeDefaultPad:()=>Z2,computeDilation2DInfo:()=>z$,computeOptimalWindowSize:()=>uM,computeOutAndReduceShapes:()=>_v,computeOutShape:()=>lM,computePool2DInfo:()=>iv,computePool3DInfo:()=>L$,convertConv2DDataFormat:()=>uv,decodeEinsumEquation:()=>$M,eitherStridesOrDilationsAreOne:()=>Pr,expandShapeToKeepDim:()=>hl,exponent:()=>EM,exponents:()=>NM,fromStringArrayToUint8:()=>VM,fromUint8ToStringArray:()=>WM,getAxesPermutation:()=>Dv,getBroadcastDims:()=>_3,getComplexWithIndex:()=>CM,getEinsumComputePath:()=>PM,getEinsumPermutation:()=>_M,getFusedBiasGradient:()=>Ef,getFusedDyActivation:()=>Nf,getImageCenter:()=>cM,getInnerMostAxes:()=>xD,getPermuted:()=>pM,getReductionAxes:()=>Xt,getReshaped:()=>dM,getReshapedPermuted:()=>hM,getSliceBeginCoords:()=>fM,getSliceSize:()=>mM,getUndoAxesPermutation:()=>c1,isIdentityPermutation:()=>FM,log:()=>p9,mergeRealAndImagArrays:()=>wM,prepareAndValidate:()=>F3,prepareSplitSize:()=>MM,segment_util:()=>mw,shouldFuse:()=>$f,slice_util:()=>Ft,splitRealAndImagArrays:()=>kM,tupleValuesAreOne:()=>_o,upcastType:()=>Bn,validateInput:()=>V2,validateUpdateShape:()=>W2,warn:()=>Co});function iM(e,t){let n=e[0].length;e.forEach((r,a)=>{M(r.length===n,()=>`Error in concat${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 ${a}.`)})}function lM(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var _1=30;function uM(e){return e<=_1?e:xh(e,Math.floor(Math.sqrt(e)))}function cM(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function dM(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function pM(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function hM(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function fM(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function mM(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var dw=1.7580993408473768,pw=1.0507009873554805,gM=.3275911,AM=.254829592,yM=-.284496736,xM=1.421413741,bM=-1.453152027,vM=1.061405429;function wM(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${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 kM(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 SM(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 IM(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 CM(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function TM(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function NM(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 EM(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 D1="->",RM=/->/g,hw=",",fw="...";function $M(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(RM,"").length)/D1.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${D1}").`);let[s,r]=e.split(D1);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 _M(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 DM(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 ${a[o]}`)}}function PM(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let o=0;o<r;++o)s.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=OM(t,i);for(let c of l)a.indexOf(c)===-1&&(s[o].push(c),a.push(c))}return{path:n,steps:s}}function FM(e){return e.every((t,n)=>t===n)}function OM(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function MM(e,t,n=0){let s=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);M(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}M(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}var mw={};Oe(mw,{collectGatherOpShapeInfo:()=>BM,computeOutShape:()=>LM,segOpComputeOptimalWindowSize:()=>zM});function zM(e,t){let n=!1,s;for(e<=_1?(s=e,n=!0):s=xh(e,Math.floor(Math.sqrt(e)));!n;)s>t||s===e?n=!0:s=xh(e,s+1);return s}function LM(e,t,n){let s=[],r=e.length;for(let a=0;a<r;a++)a!==t?s.push(e[a]):s.push(n);return s}function BM(e,t,n,s){let r=t.shape.length,a=e.shape.length;if(s!==0&&(s<-r||s>r))throw new Error(`Expect batchDims in the range of [-${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 WM(e){try{return e.map(t=>tf(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function VM(e){return e.map(t=>wd(t))}var Qs={};Oe(Qs,{nonMaxSuppressionV3Impl:()=>rw,nonMaxSuppressionV4Impl:()=>aw,nonMaxSuppressionV5Impl:()=>ow,whereImpl:()=>Xv});var gw={kernelName:fi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Bd(fe(n,"float32"),-1))}}},UM={kernelName:ou,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=At(fe(n,"float32")),r=Dn(ge($e(1),s));return Ot(me(e,r))}}}},GM={kernelName:iu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Dn(ge(At(fe(n,"float32")),1));return me(e,s)}}}},HM={kernelName:Kr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=e,l=Xt(n.shape,r);return l.length>0&&(i=Se(i,l)),H(i,n.shape)},b:()=>{let i=e,l=Xt(s.shape,r);return l.length>0&&(i=Se(i,l)),H(i,s.shape)}}}},jM={kernelName:$a,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((s,r)=>{n[r]=()=>e.clone()}),n}},qM={kernelName:_a,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>et(n)}}},XM={kernelName:cu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>et(n)}}},KM={kernelName:du,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,Dn(ge($e(1),At(fe(n,"float32")))))}}},ZM={kernelName:pu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Dn(ue($e(1),At(fe(n,"float32"))));return me(e,s)}}}},YM={kernelName:mu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=ue(At(n),At(s)),l=L(e,me(s,i)),c=Xt(n.shape,r);return c.length>0&&(l=Se(l,c)),H(l,n.shape)},b:()=>{let i=ue(At(n),At(s)),l=Ot(L(e,me(n,i))),c=Xt(s.shape,r);return c.length>0&&(l=Se(l,c)),H(l,s.shape)}}}},JM={kernelName:hu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ue(At(fe(n,"float32")),1))}}},QM={kernelName:fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ge($e(1),At(fe(n,"float32"))))}}};function ez(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(fn(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let d={dy:l,input:c},p={filterSize:n,strides:s,pad:r,dimRoundingMode:a},h=W.runKernel(wh,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var tz=V({avgPool3dGrad_:ez}),nz={kernelName:rd,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>tz(e,s,r,a,o,i)}}};function sz(e,t,n,s,r){let a=D(e,"dy","avgPoolGrad"),o=D(t,"input","avgPoolGrad");M(o.rank===a.rank,()=>`Rank of input (${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 ${i.rank}.`);let u={dy:l,input:i},d={filterSize:n,strides:s,pad:r},p=W.runKernel(vh,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var rz=V({avgPoolGrad_:sz}),az={kernelName:Da,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o}=n;return{x:()=>rz(e,s,r,a,o)}}},oz={kernelName:Pa,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[s,r]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>He(e,r,!1,!0),b:()=>He(s,e,!0,!1)}:!a&&o?{a:()=>He(e,r,!1,!1),b:()=>He(e,s,!0,!1)}:a&&!o?{a:()=>He(r,e,!1,!0),b:()=>He(s,e,!1,!1)}:{a:()=>He(r,e,!0,!0),b:()=>He(e,s,!0,!0)}}},iz={kernelName:mi,gradFunc:(e,t,n)=>{let{blockShape:s,crops:r}=n;return{x:()=>wf(e,s,r)}}},lz={kernelName:Y5,gradFunc:(e,t,n)=>{let s=n,r=s.inputShape,a=s.shape,o=Array.from(a);for(let l=r.length-1;l>=0;l--)if(r[l]===a[l])o[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>Se(e,i,!0)}}},uz={kernelName:Fa,gradFunc:e=>({x:()=>e.clone()})},cz={kernelName:Oa,gradFunc:e=>({x:()=>et(e)})},dz={kernelName:Zr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{clipValueMin:r,clipValueMax:a}=n;return{x:()=>Vn(hr(dl(s,r),pl(s,a)),e,et(e))}}},pz={kernelName:od,inputsToSave:["x"],gradFunc:gw.gradFunc},hz={kernelName:gi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let s=t.map(l=>l.shape),{axis:r}=n,a=Xs(r,t[0].shape)[0],o=s.map(l=>l[a]);return Ht(e,o,a).map(l=>()=>l)}},fz={kernelName:Ma,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return M(_o(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>t1(s.shape,e,r,o,i,l),filter:()=>R1(s,e,r.shape,o,i,l)}}},mz={kernelName:za,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>Do(e,r,a,o,i,1,l),filter:()=>R1(e,s,r.shape,a,o,i,l)}}};function gz(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(Ch,i,l)}var Az=V({conv3DBackpropFilter_:gz}),yz={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 '${s}'`);let[o,i]=t;return{x:()=>yv(o.shape,e,i,r,a),filter:()=>Az(o,e,i.shape,r,a)}}},xz={kernelName:La,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Ot(b1(fe(n,"float32"))),e)}}},bz={kernelName:Ba,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(v1(fe(n,"float32")),e)}}},vz={kernelName:Ai,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r,exclusive:a,reverse:o}=n;return{x:()=>{let i=Dv([r],s.rank),l=a1(e,r,a,!o);return i!=null&&(l=Qe(l,i)),l}}}},wz={kernelName:Wa,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a,dimRoundingMode:o}=n,i=s==null?[1,1]:s;M(_o(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${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(fn(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>tw(l.shape,e,c,r,a,i,o),filter:()=>ew(l,e,c.shape,r,a,i,o)}}},kz={kernelName:ld,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,a={x:s,filter:r,dy:e},o={x:s,filter:r,dy:e};return{x:()=>W.runKernel(_h,a,n),filter:()=>W.runKernel(Dh,o,n)}}},Sz={kernelName:Ua,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,s={dy:e,y:n};return{x:()=>W.runKernel(Ph,s)}}},Iz={kernelName:gu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(_s(Ot(At(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,s)}}},Cz={kernelName:Ga,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},Tz={kernelName:vi,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},Nz={kernelName:wi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,_s(n))}}},Ez={kernelName:Ha,gradFunc:e=>({x:()=>et(e)})},Rz={kernelName:ja,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=me(e,fe(s,"float32")),l=Xt(n.shape,r);return l.length>0?H(Se(i,l),n.shape):i},b:()=>{let i=L(e,fe(n,"float32")),l=Xt(s.shape,r);l.length>0&&(i=H(Se(i,l),s.shape));let c=At(s);return Ot(me(i,fe(c,"float32")))}}}},$z={kernelName:qa,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:s}=n,[r,a,o,i]=t,l=i==null?$e(1):i,c=Xt(a.shape,r.shape),u=[];if(a.rank===1){for(let b=0;b<r.shape.length-1;++b)u.push(r.shape[b]);u.push(1)}let d=ge(r,a),p=L(e,l),h=y1(ue(o,$e(s))),f=L(L(L(h,h),h),$e(-.5));return{x:()=>a.rank===1?H(L(L(e,Ys(H(h,[1,1,1,a.shape[0]]),u)),l),r.shape):H(L(L(e,h),l),r.shape),mean:()=>{let b=L(L(h,$e(-1)),p);return a.rank===1&&(b=Se(b,c)),H(b,a.shape)},variance:()=>{let b=L(L(f,d),p);return a.rank===1&&(b=Se(b,c)),H(b,a.shape)},scale:()=>{let b=L(d,h),w=L(e,b);return a.rank===1&&(w=Se(w,c)),H(w,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=Se(b,c)),H(b,a.shape)}}}},_z={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=Qe(A,y),w=jv(b,x,s.shape[o]),k=c1(y);return w=Qe(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 Dz={kernelName:Xa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>et(n),b:()=>et(s)}}},Pz={kernelName:Ka,gradFunc:e=>({x:()=>fe(e,"float32")})},Fz={kernelName:yu,gradFunc:e=>({x:()=>et(e)})},Oz={kernelName:xu,gradFunc:e=>({x:()=>et(e)})},Mz={kernelName:bu,gradFunc:e=>({x:()=>et(e)})},zz={kernelName:Ti,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{alpha:r}=n,a=gs(s,0);return{x:()=>Vn(a,e,L(e,r))}}},Lz={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ue(n,1))}}},Bz={kernelName:Za,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,fe(n,"float32"))}}},Wz={kernelName:J5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n;return{logits:()=>{let a=!0,o=_s(s);return ge(e,L(Se(e,r,a),o))}}}};function Vz(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(zh,i,l)}var Uz=V({localResponseNormalizationBackprop_:Vz}),Gz={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:()=>Uz(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,fe($s(n,t),e.dtype))}}var bw={kernelName:Ya,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()}}},Hz={kernelName:Ja,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,fe(dl(n,s),"float32")),b:()=>L(e,fe(i1(n,s),"float32"))}}};function jz(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(fn(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:u,input:d,output:p},m={filterSize:s,strides:r,pad:a,dimRoundingMode:o},g=W.runKernel(Bh,f,m);return h?H(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var qz=V({maxPool3dGrad_:jz}),Xz={kernelName:hd,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>qz(e,s,r,a,o,i,l)}}};function Kz(e,t,n,s,r,a,o){let i=D(e,"dy","maxPoolGrad"),l=D(t,"input","maxPoolGrad"),c=D(n,"output","maxPoolGrad");M(l.rank===i.rank,()=>`Rank of input (${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(fn(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let u={dy:i,input:l,output:c},d={filterSize:s,strides:r,pad:a,dimRoundingMode:o};return W.runKernel(Lh,u,d)}var Zz=V({maxPoolGrad_:Kz}),Yz={kernelName:Qa,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>Zz(e,s,r,a,o,i)}}},Jz={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n,a=Xs(r,s.shape),i=_v(s.shape,a)[1],l=Ut(i);return{x:()=>{let u=s.shape.slice();a.forEach(h=>{u[h]=1});let d=H(e,u);return me(L(d,As(s.shape,"float32")),l)}}}},Qz={kernelName:to,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{axis:r}=s,[a,o]=t,i=Xs(r,a.shape),l=xw(e,o,a,i);return{x:()=>l.x()}}},eL={kernelName:no,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,fe(pl(n,s),"float32")),b:()=>L(e,fe(gs(n,s),"float32"))}}},tL={kernelName:so,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>De(e,a,s.shape)}}},nL={kernelName:ku,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=Xt(n.shape,r);return i.length>0?H(Se(e,i),n.shape):e},b:()=>{let i=L(e,Ot(Fd(me(n,s)))),l=Xt(s.shape,r);return l.length>0?H(Se(i,l),s.shape):i}}}},sL={kernelName:ro,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=L(e,fe(s,"float32")),l=Xt(n.shape,r);return l.length>0?H(Se(i,l),n.shape):i},b:()=>{let i=L(e,fe(n,"float32")),l=Xt(s.shape,r);return l.length>0?H(Se(i,l),s.shape):i}}}},rL={kernelName:$i,gradFunc:e=>({x:()=>Ot(e)})},aL={kernelName:Oi,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Gt(n.shape,"float32")}}},oL={kernelName:Fi,gradFunc:e=>({x:()=>et(e)})},iL={kernelName:Mi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:s}=n;return as(e,s).map(a=>()=>a)}},vw={kernelName:ao,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>De(e,a,s.shape)}}},lL={kernelName:oo,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,s,r]=t,a=n,o=s,i=gt(a.shape,o.shape);return{a:()=>{let u=fe(o,"float32"),d=L(e,L(u,Fo(a,ge(u,$e(1))))),p=Xt(a.shape,i);return p.length>0&&(d=Se(d,p)),H(d,a.shape)},b:()=>{let u=gs(a,0),d=Vn(u,Ds(a),et(a)),p=L(e,L(r,d)),h=Xt(o.shape,i);return h.length>0&&(p=Se(p,h)),H(p,o.shape)}}}},uL={kernelName:io,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,s]=t,r=gs(n,0);return{x:()=>Vn(r,e,L(e,s)),alpha:()=>{let a=Vn(r,et(e),L(e,n)),o=Xt(s.shape,e.shape);return o.length>0&&(a=Se(a,o)),H(a,s.shape)}}}},cL={kernelName:Va,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=me(e,fe(s,"float32")),l=Xt(n.shape,r);return l.length>0?H(Se(i,l),n.shape):i},b:()=>{let i=L(e,fe(n,"float32")),l=Xt(s.shape,r);l.length>0&&(i=H(Se(i,l),s.shape));let c=At(s);return Ot(me(i,fe(c,"float32")))}}}},dL={kernelName:Cu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,Ot(At(n)))}}},pL={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(pl(n,6),Bd(n));return{x:()=>L(e,fe(s,"float32"))}}},hL={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,fe(Bd(n),"float32"))}}},fL={kernelName:Li,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},mL={kernelName:uo,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Gh,r,n)}}},gL={kernelName:Tu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Uh,r,n)}}},AL={kernelName:Bi,gradFunc:(e,t,n)=>{let{dims:s}=n,r=Xs(s,e.shape);return{x:()=>Fs(e,r)}}},yL={kernelName:Wi,gradFunc:e=>({x:()=>et(e)})},xL={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ot(me(e,L(Fo(n,1.5),2)))}}},bL={kernelName:Ui,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>fe(et(n),"float32"),t:()=>L(e,fe(n,e.dtype)),e:()=>L(e,fe(xf(n),e.dtype))}}},vL={kernelName:Nu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=gs(n,$e(0)),r=$e(dw),a=$e(pw),o=L(e,a),i=L(L(e,r),_s(fe(n,"float32")));return Vn(s,o,i)}}}},wL={kernelName:fo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,ge($e(1),n)))}}},kL={kernelName:Eu,gradFunc:e=>({x:()=>et(e)})},SL={kernelName:ho,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ff(fe(n,"float32")),e)}}},IL={kernelName:Hi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(r1(fe(n,"float32")),e)}}},CL={kernelName:Gi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{begin:r,size:a}=n,o=s.shape,[i,l]=H3(s,r,a),c=[];for(let u=0;u<e.rank;u++)c.push([i[u],o[u]-i[u]-l[u]]);return{x:()=>Js(e,c)}}},TL={kernelName:Ao,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{dim:r}=n,a=!0,o=L(e,s);return{logits:()=>ge(o,L(Se(o,[r],a),s))}}},NL={kernelName:Ru,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,fs(n))}}},ww={kernelName:ji,gradFunc:(e,t,n)=>{let{blockShape:s,paddings:r}=n;return{x:()=>hf(e,s,r)}}},kw={kernelName:qi,gradFunc:(e,t,n)=>{let{axis:s}=n;return{x:()=>wt(e,s)}}},EL={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,L(Dn(fe(n,"float32")),2))}}},RL={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(fe(n,"float32"),2))}}},$L={kernelName:yo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=$e(2);return{a:()=>L(e,L(r,ge(n,s))),b:()=>L(e,L(r,ge(s,n)))}}},_L={kernelName:wo,gradFunc:e=>({x:()=>et(e)})},DL={kernelName:xo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=gt(n.shape,s.shape);return{a:()=>{let i=e,l=Xt(n.shape,r);return l.length>0&&(i=Se(i,l)),H(i,n.shape)},b:()=>{let i=e,l=Xt(s.shape,r);return l.length>0&&(i=Se(i,l)),H(Ot(i),s.shape)}}}},PL={kernelName:go,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,As(s.shape,"float32"));return{x:()=>l}}},FL={kernelName:Ki,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,At(ff(n)))}}},OL={kernelName:bo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ge($e(1),At(n)),e)}}},ML={kernelName:Yr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{reps:r}=n;return{x:()=>{let o=et(s);if(s.rank===1)for(let i=0;i<r[0];++i)o=ue(o,De(e,[i*s.shape[0]],[s.shape[0]]));else if(s.rank===2)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)o=ue(o,De(e,[i*s.shape[0],l*s.shape[1]],[s.shape[0],s.shape[1]]));else if(s.rank===3)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)o=ue(o,De(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else if(s.rank===4)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)for(let u=0;u<r[3];++u)o=ue(o,De(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2],u*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);return o}}}},zL={kernelName:vo,gradFunc:(e,t,n)=>{let s=n,{perm:r}=s,a=c1(r);return{x:()=>Qe(e,a)}}},LL={kernelName:Ji,gradFunc:(e,t,n)=>{let s=n,{axis:r}=s;return{value:()=>yn(e,r)}}},BL={kernelName:Ad,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>WL(e,n)}}};function WL(e,t){let n=ea(t,et(t)),s=Uu(e,n),r=dl(t,$e(0,"int32")),a=s.rank-r.rank;for(let i=0;i<a;++i)r=Kt(r,i+1);r=hr(r,As(s.shape,"bool"));let o=et(s);return Vn(r,s,o)}var VL={kernelName:Qi,gradFunc:e=>({x:()=>et(e)})},UL=[gw,UM,GM,HM,jM,qM,XM,KM,ZM,YM,JM,QM,nz,az,oz,iz,lz,uz,cz,dz,pz,hz,mz,fz,yz,xz,bz,vz,wz,kz,cL,Sz,Iz,Cz,Tz,Nz,Rz,Ez,$z,_z,Dz,Pz,Fz,Oz,Mz,zz,Lz,Bz,Wz,Gz,bw,bw,Hz,Xz,Yz,Jz,Qz,eL,tL,nL,sL,rL,aL,oL,iL,vw,vw,lL,uL,dL,pL,hL,fL,mL,gL,AL,yL,xL,bL,vL,wL,kL,SL,IL,CL,TL,NL,ww,ww,kw,kw,EL,$L,RL,_L,DL,PL,FL,OL,ML,zL,LL,BL,VL];for(let e of UL)Q5(e);var Sw={};Oe(Sw,{maxNorm:()=>qL,minMaxNorm:()=>ZL,nonNeg:()=>KL,unitNorm:()=>XL});var P1;function rn(){return P1==null&&(P1=Dr().epsilon()),P1}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 os(e){return e.length===1?e[0]:e}function St(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 F1(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function O1(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>O1(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:O1(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];O1(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 GL(e,t){return e<t?-1:e>t?1:0}function Lf(e,t){return-1*GL(e,t)}function zo(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function HL(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 ${e} or null/undefined.`)}function M1(e,t,n=0,s=1/0){return Mr(n>=0),Mr(s>=n),Array.isArray(e)&&e.length>=n&&e.length<=s&&e.every(r=>typeof r===t)}function xn(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,s)=>xn(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 jL(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 z1(e,t){return X(()=>Dn(Se(L(e,e),t,!0)))}var Gd=class extends ce.Serializable{getConfig(){return{}}},L1=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=z1(e,this.axis),n=ms(t,0,this.maxValue);return L(e,me(n,ue(rn(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};L1.className="MaxNorm";ce.registerClass(L1);var B1=class extends Gd{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return X(()=>me(e,ue(rn(),z1(e,this.axis))))}getConfig(){return{axis:this.axis}}};B1.className="UnitNorm";ce.registerClass(B1);var W1=class extends Gd{apply(e){return Or(e)}};W1.className="NonNeg";ce.registerClass(W1);var V1=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=z1(e,this.axis),n=ue(L(this.rate,ms(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,me(n,ue(rn(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};V1.className="MinMaxNorm";ce.registerClass(V1);var Ew={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function an(e){return F1(e)}function Rw(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"constraint")}function on(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 qL(e){return new L1(e)}function XL(e){return new B1(e)}function KL(){return new W1}function ZL(e){return new V1(e)}var $w={};Oe($w,{constant:()=>xB,glorotNormal:()=>CB,glorotUniform:()=>IB,heNormal:()=>TB,heUniform:()=>NB,identity:()=>kB,leCunNormal:()=>EB,leCunUniform:()=>RB,ones:()=>yB,orthogonal:()=>$B,randomNormal:()=>vB,randomUniform:()=>bB,truncatedNormal:()=>wB,varianceScaling:()=>SB,zeros:()=>AB});var YL=["channelsFirst","channelsLast"],JL=["nearest","bilinear"],QL=["valid","same","causal"],eB=["max","avg"],tB=["sum","mul","concat","ave"],Zu=new Map;function jt(e){xl(YL,"DataFormat",e)}function nB(e){xl(JL,"InterpolationFormat",e)}function Os(e){xl(QL,"PaddingMode",e)}function _w(e){xl(eB,"PoolMode",e)}var Hd=[],Dw="/";function bl(e,t){Hd.push(e);try{let n=t();return Hd.pop(),n}catch(n){throw Hd.pop(),n}}function sB(){return Hd.length===0?"":Hd.join(Dw)+Dw}function Pw(e){if(!Ow(e))throw new Error("Not a valid tensor name: '"+e+"'");return sB()+e}function Fw(e){if(!Ow(e))throw new Error("Not a valid tensor name: '"+e+"'");Zu.has(e)||Zu.set(e,0);let t=Zu.get(e);if(Zu.set(e,Zu.get(e)+1),t>0){let n=`${e}_${t}`;return Zu.set(n,1),n}else return e}var rB=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Ow(e){return!!e.match(rB)}function aB(e){return e===parseInt(e.toString(),10)}function Lo(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 Bo(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 Bf(e,t){return fe(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 oB(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 H1(n,[1,t,1])})}function iB(e){let t=[Lo(e.shape)];return H(e,t)}function lB(e){if(e.rank<=1)throw new j(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Lo(e.shape,1)];return H(e,t)}function vl(e,t,n){return X(()=>{switch(e.rank){case 1:return Sf(e,t,n);case 2:return w1(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 De(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return De(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 U1(e,t,n){return X(()=>{switch(e.rank){case 1:return Sf(e,t,n);case 2:return w1(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 Wf(e,t,n,s){return X(()=>{switch(e.rank){case 1:return Sf(e,t,n);case 2:switch(s){case 1:return vl(e,t,n);case 2:return U1(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 U1(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 U1(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 G1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),wt(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: ${e.rank}`)}}function H1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new j(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Ys(e,t)}function Vf(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 Oo.matMul({a:e,b:t,transposeA:r,transposeB:a,bias:s?j1(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(Qe(t,u),[l,-1]);let d=[...r,...c],p=!1,h=!1;return H(Oo.matMul({a:e,b:t,transposeA:p,transposeB:h,bias:s?j1(e.rank,s,mr()):null,activation:n}),d)}}function zw(e,t,n){return X(()=>(Array.isArray(t)?t=Zt(t,"int32"):t=fe(t,"int32"),Uu(e,t,n)))}function qd(e){return L(e,e)}function j1(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: ${t.rank}`)}function yr(e,t,n){return X(()=>(n==null&&(n=mr()),jt(n),ue(e,j1(e.rank,t,n))))}function uB(e,t=1){if(t!==1)throw new Le(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Pd(e)}function cB(e){return X(()=>me(e,ue(sn(e),1)))}function Lw(e,t,n,s){return X(()=>Jv(e,t,n,s))}function dB(e){return X(()=>{let t=ue(.5,L(.2,e));return ms(t,0,1)})}function Xd(e,t,n=!1){return n?e():t()}var pB=["fanIn","fanOut","fanAvg"],hB=["normal","uniform","truncatedNormal"];function fB(e){xl(pB,"FanMode",e)}function mB(e){xl(hB,"Distribution",e)}var tr=class extends ce.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},q1=class extends tr{apply(e,t){return Gt(e,t)}};q1.className="Zeros";ce.registerClass(q1);var Uf=class extends tr{apply(e,t){return As(e,t)}};Uf.className="Ones";ce.registerClass(Uf);var X1=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 ${e}`);this.value=e.value}apply(e,t){return X(()=>L($e(this.value),As(e,t)))}getConfig(){return{value:this.value}}};X1.className="Constant";ce.registerClass(X1);var K1=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}}};K1.className="RandomUniform";ce.registerClass(K1);var Z1=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 Vf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Z1.className="RandomNormal";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(`truncatedNormal does not support dType ${t}.`);return Tf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Y1.className="TruncatedNormal";ce.registerClass(Y1);var J1=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,o1(e[0]))})}getConfig(){return{gain:this.gain}}};J1.className="Identity";ce.registerClass(J1);function gB(e,t="channelsLast"){let n,s;if(jt(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=Lo(e,2);n=e[1]*r,s=e[0]*r}else if(t==="channelsLast"){let r=Lo(e,0,e.length-2);n=e[e.length-2]*r,s=e[e.length-1]*r}}else{let r=Lo(e);n=Math.sqrt(r),s=Math.sqrt(r)}return[n,s]}var is=class extends tr{constructor(e){super();if(e.scale<0)throw new j(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,fB(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,mB(this.distribution),this.seed=e.seed}apply(e,t){let n=gB(e),s=n[0],r=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,s):this.mode==="fanOut"?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Le(`${this.getClassName()} does not support dType ${t}.`);return Tf(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return ju(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};is.className="VarianceScaling";ce.registerClass(is);var Gf=class extends is{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return is.className}};Gf.className="GlorotUniform";ce.registerClass(Gf);var Hf=class extends is{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return is.className}};Hf.className="GlorotNormal";ce.registerClass(Hf);var jf=class extends is{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return is.className}};jf.className="HeNormal";ce.registerClass(jf);var qf=class extends is{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return is.className}};qf.className="HeUniform";ce.registerClass(qf);var Xf=class extends is{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return is.className}};Xf.className="LeCunNormal";ce.registerClass(Xf);var Kf=class extends is{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return is.className}};Kf.className="LeCunNormal";ce.registerClass(Kf);var Q1=class extends tr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Le("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return X(()=>{if(e.length<2)throw new Le("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,s=Vf(n,0,1,"float32"),r=uw.gramSchmidt(s);return e[0]>e[1]&&(r=Qe(r)),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Q1.className="Orthogonal";ce.registerClass(Q1);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 Mt(e){return F1(e)}function Et(e){if(typeof e=="string"){let t=e in Bw?Bw[e]:e;if(t==="GlorotNormal")return new Hf;if(t==="GlorotUniform")return new Gf;if(t==="HeNormal")return new jf;if(t==="HeUniform")return new qf;if(t==="LeCunNormal")return new Xf;if(t==="LeCunUniform")return new Kf;{let n={};return n.className=t,n.config={},Ww(n)}}else return e instanceof tr?e:Ww(e)}function AB(){return new q1}function yB(){return new Uf}function xB(e){return new X1(e)}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 is(e)}function IB(e){return new Gf(e)}function CB(e){return new Hf(e)}function TB(e){return new jf(e)}function NB(e){return new qf(e)}function EB(e){return new Xf(e)}function RB(e){return new Kf(e)}function $B(e){return new Q1(e)}var Vw={};Oe(Vw,{Layer:()=>tt,RNN:()=>oa,RNNCell:()=>tp,activation:()=>pV,add:()=>vV,alphaDropout:()=>rU,average:()=>wV,averagePooling1d:()=>by,averagePooling2d:()=>vy,averagePooling3d:()=>wy,avgPool1d:()=>$V,avgPool2d:()=>DV,avgPool3d:()=>FV,avgPooling1d:()=>_V,avgPooling2d:()=>PV,avgPooling3d:()=>OV,batchNormalization:()=>NV,bidirectional:()=>ZV,concatenate:()=>kV,conv1d:()=>sV,conv2d:()=>rV,conv2dTranspose:()=>aV,conv3d:()=>oV,conv3dTranspose:()=>iV,convLstm2d:()=>jV,convLstm2dCell:()=>qV,cropping2D:()=>uV,dense:()=>hV,depthwiseConv2d:()=>dV,dot:()=>TV,dropout:()=>fV,elu:()=>YW,embedding:()=>bV,flatten:()=>gV,gaussianDropout:()=>sU,gaussianNoise:()=>nU,globalAveragePooling1d:()=>MV,globalAveragePooling2d:()=>zV,globalMaxPool1d:()=>JV,globalMaxPool2d:()=>QV,globalMaxPooling1d:()=>t7,globalMaxPooling2d:()=>n7,gru:()=>BV,gruCell:()=>WV,input:()=>vk,inputLayer:()=>ZW,layerNormalization:()=>EV,leakyReLU:()=>QW,lstm:()=>VV,lstmCell:()=>UV,masking:()=>aU,maxPool1d:()=>eU,maxPool2d:()=>tU,maxPooling1d:()=>s7,maxPooling2d:()=>r7,maxPooling3d:()=>LV,maximum:()=>SV,minimum:()=>IV,multiply:()=>CV,permute:()=>xV,prelu:()=>eV,reLU:()=>JW,repeatVector:()=>AV,reshape:()=>yV,rnn:()=>XV,separableConv2d:()=>lV,simpleRNN:()=>GV,simpleRNNCell:()=>HV,softmax:()=>tV,spatialDropout1d:()=>mV,stackedRNNCells:()=>KV,thresholdedReLU:()=>nV,timeDistributed:()=>YV,upSampling2d:()=>cV,zeroPadding2d:()=>RV});var _B=0;function Uw(){return _B++}var Zf={};function Yf(e=""){return e in Zf||(Zf[e]=0),Zf[e]+=1,e+Zf[e].toString()}function eA(e){return Array.isArray(e)&&Array.isArray(e[0])}function Jf(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 ht(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 Qf(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(),DB(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 DB(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function tA(e){return e.map(t=>t.read())}function nA(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}},PB=0,em=class{constructor(e,t){this.callArgs=t,this.id=PB++,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}}},FB=0,tt=class extends ce.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=FB++,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)+"_"+Yf(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 os(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return os(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 os(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 os(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=St(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=St(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=St(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 St(e))a.push(o.shape);this.build(os(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=St(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=os(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=OB(e),o=this.computeOutputShape(a),i,l=MB(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,St(e),t,this.name,u)):i=new xr(l,o,this,St(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 ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Qf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return tA(e?this.trainableWeights:this.weights)}setWeights(e){X(()=>{let t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${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=tA(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])}nA(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():Et("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=St(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=St(e);t=St(t),n=St(n),s=St(s),r=Jf(r),a=Jf(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new em({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 OB(e){e=St(e);let t=[];for(let n of e)t.push(n.shape);return os(t)}function MB(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 tt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Yf("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 em({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 Wo(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];te(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 zB=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)}},LB=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(me(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),gn(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=zB),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=jL(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 Wo(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 Wo(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Wo(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 Wo(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Wo(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 Wo(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Wo(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:St(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)}},sA=Lr;sA.constructors={};function ek(e,t,n,s,r,a,o,i,l){let c=new Yw,u=[new LB,...sA.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 tm(e,t){return X(()=>{e.dtype!=="float32"&&(e=fe(e,"float32"));let n=Se(qd(e),t,!0),s=Vu(n.shape,rn()),r=Dn(ea(n,s));return me(e,r)})}function wl(e,t){return X(()=>Wt(qd(ge(t,e)),-1))}function nm(e,t){return X(()=>Wt(sn(ge(t,e)),-1))}function ec(e,t){return X(()=>{let n=ge(e,t),s=ms(sn(e),rn(),Number.MAX_VALUE),r=sn(me(n,s));return L(100,Wt(r,-1))})}function BB(e,t){return X(()=>{let n=ms(t,rn(),Number.MAX_VALUE),s=Ds(ue(1,n)),r=ms(e,rn(),Number.MAX_VALUE),a=Ds(ue(1,r));return Wt(qd(ge(s,a)),-1)})}function WB(e,t){return X(()=>{let n=ea(0,ge(1,L(e,t)));return Wt(qd(n),-1)})}function VB(e,t){return X(()=>{let n=ea(0,ge(1,L(e,t)));return Wt(n,-1)})}function UB(e,t){return X(()=>{let n=Se(L(e,t),-1),s=An(L(ge(1,e),t),-1);return ea(0,ue(1,ge(s,n)))})}function GB(e,t){return X(()=>{let n=Math.log(2),s=ge(t,e),r=ge(ue(s,Gu(L(-2,s))),n);return Wt(r,-1)})}function Kd(e,t,n=!1){return X(()=>{if(n)t=Xu(t);else{let s=Se(t,t.shape.length-1,!0);t=me(t,s)}return t=ms(t,rn(),1-rn()),Ot(Se(L(fe(e,"float32"),Ds(t)),t.shape.length-1))})}function sm(e,t,n=!1){return X(()=>{let s=fe(Fd(iB(e)),"int32");t=ms(t,rn(),1-rn());let r=t.shape,a=H(Rd(s,r[r.length-1]),r);return Kd(a,t,n)})}function HB(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=Ot(sn(t));return ue(ge(n,L(t,e)),Af(_s(s)))})}function rm(e,t){return X(()=>{let n;return n=ms(t,rn(),1-rn()),n=Ds(me(n,ge(1,n))),Wt(HB(e,n),-1)})}function jB(e,t){return X(()=>{let n=ms(e,rn(),1),s=ms(t,rn(),1);return Se(L(e,Ds(me(n,s))),-1)})}function qB(e,t){return X(()=>{let n=Ds(ue(rn(),t));return Wt(ge(t,L(e,n)),-1)})}function rA(e,t){return X(()=>{let n=tm(e,-1),s=tm(t,-1),r=L(n,s);return Ot(Se(r,-1))})}var am={meanSquaredError:wl,meanAbsoluteError:nm,meanAbsolutePercentageError:ec,meanSquaredLogarithmicError:BB,squaredHinge:WB,hinge:VB,categoricalHinge:UB,logcosh:GB,categoricalCrossentropy:Kd,sparseCategoricalCrossentropy:sm,binaryCrossentropy:rm,kullbackLeiblerDivergence:jB,poisson:qB,cosineProximity:rA};function aA(e){if(typeof e=="string"){if(e in am)return am[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new j(t)}else return e}function oA(e,t){return X(()=>{let n=L(.5,Ps(t)),s=Bf(gs(t,n),e.dtype);return Wt($s(e,s),-1)})}function iA(e,t){return X(()=>Bf($s(Zs(e,-1),Zs(t,-1)),"float32"))}function tk(e,t){return X(()=>fe(Se(hr($s(e,1),$s(t,1))),"float32"))}function XB(e,t){return X(()=>fe(Se(hr($s(e,1),$s(t,0))),"float32"))}function KB(e,t){return X(()=>fe(Se(hr($s(e,0),$s(t,1))),"float32"))}function nk(e,t){return X(()=>{let n=tk(e,t),s=KB(e,t),r=ue(n,s);return fe(Vn(gs(r,0),me(n,r),0),"float32")})}function ZB(e,t){return X(()=>{let n=tk(e,t),s=XB(e,t),r=ue(n,s);return fe(Vn(gs(r,0),me(n,r),0),"float32")})}function sk(e,t){return rm(e,t)}function rk(e,t){return e.rank===t.rank&&(e=ot(e,[e.rank-1])),t=Zs(t,-1),t.dtype!==e.dtype&&(t=fe(t,e.dtype)),fe($s(e,t),"float32")}var YB=wl,JB=wl,QB=nm,eW=nm,tW=ec,nW=ec,lA=Kd,sW=rA,ak=sm,om={binaryAccuracy:oA,categoricalAccuracy:iA,precision:nk,categoricalCrossentropy:lA,sparseCategoricalCrossentropy:ak,mse:YB,MSE:JB,mae:QB,MAE:eW,mape:tW,MAPE:nW,cosine:sW};function rW(e){if(typeof e=="string"&&e in om)return om[e];if(typeof e!="string"&&e!=null)return e;throw new j(`Unknown metric ${e}`)}function im(e){if(Mr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(am))if(am[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(om))if(om[n]===e){t=n;break}return t!==void 0?t:e.name}}function aW(e){let t={Adagrad:()=>gl.adagrad(.01),Adadelta:()=>gl.adadelta(1,.95,rn()),Adam:()=>gl.adam(.001,.9,.999,rn()),Adamax:()=>gl.adamax(.002,.9,.999,rn(),0),RMSProp:()=>gl.rmsprop(.001,.9,0,rn()),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 ${e}`)}var ok=1*1024*1024;function ik(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!uA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let s=JSON.stringify(e);s.length>ok&&console.warn(`User-defined metadata of model "${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 uA(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"||!uA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!uA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function oW(e,t,n,s=console.log){let r=lW(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)),lm(a,n,s),s("=".repeat(t));let i=e.layers;for(let u=0;u<i.length;++u)r?uW(i[u],n,s):cW(i[u],n,o,s),s((u===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=iW(e),c=Qf(e.nonTrainableWeights);s(`Total params: ${l+c}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${c}`),s("_".repeat(t))}function iW(e){let t;return e.collectedTrainableWeights!=null?t=Qf(e.collectedTrainableWeights):t=Qf(e.trainableWeights),t}function lW(e){let t=!0,n=[],s=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}s.push(...r)}if(t)for(let r of e.layers){let a=!1;for(let o of r.inboundNodes)if(s.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function lm(e,t,n=console.log){let s="";for(let r=0;r<e.length;++r)r>0&&(s=s.slice(0,s.length-1)+" "),s+=e[r],s=s.slice(0,t[r]),s+=" ".repeat(t[r]-s.length);n(s)}function uW(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(i){s="multiple"}let r=e.name,a=e.getClassName(),o=[`${r} (${a})`,s,e.countParams().toString()];lm(o,t,n)}function cW(e,t,n,s){let r;try{r=JSON.stringify(e.outputShape)}catch(u){r="multiple"}let a=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let d=0;d<u.inboundLayers.length;++d){let p=u.inboundLayers[d].name,h=u.nodeIndices[d],f=u.tensorIndices[d];a.push(`${p}[${h}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],c=[`${o} (${i})`,r,e.countParams().toString(),l];lm(c,t,s);for(let u=1;u<a.length;++u)lm(["","","",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 cA(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(cA(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]=cA(r,s)}return n}}var dA="0.0.0";function dW(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return fe(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}' (${e.dtype}).`)}}var kl=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof kl)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=dW(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new j(`Duplicate key: name=${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&&te(this.id2Mask)}},pA={},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(pA[u]==null){let f=pW(o,t);d=f.sorted,p=f.recipientCounts,pA[u]=d,uk[u]=p}d=pA[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=of().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=St(g.apply(A,n)),k=null;g.supportsMasking&&(k=g.computeMask(A,x));let I=fW(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||te(y)}return h.disposeMasks(),a?l:l[0]}function pW(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:hW(s)}}function hW(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 fW(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 tt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=Yf(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],zo(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)}`);zo(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(Lf);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(Lf);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 ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new em({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}nA(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${dA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=cA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return X(()=>{e=St(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=St(e);let n;return t==null?n=Al(null,e.length):n=St(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Jf(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Lf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],x=`${m.name}_${g}_${A}`,y=n[x];u.push(y)}let d=c.computeOutputShape(os(u)),p=Jf(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 os(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(Lf);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=St(u.call(y,f)),x=St(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=St(u.call(m,f)),x=St(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(os(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(;!HL(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 mW(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: ${JSON.stringify(e)}`)}function dk(e,t){return mW(e,t,"classWeight")}async function pk(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=X(()=>{if(e.shape.length===1)return Wn(e);if(e.shape.length===2){if(e.shape[1]>1)return Zs(e,1);if(e.shape[1]===1)return H(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());te(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])}),Zt(o,"float32")}else return null}function gW(e,t){return L(e,t)}var AW=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 Je)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 yW(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 xW(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=yW(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,bW(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, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(y.value!=null){let{xs:b,ys:w}=hk(e,y.value),k={};k.batch=x,k.size=b[0].shape[0],await p.onBatchBegin(x,k);let I=[];if(n.classWeight!=null){let O=dk(n.classWeight,e.outputNames);for(let _=0;_<O.length;++_)I.push(await pk(w[_],null,O[_]))}let N=b.concat(w).concat(I),R=i(N);te(N);for(let O=0;O<l.length;++O){let _=l[O],P=R[O];k[_]=P,gn(P)}await p.onBatchEnd(x,k),Xw(k),x++,A++}if(s?A>=n.batchesPerEpoch:y.done){if(r){let b;mk(n.validationData)?b=St(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=St(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?AW:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${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 bW(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 vW(e){return typeof e.next=="function"}async function wW(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 ${JSON.stringify(n.batches)}`);let o=vW(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let c=await o.next();if(a=X(()=>{if(c.value){let{xs:u,ys:d}=hk(e,c.value),p=u.concat(d),h=X(()=>r(p));if(te(p),l===0)for(let m=0;m<h.length;++m)a.push($e(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&&te(A)}te(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]=me(a[c],i),te(u)}return os(a)}function hA(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 fA(e,t){return X(()=>e==null?null:Array.isArray(e)?e.map(n=>fA(n,t)):zw(e,t.dtype==="int32"?t:fe(t,"int32")))}function mA(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 kW(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=Zt(A),I=mA(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=fA(n,P),F=t(T);for(let U=0;U<s.length;++U){let q=s[U],z=F[U];R[q]=z,gn(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];gn(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 SW(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;hA(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 kW(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&&te(u)}}function gk(e){let t=[];e instanceof Je&&(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 Je)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 Je)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 IW(e){return e instanceof Je}function gA(e){return Array.isArray(e)}function Ak(e){return!IW(e)&&!gA(e)}function yk(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(gA(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(gA(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 CW(e,t,n){let s=zo(e.map(a=>a.shape[0]));s.sort();let r=zo(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 TW(e,t,n){let s=[wl,rm,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 NW(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: ${e}`);if(Array.isArray(n))return t.map(s=>n);{let s=[];for(let r of t){let a=n.hasOwnProperty(r)?n[r]:[];Array.isArray(a)||(a=[a]),s.push(a)}return s}}var EW="layers-model",aa=class extends Br{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new j("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");oW(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=aW(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof na))throw new j("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new j(`Unknown entry in loss dictionary: "${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(aA(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=>aA(o))}else{let a=aA(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=NW(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]===rm?["accuracy","acc"].indexOf(h)!==-1?d=oA:["crossentropy","ce"].indexOf(h)!==-1&&(d=sk):this.lossFunctions[a]===sm?["accuracy","acc"].indexOf(h)!==-1?d=rk:["crossentropy","ce"].indexOf(h)!==-1&&(d=ak):["accuracy","acc"].indexOf(h)!==-1?d=iA:["crossentropy","ce"].indexOf(h)!==-1&&(d=lA);let g;["accuracy","acc"].indexOf(h)!==-1?g="acc":["crossentropy","ce"].indexOf(h)!==-1&&(g="ce"),p=d,u=c+g}else p=rW(h),u=c+im(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;hA(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 os(l)}finally{Sl(a[0],e),Sl(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),wW(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 ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new j("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new kl;if(e instanceof Je&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new j(`The number of inputs provided (${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=mA(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 os(a.map(o=>wt(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 hA(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]===sm?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"),CW(e,t,null),TW(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: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let c=dk(s,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await pk(i[u],null,c[u]))}return[o,i,l]}testLoop(e,t,n,s=0,r){return X(()=>{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new Le("Verbose mode is not implemented yet.");if(r!=null)throw new Le("steps mode in testLoop() is not implemented yet");{let i=mA(a,n),l=Zt(Ar(0,a));for(let c=0;c<i.length;++c){let u=i[c][0],d=i[c][1],p=vl(l,u,d-u),h=fA(t,p),f=e(h);if(c===0)for(let m=0;m<f.length;++m)o.push($e(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]=me(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=gW(g,r[f]));let A=Wt(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=Wt(g(s[A],p[A]))}gn(m),a.push(m)}return h=Wt(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=Wt(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=Wt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return SW(this,e,t,n)}async fitDataset(e,t){return xW(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 te(o),os(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=of().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-of().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(im(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ra(im(e)));{let e={};for(let t in this.metrics)e[t]=ra(im(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=ss.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 ss.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:EW,generatedBy:`TensorFlow.js tfjs-layers v${dA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await ss.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=ss.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 RW(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 ss.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),te(a)}return r}async function $W(e,t){if(t==null&&(t={}),typeof e=="string"){let n=ss.getLoadHandlers(e,t);if(n.length===0)n.push(ss.browserHTTPRequest(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return _W(e,void 0,t)}async function _W(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}=DW(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),te(c),te(u.map(d=>d.tensor))}return i}function DW(e,t){let n=ss.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 AA=class extends aa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Yf("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 AA||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 em({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(ht(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new 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 AA))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=br(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},um=AA;um.className="Sequential";ce.registerClass(um);function PW(e){return new aa(e)}function FW(e){return new um(e)}function OW(e,t){return t==null&&(t={}),$W(e,t)}function vk(e){return qw(e)}function MW(e,t){sA.registerCallbackConstructor(e,t)}var ls=class extends ce.Serializable{getConfig(){return{}}},wk=class extends ls{apply(e,t=1){return uB(e,t)}};wk.className="elu";ce.registerClass(wk);var kk=class extends ls{apply(e){return x1(e)}};kk.className="selu";ce.registerClass(kk);var Sk=class extends ls{apply(e){return Or(e)}};Sk.className="relu";ce.registerClass(Sk);var Ik=class extends ls{apply(e){return X(()=>Od(6,Or(e)))}};Ik.className="relu6";ce.registerClass(Ik);var Ck=class extends ls{apply(e){return e}};Ck.className="linear";ce.registerClass(Ck);var Tk=class extends ls{apply(e){return fs(e)}};Tk.className="sigmoid";ce.registerClass(Tk);var Nk=class extends ls{apply(e){return dB(e)}};Nk.className="hardSigmoid";ce.registerClass(Nk);var Ek=class extends ls{apply(e){return Gu(e)}};Ek.className="softplus";ce.registerClass(Ek);var Rk=class extends ls{apply(e){return cB(e)}};Rk.className="softsign";ce.registerClass(Rk);var $k=class extends ls{apply(e){return Lu(e)}};$k.className="tanh";ce.registerClass($k);var yA=class extends ls{apply(e,t=-1){return Xu(e,t)}};yA.className="softmax";ce.registerClass(yA);var _k=class extends ls{apply(e,t=-1){return l1(e,t)}};_k.className="logSoftmax";ce.registerClass(_k);var Dk=class extends ls{apply(e,t=1){return X(()=>L(fs(L(e,t)),e))}};Dk.className="swish";ce.registerClass(Dk);var Pk=class extends ls{apply(e){return X(()=>L(e,Lu(Gu(e))))}};Pk.className="mish";ce.registerClass(Pk);function Vo(e){return e.getClassName()}function xA(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"activation")}function Uo(e){if(e==null){let t={};return t.className="linear",t.config={},xA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},xA(t)}else return e instanceof ls?e:xA(e)}function bA(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();bA(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=Gt([1]);return this.hasL1&&(t=ue(t,Se(L(this.l1,sn(e))))),this.hasL2&&(t=ue(t,Se(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 zW(e){return bA(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function LW(e){return bA(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var Ok={l1l2:"L1L2"};function yt(e){return F1(e)}function Mk(e,t={}){return Ud(e,ce.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(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 vA=class extends tt{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=ms(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};vA.className="ReLU";ce.registerClass(vA);var wA=class extends tt{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 gf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wA.className="LeakyReLU";ce.registerClass(wA);var kA=class extends tt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=on(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 ${e.sharedAxes}`)}build(e){e=ht(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Yt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),kf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Mt(this.alphaInitializer),alphaRegularizer:yt(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};kA.className="PReLU";ce.registerClass(kA);var SA=class extends tt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Le(`Non-default alpha value (${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}};SA.className="ELU";ce.registerClass(SA);var IA=class extends tt{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,fe(gs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};IA.className="ThresholdedReLU";ce.registerClass(IA);var CA=class extends tt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new yA().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}};CA.className="Softmax";ce.registerClass(CA);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(!aB(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+Bo([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function TA(e,t){return X(()=>(jt(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function zk(e,t){return X(()=>(jt(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function BW(e,t,n,s=1,r="valid",a,o=1){return X(()=>{if(a==null&&(a=mr()),jt(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=Qe(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=e1(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()),jt(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=TA(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Oo.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Qe(l,[0,3,1,2])),l})}function WW(e,t,n,s=[1,1,1],r="valid",a,o){return X(()=>{if(a==null&&(a=mr()),jt(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 ${e.rank}.`);let i=zk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=s1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=yr(i,n)),a==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var NA=class extends tt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",NA.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${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,jt(this.dataFormat),this.activation=Uo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(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"&&!M1(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:Vo(this.activation),useBias:this.useBias,biasInitializer:Mt(this.biasInitializer),biasRegularizer:yt(this.biasRegularizer),activityRegularizer:yt(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ep=class extends NA{constructor(e,t){super(e,t);this.kernel=null,ep.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ht(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=BW(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=WW(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=ht(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:Mt(this.kernelInitializer),kernelRegularizer:yt(this.kernelRegularizer),kernelConstraint:an(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"&&!M1(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)}.`)}},cm=Bk;cm.className="Conv2D";ce.registerClass(cm);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)}.`)}},dm=Wk;dm.className="Conv3D";ce.registerClass(dm);var EA=class extends cm{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 ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Qe(n,[0,2,3,1]));let g=n1(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Qe(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=ht(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}};EA.className="Conv2DTranspose";ce.registerClass(EA);var RA=class extends dm{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 ${this.padding}`)}build(e){if(e=ht(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Qe(n,[0,2,3,4,1]));let w=xv(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Qe(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=ht(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}};RA.className="Conv3DTranspose";ce.registerClass(RA);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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${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=Qe(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=Qe(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=Mt(this.depthwiseInitializer),e.pointwiseInitializer=Mt(this.pointwiseInitializer),e.depthwiseRegularizer=yt(this.depthwiseRegularizer),e.pointwiseRegularizer=yt(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(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"&&!M1(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)}.`)}},_A=Uk;_A.className="Conv1D";ce.registerClass(_A);var DA=class extends tt{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=Wf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Wf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Wf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Wf(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}};DA.className="Cropping2D";ce.registerClass(DA);var PA=class extends tt{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,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,nB(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=Qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(n,[r,a]):Ie.resizeBilinear(n,[r,a]);return Qe(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(n,[r,a]):Ie.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};PA.className="UpSampling2D";ce.registerClass(PA);function VW(e,t,n=[1,1],s="valid",r,a){return X(()=>{r==null&&(r=mr()),jt(r);let o=TA(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=Qe(o,[0,3,1,2])),o})}var FA=class extends NA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ht(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=VW(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=ht(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=Mt(this.depthwiseInitializer),e.depthwiseRegularizer=yt(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};FA.className="DepthwiseConv2D";ce.registerClass(FA);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=Qe(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=fe(fe(r,"bool"),"float32"),r.rank===l-1&&(r=Kt(r,-1)),r=Qe(r,c)),s&&(t=Fs(t,0),r!=null&&(r=Fs(r,0)));let u=[],d,p=n,h=t.shape[0],f=as(t),m;r!=null&&(m=as(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=ge(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=yn(u,1)),[d,g,p]})}var jk=class extends tt{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 fm({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){eA(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.");eA(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=>Gt([n,s])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_[0]=Gt([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()):te(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=>gn(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=Gt(e.shape);return t=Se(t,[1,2]),t=jd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?H1(t,[1,n]):t):this.cell.stateSize>1?[H1(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 tt{},pm=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,xn(this.units,"units"),this.activation=Uo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Yu([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Bo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return 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=Go({ones:()=>Ps(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({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:Vo(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:yt(this.kernelRegularizer),recurrentRegularizer:yt(this.recurrentRegularizer),biasRegularizer:yt(this.biasRegularizer),activityRegularizer:yt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};pm.className="SimpleRNNCell";ce.registerClass(pm);var OA=class extends oa{constructor(e){e.cell=new pm(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};OA.className="SimpleRNN";ce.registerClass(OA);var hm=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,xn(this.units,"units"),this.activation=Uo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Uo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Yu([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Bo([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=ht(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return 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=Go({ones:()=>Ps(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({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]=Ht(u,[2*this.units,this.units],u.rank-1),h=zr(s,d),[f,m,g]=Ht(c,3,c.rank-1),[A,x]=Ht(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,Ot(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vo(this.activation),recurrentActivation:Vo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:yt(this.kernelRegularizer),recurrentRegularizer:yt(this.recurrentRegularizer),biasRegularizer:yt(this.biasRegularizer),activityRegularizer:yt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};hm.className="GRUCell";ce.registerClass(hm);var MA=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 hm(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};MA.className="GRU";ce.registerClass(MA);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,xn(this.units,"units"),this.activation=Uo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Uo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Yu([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,Bo([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=ht(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let 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 Uf().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=Go({ones:()=>Ps(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({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]=Ht(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:Vo(this.activation),recurrentActivation:Vo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:yt(this.kernelRegularizer),recurrentRegularizer:yt(this.recurrentRegularizer),biasRegularizer:yt(this.biasRegularizer),activityRegularizer:yt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};np.className="LSTMCell";ce.registerClass(np);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 np(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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="LSTM";ce.registerClass(zA);var fm=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){eA(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 tA(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]])}nA(t)}};fm.className="StackedRNNCells";ce.registerClass(fm);function Go(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?gn(i().clone()):Array(r).fill(void 0).map(i).map(c=>gn(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&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new 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=Gt(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(()=>Gt(r)):this.states_=[Gt(r)];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(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()):te(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=>gn(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 mm=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,xn(this.filters,"filters"),this.kernelSize=tc(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=tc(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",Os(this.padding),this.dataFormat=a||"channelsLast",jt(this.dataFormat),this.dilationRate=tc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>xn(i,"dilationRate"))}build(e){var t;e=ht(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=As([c]),f=l.apply([c*2]);return G1([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=Go({ones:()=>Ps(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Y,J,ne)=>!J||!J[ne]?Y:L(J[ne],Y),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=Go({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]=Ht(this.kernel.read(),o,x),[I,N,R,O]=this.useBias?Ht(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]=Ht(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=Do(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 Do(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};mm.className="ConvLSTM2DCell";ce.registerClass(mm);var LA=class extends qk{constructor(e){let t=new mm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};LA.className="ConvLSTM2D";ce.registerClass(LA);var gm=class extends tt{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()}};gm.className="Dropout";ce.registerClass(gm);var BA=class extends gm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};BA.className="SpatialDropout1D";ce.registerClass(BA);var WA=class extends tt{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,xn(this.units,"units"),this.activation=Uo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ht(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ht(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return 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:Vo(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:yt(this.kernelRegularizer),biasRegularizer:yt(this.biasRegularizer),activityRegularizer:yt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Dense";ce.registerClass(WA);var VA=class extends tt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ht(e);for(let t of e.slice(1))if(t==null)throw new 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],Lo(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=Qe(n,s)}return lB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};VA.className="Flatten";ce.registerClass(VA);var UA=class extends tt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Uo(e.activation)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Vo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};UA.className="Activation";ce.registerClass(UA);var GA=class extends tt{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),oB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};GA.className="RepeatVector";ce.registerClass(GA);var HA=class extends tt{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=Lo(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}};HA.className="Reshape";ce.registerClass(HA);var jA=class extends tt{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=ht(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Qe(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};jA.className="Permute";ce.registerClass(jA);var qA=class extends tt{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 cf(Hu(n,this.maskValue),s)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=cf(Hu(n,this.maskValue),s,r);return L(n,fe(a,n.dtype))})}};qA.className="Masking";ce.registerClass(qA);var XA=class extends tt{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(St(e.inputLength))}this.inputDim=e.inputDim,xn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,xn(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=on(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,et(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=St(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=Bf(n,"int32"));let s=zw(this.embeddings.read(),H(n,[n.size]));return H(s,ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Mt(this.embeddingsInitializer),embeddingsRegularizer:yt(this.embeddingsRegularizer),activityRegularizer:yt(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};XA.className="Embedding";ce.registerClass(XA);var Il=class extends tt{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=[ht(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=zo(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&zo(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return X(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Bo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=jd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=H(i,[u].concat(Lo(c.slice(1))));p=Qe(p,[1,0]),p=H(p,d),n.push(p),r=!0}else if(l>1){let c=Ar(1,l).concat([0]);n.push(Qe(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=H(Qe(H(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(Ar(0,o-1));a=Qe(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=zo(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return X(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Kt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=hr(n,t[s]);return n})}},KA=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})}};KA.className="Add";ce.registerClass(KA);var 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=L(t,e[n]);return t})}};ZA.className="Multiply";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=ue(t,e[n]);return L(1/e.length,t)})}};YA.className="Average";ce.registerClass(YA);var JA=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})}};JA.className="Maximum";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=Od(t,e[n]);return t})}};QA.className="Minimum";ce.registerClass(QA);var ey=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(()=>G1(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(fe(Ps(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Kt(t[a],-1)):s.push(t[a]);let r=wt(s,this.axis);return K2(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ey.className="Concatenate";ce.registerClass(ey);function sp(e,t){for(;e<0;)e+=t;return e}function UW(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=Se(L(e,t),a[0]):i=Se(L(Qe(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=ot(i,c)}return i.shape.length===1&&(i=Kt(i,1)),i})}var ty=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=tm(t,s[0]),n=tm(n,s[1])),UW(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}};ty.className="Dot";ce.registerClass(ty);var ny=class extends tt{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(Vf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};ny.className="GaussianNoise";ce.registerClass(ny);var sy=class extends tt{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,Vf(n.shape,1,r))},()=>n,t.training||!1):n})}};sy.className="GaussianDropout";ce.registerClass(sy);var ry=class extends tt{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=Bf(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})}};ry.className="AlphaDropout";ce.registerClass(ry);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 GW(e,t,n,s,r=.001){return X(()=>{let a=vf(e,s),o=a.mean,i=a.variance;return[rp(e,o,i,n,t,r),o,i]})}function HW(e,t,n,s,r=.001){return X(()=>{let a=vf(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 jW(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),Ar(0,e.rank-1))?GW(e,t,n,s,r):HW(e,t,n,s,r)}var ay=class extends tt{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=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new 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]=jW(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,x,y)=>{X(()=>{let b=1-y,w=A.read(),k=L(ge(w,x),b);A.write(ge(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:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),movingMeanInitializer:Mt(this.movingMeanInitializer),movingVarianceInitializer:Mt(this.movingVarianceInitializer),betaRegularizer:yt(this.betaRegularizer),gammaRegularizer:yt(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ay.className="BatchNormalization";ce.registerClass(ay);var oy=class extends tt{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=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(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!==zo(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}=vf(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:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),betaRegularizer:yt(this.betaRegularizer),gammaRegularizer:yt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};oy.className="LayerNormalization";ce.registerClass(oy);function qW(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: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Js(e,s)})}var iy=class extends tt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?mr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return X(()=>qW(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};iy.className="ZeroPadding2D";ce.registerClass(iy);function Am(e,t,n,s,r,a){return X(()=>{jt(r),_w(a),Os(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=TA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=bf(e,t,n,i):o=pf(e,t,n,i),r==="channelsFirst"&&(o=Qe(o,[0,3,1,2])),o})}function Xk(e,t,n,s,r,a){return X(()=>{jt(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=p1(e,t,n,i):o=J2(e,t,n,i),r==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var Kk=class extends tt{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(xn(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)}`);xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Os(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=ht(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 ot(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ly=class extends Kk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Am(e,t,n,s,r,"max")}};ly.className="MaxPooling1D";ce.registerClass(ly);var uy=class extends Kk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Am(e,t,n,s,r,"avg")}};uy.className="AveragePooling1D";ce.registerClass(uy);var Zk=class extends tt{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];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Os(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=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}},cy=class extends Zk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Am(e,t,n,s,r,"max")}};cy.className="MaxPooling2D";ce.registerClass(cy);var dy=class extends Zk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Am(e,t,n,s,r,"avg")}};dy.className="AveragePooling2D";ce.registerClass(dy);var Yk=class extends tt{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];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Os(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=ht(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}},py=class extends Yk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Xk(e,t,n,s,r,"max")}};py.className="MaxPooling3D";ce.registerClass(py);var hy=class extends Yk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),Os(s),Xk(e,t,n,s,r,"avg")}};hy.className="AveragePooling3D";ce.registerClass(hy);var Jk=class extends tt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},fy=class extends Jk{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return Wt(n,1)})}};fy.className="GlobalAveragePooling1D";ce.registerClass(fy);var my=class extends Jk{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return An(n,1)})}};my.className="GlobalMaxPooling1D";ce.registerClass(my);var Qk=class extends tt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(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}},gy=class extends Qk{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Wt(n,[1,2]):Wt(n,[2,3])})}};gy.className="GlobalAveragePooling2D";ce.registerClass(gy);var Ay=class extends Qk{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?An(n,[1,2]):An(n,[2,3])})}};Ay.className="GlobalMaxPooling2D";ce.registerClass(Ay);var e7=class extends tt{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)}},yy=class extends e7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(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=ht(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]))}};yy.className="TimeDistributed";ce.registerClass(yy);function XW(e){xl(tB,"BidirectionalMergeMode",e)}var KW="concat",xy=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?KW:e.mergeMode,XW(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()):os(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=G1([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)}};xy.className="Bidirectional";ce.registerClass(xy);function ZW(e){return new Ju(e)}function YW(e){return new SA(e)}function JW(e){return new vA(e)}function QW(e){return new wA(e)}function eV(e){return new kA(e)}function tV(e){return new CA(e)}function nV(e){return new IA(e)}function sV(e){return new _A(e)}function rV(e){return new cm(e)}function aV(e){return new EA(e)}function oV(e){return new dm(e)}function iV(e){return new RA(e)}function lV(e){return new $A(e)}function uV(e){return new DA(e)}function cV(e){return new PA(e)}function dV(e){return new FA(e)}function pV(e){return new UA(e)}function hV(e){return new WA(e)}function fV(e){return new gm(e)}function mV(e){return new BA(e)}function gV(e){return new VA(e)}function AV(e){return new GA(e)}function yV(e){return new HA(e)}function xV(e){return new jA(e)}function bV(e){return new XA(e)}function vV(e){return new KA(e)}function wV(e){return new YA(e)}function kV(e){return new ey(e)}function SV(e){return new JA(e)}function IV(e){return new QA(e)}function CV(e){return new ZA(e)}function TV(e){return new ty(e)}function NV(e){return new ay(e)}function EV(e){return new oy(e)}function RV(e){return new iy(e)}function by(e){return new uy(e)}function $V(e){return by(e)}function _V(e){return by(e)}function vy(e){return new dy(e)}function DV(e){return vy(e)}function PV(e){return vy(e)}function wy(e){return new hy(e)}function FV(e){return wy(e)}function OV(e){return wy(e)}function MV(e){return new fy(e)}function zV(e){return new gy(e)}function t7(e){return new my(e)}function n7(e){return new Ay(e)}function s7(e){return new ly(e)}function r7(e){return new cy(e)}function LV(e){return new py(e)}function BV(e){return new MA(e)}function WV(e){return new hm(e)}function VV(e){return new zA(e)}function UV(e){return new np(e)}function GV(e){return new OA(e)}function HV(e){return new pm(e)}function jV(e){return new LA(e)}function qV(e){return new mm(e)}function XV(e){return new oa(e)}function KV(e){return new fm(e)}function ZV(e){return new xy(e)}function YV(e){return new yy(e)}var JV=t7,QV=n7,eU=s7,tU=r7;function nU(e){return new ny(e)}function sU(e){return new sy(e)}function rU(e){return new ry(e)}function aU(e){return new qA(e)}var a7={};Oe(a7,{MAPE:()=>gU,MSE:()=>xU,binaryAccuracy:()=>oU,binaryCrossentropy:()=>iU,categoricalAccuracy:()=>uU,categoricalCrossentropy:()=>cU,cosineProximity:()=>hU,mape:()=>AU,meanAbsoluteError:()=>fU,meanAbsolutePercentageError:()=>mU,meanSquaredError:()=>yU,mse:()=>bU,precision:()=>dU,recall:()=>pU,sparseCategoricalAccuracy:()=>lU});function oU(e,t){return oA(e,t)}function iU(e,t){return sk(e,t)}function lU(e,t){return rk(e,t)}function uU(e,t){return iA(e,t)}function cU(e,t){return lA(e,t)}function dU(e,t){return nk(e,t)}function pU(e,t){return ZB(e,t)}function hU(e,t){return rA(e,t)}function fU(e,t){return nm(e,t)}function mU(e,t){return ec(e,t)}function gU(e,t){return ec(e,t)}function AU(e,t){return ec(e,t)}function yU(e,t){return wl(e,t)}function xU(e,t){return wl(e,t)}function bU(e,t){return wl(e,t)}var o7={};Oe(o7,{modelFromJSON:()=>RW});var i7={};Oe(i7,{l1:()=>wU,l1l2:()=>vU,l2:()=>kU});function vU(e){return new Qd(e)}function wU(e){return zW(e)}function kU(e){return LW(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 ym(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=ym:this.mode==="max"?this.monitorFunc=u7:this.monitor.indexOf("acc")!==-1?this.monitorFunc=u7:this.monitorFunc=ym,this.monitorFunc===ym&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===ym?1/0:-1/0}async onEpochEnd(e,t){await Wo(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 SU(e){return new c7(e)}var IU={earlyStopping:SU},CU=Z();CU.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 ky={};function TU(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};ky[e]=n}function p7(e){return ky[e]}function NU(e){delete ky[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 Gn(t.inputNames[a.inputIndexStart],n,s,r);if(a.type==="tensors")return t.inputNames.slice(i,l).map(p=>Gn(p,n,s,r));let c=Gn(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 Gn(e,t,n,s){let[r,a]=ys(e);if(s!=null){let i=s.getHashTableHandleByName(r);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[xm(r,i)]);return o!==void 0?t[xm(r,o)][a]:void 0}function EU(e,t,n){return t[xm(e,n.currentContextId)]}function Vr(e,t){let[n,s,r]=ys(e);return[xm(n,t&&t.currentContextId),s,r]}function xm(e,t){return t?`${e}-${t}`:e}function ys(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 bm(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:Wn(e)}var h7={};Oe(h7,{json:()=>RU});var RU=[{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:()=>_U});var _U=[{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:()=>DU});var DU=[{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:()=>PU});var PU=[{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:()=>FU});var FU=[{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:()=>OU});var OU=[{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:()=>MU});var MU=[{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:()=>zU});var zU=[{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:()=>LU});var LU=[{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:()=>BU});var BU=[{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:()=>WU});var WU=[{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:()=>VU});var VU=[{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:()=>UU});var UU=[{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:()=>GU});var GU=[{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:()=>HU});var HU=[{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:()=>jU});var jU=[{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:()=>qU});var qU=[{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:()=>XU});var XU=[{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}:${w}`;m.inputNames[A]=k}}m.inputs.push(b),b.children.push(m)})}),Object.keys(u).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=Vr(f),g=o[m];g!=null&&(g.signatureKey=u[f],l.push(g))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=Vr(f),g=o[m];g&&(g.signatureKey=c[f],i.push(g))}):i=s;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let h={nodes:o,inputs:i,outputs:l,weights:r,placeholders:s,signature:t,functions:p};return a.length>0&&(h.initNodes=a),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=p7(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,r)=>(s[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,r)=>{let a=r.type,o;switch(r.type){case"string":o=Sy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Sy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":o=_y(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=_y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":o=Cy(e.attr,r.tfName,r.defaultValue||0),o===void 0&&!!r.tfDeprecatedName&&(o=Cy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":o=$y(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=$y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":o=Iy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Iy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":o=Py(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Py(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":o=Ry(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Ry(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":o=Dy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Dy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":o=Ny(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Ny(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"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: ${e.op}`)}return s[r.name]={value:o,type:a},s},{})),n}mapFunction(e){let t=e.nodeDef,n=[],s=[],r={};t!=null&&(r=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&s.push(u[d.name]),u),{}));let a=[],o=[];e.signature.inputArg.forEach(u=>{let[d]=Vr(u.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Ty(u.type),type:"dtype"}},children:[]};p.signatureKey=u.name,a.push(p),r[d]=p}),Object.keys(r).forEach(u=>{let d=r[u];d.inputNames.forEach((p,h)=>{let[f,,m]=Vr(p),g=r[f];if(g.outputs!=null){let A=g.outputs.indexOf(m);if(A!==-1){let x=`${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 KU(e){let t=Z().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):KU(e);return t?n:n.toLowerCase()}function Sy(e,t,n,s=!1){let r=e[t];return r!=null?D7(r.s,s):n}function Iy(e,t,n){let s=e[t];return s?s.b:n}function Cy(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 Ty(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 Ny(e,t,n){let s=e[t];return s&&s.type?Ty(s.type):n}function Ey(e,t,n){let s=e[t];return s&&s.list&&s.list.type?s.list.type.map(r=>Ty(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 Ry(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 _y(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 Dy(e,t,n){let s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(r=>F7(r)):n}function Py(e,t,n){let s=e[t];return s&&s.list&&s.list.b?s.list.b:n}var ZU=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 Gn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Gn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Cy(this.node.rawAttrs,e,t);if(n.s!=null)return Sy(this.node.rawAttrs,e,t);if(n.b!=null)return Iy(this.node.rawAttrs,e,t);if(n.shape!=null)return Ry(this.node.rawAttrs,e,t);if(n.type!=null)return Ny(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 _y(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Dy(this.node.rawAttrs,e,t);if(n.list.b!=null)return Py(this.node.rawAttrs,e,t);if(n.list.type!=null)return Ey(this.node.rawAttrs,e,t)}return t}},YU=(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[Mu(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[me(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[uf(S("a",e,t,n),S("b",e,t,n))];case"Sub":return[ge(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[Fo(S("a",e,t,n),S("b",e,t,n))];case"SquaredDifference":return[S1(S("a",e,t,n),S("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JU=(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[No(S("real",e,t,n),S("imag",e,t,n))];case"Cos":return[ff(S("x",e,t,n))];case"Cosh":return[r1(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[Af(S("x",e,t,n))];case"Imag":return[mf(S("x",e,t,n))];case"Neg":return[Ot(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[A1(S("x",e,t,n))];case"Selu":return[x1(S("x",e,t,n))];case"Sigmoid":return[fs(S("x",e,t,n))];case"Sin":return[b1(S("x",e,t,n))];case"Sign":return[Vv(S("x",e,t,n))];case"Sinh":return[v1(S("x",e,t,n))];case"Softplus":return[Gu(S("x",e,t,n))];case"Sqrt":return[Dn(S("x",e,t,n))];case"Square":return[At(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[ms(S("x",e,t,n),S("clipValueMin",e,t,n),S("clipValueMax",e,t,n))];case"Relu6":return[g1(S("x",e,t,n))];case"Rsqrt":return[y1(Gn(e.inputNames[0],t,n))];case"Prod":return[h1(S("x",e,t,n),S("axes",e,t,n))];case"LeakyRelu":return[gf(S("x",e,t,n),S("alpha",e,t,n))];case"Prelu":return[kf(S("x",e,t,n),S("alpha",e,t,n))];case"IsNan":return[Tv(Gn(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=Fy(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=Fy(a.shape,s)}),!O7(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Fy(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. ${t}`);n[s]=r>=0?r:a}return n}var QU=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=$e(0),gn(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,gn(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 ut([],[0].concat(this.elementShape));let n=this.readMany(e);return nr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),yn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return ut([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return nr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),wt(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,as(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(De(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 ${r.dtype}`);nr(t,r.shape,"TensorList shape mismatch: "),gn(r)}),this.idTensor=$e(0),this.maxNumElements=s,gn(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 yn(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.");gn(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: "),gn(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?ut([],[0].concat(s)):X(()=>{let r=e.map(a=>H(this.tensors[a],s));return yn(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?ut([],[0].concat(n)):X(()=>{let s=this.tensors.map(r=>H(r,n));return wt(s,0)})}};function eG(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=as(e);return new op(a,t,s)}function tG(e,t,n){return new op([],e,t,n)}function nG(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=as(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function sG(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=Fy(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(De(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 rG=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=>Gn(r,t,n)!==void 0);if(s){let r=Gn(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 QU(c,r,s,a,l,o,i);return n.addTensorArray(u),[u.idTensor,$e(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[$e(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=nG(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=tG(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=eG(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=sG(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=bm(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 aG=(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[e1(S("x",e,t,n),S("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let s=S("strides",e,t,n),r=bm(e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilations",e,t,n);return[Do(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[Oo.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[Oo.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=bm(e,t,n);return[n1(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=bm(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[s1(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[pf(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[bf(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[J2(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[p1(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`)}},oG=(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[As(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[Tf(s,r,a,S("dtype",e,t,n),o)]}case"Zeros":return[Gt(S("shape",e,t,n),S("dtype",e,t,n))];case"ZerosLike":return[et(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Oy(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 iG=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=Oy(e,t,n),c=await Ie.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}=Oy(e,t,n),l=S("padToMaxOutputSize",e,t,n),c=await Ie.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}=Oy(e,t,n);return[await Ie.nonMaxSuppressionAsync(s,r,a,o,i)]}case"Where":{let s=fe(S("condition",e,t,n),"bool"),r=[await C1(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`)}},lG=(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=I1(s);return[r.values,r.indices]}case"UniqueV2":{let s=S("x",e,t,n),r=S("axis",e,t,n),a=I1(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},uG=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let s=S("default",e,t,n);return[Gn(e.name,t,n)||s];case"Placeholder":return[Gn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=S("x",e,t,n);return[ia(c)]}case"IdentityN":return S("x",e,t,n).map(c=>ia(c));case"Snapshot":let r=S("x",e,t,n);return[ia(r)];case"Shape":return[Zt(S("x",e,t,n).shape,"int32")];case"ShapeN":return S("x",e,t,n).map(c=>Zt(c.shape));case"Size":return[$e(S("x",e,t,n).size,"int32")];case"Rank":return[$e(S("x",e,t,n).rank,"int32")];case"NoOp":return[$e(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 ${e.op} is not implemented`)}},cG=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=$e(0),this.tensorMap=new Map,gn(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 $e(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=as(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];gn(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 yn(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}`)}},dG=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 cG(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`)}},pG=(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[Ie.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[Ie.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[Ie.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hG=(e,t,n)=>{switch(e.op){case"Equal":return[$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[gs(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[i1(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[xf(S("a",e,t,n))];case"LogicalOr":return[d1(S("a",e,t,n),S("b",e,t,n))];case"Select":case"SelectV2":return[Vn(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`)}},fG=(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[Qe(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[Oo.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`)}},mG=(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[l1(S("x",e,t,n))];case"SparseToDense":return[N1(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`)}},gG=(e,t,n)=>{switch(e.op){case"Max":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[An(S("x",e,t,n),o,i)]}case"Mean":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Wt(S("x",e,t,n),o,i)]}case"Min":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Po(S("x",e,t,n),o,i)]}case"Sum":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Se(S("x",e,t,n),o,i)]}case"All":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[K2(S("x",e,t,n),o,i)]}case"Any":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[cf(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[h1(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[a1(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[Q2(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`)}},AG=(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),[wt(a,r)]}case"Gather":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[Uu(s,fe(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,fe(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[De(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=ot(r[0]).shape,i=r.map(l=>{let c=v.arraysEqual(l.shape,a);if(!c&&!v.arraysEqual(ot(l).shape,o))throw new Error("the input tensors shape does not match");return c?l:H(l,a)});return[yn(i,s)]});case"Unpack":{let s=S("axis",e,t,n),r=S("tensor",e,t,n);return as(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 Ht(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[N1(s,a,r,a.dtype===o.dtype?o:fe(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},yG=(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`)}},xG=(e,t,n)=>{switch(e.op){case"FFT":return[If(S("x",e,t,n))];case"IFFT":return[Ld(S("x",e,t,n))];case"RFFT":return[Cf(S("x",e,t,n))];case"IRFFT":return[k1(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=_f.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}=_f.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[_f.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vG=(e,t,n)=>{switch(e.op){case"Cast":return[fe(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Kt(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[ot(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[wf(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[hf(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(()=>YU(a,o,i));case"basic_math":return X(()=>JU(a,o,i));case"control":return rG(a,o,i);case"convolution":return X(()=>aG(a,o,i));case"creation":return X(()=>oG(a,o,i));case"dynamic":return iG(a,o,i);case"evaluation":return X(()=>lG(a,o,i));case"image":return X(()=>pG(a,o,i));case"graph":return X(()=>uG(a,o,i));case"logical":return X(()=>hG(a,o,i));case"matrices":return X(()=>fG(a,o,i));case"normalization":return X(()=>mG(a,o,i));case"reduction":return X(()=>gG(a,o,i));case"slice_join":return X(()=>AG(a,o,i));case"sparse":return X(()=>yG(a,o,i));case"spectral":return X(()=>xG(a,o,i));case"string":return X(()=>bG(a,o,i));case"transformation":return X(()=>vG(a,o,i));case"hash_table":return dG(a,o,i,s);case"custom":let l=p7(a.op);if(l&&l.customExecutor)return l.customExecutor(new ZU(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=>ys(p)[0]),u=[];s!=null&&(u=s.map(p=>ys(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((W7(p)||CG(p)||TG(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 wG(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>ys(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 kG=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],SG=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],IG=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function W7(e){return kG.indexOf(e.op)>=0}function CG(e){return SG.indexOf(e.op)>=0}function TG(e){return IG.indexOf(e.op)>=0}var My=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 My(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 wG(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[ys(u)[0]]),r=t.map(u=>ys(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]=ys(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=>Gn(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=EU(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=Z().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=>Gn(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[ys(x)[0]]),o=n.map(x=>ys(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]=ys(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)&&!Gn(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}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&S("isConstant",u.node,s,n)&&([d]=Vr(u.node.name,n)),s[u.node.name]==null){let p=z7(u.node,s,n,this._resourceManager);d||([d]=Vr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Vr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Gn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Gn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=ys(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ys(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ys(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},NG=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},EG="?tfjs-format=file",RG="model.json",V7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new NG}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=ss.browserHTTPRequest(e,this.loadOptions);else{let t=ss.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(ss.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=ss.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new My(_7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=_7.Instance.transformGraph(e.modelInitializer);this.initializer=new My(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=ss.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Je)&&!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}${RG}${EG}`);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:()=>eH,csv:()=>dH,func:()=>pH,generator:()=>hH,microphone:()=>mH,version_data:()=>gH,webcam:()=>fH,zip:()=>tH});var _G=di(gh()),DG=di(gh());function PG(e,t){return vm(e,t)}function vm(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=vm(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 FG(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;vm(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 vm(e,t,n)}function nc(e){let t=!1;if(Z().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 Je)&&!(e instanceof Promise)&&!t)}function OG(e){return e==null||MG(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Je||v.isTypedArray(e)}function MG(e){return e===null||typeof e!="object"&&typeof e!="function"}function zG(e){return PG(e,LG)}function LG(e){return e instanceof Je?{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 VG(e)}function zy(e){return new UG(e)}function BG(e,t){return new J7(e,t)}function WG(e,t=wm.FAIL){return new JG(e,t)}var bn=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 ZG(this,e)}filter(e){return new XG(this,e)}map(e){return new KG(this,e)}mapAsync(e){return new Y7(this,e)}serialMapAsync(e){return new Y7(this,e).serial()}flatmap(e){return new YG(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 qG(this,e,t)}columnMajorBatch(e,t=!0,n=H7){return this.rowMajorBatch(e,t).map(r=>FG(r,n))}concatenate(e,t){return new J7(Z7([this,e]),t)}take(e){return e<0||e==null?this:new jG(this,e)}skip(e){return e<0||e==null?this:new HG(this,e)}prefetch(e){return new Q7(this,e)}shuffle(e,t){return new QG(this,e,t)}serial(){return new GG(this)}},VG=class extends bn{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:zG(e),done:!1}}},UG=class extends bn{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: ${e.message}`,e}}},GG=class extends bn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},HG=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;te(e.value)}return this.upstream.next()}},jG=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},qG=class extends bn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},XG=class extends bn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;te(e.value)}}},KG=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},ZG=class extends bn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Ly=class extends bn{constructor(){super();this.outputQueue=new K7,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},YG=class extends Ly{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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 bn{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}},wm;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(wm||(wm={}));var JG=class extends bn{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 bn?{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 ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Q7=class extends bn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new q7(t)}summary(){return`${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()}},QG=class extends Q7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=DG.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
|
|
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),xs(async()=>(await n.iterator()).columnMajorBatch(e,t,nH),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,xs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,xs(async()=>(await t.iterator()).filter(s=>X(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return xs(async()=>(await t.iterator()).map(n=>X(()=>e(n))),this.size)}mapAsync(e){let t=this;return xs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return xs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,xs(async()=>{let s=zy(async()=>({value:await t.iterator(),done:!1}));return BG(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,xs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=_G.alea(t||v.now().toString());return xs(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,xs(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 xs(e,t=null){return new class extends sc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function eH(e){return xs(async()=>Z7(e),e.length)}function tH(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 xs(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 WG(n,wm.SHORTEST)},t)}function nH(e){if(e===null)return null;let t=e[0];return OG(t)?{value:sH(e),recurse:!1}:{value:null,recurse:!0}}function sH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Je?yn(e):ut(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))}},km='"',ip=Symbol("out"),tS=Symbol("field"),Sm=Symbol("quote"),By=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 km:s=o+1,a=Sm;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 Sm:switch(e.charAt(o)){case km:a=By;break;default:}break;case By:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=ip,s=o+1;break;case km:a=Sm;break;default:a=nS;break}break;case nS:switch(e.charAt(o)){case km:a=Sm;break;default:}break;default:}if(a===By?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 bn{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(Z().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),ut(n,t)}},aS=class extends bn{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=Zt([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(Z().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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Kt(fe(e,"float32"),0),n;n=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return H(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},oS=class{},iS=class extends bn{split(e){return new rH(this,e)}},rH=class extends iS{constructor(e,t){super();this.upstream=e,this.impl=new aH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},aH=class extends Ly{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oH=class extends bn{decodeUTF8(){return new iH(this)}},iH=class extends iS{constructor(e){super();this.upstream=e,this.impl=new lH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lH=class extends Ly{constructor(e){super();if(this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=M5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Z().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 oH{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Z().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function uH(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=cH(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new lS(o,t)}else throw new Error(a.statusText)}var cH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function uS(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var cS=class extends oS{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(uS(this.input)&&Z().get("IS_NODE")){let e=js("fs");this.input=e.readFileSync(this.input.substr(7))}return new lS(this.input,this.options)}},dS=class extends oS{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return uS(this.url)?new cS(this.url,this.fileOptions).iterator():uH(this.url,this.fileOptions)}};function dH(e,t={}){return new sS(new dS(e),t)}function pH(e){let t=zy(e);return xs(async()=>t)}function hH(e){return xs(async()=>{let t=await e();return zy(()=>t.next())})}async function fH(e,t){return aS.create(e,t)}async function mH(e){return rS.create(e)}var gH="0.0.0";function Ee(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var AH=Qs.whereImpl,pS=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new td(this,rs())}nextDataId(){return pS.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Z().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 rs().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){Ee([e],"where");let t=this.readSync(e.dataId);return AH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},Wy=pS;Wy.nextDataId=0;var Im={};Oe(Im,{addImpl:()=>fS,bincountImpl:()=>Uy,bincountReduceImpl:()=>mS,ceilImpl:()=>gS,concatImpl:()=>Gy,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:()=>Hy,negImpl:()=>PS,notEqualImpl:()=>FS,prodImpl:()=>OS,rangeImpl:()=>qy,rsqrtImpl:()=>MS,sigmoidImpl:()=>aj,simpleAbsImpl:()=>hS,sliceImpl:()=>Nm,sparseFillEmptyRowsImpl:()=>LS,sparseReshapeImpl:()=>BS,sparseSegmentReductionImpl:()=>Xy,sqrtImpl:()=>lj,squaredDifferenceImpl:()=>WS,stridedSliceImpl:()=>VS,stringNGramsImpl:()=>US,stringSplitImpl:()=>GS,stringToHashBucketFastImpl:()=>HS,subImpl:()=>jS,tileImpl:()=>qS,topKImpl:()=>KS,transposeImpl:()=>jy,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 yH=e=>{let{x:t}=e.inputs,n=e.backend;Ee(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)},xH={kernelName:fi,backendName:"cpu",kernelFunc:yH};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 bs(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 bH={kernelName:ad,backendName:"cpu",kernelFunc:bs};function Cm(e,t,n="float32"){if(n==="complex64"){let r=Cm(e,t,"float32"),a=Cm(e,t,"float32");return bs({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 vH={kernelName:Ka,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 wH={kernelName:fd,backendName:"cpu",kernelFunc:Cl};function Ho(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=Cm(n,r.shape,r.dtype),i=Ho({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=bs({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=Ho({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 kH={kernelName:Fa,backendName:"cpu",kernelFunc:Ho};function vn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ee([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=Ho({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=Ho({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=bs({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 Vy(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),SH=Vy((e,t,n,s)=>({real:e+n,imag:t+s})),lp=vn(Kr,fS,SH),IH={kernelName:Kr,backendName:"cpu",kernelFunc:lp};function Uy(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 jo(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 ft(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Ee(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(Ee(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=jo(e=>Math.ceil(e)),CH=rc(Oa,gS),TH={kernelName:Oa,backendName:"cpu",kernelFunc:CH};function Gy(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=vn(bi,AS,null,"bool"),NH={kernelName:bi,backendName:"cpu",kernelFunc:yS},xS=jo(e=>Math.exp(e)),bS=rc(Ga,xS,"float32"),EH={kernelName:Ga,backendName:"cpu",kernelFunc:bS},vS=jo(e=>Math.expm1(e)),RH=rc(wi,vS),$H={kernelName:wi,backendName:"cpu",kernelFunc:RH},wS=jo(e=>Math.floor(e)),_H=rc(Ha,wS),DH={kernelName:Ha,backendName:"cpu",kernelFunc:_H};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),PH=vn(Ci,IS,null,"bool"),FH={kernelName:Ci,backendName:"cpu",kernelFunc:PH},CS=Jt((e,t)=>e>=t?1:0),OH=vn(Xa,CS,null,"bool"),MH={kernelName:Xa,backendName:"cpu",kernelFunc:OH},TS=Jt((e,t)=>e<t?1:0),zH=vn(Ni,TS,null,"bool"),LH={kernelName:Ni,backendName:"cpu",kernelFunc:zH},NS=Jt((e,t)=>e<=t?1:0),BH=vn(Ei,NS,null,"bool"),WH={kernelName:Ei,backendName:"cpu",kernelFunc:BH};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=jo(e=>Math.log(e)),VH=rc(Za,RS),UH={kernelName:Za,backendName:"cpu",kernelFunc:VH};function $S(e,t,n,s){let r=v.getTypedArrayFromDType(s,v.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let c=e[o+l];(Number.isNaN(c)||c>i)&&(i=c)}r[a]=i}return r}var _S=Jt((e,t)=>Math.max(e,t)),GH=vn(Ja,_S),HH={kernelName:Ja,backendName:"cpu",kernelFunc:GH},DS=Jt((e,t)=>Math.min(e,t)),jH=vn(no,DS),qH={kernelName:no,backendName:"cpu",kernelFunc:jH},Hy=Jt((e,t)=>e*t),XH=Vy((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Tm=vn(ro,Hy,XH),KH={kernelName:ro,backendName:"cpu",kernelFunc:Tm};function PS(e,t,n){let s=v.createScalarValue(-1,n);return Hy([],t,s,e,n)}function ZH(e){let{inputs:t,backend:n}=e,{x:s}=t;Ee(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=PS(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var YH={kernelName:$i,backendName:"cpu",kernelFunc:ZH},FS=Jt((e,t)=>e!==t?1:0),JH=vn(_i,FS,null,"bool"),QH={kernelName:_i,backendName:"cpu",kernelFunc:JH};function jy(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;Ee(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=jy(l,r.shape,r.dtype,a,i);return{dataId:s.write(c,i,r.dtype),shape:i,dtype:r.dtype}}var ej={kernelName:vo,backendName:"cpu",kernelFunc:Ms};function OS(e,t,n,s){let[r,a]=E.computeOutAndReduceShapes(e,s),o=Bn(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 tj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ee(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 nj={kernelName:zi,backendName:"cpu",kernelFunc:tj};function qy(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=jo(e=>1/Math.sqrt(e)),sj=rc(po,MS),rj={kernelName:po,backendName:"cpu",kernelFunc:sj},aj=jo(e=>1/(1+Math.exp(-e))),zS=ft(fo,e=>1/(1+Math.exp(-e))),oj={kernelName:fo,backendName:"cpu",kernelFunc:zS};function Nm(e,t,n,s,r){let a=Ft.isSliceContinous(s,t,n),o=v.sizeFromShape(n),i=v.computeStrides(s);if(a){let d=Ft.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;Ee(r,"slice");let[i,l]=Ft.parseSliceParams(r,a,o);Ft.assertParamsValid(r,i,l);let c=n.data.get(r.dataId).values,u=Nm(c,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var ij={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 Xy(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 lj=jo(e=>Math.sqrt(e)),uj=ft(mo,e=>Math.sqrt(e)),cj={kernelName:mo,backendName:"cpu",kernelFunc:uj},WS=Jt((e,t)=>{let n=e-t;return n*n}),dj=vn(yo,WS),pj={kernelName:yo,backendName:"cpu",kernelFunc:dj};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 hj=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 hj(n,s,r,a,o,i).compute(e,t)}function fj(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;fj(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),mj=Vy((e,t,n,s)=>({real:e-n,imag:t-s})),Ky=vn(xo,jS,mj),gj={kernelName:xo,backendName:"cpu",kernelFunc:Ky};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 Aj="0.0.0";ul("cpu",()=>new Wy,1);var YS=ft(Ua,e=>e>=0?e:Math.exp(e)-1),yj={kernelName:Ua,backendName:"cpu",kernelFunc:YS};function JS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ee([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 xj={kernelName:Ti,backendName:"cpu",kernelFunc:JS},bj=Jt((e,t)=>e<0?t*e:e);function QS(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Ee([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=bj(s.shape,r.shape,a,o,"float32");return n.makeTensorInfo(l,"float32",i)}var vj={kernelName:io,backendName:"cpu",kernelFunc:QS},eI=ft(lo,e=>Math.max(0,e)),wj={kernelName:lo,backendName:"cpu",kernelFunc:eI},tI=ft(co,e=>Math.min(Math.max(0,e),6)),kj={kernelName:co,backendName:"cpu",kernelFunc:tI};function Zy(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 $t(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,o),l=v.sizeFromShape(i);v.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let c=n.data.get(r.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,d=c.complexTensorInfos.imag;u.shape=i,d.shape=i}return{dataId:r.dataId,shape:i,dtype:r.dtype}}var Sj={kernelName:Li,backendName:"cpu",kernelFunc:$t};function nI(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;Ee([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=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[A,h,d]:[A,d,h],k=$t({inputs:{x:r},backend:n,attrs:{shape:b}}),I=$t({inputs:{x:a},backend:n,attrs:{shape:w}}),N=o?k.shape[1]:k.shape[2],R=o?k.shape[2]:k.shape[1],O=i?I.shape[1]:I.shape[2],_=Math.max(g,A),P=n.data.get(k.dataId).values,T=n.data.get(I.dataId).values,F=v.computeStrides(k.shape),U=v.computeStrides(I.shape),[q,z,K]=o?[F[0],1,F[1]]:[F[0],F[1],1],[Y,J,ne]=i?[1,U[1],U[0]]:[U[1],1,U[0]],re=R*O,G=ze([_,R,O],k.dtype),se=G.values,oe=n.blockSize;for(let pe=0;pe<_;pe++)for(let ye=0;ye<R;ye+=oe)for(let we=0;we<O;we+=oe)for(let Te=0;Te<N;Te+=oe){let Me=Math.min(ye+oe,R),Ue=Math.min(we+oe,O),qe=Math.min(Te+oe,N);for(let Ke=ye;Ke<Me;Ke++)for(let dt=we;dt<Ue;dt++){let pt=0;for(let rt=Te;rt<qe;rt++){let kt=Math.min(pe,g-1)*q,mt=Math.min(pe,A-1)*ne,Tt=P[kt+Ke*z+rt*K],Dt=T[rt*Y+dt*J+mt];pt+=Tt*Dt}se[pe*re+(Ke*O+dt)]+=pt}}return n.disposeIntermediateTensorInfo(k),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(y,G.dtype,G.values)}var Ij={kernelName:Pa,backendName:"cpu",kernelFunc:nI};function Cj(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p,h,f,m=[];p=nI({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=lp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=Zy(n,p,u,i,d),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var Tj={kernelName:ko,backendName:"cpu",kernelFunc:Cj},Nj=ft(ou,e=>Math.acos(e)),Ej={kernelName:ou,backendName:"cpu",kernelFunc:Nj},Rj=ft(iu,e=>Math.acosh(e)),$j={kernelName:iu,backendName:"cpu",kernelFunc:Rj};function _j(e){let{inputs:t,backend:n}=e,s=t;Ee(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 Dj={kernelName:$a,backendName:"cpu",kernelFunc:_j};function Pj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ee(r,"all");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Ms({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("all",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let x=A*h,y=m[x];for(let b=0;b<h;++b){let w=m[x+b];y=y&&w}f[A]=y}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let A=E.expandShapeToKeepDim(d,i),x=$t({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var Fj={kernelName:lu,backendName:"cpu",kernelFunc:Pj};function Oj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ee(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=$t({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var Mj={kernelName:uu,backendName:"cpu",kernelFunc:Oj};function zj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ee(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 Lj={kernelName:_a,backendName:"cpu",kernelFunc:zj};function Bj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ee(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 Wj={kernelName:cu,backendName:"cpu",kernelFunc:Bj},Vj=ft(du,e=>Math.asin(e)),Uj={kernelName:du,backendName:"cpu",kernelFunc:Vj},Gj=ft(pu,e=>Math.asinh(e)),Hj={kernelName:pu,backendName:"cpu",kernelFunc:Gj},jj=ft(hu,e=>Math.atan(e)),qj={kernelName:hu,backendName:"cpu",kernelFunc:jj},Xj=Jt((e,t)=>Math.atan2(e,t)),Kj=vn(mu,Xj),Zj={kernelName:mu,backendName:"cpu",kernelFunc:Kj},Yj=ft(fu,e=>Math.atanh(e)),Jj={kernelName:fu,backendName:"cpu",kernelFunc:Yj};function Yy(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,Y=0;for(let ne=O;ne<_;ne+=l){let re=k+ne*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,Y++)}if(isNaN(z))break}let J=P+T*y+I;g[J]=a==="avg"?K/Y: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 Y=K*i-g,J=Y;for(;J<0;)J+=u;let ne=Math.min(r.inHeight,h+Y),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,Te=0,Me=0;for(let qe=U;qe<q;qe+=c){let Ke=_+qe*s[1];for(let dt=J;dt<ne;dt+=u){let pt=Ke+dt*s[2];for(let rt=oe;rt<pe;rt+=d){let kt=pt+rt*s[3],mt=e[kt+P];if(a==="max"&&mt>we?we=mt:a==="avg"&&(Te+=mt,Me++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Ue=ye+P;b[Ue]=a==="avg"?Te/Me:we}}}}return y}function Qj(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 Y=_;Y<P;Y+=l){let J=Y-O,ne=e.get(m,U,z,Y,g);ne>=T&&(T=ne,F=q*u*d+K*u+J)}}}n.set(F,m,A,w,R,g)}}}return n}function eq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ee(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=Yy(p,r.shape,r.dtype,h,u,"avg");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var tq={kernelName:Da,backendName:"cpu",kernelFunc:eq};function nq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Ee(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 sq={kernelName:rd,backendName:"cpu",kernelFunc:nq};function rq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Ee([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,Y=q-R,J=z-N,ne=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=(Y+se)/p;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let pe=0;pe<k;pe+=y){let ye=(J+pe)/h;if(ye<0||ye>=u.outWidth||Math.floor(ye)!==ye)continue;ne+=P.get(T,G,oe,ye,F)}}}O.set(ne*_,T,U,q,z,F)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var aq={kernelName:wh,backendName:"cpu",kernelFunc:rq};function oq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ee([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 Y=(F+K)/p;if(Y<0||Y>=u.outWidth||Math.floor(Y)!==Y)continue;U+=N.get(R,z,Y,O)}}w.set(U*k,R,_,P,O)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var iq={kernelName:vh,backendName:"cpu",kernelFunc:oq};function lq(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."),Ee([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 uq={kernelName:qa,backendName:"cpu",kernelFunc:lq};function cq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ee([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=$t({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ms({inputs:{x:h},backend:n,attrs:{perm:c}}),m=$t({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 dq={kernelName:mi,backendName:"cpu",kernelFunc:cq};function pq(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=Uy(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var hq={kernelName:kh,backendName:"cpu",kernelFunc:pq};function fq(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 mq={kernelName:Sh,backendName:"cpu",kernelFunc:fq},gq=ft(Zr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),Aq={kernelName:Zr,backendName:"cpu",kernelFunc:gq},yq=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")},xq={kernelName:od,backendName:"cpu",kernelFunc:yq};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 bq={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=bs({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 $t({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=Gy(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 vq={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;Ee([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 Y=K*I,J=K*_;for(let ne=0;ne<p.outHeight;++ne){let re=J+ne*P,G=ne*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=Y+oe*N;for(let we=0;we<p.outWidth;++we){let Te=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],dt=ye+qe*R,pt=Ke;for(let rt=0;rt<p.inChannels;++rt){let kt=U[dt+rt*O];for(let mt=0;mt<p.outChannels;++mt)z[Te+mt*F]+=kt*q[pt+mt];pt+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var wq={kernelName:Ma,backendName:"cpu",kernelFunc:aI};function kq(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;Ee([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 Y=O;Y<_;++Y){let J=R+Y*h-b;for(let ne=T;ne<F;++ne){let re=P+ne*f-y;A?z+=I.get(K,J,re,U)*N.get(K,Y,ne,q):z+=I.get(K,U,J,re)*N.get(K,q,Y,ne)}}x.set(z,R,P,U,q)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var Sq={kernelName:Ih,backendName:"cpu",kernelFunc:kq};function Iq(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;Ee([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,Y=h==="channelsLast",J=m.strides[0],ne=Y?m.strides[1]:m.strides[2],re=Y?m.strides[2]:1,G=Y?1:m.strides[1],se=p[0],oe=Y?p[1]:p[2],pe=Y?p[2]:1,ye=Y?1:p[1];for(let we=0;we<k;++we)for(let Te=0;Te<R;++Te)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 dt=0;dt<_;++dt){let pt=dt-K,rt=Math.max(0,Math.ceil(pt/q)),kt=Math.min(F,(N+pt)/q),mt=0;for(let Dt=qe;Dt<Ke;++Dt){let Ts=Dt*U-Ue;for(let kn=rt;kn<kt;++kn){let lr=kn*q-pt,On=se*we+oe*Dt+pe*kn,ps=y*(I-1-Ts)+b*(N-1-lr)+w*Te;for(let Gs=0;Gs<P;++Gs){let Ns=A[On+ye*Gs],Sn=x[ps+Gs];mt+=Ns*Sn}}}let Tt=J*we+ne*Me+re*dt+G*Te;g[Tt]=mt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Cq={kernelName:za,backendName:"cpu",kernelFunc:Iq};function Tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Ee([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],Y=_+z*N[1];for(let J=0;J<c.outHeight;++J){let ne=F+J*b.strides[2],re=J*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=Y+se*N[2];for(let ye=0;ye<c.outWidth;++ye){let we=ne+ye*c.outChannels,Te=ye*c.strideWidth-x;for(let Me=0;Me<p;++Me){let Ue=Te+Me*m;if(Ue<0||Ue>=c.inWidth)continue;let qe=oe+Me*R[2],Ke=pe+Ue*c.inChannels,dt=qe;for(let pt=0;pt<c.inChannels;++pt){let rt=w[Ke+pt];for(let kt=0;kt<c.outChannels;++kt)I[we+kt]+=rt*k[dt+kt];dt+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Nq={kernelName:id,backendName:"cpu",kernelFunc:Tq};function Eq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ee([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,Y=d.padInfo.left,J=d.padInfo.top;for(let ne=0;ne<m;++ne){let re=Math.max(0,Math.ceil((K-ne)/p)),G=Math.min(d.outDepth,(d.inDepth+K-ne)/p),se=ne*b;for(let oe=0;oe<g;++oe){let pe=Math.max(0,Math.ceil((J-oe)/h)),ye=Math.min(d.outHeight,(d.inHeight+J-oe)/h),we=oe*w+se;for(let Te=0;Te<A;++Te){let Me=Math.max(0,Math.ceil((Y-Te)/f)),Ue=Math.min(d.outWidth,(d.inWidth+Y-Te)/f),qe=Te*k+we;for(let Ke=0;Ke<d.inChannels;++Ke){let dt=Ke*I+qe;for(let pt=0;pt<d.outChannels;++pt){let rt=0;for(let kt=0;kt<d.batchSize;++kt){let mt=kt*F,Tt=kt*R;for(let Dt=re;Dt<G;++Dt){let kn=(ne+Dt*p-K)*U+mt,lr=Dt*O+Tt;for(let On=pe;On<ye;++On){let Gs=(oe+On*h-J)*q+kn,Ns=On*_+lr;for(let Sn=Me;Sn<Ue;++Sn){let Rn=(Te+Sn*f-Y)*z+Gs,Nr=Sn*P+Ns;rt+=T[Rn+Ke]*N[Nr+pt]}}}}y[dt+pt]=rt}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var Rq={kernelName:Ch,backendName:"cpu",kernelFunc:Eq};function $q(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ee([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(l,a.shape,i,1,o),p=new nn(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,x=n.data.get(r.dataId).values,[y,b,w,k]=c,I=n.data.get(a.dataId).values,[N,R,O,_]=u,{batchSize:P,filterDepth:T,filterHeight:F,filterWidth:U,inChannels:q,inDepth:z,inHeight:K,inWidth:Y,outChannels:J,outDepth:ne,outHeight:re,outWidth:G,strideDepth:se,strideHeight:oe,strideWidth:pe}=d,ye=T-1-d.padInfo.front,we=F-1-d.padInfo.top,Te=U-1-d.padInfo.left;for(let Me=0;Me<P;++Me)for(let Ue=0;Ue<q;++Ue)for(let qe=0;qe<z;++qe){let Ke=qe-ye,dt=Math.max(0,Math.ceil(Ke/se)),pt=Math.min(ne,(T+Ke)/se);for(let rt=0;rt<K;++rt){let kt=rt-we,mt=Math.max(0,Math.ceil(kt/oe)),Tt=Math.min(re,(F+kt)/oe);for(let Dt=0;Dt<Y;++Dt){let Ts=Dt-Te,kn=Math.max(0,Math.ceil(Ts/pe)),lr=Math.min(G,(U+Ts)/pe),On=0;for(let ps=dt;ps<pt;++ps){let Gs=ps*se-Ke;for(let Ns=mt;Ns<Tt;++Ns){let Sn=Ns*oe-kt;for(let Tr=kn;Tr<lr;++Tr){let Rn=Tr*pe-Ts,Nr=y*Me+b*ps+w*Ns+k*Tr,Er=N*(T-1-Gs)+R*(F-1-Sn)+O*(U-1-Rn)+_*Ue;for(let xa=0;xa<J;++xa){let Lc=x[Nr+xa],ur=I[Er+xa];On+=Lc*ur}}}}h[f*Me+m*qe+g*rt+A*Dt+Ue]=On}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var _q={kernelName:Th,backendName:"cpu",kernelFunc:$q},Dq=ft(La,e=>Math.cos(e)),Pq={kernelName:La,backendName:"cpu",kernelFunc:Dq},Fq=ft(Ba,e=>Math.cosh(e)),Oq={kernelName:Ba,backendName:"cpu",kernelFunc:Fq};function Mq(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 Y=0;Y<h;Y++){let J=Y+K*k[2]+q*k[1]+I*k[0];A.values[J]=c}continue}if(l==="bilinear"){let K=Math.floor(z),Y=Math.ceil(z),J=z-K;for(let ne=0;ne<g;ne++){let re=g>1?O*(p-1)+ne*U:.5*(O+P)*(p-1);if(re<0||re>p-1){for(let pe=0;pe<h;pe++){let ye=pe+ne*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 Te=b[ye];ye=pe+G*w[2]+Y*w[1]+T*w[0];let Me=b[ye];ye=pe+se*w[2]+Y*w[1]+T*w[0];let Ue=b[ye],qe=we+(Te-we)*oe,Ke=Me+(Ue-Me)*oe;ye=pe+ne*k[2]+q*k[1]+I*k[0],A.values[ye]=qe+(Ke-qe)*J}}}else for(let K=0;K<g;++K){let Y=g>1?O*(p-1)+K*U:.5*(O+P)*(p-1);if(Y<0||Y>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 J=Math.round(Y),ne=Math.round(z);for(let re=0;re<h;re++){let G=re+J*w[2]+ne*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 zq={kernelName:yi,backendName:"cpu",kernelFunc:Mq};function Lq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ee(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=Bn(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 Bq={kernelName:Ai,backendName:"cpu",kernelFunc:Lq};function Wq(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=Uy(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 Vq={kernelName:Nh,backendName:"cpu",kernelFunc:Wq};function Uq(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 Gq={kernelName:xi,backendName:"cpu",kernelFunc:Uq};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;Ee([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],Y=_+z*u[1];for(let J=0;J<h.outWidth;++J){let ne=F+J*k.strides[2],re=J*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=Y+se*h.inChannels,ye=ne,we=oe;for(let Te=0;Te<h.inChannels;++Te){let Me=I[pe+Te];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 Hq={kernelName:Wa,backendName:"cpu",kernelFunc:oI};function jq(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;Ee([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 Y=R;Y<O;++Y){let J=N+Y*p-x;for(let ne=P;ne<T;++ne){let re=_+ne*h-A;z+=w.get(K,J,re,U)*I.get(K,Y,ne,F)}}g.set(z,N,_,U,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var qq={kernelName:Eh,backendName:"cpu",kernelFunc:jq};function Xq(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;Ee([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:Y,strideHeight:J,strideWidth:ne}=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,Te=Math.max(0,Math.ceil(we/J)),Me=Math.min(K,(P+we)/J);for(let Ue=0;Ue<q;++Ue){let qe=Ue-G,Ke=Math.max(0,Math.ceil(qe/ne)),dt=Math.min(Y,(T+qe)/ne),pt=0;for(let rt=Te;rt<Me;++rt){let kt=rt*J-we;for(let mt=Ke;mt<dt;++mt){let Tt=mt*ne-qe,Dt=b*oe+w*rt+k*mt,Ts=N*(P-1-kt)+R*(T-1-Tt)+O*pe;for(let kn=0;kn<se;++kn){let lr=pe*se+kn,On=y[Dt+lr],ps=I[Ts+kn];pt+=On*ps}}}m[g*oe+A*ye+x*Ue+pe]=pt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var Kq={kernelName:Rh,backendName:"cpu",kernelFunc:Xq};function Zq(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 Yq={kernelName:$h,backendName:"cpu",kernelFunc:Zq},Jq={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 Y=K*w-y.left;for(let J=0;J<g;++J){let ne=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=Y+oe*R;if(pe>=0&&pe<m){let ye=v.locToIndex([U,se,pe,J],u,v.computeStrides(s.shape)),we=v.locToIndex([G,oe,J],p,v.computeStrides(r.shape)),Te=c[ye]+d[we];Te>ne&&(ne=Te)}}}let re=v.locToIndex([U,q,K,J],P,v.computeStrides(O));T[re]=ne}}}return{dataId:l.write(v.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},Qq={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(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 Y=Number.MIN_SAFE_INTEGER,J=0,ne=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>Y&&(Y=pe,J=re,ne=se)}}}_[J][ne][K]+=O[T][F][q][K]}}}return{dataId:c.write(v.toTypedArray(_,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},eX={kernelName:_h,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 ${_h}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let O=v.toNestedArray(R,c.data.get(a.dataId).values),_=v.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T<p;++T)for(let F=0;F<g;++F){let U=F*y-x.top;for(let q=0;q<A;++q){let z=q*b-x.left;for(let K=0;K<m;++K){let Y=Number.MIN_SAFE_INTEGER,J=U<0?0:U,ne=z<0?0:z;for(let re=0;re<w;++re){let G=U+re*I;if(G>=0&&G<h)for(let se=0;se<k;++se){let oe=z+se*N;if(oe>=0&&oe<f){let pe=u[T][G][oe][K]+d[re][se][K];pe>Y&&(Y=pe,J=G,ne=oe)}}}_[T][J][ne][K]+=O[T][F][q][K]}}}return{dataId:c.write(v.toTypedArray(_,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function cp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ee(r,"sum");let i;r.dtype==="bool"?i=Ho({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=Ur({inputs:{x:r},backend:n});let l=i.shape.length,c=v.parseAxisParam(a,i.shape),u=E.getAxesPermutation(c,l),d=c,p=i;u!=null&&(p=Ms({inputs:{x:i},backend:n,attrs:{perm:u}}),d=E.getInnerMostAxes(d.length,l)),E.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=E.computeOutAndReduceShapes(p.shape,d),m=E.upcastType(p.dtype,"int32"),g=Cm(n,h,m),A=v.sizeFromShape(f),x=n.data.get(g.dataId).values,y=n.data.get(p.dataId).values;for(let b=0;b<x.length;++b){let w=b*A,k=0;for(let I=0;I<A;++I)k+=y[w+I];x[b]=k}if(o){let b=E.expandShapeToKeepDim(g.shape,c),w=g;g=$t({inputs:{x:g},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(i),u!=null&&n.disposeIntermediateTensorInfo(p),g}var tX={kernelName:go,backendName:"cpu",kernelFunc:cp};function nX(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=$t({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=Tm({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=cp({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var sX={kernelName:ud,backendName:"cpu",kernelFunc:nX};function rX(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Ee([s,r],"eluGrad");let a=new Float32Array(v.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l<o.length;++l){let c=o[l];c>=1?a[l]=i[l]:a[l]=i[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",a)}var aX={kernelName:Ph,backendName:"cpu",kernelFunc:rX},oX=E.ERF_P,iX=E.ERF_A1,lX=E.ERF_A2,uX=E.ERF_A3,cX=E.ERF_A4,dX=E.ERF_A5,pX=ft(gu,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+oX*n);return t*(1-((((dX*s+cX)*s+uX)*s+lX)*s+iX)*s*Math.exp(-n*n))}),hX={kernelName:gu,backendName:"cpu",kernelFunc:pX};function Em(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),$t({inputs:{x:r},backend:n,attrs:{shape:i}})}var fX={kernelName:vi,backendName:"cpu",kernelFunc:Em},mX=Jt((e,t)=>e/t),Jy=vn(Va,mX),Qy={kernelName:Va,backendName:"cpu",kernelFunc:Jy};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=bs({inputs:{real:A,imag:x},backend:n}),{real:b,imag:w}=gX(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=bs({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function gX(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(AX(s)){let i=ex(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=Qy.kernelFunc({inputs:{a:c,b:d},backend:n}),f=Qy.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=yX(i,s,t);return E.splitRealAndImagArrays(l)}}function AX(e){return(e&e-1)==0}function ex(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=bs({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=bs({inputs:{real:x,imag:y},backend:r}),w=ex(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),_=bs({inputs:{real:R,imag:O},backend:r}),P=ex(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=bs({inputs:{real:q,imag:z},backend:r}),Y=E.exponents(n,s),J=[Y.real.length],ne=r.makeTensorInfo(J,"float32",Y.real),re=r.makeTensorInfo(J,"float32",Y.imag),G=bs({inputs:{real:ne,imag:re},backend:r}),se=Tm({inputs:{a:G,b:K},backend:r}),oe=lp({inputs:{a:_,b:se},backend:r}),pe=Ky({inputs:{a:_,b:se},backend:r}),ye=Cl({inputs:{input:oe},backend:r}),we=Cl({inputs:{input:pe},backend:r}),Te=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:[Te,Me],backend:r,attrs:{axis:0}}),Ke=r.data.get(Ue.dataId).values,dt=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(ne),r.disposeIntermediateTensorInfo(re),r.disposeIntermediateTensorInfo(G),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(pe),r.disposeIntermediateTensorInfo(ye),r.disposeIntermediateTensorInfo(Te),r.disposeIntermediateTensorInfo(we),r.disposeIntermediateTensorInfo(Me),r.disposeIntermediateTensorInfo(Ue),r.disposeIntermediateTensorInfo(qe),{real:Ke,imag:dt}}function yX(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 xX(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=$t({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=iI(i,!1,n),c=$t({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var bX={kernelName:Fh,backendName:"cpu",kernelFunc:xX};function tx(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 wX(i,r,o),t.makeTensorInfo(s,o,i)}var vX={kernelName:Au,backendName:"cpu",kernelFunc:tx};function wX(e,t,n){e.fill(t)}var kX={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}}},SX=Jt((e,t)=>Math.floor(e/t)),IX=vn(ja,SX,null,"int32"),CX={kernelName:ja,backendName:"cpu",kernelFunc:IX};function TX(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=Zy(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var NX={kernelName:So,backendName:"cpu",kernelFunc:TX};function EX(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=Zy(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var RX={kernelName:Io,backendName:"cpu",kernelFunc:EX};function $X(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=v.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,c,u,d]=E.prepareAndValidate(s,r);if(c===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),f=kS(p,h,s.dtype,c,i,u,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var _X={kernelName:Ii,backendName:"cpu",kernelFunc:$X};function DX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Ee([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, ${u-1}]`)}let d=i;i==null&&(d=0);let p=v.sizeFromShape(a.shape),h=E.segment_util.collectGatherOpShapeInfo(r,a,l,d),f=$t({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=$t({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,p/h.batchSize]}}),g=[h.batchSize,h.outerSize,p/h.batchSize,h.sliceSize],A=n.bufferSync(m),x=n.bufferSync(f),y=SS(x,A,g);return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var PX={kernelName:Si,backendName:"cpu",kernelFunc:DX};function FX(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=$t({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=iI(i,!0,n),c=$t({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var OX={kernelName:Oh,backendName:"cpu",kernelFunc:FX},MX=ft(yu,e=>Number.isFinite(e)?1:0,"bool"),zX={kernelName:yu,backendName:"cpu",kernelFunc:MX},LX=ft(xu,e=>Math.abs(e)===1/0?1:0,"bool"),BX={kernelName:xu,backendName:"cpu",kernelFunc:LX},WX=ft(bu,e=>Number.isNaN(e)?1:0,"bool"),VX={kernelName:bu,backendName:"cpu",kernelFunc:WX};function UX(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 GX={kernelName:Mh,backendName:"cpu",kernelFunc:UX},HX=ft(vu,e=>Math.log1p(e)),jX={kernelName:vu,backendName:"cpu",kernelFunc:HX},qX=Jt((e,t)=>e&&t),XX=vn(Ri,qX,null,"bool"),KX={kernelName:Ri,backendName:"cpu",kernelFunc:XX},ZX=ft(wu,e=>e?0:1,"bool"),YX={kernelName:wu,backendName:"cpu",kernelFunc:ZX},JX=Jt((e,t)=>e||t),QX=vn(dd,JX,null,"bool"),eK={kernelName:dd,backendName:"cpu",kernelFunc:QX};function tK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Ee(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 nK={kernelName:pd,backendName:"cpu",kernelFunc:tK};function sK(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;Ee(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 rK={kernelName:zh,backendName:"cpu",kernelFunc:sK};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=jy(h,l,r.dtype,p,b),d=E.getInnerMostAxes(d.length,c),l=b}Ee(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 aK={kernelName:Ya,backendName:"cpu",kernelFunc:lI};function oK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ee(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=Yy(p,r.shape,r.dtype,h,u,"max");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var iK={kernelName:Qa,backendName:"cpu",kernelFunc:oK};function lK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Ee(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 uK={kernelName:hd,backendName:"cpu",kernelFunc:lK};function cK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Ee([r,a],"maxPool3DGrad");let u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=n.bufferSync(a),p=Qj(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,Y=0;for(let J=0;J<y;J+=g){let ne=(q+J)/h;if(!(ne<0||ne>=u.outDepth||Math.floor(ne)!==ne))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(_,ne,G,oe,P),ye=J*b*w+re*w+se,we=pe===ye?1:0;if(we===0)continue;Y+=O.get(_,ne,G,oe,P)*we}}}R.set(Y,_,T,F,U,P)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var dK={kernelName:Bh,backendName:"cpu",kernelFunc:cK};function pK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Ee([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 Y=0;Y<b;Y+=x){let J=(U+Y)/g;if(J<0||J>=p.outWidth||Math.floor(J)!==J)continue;let ne=y*b-1-f.get(O,K,J,_),re=z*b+Y,G=ne===re?1:0;if(G===0)continue;q+=R.get(O,K,J,_)*G}}I.set(q,O,P,T,_)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var hK={kernelName:Lh,backendName:"cpu",kernelFunc:pK};function fK(e,t,n,s,r){let a=v.computeStrides(t),o=Yy(e,t,n,a,r,"max"),i=sI(e,t,n,r,!0,s);return[o.values,i.values]}var mK={kernelName:Wh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Ee(s,"MaxPoolWithArgmax");let c=l.data.get(s.dataId).values,u=E.computePool2DInfo(s.shape,r,a,[1,1],o),[d,p]=fK(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 gK(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=Ho({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(h);let f=Jy({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 AK={kernelName:eo,backendName:"cpu",kernelFunc:gK};function yK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ee(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=$t({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),x}return g}var xK={kernelName:to,backendName:"cpu",kernelFunc:yK};function bK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,mode:o}=s;Ee(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 vK={kernelName:so,backendName:"cpu",kernelFunc:bK},wK=Jt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),kK=vn(ku,wK),SK={kernelName:ku,backendName:"cpu",kernelFunc:kK},IK=di(gh());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 ${i}`);let l=v.parseAxisParam([i],r.shape),c=lI({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=E.expandShapeToKeepDim(c.shape,l),d=$t({inputs:{x:c},backend:n,attrs:{shape:u}}),p=Ky({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=$t({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Jy({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 CK={kernelName:Ao,backendName:"cpu",kernelFunc:uI};function TK(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;Ee(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=IK.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 NK={kernelName:Vh,backendName:"cpu",kernelFunc:TK},EK=Qs.nonMaxSuppressionV3Impl;function RK(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;Ee(r,"NonMaxSuppression");let c=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,{selectedIndices:d}=EK(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var $K={kernelName:Di,backendName:"cpu",kernelFunc:RK},_K=Qs.nonMaxSuppressionV4Impl;function DK(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s;Ee(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:p,validOutputs:h}=_K(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var PK={kernelName:Su,backendName:"cpu",kernelFunc:DK},FK=Qs.nonMaxSuppressionV5Impl;function OK(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s;Ee(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}=FK(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var MK={kernelName:Pi,backendName:"cpu",kernelFunc:OK};function zK(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s;Ee(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 LK={kernelName:Oi,backendName:"cpu",kernelFunc:zK};function Rm(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=Rm({inputs:{x:r},backend:n}),o=ac({inputs:{input:s},backend:n}),i=Rm({inputs:{x:o},backend:n}),l=bs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return tx({backend:n,attrs:{shape:s.shape,value:0,dtype:s.dtype}})}var BK={kernelName:Qi,backendName:"cpu",kernelFunc:Rm};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=Rm({inputs:{x:o},backend:n}),l=bs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return tx({backend:n,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}var WK={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 Em({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=Em({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 VK={kernelName:Mi,backendName:"cpu",kernelFunc:dI};function UK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;Ee(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:ao,backendName:"cpu",kernelFunc:UK},GK=Jt((e,t)=>Math.pow(e,t)),HK=vn(oo,GK),jK={kernelName:oo,backendName:"cpu",kernelFunc:HK};function qK(e){let{backend:t,attrs:n}=e,{start:s,stop:r,dtype:a,step:o}=n,i=qy(s,r,o,a);return t.makeTensorInfo([i.length],a,i)}var XK={kernelName:Iu,backendName:"cpu",kernelFunc:qK},KK=ft(Cu,e=>1/e),ZK={kernelName:Cu,backendName:"cpu",kernelFunc:KK};function YK(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ee(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)),Y=P+q*l[2],J=T+q*l[2],ne=P+K*l[2],re=T+K*l[2];for(let G=0;G<f;G++){let se=m[Y+G],oe=m[J+G],pe=m[ne+G],ye=m[re+G],we=se+(pe-se)*z,Te=oe+(ye-oe)*z,Me=we+(Te-we)*O;g[y++]=Me}}}return n.makeTensorInfo([d,c,u,f],"float32",g)}var JK={kernelName:uo,backendName:"cpu",kernelFunc:YK};function QK(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ee([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),Y=q-z,J=1-Y,ne=_+z*i[2],re=_+K*i[2],G=P+z*i[2],se=P+K*i[2],oe=F*J,pe=F*Y,ye=T*J,we=T*Y;for(let Te=0;Te<d;Te++){let Me=y[b++];f[ne+Te]+=Me*oe,f[re+Te]+=Me*pe,f[G+Te]+=Me*ye,f[se+Te]+=Me*we}}}}return n.makeTensorInfo([l,u,c,d],"float32",f)}var eZ={kernelName:Gh,backendName:"cpu",kernelFunc:QK};function tZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ee(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 nZ={kernelName:Tu,backendName:"cpu",kernelFunc:tZ};function sZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ee([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 Y=0;Y<p;Y++){let J=0;for(let ne=0;ne<I;ne++){let re=ne+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],Te=ye*b,Me=Math.min(d-1,o?Math.round(Te):Math.floor(Te));U===Me&&(J+=g[we+Y])}}m[q+Y]=J}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var rZ={kernelName:Uh,backendName:"cpu",kernelFunc:sZ};function aZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;Ee(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 oZ={kernelName:Bi,backendName:"cpu",kernelFunc:aZ},iZ={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),Y=F*p,J=w+K+Y+O;q=x[J]}let z=w+I+R+O;l[z]=q}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},lZ=ft(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}),uZ={kernelName:Wi,backendName:"cpu",kernelFunc:lZ};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 ${n}`);for(let A=0;A<r;A++)c?h.values[g*r+A]+=p[f*r+A]:h.values[g*r+A]=t.rank===0?p[0]:p[f*r+A]}return h}function cZ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=!0,h=n.bufferSync(r),f=n.bufferSync(a),m=hI(h,f,o,d,c,l,i,u,0,p);return n.makeTensorInfo(o,m.dtype,m.values)}var dZ={kernelName:Vi,backendName:"cpu",kernelFunc:cZ};function pZ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ee([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Bn(r.dtype,a.dtype),d=v.makeZerosTypedArray(v.sizeFromShape(r.shape),u),p=0,h=o===0||o>1||r.shape.length===1?1:v.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=c[f];return n.makeTensorInfo(r.shape,u,d)}var hZ={kernelName:Ui,backendName:"cpu",kernelFunc:pZ},fZ=E.SELU_SCALEALPHA,mZ=E.SELU_SCALE,gZ=ft(Nu,e=>e>=0?mZ*e:fZ*(Math.exp(e)-1)),AZ={kernelName:Nu,backendName:"cpu",kernelFunc:gZ},yZ=ft(Eu,e=>e<0?-1:e>0?1:0),xZ={kernelName:Eu,backendName:"cpu",kernelFunc:yZ},bZ=ft(ho,e=>Math.sin(e)),vZ={kernelName:ho,backendName:"cpu",kernelFunc:bZ},wZ=ft(Hi,e=>Math.sinh(e)),kZ={kernelName:Hi,backendName:"cpu",kernelFunc:wZ},SZ=11920928955078125e-23,fI=Math.log(SZ)+2,IZ=ft(Ru,e=>{let t=e>-fI,n=e<fI,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),CZ={kernelName:Ru,backendName:"cpu",kernelFunc:IZ};function TZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ee([r],"spaceToBatchND");let i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=pI.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,a,i,!1),d=E.getPermuted(u.length,a.length,!1),p=E.getReshapedPermuted(c.shape,a,i,!1),m=$t({inputs:{x:c},backend:n,attrs:{shape:u}}),x=Ms({inputs:{x:m},backend:n,attrs:{perm:d}}),w=$t({inputs:{x},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(x),w}var NZ={kernelName:ji,backendName:"cpu",kernelFunc:TZ};function EZ(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 RZ={kernelName:Hh,backendName:"cpu",kernelFunc:EZ};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 ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[c,u,d]=BS(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var _Z={kernelName:jh,backendName:"cpu",kernelFunc:$Z};function DZ(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]=Xy(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var PZ={kernelName:qh,backendName:"cpu",kernelFunc:DZ};function FZ(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
|
|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=Xy(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var OZ={kernelName:Xh,backendName:"cpu",kernelFunc:FZ};function MZ(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=E.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=hI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var zZ={kernelName:md,backendName:"cpu",kernelFunc:MZ};function LZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let h=Tl({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=d,h})}var BZ={kernelName:qi,backendName:"cpu",kernelFunc:LZ},WZ={kernelName:$u,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Ee(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}}},VZ=ft(wo,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),UZ={kernelName:wo,backendName:"cpu",kernelFunc:VZ};function GZ(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;Ee(r,"stridedSlice");let{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=$t({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: ${r.shape.length}`);let k=Ft.computeOutShape(x,y,b),I=Tl({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=$t({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 HZ={kernelName:Xi,backendName:"cpu",kernelFunc:GZ};function jZ(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 qZ={kernelName:gd,backendName:"cpu",kernelFunc:jZ};function XZ(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 KZ={kernelName:Kh,backendName:"cpu",kernelFunc:XZ};function ZZ(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 YZ={kernelName:Zh,backendName:"cpu",kernelFunc:ZZ},JZ=ft(Ki,e=>Math.tan(e)),QZ={kernelName:Ki,backendName:"cpu",kernelFunc:JZ},eY=ft(bo,e=>Math.tanh(e)),tY={kernelName:bo,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;Ee(r,"tile");let o=qS(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var sY={kernelName:Yr,backendName:"cpu",kernelFunc:nY};function rY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;Ee(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 aY={kernelName:Zi,backendName:"cpu",kernelFunc:rY};function oY(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),Y=mI(z,d,i);switch(o){case"nearest":F=pY(k,d,p,x,y,b,R,Y,K,T,l);break;case"bilinear":F=hY(k,d,p,x,y,b,R,Y,K,T,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let J=R*x+_*y+P*b+T;w[J]=F}return s.makeTensorInfo(g,r.dtype,w)}return{dataId:s.write(w,g,r.dtype),shape:r.shape,dtype:r.dtype}}var iY={kernelName:Yi,backendName:"cpu",kernelFunc:oY};function mI(e,t,n){switch(n){case"reflect":return lY(e,t);case"wrap":return uY(e,t);case"nearest":return dY(e,t);case"constant":default:return cY(e,t)}}function lY(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=2*t;n<s&&(n=s*Math.trunc(-n/s)+n),n=n<-t?n+s:-n-1}else if(n>t-1)if(t<=1)n=0;else{let s=2*t;n-=s*Math.trunc(n/s),n>=t&&(n=s-n-1)}return v.clamp(0,n,t-1)}function uY(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=t-1;n+=t*(Math.trunc(-n/s)+1)}else if(n>t-1)if(t<=1)n=0;else{let s=t-1;n-=t*Math.trunc(n/s)}return v.clamp(0,n,t-1)}function cY(e,t){return e}function dY(e,t){return v.clamp(0,e,t-1)}function dp(e,t,n,s,r,a,o,i,l,c,u){let d=o*s+i*r+l*a+c;return 0<=i&&i<t&&0<=l&&l<n?e[d]:u}function pY(e,t,n,s,r,a,o,i,l,c,u){let d=Math.round(i),p=Math.round(l);return dp(e,t,n,s,r,a,o,d,p,c,u)}function hY(e,t,n,s,r,a,o,i,l,c,u){let d=Math.floor(i),p=Math.floor(l),h=d+1,f=p+1,m=(f-l)*dp(e,t,n,s,r,a,o,d,p,c,u)+(l-p)*dp(e,t,n,s,r,a,o,d,f,c,u),g=(f-l)*dp(e,t,n,s,r,a,o,h,p,c,u)+(l-p)*dp(e,t,n,s,r,a,o,h,f,c,u);return(h-i)*m+(i-d)*g}function fY(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Ee(a,"unique");let o=s.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:c}=ZS(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var mY={kernelName:Yh,backendName:"cpu",kernelFunc:fY};function gY(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape.length,i=r.shape[a],l=new Array(o-1),c=0;for(let h=0;h<o;h++)h!==a&&(l[c++]=r.shape[h]);let u=new Array(o).fill(0),d=r.shape.slice();d[a]=1;let p=new Array(i);for(let h=0;h<p.length;h++){u[a]=h;let f=Tl({inputs:{x:r},backend:n,attrs:{begin:u,size:d}});p[h]=$t({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return p}var AY={kernelName:Ji,backendName:"cpu",kernelFunc:gY};function yY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s;Ee(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=Em({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=Ho({inputs:{x:A},backend:n,attrs:{dtype:"float32"}}),y=Tm({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 xY={kernelName:Ad,backendName:"cpu",kernelFunc:yY},bY=[Tj,xH,Ej,$j,IH,Dj,Fj,Mj,Lj,Wj,Uj,Hj,qj,Zj,Jj,tq,sq,aq,iq,Ij,uq,dq,hq,mq,kH,TH,Aq,bH,xq,vq,Sq,Cq,wq,Rq,_q,Nq,Pq,Oq,zq,Bq,Vq,Gq,Hq,qq,Kq,Yq,Jq,eX,Qq,Qy,sX,yj,aX,NH,hX,EH,fX,$H,bX,vX,kX,DH,CX,NX,RX,_X,PX,FH,MH,vH,OX,bq,zX,BX,VX,xj,LH,WH,GX,UH,jX,KX,YX,eK,nK,rK,HH,iK,uK,dK,hK,mK,aK,AK,xK,qH,vK,SK,NK,KH,YH,$K,PK,MK,QH,LK,WK,VK,pI,jK,vj,nj,XK,wH,ZK,wj,kj,Sj,JK,eZ,nZ,rZ,oZ,iZ,uZ,rj,dZ,hZ,AZ,oj,xZ,vZ,kZ,ij,CK,CZ,NZ,RZ,_Z,PZ,OZ,zZ,BZ,cj,WZ,pj,UZ,HZ,qZ,KZ,YZ,gj,tX,QZ,tY,sY,aY,ej,iY,mY,AY,xY,BK];for(let e of bY)cr(e);var gI={};Oe(gI,{assertNotComplex:()=>lc,bindCanvasToFramebuffer:()=>_Y,bindColorTextureToFramebuffer:()=>Pm,bindTextureToProgramUniformSampler:()=>$I,bindTextureUnit:()=>NI,bindVertexBufferToProgramAttribute:()=>rx,callAndCheck:()=>Ce,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:()=>RY,getProgramUniformLocation:()=>RI,getProgramUniformLocationOrThrow:()=>EI,getRowsCols:()=>Rl,getShapeAs3D:()=>Fm,getTextureShapeFromLogicalShape:()=>PI,getWebGLDisjointQueryTimerVersion:()=>MI,getWebGLErrorMessage:()=>yI,getWebGLMaxTextureSize:()=>FI,hasExtension:()=>Ls,isCapableOfRenderingToFloatTexture:()=>zI,isDownloadFloatTextureEnabled:()=>LI,isReshapeFree:()=>gp,isWebGLFenceEnabled:()=>BI,isWebGLVersionEnabled:()=>ox,linkProgram:()=>wI,resetMaxTextureSize:()=>DY,resetMaxTexturesInShader:()=>PY,unbindColorTextureFromFramebuffer:()=>ax,unbindTextureUnit:()=>$Y,validateFramebuffer:()=>mp,validateProgram:()=>Dm,validateTextureSize:()=>CI});var Nl={},nx={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function $m(e,t){Nl[e]=t}function Gr(e){if(!(e in Nl)){let n=wY(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 vY(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 wY(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=vY(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Nl[e]},!1),e===1?t.getContext("webgl",nx)||t.getContext("experimental-webgl",nx):t.getContext("webgl2",nx)}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 Cn;(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"})(Cn||(Cn={}));function hp(e,t){return[t,e]}function kY(e,t){return e*t}function _m(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 SY(e,t){let[n,s]=ic(e,t);return n*s*4}function sx(e,t){let n=e,s,r,a,o,i,l,c,u,d,p;return Z().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 Ce(e,t){let n=t();return Z().getBool("DEBUG")&&IY(e),n}function IY(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+yI(e,t))}var CY=596e-10,TY=65504;function AI(e){return!!(Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||CY<Math.abs(e)&&Math.abs(e)<TY)}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(Ce(e,()=>e.shaderSource(n,t)),Ce(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(Ce(e,()=>e.shaderSource(n,t)),Ce(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw EY(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var NY=/ERROR: [0-9]+:([0-9]+):/g;function EY(e,t){let n=NY.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 ${v.rightPad(c[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function vI(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function wI(e,t){if(Ce(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Dm(e,t){if(Ce(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function kI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ce(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function SI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Ce(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function RY(){return Z().getNumber("WEBGL_VERSION")===2?1:4}function II(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function CI(e,t){let n=Z().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let s=`[${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 rx(e,t,n,s,r,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(Ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s)),Ce(e,()=>e.vertexAttribPointer(i,r,e.FLOAT,!1,a,o)),Ce(e,()=>e.enableVertexAttribArray(i)),!0)}function NI(e,t,n){DI(e,n),Ce(e,()=>e.activeTexture(e.TEXTURE0+n)),Ce(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function $Y(e,t){DI(e,t),Ce(e,()=>e.activeTexture(e.TEXTURE0+t)),Ce(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){Ce(e,()=>NI(e,t,s)),Ce(e,()=>e.uniform1i(n,s))}function _Y(e){Ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ce(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),Ce(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function Pm(e,t,n){Ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Ce(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function ax(e,t){Ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Ce(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 ${t}`}}function la(e,t,n){let s=Ce(e,()=>t());if(s==null)throw new Error(n);return s}function DI(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${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 Fm(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=Z().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 Om(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||Om(n)&&Om(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Om(e[0])&&Om(t[0])}var Mm,zm;function FI(e){if(Mm==null){let t=Gr(e);Mm=t.getParameter(t.MAX_TEXTURE_SIZE)}return Mm}function DY(){Mm=null}function PY(){zm=null}function OI(e){if(zm==null){let t=Gr(e);zm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,zm)}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 ox(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 ix(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 ix(t);let s="EXT_color_buffer_half_float";if(Ls(t,s)){let r=t.getExtension(s);return FY(t,r)}return!1}return ix(t)}function ix(e){let t=sx(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 FY(e,t){let n=sx(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=Z();_e.registerFlag("HAS_WEBGL",()=>_e.getNumber("WEBGL_VERSION")>0);_e.registerFlag("WEBGL_VERSION",()=>ox(2)?2:ox(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&&!Pu.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",()=>Pu.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 Hn(){let e,t,n,s,r,a,o,i,l,c;return Z().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 Lm(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}; ${i};`}).join("")}function OY(e,t){let n=e.length,s=e.map(a=>`${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 MY(e,t,n="index"){let s=e.map((a,o)=>o),r=OY(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 lx(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 ux(){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 zY(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}=cx(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?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>LY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Hn(),l=VY(i),c,u,d=HY(i);return t.isPacked?(c=BY(t.logicalShape,o,n.enableShapeUniforms),u=GY(i)):(c=WY(t.logicalShape,o,n.enableShapeUniforms),u=UY(i)),n.packedInputs&&(d+=KY),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function uc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return iJ(e,t);case 1:return uJ(e,t);case 2:return dJ(e,t);case 3:return hJ(e,t);case 4:return mJ(e,t);case 5:return gJ(e);case 6:return AJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function UI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return oJ(e);case 1:return lJ(e,t);case 2:return cJ(e,t);case 3:return pJ(e,t);default:return fJ(e,t)}}function LY(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+=yJ(e,t):r+=xJ(e,t)),r}function BY(e,t,n){switch(e.length){case 0:return GI();case 1:return ZY(e,t,n);case 2:return rJ(e,t,n);case 3:return JY(e,t,n);default:return eJ(e,t,n)}}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);case 4:return tJ(e,t,n);case 5:return nJ(e,t);case 6:return sJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function VY(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function UY(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function GY(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function HY(e){return`${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);
|
|
}
|
|
|
|
${jY}
|
|
${qY}
|
|
${XY}
|
|
`}var jY=`
|
|
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);
|
|
}
|
|
`,qY=`
|
|
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);
|
|
}
|
|
`,XY=`
|
|
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);
|
|
}
|
|
`,KY=`
|
|
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 ZY(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 YY(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function JY(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 QY(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;
|
|
${Lm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=$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;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function eJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${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 tJ(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;
|
|
${Lm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=$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;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function nJ(e,t){let n=$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;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function sJ(e,t){let n=$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;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function rJ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function aJ(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 oJ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Hn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function iJ(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 lJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Hn();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 uJ(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 cJ(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=Hn();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 dJ(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 pJ(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=Hn();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 hJ(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 fJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Hn();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 mJ(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 gJ(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 AJ(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function yJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=VI(e.shapeInfo.logicalShape,t.logicalShape),l=xt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${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 xJ(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=xt(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 xt(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 ${e} is not yet supported`)}function cx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function dc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pc(e,t){return t.map(n=>e[n]).join(", ")}function bJ(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=zY(r,o,t),l=bI(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);Z().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${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 ${l} must match`)})}function vJ(e,t,n,s,r){t.program.enableShapeUniforms||(HI(t.inShapeInfos,n),HI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=cx(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 ${l.type} is not supported yet.`)}),e.executeProgram()}function wJ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=cx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${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+`${Z().getNumber("WEBGL_VERSION")}`,a}function Bs(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var kJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=pp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Lm(["r","c","d"],e):$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;
|
|
}
|
|
`}},SJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=pp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Lm(["r","c","d"],e):$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;
|
|
}
|
|
`}},IJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=zs.DOWNLOAD;let t=Hn();this.outputShape=e,this.userCode=`
|
|
${WI}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},CJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=zs.DOWNLOAD;let t=Hn();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);
|
|
}
|
|
`}},TJ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Hn();this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?ux():lx(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.);
|
|
}
|
|
`}},NJ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Hn();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?ux():lx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},jI={};Oe(jI,{bindVertexProgramAttributeStreams:()=>t4,createBufferFromOutputTexture:()=>r4,createFloat16MatrixTexture:()=>YI,createFloat16PackedMatrixTexture:()=>e4,createFloat32MatrixTexture:()=>ZI,createIndexBuffer:()=>KI,createPackedMatrixTexture:()=>QI,createUnsignedBytesMatrixTexture:()=>JI,createVertexBuffer:()=>XI,createVertexShader:()=>qI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>o4,downloadFloat32MatrixFromBuffer:()=>a4,downloadMatrixFromPackedOutputTexture:()=>l4,downloadPackedMatrixFromBuffer:()=>i4,getInternalFormatForFloat16MatrixTexture:()=>px,getInternalFormatForFloat16PackedMatrixTexture:()=>mx,getInternalFormatForFloat32MatrixTexture:()=>dx,getInternalFormatForPackedMatrixTexture:()=>fx,getInternalFormatForUnsignedBytesMatrixTexture:()=>hx,uploadDenseMatrixToTexture:()=>n4,uploadPixelDataToTexture:()=>s4});function qI(e){let t=Hn(),n=`${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 Ce(e,()=>e.bindTexture(i,o)),Ce(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ce(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ce(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ce(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ce(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function dx(e){return e.internalFormatFloat}function ZI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,dx(s),s.textureFormatFloat,e.FLOAT)}function px(e){return e.internalFormatHalfFloat}function YI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,px(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function hx(e){return e.downloadTextureFormat}function JI(e,t,n,s){let[r,a]=hp(t,n);return Ap(e,r,a,hx(s),e.RGBA,e.UNSIGNED_BYTE)}function fx(e){return e.internalFormatPackedFloat}function QI(e,t,n,s){let[r,a]=ic(t,n);return Ap(e,r,a,fx(s),e.RGBA,e.FLOAT)}function mx(e){return e.internalFormatPackedHalfFloat}function e4(e,t,n,s){let[r,a]=ic(t,n);return Ap(e,r,a,mx(s),e.RGBA,s.textureTypeHalfFloat)}function t4(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rx(e,t,"clipSpacePos",n,3,a,s)&&rx(e,t,"uv",n,2,a,r)}function n4(e,t,n,s,r,a){Ce(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),Ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function s4(e,t,n){Ce(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function r4(e,t,n,s){let r=e.createBuffer();Ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ce(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ce(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ce(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(kY(t*n,o));return Ce(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(SY(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 Ce(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Bm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Z().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,$m(t,e)):this.gl=Gr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Z().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(Z().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(Z().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=sx(this.gl,this.textureHalfFloatExtension)}get debug(){return Z().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;Ce(e,()=>e.finish()),Ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ce(e,()=>e.deleteFramebuffer(this.framebuffer)),Ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ce(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&&(ax(this.gl,this.framebuffer),this.outputTexture=null),Ce(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(Z().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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Z().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 Ce(t,()=>t.attachShader(n,this.vertexShader)),Ce(t,()=>t.attachShader(n,e)),wI(t,n),this.debug&&Dm(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&&Ce(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Dm(this.gl,this.program),Ce(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(),Ce(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&&Dm(this.gl,this.program),mp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,Z().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(Z().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(Z().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,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Z().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=EJ(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(),Pm(this.gl,e,this.framebuffer),this.debug&&mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Pm(this.gl,this.outputTexture,this.framebuffer),this.debug&&mp(this.gl)):ax(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;Pm(s,e,this.framebuffer),this.debug&&mp(s),this.outputTexture=e,Ce(s,()=>s.viewport(0,0,t,n)),Ce(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ce(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 EJ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:RJ,bincountImpl:u4,bincountReduceImpl:$J,ceilImpl:_J,concatImpl:DJ,equalImpl:PJ,expImpl:FJ,expm1Impl:OJ,floorImpl:MJ,gatherNdImpl:zJ,gatherV2Impl:LJ,greaterImpl:BJ,greaterEqualImpl:WJ,lessImpl:VJ,lessEqualImpl:UJ,linSpaceImpl:GJ,logImpl:HJ,maxImpl:jJ,maximumImpl:qJ,minimumImpl:XJ,multiplyImpl:KJ,negImpl:ZJ,notEqualImpl:YJ,prodImpl:JJ,rangeImpl:QJ,rsqrtImpl:eQ,sigmoidImpl:tQ,simpleAbsImpl:c4,sliceImpl:nQ,sparseFillEmptyRowsImpl:sQ,sparseReshapeImpl:rQ,sparseSegmentReductionImpl:d4,sqrtImpl:aQ,stridedSliceImpl:oQ,stringNGramsImpl:iQ,stringSplitImpl:lQ,stringToHashBucketFastImpl:uQ,subImpl:cQ,tileImpl:dQ,topKImpl:pQ,transposeImpl:gx,uniqueImpl:hQ}=Im;function p4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function jn(e,t){return t===1?[e]:p4(e,t)}function fQ(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 mQ=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=jn("rc",t),s=xt(t),r=AQ(t,e,n),a=yQ(t,e[e.length-1],e[e.length-2],n),o=xQ(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function gQ(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 AQ(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 yQ(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 xQ(e,t){let n=e.length,s=gQ(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=`
|
|
${bQ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?ux():lx(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 bQ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?MY(["r","c","d"],"inputShape"):$l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var vQ=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===Cn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Cn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Cn.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=Z().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 wQ(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=kQ(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=wQ(n,a);return o*i}function kQ(e,t){switch(e){case Cn.PACKED_2X2_FLOAT32:return fx(t);case Cn.PACKED_2X2_FLOAT16:return mx(t);case Cn.UNPACKED_FLOAT32:return dx(t);case Cn.UNPACKED_FLOAT16:return px(t);case Cn.PACKED_4X1_UNSIGNED_BYTE:return hx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SQ(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Cn.PACKED_2X2_FLOAT32:Cn.UNPACKED_FLOAT32:e?Cn.PACKED_2X2_FLOAT16:Cn.UNPACKED_FLOAT16}function m4(e,t){if(e===zs.UPLOAD)return Cn.PACKED_2X2_FLOAT32;if(e===zs.RENDER||e==null)return SQ(t);if(e===zs.DOWNLOAD||e===zs.PIXELS)return Cn.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 qo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Bs(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},kr="if (isnan(x)) return x;",IQ="return x;",A4="return abs(x);",CQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",TQ=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,NQ=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Wm="return x;",EQ="return 1.0 / (1.0 + exp(-1.0 * x));",RQ="return x;",$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;
|
|
`,_Q=`
|
|
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;
|
|
`,DQ=`
|
|
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;
|
|
`,PQ="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) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},FQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=jn("rc",t),s=xt(t),r=fQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},OQ=Qs.whereImpl,MQ=1e-7,zQ=1e-4,Vm={};function LQ(e){return e in Vm||(Vm[e]={}),Vm[e]}var BQ=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),WQ=600;function VQ(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*WQ/1024/1024}var y4=class extends su{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,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Gr(Z().getNumber("WEBGL_VERSION"));this.binaryCache=LQ(Z().getNumber("WEBGL_VERSION")),this.gpgpu=new Bm(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 vQ(this.gpgpu),this.numMBBeforeWarning=VQ(),this.texData=new td(this,rs())}nextDataId(){return y4.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().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(Z().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,Wm):d=new qo(o,Wm);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,Wm):h=new qo(s,Wm);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(Z().getBool("DEBUG")&&!Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Z().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"&&Z().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,..._m(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;Ce(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)&&rs().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 Z().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,..._m(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Z().getBool("WEBGL_PACK")&&s===!0,o=a?Fm(t):t,i=a?new CJ(o):new IJ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().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=BQ){return Z().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 OQ(e.shape,t)}packedUnaryOp(e,t,n){let s=new hc(e.shape,t),r=this.compileAndRun(s,[e],n);return rs().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(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,A4,e.dtype);let t=new qo(e.shape,A4),n=this.compileAndRun(t,[e]);return rs().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 rs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new FQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mQ(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=Fm(s),o,i=_m(a);n?o=new SJ(a):o=new kJ(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=_m(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)<=Z().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=wJ(e,l,c),d=this.getAndSaveBinary(u,()=>bJ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),vJ(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=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Z().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||(Z().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(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs($e(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?MQ:zQ}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=Fm(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 NJ(d,m)):p=new TJ(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=UQ(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 UQ(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 GQ="0.0.0";function x4(){Z().set("WEBGL_FORCE_F16_TEXTURES",!0)}Pu.isBrowser()&&ul("webgl",()=>new yp,2);var HQ={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));
|
|
}
|
|
`}},Um=`
|
|
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=`
|
|
${xt(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) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=jn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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 vs(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 jQ={kernelName:Ka,backendName:"webgl",kernelFunc:vs};function Xo(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=vs({inputs:{x:s},backend:n}),l=vs({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var qQ={kernelName:ad,backendName:"webgl",kernelFunc:Xo},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 XQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Z().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 KQ={kernelName:Ti,backendName:"webgl",kernelFunc:XQ},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 ZQ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Z().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 YQ={kernelName:io,backendName:"webgl",kernelFunc:ZQ},I4="if (isnan(x)) return x;",JQ=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,QQ=`
|
|
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 st({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=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new hc(o.shape,t):u=new qo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Tn({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],Bn(b.dtype,w.dtype))}),x=Xo({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),x}let d=a||Bn(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=Z().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 Gm(e,t=!1){if(e==="linear")return t?RQ:IQ;if(e==="relu")return t?_Q:TQ;if(e==="elu")return t?$Q:CQ;if(e==="relu6")return t?DQ:NQ;if(e==="prelu")return t?S4:k4;if(e==="leakyrelu")return t?w4:v4;if(e==="sigmoid")return t?PQ:EQ;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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},E4="return a * b;";function Ax(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=E.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new N4(T4.REAL,s.shape,r.shape),u=new N4(T4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Xo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=KJ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new xp(E4,s.shape,r.shape):o=new fc(E4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var eee={kernelName:ro,backendName:"webgl",kernelFunc:Ax};function tee(e,t,n){let s=[El(e.shape),...Rl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[El(t),...Rl(t)],o=new h4(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${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))?tee(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var nee={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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},see=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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 ree(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=ree(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 see({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 aee=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=xt(this.rank),r=oee(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function oee(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 iee=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 ${this.rank} is not yet supported.`);let s=xt(this.rank),r=p4("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${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 Hm(e,t,n){let s=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iee(e.shape,t):new aee(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function lee(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=Hm(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 jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return lee(r,a,o,n)}var uee={kernelName:go,backendName:"webgl",kernelFunc:jm};function qn(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=gx(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=Hm(r,a,o);return c}var cee={kernelName:vo,backendName:"webgl",kernelFunc:qn},$4=1e3;function qm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${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?Gm(l,!0):null,q=P||T||F||U!=null,z;if((h===1||f===1)&&_>$4&&q===!1){let Y=I,J=N;n&&(Y=qn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),R.push(Y)),s&&(J=qn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),R.push(J));let ne=f!==1,re=f===1,G=Y;ne&&(G=ve({inputs:{x:Y},backend:r,attrs:{shape:[O,_,1]}}),R.push(G));let se=f===1?2:1,oe=J;re&&(oe=ve({inputs:{x:J},backend:r,attrs:{shape:[O,1,_]}}),R.push(oe));let pe=Ax({inputs:{a:G,b:oe},backend:r});z=jm({inputs:{x:pe},backend:r,attrs:{axis:se,keepDims:!0}}),R.push(pe)}else{let Y=Bn(e.dtype,t.dtype),J=new C4(w,k,[O,h,f],n,s,P,U,T,F),ne=[I,N];if(a!=null&&ne.push(a),T&&ne.push(o),F){let re=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ne.push(re),R.push(re)}z=r.runWebGLProgram(J,ne,Y)}let K=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let Y of R)r.disposeIntermediateTensorInfo(Y);return K}function dee(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 qm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var pee={kernelName:ko,backendName:"webgl",kernelFunc:dee},_4="return abs(x);";function hee(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 Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,_4):r=new qo(s.shape,_4),n.runWebGLProgram(r,[s],s.dtype)}var fee={kernelName:fi,backendName:"webgl",kernelFunc:hee},mee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,gee=st({opSnippet:mee}),Aee={kernelName:ou,backendName:"webgl",kernelFunc:gee},yee=kr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,xee=st({opSnippet:yee}),bee={kernelName:iu,backendName:"webgl",kernelFunc:xee},D4="return a + b;",vee=Tn({opSnippet:D4,packedOpSnippet:D4,supportsComplex:!0,cpuKernelImpl:RJ}),wee={kernelName:Kr,backendName:"webgl",kernelFunc:vee},kee=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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},See=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}};function Xm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return vs({inputs:{x:s[0]},backend:n});if(s.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Xm({inputs:s.slice(0,l),backend:n}),u=Xm({inputs:s.slice(l),backend:n});return Xm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Bn(l,c)),a=s.map(l=>l.shape),i=Z().getBool("WEBGL_PACK")?new See(s[0].shape,a):new kee(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Iee={kernelName:$a,backendName:"webgl",kernelFunc:Xm};function Cee(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=qn({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 Tee={kernelName:lu,backendName:"webgl",kernelFunc:Cee};function Nee(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=qn({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 Eee={kernelName:uu,backendName:"webgl",kernelFunc:Nee},Ree=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=xt(i),c=jn("coords",i),u,d;if(a===1){d=i+1;let I=xt(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=jn("sourceLocR",d-1).concat("inIdx.r"),g=jn("sourceLocG",d-1).concat("inIdx.g"),A=jn("sourceLocB",d-1).concat("inIdx.b"),x=jn("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 Ree(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),!Z().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 _ee(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=qn({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 Dee={kernelName:_a,backendName:"webgl",kernelFunc:_ee};function Pee(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=qn({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 Fee={kernelName:cu,backendName:"webgl",kernelFunc:Pee},Oee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,Mee=st({opSnippet:Oee}),zee={kernelName:du,backendName:"webgl",kernelFunc:Mee},Lee=kr+"return log(x + sqrt(x * x + 1.0));",Bee=st({opSnippet:Lee}),Wee={kernelName:pu,backendName:"webgl",kernelFunc:Bee},Vee=kr+`
|
|
return atan(x);
|
|
`,Uee=st({opSnippet:Vee}),Gee={kernelName:hu,backendName:"webgl",kernelFunc:Uee},Hee=JQ+`
|
|
return atan(a, b);
|
|
`,jee=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+QQ+`
|
|
return result;
|
|
`,qee=Tn({opSnippet:Hee,packedOpSnippet:jee}),Xee={kernelName:mu,backendName:"webgl",kernelFunc:qee},Kee=kr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zee=st({opSnippet:Kee}),Yee={kernelName:fu,backendName:"webgl",kernelFunc:Zee},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});
|
|
}
|
|
`}},yx=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 Jee(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 vs({inputs:{x:r},backend:n});let d=new bp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var Qee={kernelName:Da,backendName:"webgl",kernelFunc:Jee};function ete(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 yx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var tte={kernelName:rd,backendName:"webgl",kernelFunc:ete},nte=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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},ste=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 rte(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 ste(p);return n.runWebGLProgram(h,[r],o.dtype)}var ate={kernelName:wh,backendName:"webgl",kernelFunc:rte};function ote(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 nte(u);return n.runWebGLProgram(d,[r],o.dtype)}var ite={kernelName:vh,backendName:"webgl",kernelFunc:ote};function lte(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return qm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var ute={kernelName:Pa,backendName:"webgl",kernelFunc:lte},cte=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)));
|
|
}
|
|
`}},dte=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);
|
|
}
|
|
`}},pte=({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=Z().getBool("WEBGL_PACK_NORMALIZATION")?new dte(s.shape,r.shape,a.shape,u,d,l):new cte(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},hte={kernelName:qa,backendName:"webgl",kernelFunc:pte},fte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=xt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=mte(this.rank),s,r=e.map((a,o)=>`sourceLoc.${xx[o]} = start[${o}] + coords.${xx[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},xx=["x","y","z","w","u","v"];function mte(e){if(e===1)return"sourceLoc";if(e<=6)return xx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var gte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=xt(this.rank),n=jn("coords",this.rank),s=jn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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 Ate(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=Ft.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]=Ft.parseSliceParams(r,a,o);if(Ft.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=nQ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Ft.isSliceContinous(r.shape,i,l);if(c||!u){let d=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gte(l):new fte(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Ate(r,i,l,n)}var yte={kernelName:Gi,backendName:"webgl",kernelFunc:mc},xte=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=qn({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},bte={kernelName:mi,backendName:"webgl",kernelFunc:xte};function vte(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 wte={kernelName:kh,backendName:"webgl",kernelFunc:vte};function kte(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 Ste={kernelName:Sh,backendName:"webgl",kernelFunc:kte},Ite="return float(a != b);",M4=Tn({opSnippet:Ite,cpuKernelImpl:YJ,dtype:"bool"}),Cte={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 vs({inputs:{x:r.complexTensorInfos.real},backend:n})}var Tte={kernelName:fd,backendName:"webgl",kernelFunc:vp},Nte="return float(int(x));";function Ete(e,t){let n=new qo(e.shape,Nte),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function bx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return vs({inputs:{x:r},backend:n});let o=Gt(r.shape),i=bx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Xo({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=bx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=vs({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Ete(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 ${a}`)}var Rte={kernelName:Fa,backendName:"webgl",kernelFunc:bx},z4="return ceil(x);",$te=st({opSnippet:z4,packedOpSnippet:z4,cpuKernelImpl:_J}),_te={kernelName:Oa,backendName:"webgl",kernelFunc:$te},Dte=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},Pte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function Fte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Z().getBool("WEBGL_PACK_CLIP")?i=new Pte(r.shape):i=new Dte(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Ote={kernelName:Zr,backendName:"webgl",kernelFunc:Fte},Mte=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function L4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new Mte(s.shape),o=[L4(s,r.complexTensorInfos.real),L4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var Lte={kernelName:od,backendName:"webgl",kernelFunc:zte},Bte=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},Wte=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=xt(s),a=jn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${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}(${Km(o,l,m)}),
|
|
vec2(${Km(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${Km(o,l,h)}),
|
|
vec2(${Km(c,l,h)}));`,this.userCode=`
|
|
float getValue(${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 Km(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Zm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return vs({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Vte={kernelName:cd,backendName:"webgl",kernelFunc:Zm};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=>Zm({inputs:{input:m},backend:n})),p=gc(u,t,n),h=gc(d,t,n),f=Xo({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=DJ(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>Z().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(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new Wte(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=Ute(e,t,n),i=new Bte(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 Ute(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 vs({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),gc(i,a,n)}var Gte={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}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},Hte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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);
|
|
}
|
|
`}},jte=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=Hn(),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=qm({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=vs({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=qm({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 jte(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?Gm(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 J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));q.push(J),b.push(J)}let z=s.runWebGLProgram(U,q,"float32"),K=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Y=ve({inputs:{x:z},backend:s,attrs:{shape:K}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return Y}function qte(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(Z().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 Xte={kernelName:Ma,backendName:"webgl",kernelFunc:qte},Kte=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);
|
|
}
|
|
`}},Zte=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);
|
|
}
|
|
`}},Yte=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Jte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Qte(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 Kte(p);return n.runWebGLProgram(h,[r,a],"float32")}var ene={kernelName:Ih,backendName:"webgl",kernelFunc:Qte};function tne(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 Zte(p);return n.runWebGLProgram(h,[r,a],"float32")}var nne={kernelName:za,backendName:"webgl",kernelFunc:tne};function sne(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 Hte(c);return n.runWebGLProgram(u,[r,a],"float32")}var rne={kernelName:id,backendName:"webgl",kernelFunc:sne};function ane(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 Yte(c);return n.runWebGLProgram(u,[r,a],"float32")}var one={kernelName:Ch,backendName:"webgl",kernelFunc:ane};function ine(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 Jte(c);return n.runWebGLProgram(u,[r,a],"float32")}var lne={kernelName:Th,backendName:"webgl",kernelFunc:ine},une=I4+`
|
|
return cos(x);
|
|
`,cne=st({opSnippet:une}),dne={kernelName:La,backendName:"webgl",kernelFunc:cne},pne=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,hne=st({opSnippet:pne}),fne={kernelName:Ba,backendName:"webgl",kernelFunc:hne},mne=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(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},gne=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new mne(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Ane={kernelName:yi,backendName:"webgl",kernelFunc:gne},G4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${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() {
|
|
${xt(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 ${e} is not yet supported`)}function yne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=qn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=vs({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new G4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new G4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=qn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var xne={kernelName:Ai,backendName:"webgl",kernelFunc:yne};function bne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=u4(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=$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 vne={kernelName:Nh,backendName:"webgl",kernelFunc:bne},wne=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 kne(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 wne(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Sne={kernelName:xi,backendName:"webgl",kernelFunc:kne},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 Ine(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;Z().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 Cne={kernelName:Wa,backendName:"webgl",kernelFunc:Ine},Tne=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Nne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 Ene(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 Tne(d);return n.runWebGLProgram(p,[r,a],"float32")}var Rne={kernelName:Eh,backendName:"webgl",kernelFunc:Ene};function $ne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Nne(d);return n.runWebGLProgram(p,[r,a],"float32")}var _ne={kernelName:Rh,backendName:"webgl",kernelFunc:$ne},Dne=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 Pne(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 Dne(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 Fne={kernelName:$h,backendName:"webgl",kernelFunc:Pne},One=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 Mne(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 One(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 zne={kernelName:ld,backendName:"webgl",kernelFunc:Mne};function Lne(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=qn({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=Ax({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=jm({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 Bne={kernelName:ud,backendName:"webgl",kernelFunc:Lne},Wne="return (x >= 0.0) ? x : (exp(x) - 1.0);",Vne=`
|
|
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;
|
|
`,Une=st({opSnippet:Wne,packedOpSnippet:Vne}),Gne={kernelName:Ua,backendName:"webgl",kernelFunc:Une},Hne="return (b >= 1.0) ? a : a * (b + 1.0);",jne=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,qne=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new xp(jne,s.shape,r.shape):new fc(Hne,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},Xne={kernelName:Ph,backendName:"webgl",kernelFunc:qne},Kne=`
|
|
return vec4(equal(a, b));
|
|
`,Zne="return float(a == b);",Yne=Tn({opSnippet:Zne,packedOpSnippet:Kne,dtype:"bool",cpuKernelImpl:PJ}),Jne={kernelName:bi,backendName:"webgl",kernelFunc:Yne},Qne=`
|
|
// 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 = ${E.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,ese=st({opSnippet:Qne}),tse={kernelName:gu,backendName:"webgl",kernelFunc:ese},K4="return exp(x);",Z4=st({opSnippet:K4,packedOpSnippet:K4,cpuKernelImpl:FJ,dtype:"float32"}),nse={kernelName:Ga,backendName:"webgl",kernelFunc:Z4};function vx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var sse={kernelName:vi,backendName:"webgl",kernelFunc:vx},Y4="return exp(x) - 1.0;",rse=st({opSnippet:Y4,packedOpSnippet:Y4,cpuKernelImpl:OJ}),ase={kernelName:wi,backendName:"webgl",kernelFunc:rse},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=Xo({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 ose(e){let{inputs:t,backend:n}=e,{input:s}=t;return Q4(s,!1,n)}var ise={kernelName:Fh,backendName:"webgl",kernelFunc:ose},lse=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 lse(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var use={kernelName:Au,backendName:"webgl",kernelFunc:wp},cse=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},dse={kernelName:ki,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new cse(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},eC="return floor(x);",pse=st({opSnippet:eC,packedOpSnippet:eC,cpuKernelImpl:MJ}),hse={kernelName:Ha,backendName:"webgl",kernelFunc:pse},fse=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,mse=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,gse=Tn({opSnippet:fse,packedOpSnippet:mse,dtype:"int32"}),Ase={kernelName:ja,backendName:"webgl",kernelFunc:gse},yse=class{constructor(e){this.variableNames=["A"];let t=Hn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},xse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Hn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},bse={kernelName:yd,backendName:"webgl",kernelFunc:vse},Ac;function vse(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=Z().getBool("WEBGL_PACK")?new xse(d):new yse(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function wse(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(Z().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?Gm(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 kse={kernelName:So,backendName:"webgl",kernelFunc:wse};function Sse(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=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?Gm(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 Ise={kernelName:Io,backendName:"webgl",kernelFunc:Sse},Cse=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=xt(t.length),r=xt(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 Tse(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=zJ(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,y.values)}let f=new Cse(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 Nse={kernelName:Ii,backendName:"webgl",kernelFunc:Tse},Ese=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=xt(this.rank),s=Rse(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function Rse(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=LJ(w,b,g);return h.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new Ese(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},_se="return float(a > b);",Dse=`
|
|
return vec4(greaterThan(a, b));
|
|
`,Pse=Tn({opSnippet:_se,packedOpSnippet:Dse,cpuKernelImpl:BJ,dtype:"bool"}),Fse={kernelName:Ci,backendName:"webgl",kernelFunc:Pse},Ose="return float(a >= b);",Mse=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,zse=Tn({opSnippet:Ose,packedOpSnippet:Mse,dtype:"bool",cpuKernelImpl:WJ}),Lse={kernelName:Xa,backendName:"webgl",kernelFunc:zse};function Bse(e){let{inputs:t,backend:n}=e,{input:s}=t;return Q4(s,!0,n)}var Wse={kernelName:Oh,backendName:"webgl",kernelFunc:Bse},Vse="return float(!isnan(x) && !isinf(x));",Use=st({opSnippet:Vse,dtype:"bool"}),Gse={kernelName:yu,backendName:"webgl",kernelFunc:Use},Hse="return float(isinf(x));",jse=st({opSnippet:Hse,dtype:"bool"}),qse={kernelName:xu,backendName:"webgl",kernelFunc:jse},Xse="return float(isnan(x));",Kse=st({opSnippet:Xse,dtype:"bool"}),Zse={kernelName:bu,backendName:"webgl",kernelFunc:Kse},Yse="return float(a < b);",Jse=`
|
|
return vec4(lessThan(a, b));
|
|
`,Qse=Tn({opSnippet:Yse,packedOpSnippet:Jse,cpuKernelImpl:VJ,dtype:"bool"}),ere={kernelName:Ni,backendName:"webgl",kernelFunc:Qse},tre="return float(a <= b);",nre=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,sre=Tn({opSnippet:tre,packedOpSnippet:nre,cpuKernelImpl:UJ,dtype:"bool"}),rre={kernelName:Ei,backendName:"webgl",kernelFunc:sre};function are(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=GJ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var ore={kernelName:Mh,backendName:"webgl",kernelFunc:are},ire=`if (x < 0.0) return NAN;
|
|
return log(x);`,lre=`
|
|
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;
|
|
`,ure=st({opSnippet:ire,packedOpSnippet:lre,cpuKernelImpl:HJ}),cre={kernelName:Za,backendName:"webgl",kernelFunc:ure},dre="return log(1.0 + x);",pre=st({opSnippet:dre}),hre={kernelName:vu,backendName:"webgl",kernelFunc:pre},fre="return float(a >= 1.0 && b >= 1.0);",mre=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,gre=Tn({opSnippet:fre,packedOpSnippet:mre,dtype:"bool"}),Are={kernelName:Ri,backendName:"webgl",kernelFunc:gre},yre="return float(!(x >= 1.0));",xre=st({opSnippet:yre}),bre={kernelName:wu,backendName:"webgl",kernelFunc:xre},vre="return float(a >= 1.0 || b >= 1.0);",wre=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,kre=Tn({opSnippet:vre,packedOpSnippet:wre,dtype:"bool"}),Sre={kernelName:dd,backendName:"webgl",kernelFunc:kre},Ire=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 * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Cre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
|
|
}
|
|
`}},Tre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Cre(r.shape,a,o,i,l):new Ire(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Nre={kernelName:pd,backendName:"webgl",kernelFunc:Tre},Ere=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 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},Rre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Ere(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},$re={kernelName:zh,backendName:"webgl",kernelFunc:Rre};function _re(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=gx(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=Hm(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=jJ(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=_re(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Dre={kernelName:Ya,backendName:"webgl",kernelFunc:nC},Pre=b4+`
|
|
return max(a, b);
|
|
`,Fre=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Um+`
|
|
return result;
|
|
`,Ore=Tn({opSnippet:Pre,packedOpSnippet:Fre,cpuKernelImpl:qJ}),Mre={kernelName:Ja,backendName:"webgl",kernelFunc:Ore};function zre(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 vs({inputs:{x:r},backend:n});let d=new bp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Lre={kernelName:Qa,backendName:"webgl",kernelFunc:zre};function Bre(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 yx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Wre={kernelName:hd,backendName:"webgl",kernelFunc:Bre},Vre=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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Ure=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Gre(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 yx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Ure(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Hre={kernelName:Bh,backendName:"webgl",kernelFunc:Gre};function jre(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 Vre(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var qre={kernelName:Lh,backendName:"webgl",kernelFunc:jre};function Xre(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 Kre={kernelName:Wh,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]=Xre(s,i,u,l);return[d,p]}};function Zre(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 Yre={kernelName:eo,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=gx(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=Hm(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=Zre(f,g,A,o);for(let y of h)o.disposeIntermediateTensorInfo(y);return x}};function Jre(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=qn({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 Qre={kernelName:to,backendName:"webgl",kernelFunc:Jre},eae=b4+`
|
|
return min(a, b);
|
|
`,tae=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Um+`
|
|
return result;
|
|
`,nae=Tn({opSnippet:eae,packedOpSnippet:tae,cpuKernelImpl:XJ}),sae={kernelName:no,backendName:"webgl",kernelFunc:nae},rae=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=xt(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}));
|
|
}
|
|
`}},aae=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=xt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=jn("rc",s),l=jn("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);
|
|
}
|
|
`}},oae=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aae(s.shape,r,a):new rae(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},iae={kernelName:so,backendName:"webgl",kernelFunc:oae},lae=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,uae=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Um+`
|
|
return result;
|
|
`,cae=Tn({opSnippet:lae,packedOpSnippet:uae}),dae={kernelName:ku,backendName:"webgl",kernelFunc:cae},pae=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}));
|
|
}
|
|
`}},hae=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,fae=`
|
|
// 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=Tn({opSnippet:hae,packedOpSnippet:fae,checkOutOfBounds:!0}),mae={kernelName:Va,backendName:"webgl",kernelFunc:sC},rC="return a - b;",aC=Tn({opSnippet:rC,packedOpSnippet:rC,supportsComplex:!0,cpuKernelImpl:cQ}),gae={kernelName:xo,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=jm({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 Aae={kernelName:Ao,backendName:"webgl",kernelFunc:oC};function yae(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 pae(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var xae={kernelName:Vh,backendName:"webgl",kernelFunc:yae},iC="return -x;";function bae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=ZJ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,iC):r=new qo(s.shape,iC),n.runWebGLProgram(r,[s],s.dtype)}var vae={kernelName:$i,backendName:"webgl",kernelFunc:bae},wae=Qs.nonMaxSuppressionV3Impl;function kae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=wae(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Sae={kernelName:Di,backendName:"webgl",kernelFunc:kae},Iae=Qs.nonMaxSuppressionV4Impl;function Cae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Iae(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Tae={kernelName:Su,backendName:"webgl",kernelFunc:Cae},Nae=Qs.nonMaxSuppressionV5Impl;function Eae(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Nae(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Rae={kernelName:Pi,backendName:"webgl",kernelFunc:Eae},$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)));
|
|
}
|
|
`}},_ae=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},Dae={kernelName:Oi,backendName:"webgl",kernelFunc:_ae};function Ym(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=vp({inputs:{input:s},backend:n}),a=Ym({inputs:{x:r},backend:n}),o=Zm({inputs:{input:s},backend:n}),i=Ym({inputs:{x:o},backend:n}),l=Xo({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 Pae={kernelName:Qi,backendName:"webgl",kernelFunc:Ym};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=Zm({inputs:{input:s},backend:n}),i=Ym({inputs:{x:o},backend:n}),l=Xo({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 Fae={kernelName:Fi,backendName:"webgl",kernelFunc:lC};function Oae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return vx({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=vx({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 Mae={kernelName:Mi,backendName:"webgl",kernelFunc:Oae},zae=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=xt(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}));
|
|
}
|
|
}
|
|
`}},Lae=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=xt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=jn("rc",s),l=jn("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=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lae(r.shape,a,o):new zae(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Bae={kernelName:ao,backendName:"webgl",kernelFunc:uC},Wae=`
|
|
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);
|
|
`,Vae=`
|
|
// 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));
|
|
`+Um+`
|
|
return result;
|
|
`,Uae=Tn({opSnippet:Wae,packedOpSnippet:Vae}),Gae={kernelName:oo,backendName:"webgl",kernelFunc:Uae};function Hae(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=qn({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}=JJ(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 jae={kernelName:zi,backendName:"webgl",kernelFunc:Hae},cC=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=QJ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},qae={kernelName:Iu,backendName:"webgl",kernelFunc:cC},Xae="return 1.0 / x;",Kae=st({opSnippet:Xae}),Zae={kernelName:Cu,backendName:"webgl",kernelFunc:Kae},Yae=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Jae=`
|
|
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;
|
|
`,Qae=st({opSnippet:Yae,packedOpSnippet:Jae}),eoe={kernelName:lo,backendName:"webgl",kernelFunc:Qae},toe=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,noe=`
|
|
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;
|
|
`,soe=st({opSnippet:toe,packedOpSnippet:noe}),roe={kernelName:co,backendName:"webgl",kernelFunc:soe},aoe=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);
|
|
}
|
|
`}},ooe=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 ioe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ooe(r.shape,l,c,a,o):new aoe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var loe={kernelName:uo,backendName:"webgl",kernelFunc:ioe},uoe=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 coe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new uoe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var doe={kernelName:Gh,backendName:"webgl",kernelFunc:coe},poe=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);
|
|
}
|
|
`}},hoe=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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function foe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new hoe(r.shape,l,c,a,o):new poe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var moe={kernelName:Tu,backendName:"webgl",kernelFunc:foe},goe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Aoe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new goe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var yoe={kernelName:Uh,backendName:"webgl",kernelFunc:Aoe},xoe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=xt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},boe=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-${n} tensor is not yet supported`);this.outputShape=e;let s=jn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=xt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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 voe(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 vs({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new boe(r.shape,i):new xoe(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var woe={kernelName:Bi,backendName:"webgl",kernelFunc:voe},koe=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Soe={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new koe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Ioe=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Coe=st({opSnippet:Ioe}),Toe={kernelName:Wi,backendName:"webgl",kernelFunc:Coe},Noe="return inversesqrt(x);",Eoe=st({opSnippet:Noe,cpuKernelImpl:eQ}),Roe={kernelName:po,backendName:"webgl",kernelFunc:Eoe},dC=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=xt(r.length),l=xt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 $oe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new dC(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),x}var _oe={kernelName:Vi,backendName:"webgl",kernelFunc:$oe},Doe=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=xt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Poe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Doe(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Bn(r.dtype,a.dtype))}var Foe={kernelName:Ui,backendName:"webgl",kernelFunc:Poe},Ooe=`
|
|
// 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);
|
|
`,Moe=st({opSnippet:Ooe}),zoe={kernelName:Nu,backendName:"webgl",kernelFunc:Moe},pC="return 1.0 / (1.0 + exp(-1.0 * x));",Loe=st({opSnippet:pC,packedOpSnippet:pC,cpuKernelImpl:tQ}),Boe={kernelName:fo,backendName:"webgl",kernelFunc:Loe},Woe=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Voe=st({opSnippet:Woe}),Uoe={kernelName:Eu,backendName:"webgl",kernelFunc:Voe},Goe=I4+`
|
|
return sin(x);
|
|
`,Hoe=st({opSnippet:Goe}),joe={kernelName:ho,backendName:"webgl",kernelFunc:Hoe},qoe=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Xoe=st({opSnippet:qoe}),Koe={kernelName:Hi,backendName:"webgl",kernelFunc:Xoe},Zoe=`
|
|
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;
|
|
`,Yoe=st({opSnippet:Zoe}),Joe={kernelName:Ru,backendName:"webgl",kernelFunc:Yoe},Qoe=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=qn({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},eie={kernelName:ji,backendName:"webgl",kernelFunc:Qoe};function tie(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]=sQ(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 nie={kernelName:Hh,backendName:"webgl",kernelFunc:tie};function sie(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]=rQ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var rie={kernelName:jh,backendName:"webgl",kernelFunc:sie};function aie(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 oie={kernelName:qh,backendName:"webgl",kernelFunc:aie};function iie(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
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=d4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var lie={kernelName:Xh,backendName:"webgl",kernelFunc:iie};function uie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=new dC(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var cie={kernelName:md,backendName:"webgl",kernelFunc:uie};function die(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=mc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var pie={kernelName:qi,backendName:"webgl",kernelFunc:die},hC="return sqrt(x);",hie=st({opSnippet:hC,packedOpSnippet:hC,cpuKernelImpl:aQ}),fie={kernelName:mo,backendName:"webgl",kernelFunc:hie},mie="return x * x;",gie=st({opSnippet:mie}),Aie={kernelName:$u,backendName:"webgl",kernelFunc:gie},fC="return (a - b) * (a - b);",yie=Tn({opSnippet:fC,packedOpSnippet:fC}),xie={kernelName:yo,backendName:"webgl",kernelFunc:yie};function bie({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=kr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new qo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var vie={kernelName:wo,backendName:"webgl",kernelFunc:bie},wie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=xt(n.length),a=xt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${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 kie(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}=Ft.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=Ft.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=oQ(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,O.values)}else{let N=new wie(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 Sie={kernelName:Xi,backendName:"webgl",kernelFunc:kie};function Iie(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]=iQ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Cie={kernelName:gd,backendName:"webgl",kernelFunc:Iie};function Tie(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: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=lQ(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Nie={kernelName:Kh,backendName:"webgl",kernelFunc:Tie};function Eie(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=uQ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Rie={kernelName:Zh,backendName:"webgl",kernelFunc:Eie},$ie="return tan(x);",_ie=st({opSnippet:$ie}),Die={kernelName:Ki,backendName:"webgl",kernelFunc:_ie},Pie=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Fie=st({opSnippet:Pie}),Oie={kernelName:bo,backendName:"webgl",kernelFunc:Fie},Mie=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=xt(this.rank),r=zie(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function zie(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=dQ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Mie(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Lie={kernelName:Yr,backendName:"webgl",kernelFunc:mC},Bie=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));
|
|
}
|
|
}
|
|
`}},Wie=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 Vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().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]=pQ(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 Bie(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 Wie([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 Uie={kernelName:Zi,backendName:"webgl",kernelFunc:Vie},Gie=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 Hie(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 Gie(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var jie={kernelName:Yi,backendName:"webgl",kernelFunc:Hie};function qie(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}=hQ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var Xie={kernelName:Yh,backendName:"webgl",kernelFunc:qie};function Kie(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 Zie={kernelName:Ji,backendName:"webgl",kernelFunc:Kie},Yie=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 Jie(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=qn({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 Yie(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=qn({inputs:{x:y},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Qie={kernelName:Ad,backendName:"webgl",kernelFunc:Jie},ele=[Nre,$re,pee,fee,Aee,bee,wee,Iee,Tee,Eee,Dee,Fee,zee,Wee,Xee,Gee,Yee,tte,Qee,ate,ite,ute,hte,bte,wte,Ste,Rte,_te,Ote,Lte,qQ,Gte,ene,nne,Xte,one,lne,rne,dne,fne,Ane,xne,vne,Sne,Rne,_ne,Cne,Fne,zne,Bne,Gne,Xne,Jne,tse,nse,sse,ase,ise,use,dse,hse,Ase,bse,kse,Ise,Nse,$se,Fse,Lse,jQ,Wse,Vte,Gse,qse,Zse,KQ,ere,rre,ore,hre,cre,Are,bre,Sre,Dre,Wre,Lre,Hre,qre,Kre,Mre,Yre,Qre,sae,iae,dae,xae,eee,vae,Sae,Tae,Rae,Cte,Dae,Fae,Mae,Bae,Gae,YQ,jae,qae,Tte,mae,Zae,roe,eoe,nee,loe,doe,moe,yoe,woe,Soe,Toe,Roe,_oe,Foe,zoe,Boe,Uoe,joe,Koe,yte,Aae,Joe,eie,nie,rie,oie,lie,cie,pie,fie,Aie,xie,vie,Sie,Cie,Nie,Rie,gae,uee,Die,Oie,Lie,Uie,jie,cee,Xie,Zie,Qie,Pae];for(let e of ele)cr(e);var Hr=Z();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 tle(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 wn(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 Jm(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Qm(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function wx(){return`
|
|
${Qm()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] numWorkgroups: vec3<u32>)
|
|
`}function Ko(){return`
|
|
${Qm()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>)
|
|
`}function nt(){return`
|
|
${wx()} {
|
|
let index = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
`}function nle(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=xC(t.shape),f=`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${Jm(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 : ${wn(e[f].shape.length)}; `}),o+=`outShape : ${wn(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
|
|
outShapeStrides: ${wn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${Jm(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
[[block]] struct Matrix${1+f} {
|
|
numbers: array<${Jm(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]=lle(t.shape,n.dispatchLayout),u=xC(t.shape),d=[AC,a.join(`
|
|
`),yC,u,l,sle(t.shape.length)];if(n.atomic||d.push(rle(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>ale(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 sle(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 rle(e,t,n){let s=e.length,r=Jm(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] = ${r}(value);
|
|
}`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=wn(s);n?a+=`
|
|
fn setOutput(${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}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function ale(e,t,n,s){let r=ole(e,n);return e.shape.length<=t.length&&(r+=ile(e,t,n,s)),r}function ole(e,t){let n=e.name,s=e.shape.length,r=wn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${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 ile(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=wn(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[${g+d}] = 0;`).join(`
|
|
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=wn(i),A=e.shape.map((x,y)=>`coords[${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 lle(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>) -> ${wn(a)}{
|
|
let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=tle(h,"uniforms.outShape");o+=`var index${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=wn(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=wn(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:()=>Cx,computeDispatch:()=>Fe,computeWorkGroupSizeForConv2d:()=>kx,computeWorkGroupSizeForMatMul:()=>Sx,computeWorkPerThreadForConv2d:()=>Ix,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>Tx,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 kx(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 Sx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Ix(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 Cx(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 Tx(){return!!navigator.gpu}var Vt;(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"})(Vt||(Vt={}));var ule="return a + b;",cle="return areal * breal - aimag * bimag;",dle="return areal * bimag + aimag * breal;",ple="return a / b;",hle="return a * b;",fle="return (a - b) * (a - b);",mle="return a - b;",gle="return f32(a == b);",Ale="return vec4<f32>(a == b);",yle="return f32(a > b);",xle="return vec4<f32>(a > b);",ble="return f32(a >= b);",vle="return vec4<f32>(a >= b);",wle="return f32(a < b);",kle="return vec4<f32>(a < b);",Sle="return f32(a <= b);",Ile="return vec4<f32>(a <= b);",Cle="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Tle=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,Nle=`
|
|
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;
|
|
}
|
|
`,Ele=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,Rle=`
|
|
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);",_le="return vec4<f32>(a != b);",Dle=`
|
|
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);
|
|
`,Ple=`
|
|
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);
|
|
${wC}
|
|
return resultTemp;
|
|
`,Fle="if (a < 0.0) { return b * a; } return a;",Ole=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function kC(e,t){let n=t?wC:Nle;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function kp(e,t){switch(e){case 0:return hle;case 1:return ule;case 2:return mle;case 3:return ple;case 4:return t?Ale:gle;case 5:return t?xle:yle;case 6:return t?vle:ble;case 7:return t?kle:wle;case 8:return t?Ile:Sle;case 9:return t?Tle:Cle;case 10:return t?_le:$le;case 11:return fle;case 12:return t?Rle:Ele;case 14:return t?Ole:Fle;case 15:return kC("max",t);case 16:return kC("min",t);case 13:return t?Ple:Dle;case 17:return cle;case 18:return dle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var bt;(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"})(bt||(bt={}));var Mle="return abs(a);",zle="return ceil(a);",Lle="return cos(a);",Ble=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Wle="return exp(a) - 1.0;",Vle="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Ule=`
|
|
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;
|
|
`,Gle="return exp(a);",Hle="return floor(a);",jle="return a;",qle=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,Xle="return f32(!(a >= 1.0));",Kle="return -a;",Zle="return (a < 0.0) ? b * a : a;",Yle="return max(a, 0.0);",Jle="return clamp(a, 0.0, 6.0);",Qle="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",eue=`
|
|
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;
|
|
`,tue="return 1.0/sqrt(a);",nue="return 1.0 / (1.0 + exp(-1.0 * a));",sue="return sin(a);",rue=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,aue="return sqrt(a);",oue="return a * a;",iue=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,lue="return f32(i32((a)));";function xc(e,t){switch(e){case 0:return Mle;case 2:return Lle;case 3:return Ble;case 1:return zle;case 4:return t?Ule:Vle;case 5:return Gle;case 6:return Wle;case 7:return Hle;case 8:return jle;case 9:return qle;case 10:return Xle;case 11:return Kle;case 12:return Zle;case 13:return t?eue:Yle;case 14:return t?Qle:Jle;case 15:return tue;case 18:return nue;case 16:return sue;case 17:return rue;case 19:return aue;case 20:return oue;case 21:return iue;case 22:return lue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ca(e,t=!1){if(e===null)return null;if(e==="linear")return xc(bt.LINEAR);if(e==="relu")return xc(bt.RELU,t);if(e==="elu")return xc(bt.ELU,t);if(e==="relu6")return xc(bt.RELU6,t);if(e==="prelu")return kp(Vt.PRELU,t);if(e==="sigmoid")return xc(bt.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};
|
|
|
|
${Ko()} {
|
|
|
|
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 uue(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${Ko()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var cue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Sx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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):uue(this.workGroupSize)}
|
|
|
|
`}};function Nx(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}>;
|
|
${Ko()} {
|
|
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 due(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${Ko()} {
|
|
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=Sx(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?Nx([this.workPerThread,this.workPerThread,1],this.workGroupSize):due(this.workGroupSize)}
|
|
`}};function pue(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${Ko()} {
|
|
let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var hue=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${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);
|
|
}
|
|
${pue()}
|
|
`}};function fue(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.
|
|
${Ko()} {
|
|
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 >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var mue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${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);
|
|
}
|
|
}
|
|
${fue(this.workGroupSize)}
|
|
`}};function je(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var gue={kernelName:Li,backendName:"webgpu",kernelFunc:je};function Ex({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],I=je({inputs:{x:e},backend:r,attrs:{shape:w}}),N=je({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,N],O=Math.max(A,x),_=d%4==0&&f%4==0&&!n&&!s&&f>=32,P;h*f<=32?P=new hue([O,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?P=new mue(w,k,[O,h,f],a,l,o):_?P=new cue(w,[O,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):P=new IC(w,[O,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[I,N];a&&T.push(a),o&&T.push(o);let F=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=r.runWebGPUProgram(P,T,e.dtype,F),q=je({inputs:{x:U},backend:r,attrs:{shape:b}});R.push(U);for(let z of R)r.disposeData(z.dataId);return q}function Aue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Ex({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var yue={kernelName:ko,backendName:"webgpu",kernelFunc:Aue},CC=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${kp(this.op,!1)}
|
|
}
|
|
|
|
${nt()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},xue=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${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}>;
|
|
${nt()}
|
|
|
|
// 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);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},bue=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${kp(this.op,this.isVec4)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},TC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${kp(this.op,!1)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function NC(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new bue(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new xue(e,t,n,a):new TC(e,t,n)}function sr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var vue={kernelName:Ka,backendName:"webgpu",kernelFunc:sr};function bc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=sr({inputs:{x:s},backend:n}),l=sr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var wue={kernelName:ad,backendName:"webgpu",kernelFunc:bc},e0=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${xc(this.op,!1)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function Nn({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 e0(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Xn({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!==Vt.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],Bn(A.dtype,x.dtype))});else{let g=new CC(Vt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new CC(Vt.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||Bn(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:kue,ceilImpl:Sue,concatImpl:Iue,equalImpl:Cue,expImpl:Tue,expm1Impl:Nue,floorImpl:Eue,gatherNdImpl:Rue,gatherV2Impl:$ue,greaterEqualImpl:_ue,greaterImpl:Due,lessEqualImpl:Pue,lessImpl:Fue,logImpl:Oue,maxImpl:Mue,maximumImpl:zue,minimumImpl:Lue,multiplyImpl:Bue,negImpl:Wue,notEqualImpl:Vue,prodImpl:Uue,rangeImpl:Gue,rsqrtImpl:Hue,simpleAbsImpl:jue,sliceImpl:que,stridedSliceImpl:Xue,stringNGramsImpl:Kue,subImpl:Zue,tileImpl:Yue,topKImpl:Jue,transposeImpl:Que,uniqueImpl:p1e}=Im,ece=Nn({opType:bt.ABS,cpuKernelImpl:jue}),tce={kernelName:fi,backendName:"webgpu",kernelFunc:ece},nce=Xn({opSnippet:Vt.ADD,cpuKernelImpl:kue,supportsComplex:!0}),sce={kernelName:Kr,backendName:"webgpu",kernelFunc:nce},rce=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`
|
|
${nt()}
|
|
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, ${t});
|
|
}
|
|
}
|
|
}
|
|
`}};function ace(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return sr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Bn(i,l)),a=s.map(i=>i.shape),o=new rce(a);return n.runWebGPUProgram(o,s,r)}var oce={kernelName:$a,backendName:"webgpu",kernelFunc:ace},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;
|
|
}
|
|
|
|
${Ko()} {
|
|
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);"}
|
|
}
|
|
`}},ice=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]}>;
|
|
${Qm()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},lce=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=uce(this.newDim);return`
|
|
${nt()}
|
|
|
|
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 uce(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=Que(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 ice(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new lce(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var cce={kernelName:vo,backendName:"webgpu",kernelFunc:Ol};function dce(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 pce={kernelName:_a,backendName:"webgpu",kernelFunc:dce};function hce(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 fce={kernelName:cu,backendName:"webgpu",kernelFunc:hce},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"),`
|
|
${nt()}
|
|
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`
|
|
${nt()}
|
|
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 mce(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 gce={kernelName:Da,backendName:"webgpu",kernelFunc:mce};function Ace(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Ex({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var yce={kernelName:Pa,backendName:"webgpu",kernelFunc:Ace},xce=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 : ${wn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=bce(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Rx[a]} = uniforms.start[${a}] + coords.${Rx[a]};`),`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},Rx=["x","y","z","w","u","v"];function bce(e){if(e===1)return"sourceLoc";if(e<=6)return Rx.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]=Ft.parseSliceParams(r,a,o);if(Ft.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=que(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 xce(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var vce={kernelName:Gi,backendName:"webgpu",kernelFunc:vc},wce=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},kce={kernelName:mi,backendName:"webgpu",kernelFunc:wce},_C=Xn({opSnippet:Vt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Vue}),Sce={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 Ice={kernelName:fd,backendName:"webgpu",kernelFunc:Sp};function Cce(e,t){let n=new e0(e.shape,bt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function $x(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return sr({inputs:{x:r},backend:n});let o=Gt(r.shape),i=$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 Cce(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 ${a}`)}var Tce={kernelName:Fa,backendName:"webgpu",kernelFunc:$x},Nce=Nn({opType:bt.CEIL,cpuKernelImpl:Sue}),Ece={kernelName:Oa,backendName:"webgpu",kernelFunc:Nce},Rce=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`
|
|
${nt()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
var clampedValue : vec4<f32>;
|
|
for (var i = 0; i < 4; i = i + 1) {
|
|
if (isNanCustom(value[i])) {
|
|
clampedValue[i] = value[i];
|
|
} else {
|
|
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
|
|
}
|
|
}
|
|
|
|
setOutputFlat(index, clampedValue);
|
|
}
|
|
}
|
|
`}},$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`
|
|
${nt()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function _ce(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 Rce(r.shape):i=new $ce(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Dce={kernelName:Zr,backendName:"webgpu",kernelFunc:_ce},Pce=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${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`
|
|
${nt()}
|
|
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 t0(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 Fce={kernelName:cd,backendName:"webgpu",kernelFunc:t0};function _x(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=>t0({inputs:{input:m},backend:n})),p=_x(u,t,n),h=_x(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=Iue(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}=Oce(e,t,n),i=new Pce(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 Oce(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),_x(i,a,n)}var Mce={kernelName:gi,backendName:"webgpu",kernelFunc:DC},zce=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`
|
|
${nt()}
|
|
|
|
for(var i = 0; i<${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=Ex({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 Lce({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 zce(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],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),I,N),K=q[1],Y=q[2],J=n.outChannels,ne=[{type:"int32",data:[K]},{type:"int32",data:[J]},{type:"int32",data:[Y]}],re=s.runWebGPUProgram(z,[U,_],U.dtype,ne),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);
|
|
}
|
|
}
|
|
${t}
|
|
`}},OC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ix(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(s,[a,i]),ua(r,[i,o])]}getUserCode(){let e=Nx(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${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);
|
|
}
|
|
}
|
|
|
|
${wx()} {
|
|
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 Bce(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(Z().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Lce({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=Z().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 Wce={kernelName:Ma,backendName:"webgpu",kernelFunc:Bce},Vce=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=kx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ix(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;
|
|
}
|
|
|
|
${Nx(this.elementsPerThread,this.workGroupSize)}
|
|
`}},Uce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
|
|
${nt()} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d1 = coords[${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 (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function Gce(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Uce(p);else{f=new Vce(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Hce={kernelName:za,backendName:"webgpu",kernelFunc:Gce},jce=Nn({opType:bt.COS}),qce={kernelName:La,backendName:"webgpu",kernelFunc:jce},Xce=Nn({opType:bt.COSH}),Kce={kernelName:Ba,backendName:"webgpu",kernelFunc:Xce},Zce=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let height_ratio = f32(${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);
|
|
}
|
|
}
|
|
}
|
|
`}},Yce=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 Zce(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Jce={kernelName:yi,backendName:"webgpu",kernelFunc:Yce},Qce=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`
|
|
${nt()}
|
|
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 = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ede(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Qce(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var tde={kernelName:xi,backendName:"webgpu",kernelFunc:ede},zC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${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}
|
|
|
|
${Qm()}
|
|
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);
|
|
}
|
|
}
|
|
|
|
${wx()} {
|
|
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}
|
|
${n}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new zC(d):p=new LC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var sde={kernelName:Wa,backendName:"webgpu",kernelFunc:nde},BC=Xn({opSnippet:Vt.MUL,cpuKernelImpl:Bue,supportsComplex:!0}),rde={kernelName:ro,backendName:"webgpu",kernelFunc:BC},ade=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${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;
|
|
}
|
|
${Ko()} {
|
|
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);
|
|
${e?a:s}
|
|
}
|
|
`}};function Ip(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=Ol({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=Mue(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:x,outDtype:y}=Uue(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,y,A);break;default:throw new Error(`${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 ade(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 Dx(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 ode={kernelName:go,backendName:"webgpu",kernelFunc:Dx};function ide(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=Dx({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 lde={kernelName:ud,backendName:"webgpu",kernelFunc:ide},ude=Nn({opType:bt.ELU}),cde={kernelName:Ua,backendName:"webgpu",kernelFunc:ude},dde=Xn({opSnippet:Vt.EQUAL,dtype:"bool",cpuKernelImpl:Cue}),pde={kernelName:bi,backendName:"webgpu",kernelFunc:dde},WC=Nn({opType:bt.EXP,cpuKernelImpl:Tue,dtype:"float32"}),hde={kernelName:Ga,backendName:"webgpu",kernelFunc:WC};function Px(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 fde={kernelName:vi,backendName:"webgpu",kernelFunc:Px},mde=Nn({opType:bt.EXPM1,cpuKernelImpl:Nue}),gde={kernelName:wi,backendName:"webgpu",kernelFunc:mde},Ade=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`
|
|
${nt()}
|
|
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 Ade(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var yde={kernelName:Au,backendName:"webgpu",kernelFunc:wc},xde=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`
|
|
${nt()}
|
|
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);
|
|
}
|
|
}
|
|
`}},bde={kernelName:ki,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new xde(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},vde=Nn({opType:bt.FLOOR,cpuKernelImpl:Eue}),wde={kernelName:Ha,backendName:"webgpu",kernelFunc:vde},kde=Xn({opSnippet:Vt.INT_DIV,dtype:"int32"}),Sde={kernelName:ja,backendName:"webgpu",kernelFunc:kde},Ide=(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=nle(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 Cde={kernelName:yd,backendName:"webgpu",kernelFunc:Tde},kc;function Tde(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(Z().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 Nde=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)"),`
|
|
${nt()}
|
|
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)));
|
|
}
|
|
}
|
|
`}},Ede={kernelName:qa,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 Nde(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function Rde(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=Z().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:So,backendName:"webgpu",kernelFunc:Rde};function _de(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 Dde={kernelName:Io,backendName:"webgpu",kernelFunc:_de},Pde=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 : ${wn(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
|
|
${nt()}
|
|
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 Fde(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=Rue(x,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Pde(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 Ode={kernelName:Ii,backendName:"webgpu",kernelFunc:Fde},Mde=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=zde(this.aShape,"i32");return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function zde(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 Mde(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 Lde={kernelName:Si,backendName:"webgpu",kernelFunc:HC},Bde=Xn({opSnippet:Vt.GREATER,cpuKernelImpl:Due,dtype:"bool"}),Wde={kernelName:Ci,backendName:"webgpu",kernelFunc:Bde},Vde=Xn({opSnippet:Vt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:_ue}),Ude={kernelName:Xa,backendName:"webgpu",kernelFunc:Vde},Gde=Xn({opSnippet:Vt.LESS,dtype:"bool",cpuKernelImpl:Fue}),Hde={kernelName:Ni,backendName:"webgpu",kernelFunc:Gde},jde=Xn({opSnippet:Vt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Pue}),qde={kernelName:Ei,backendName:"webgpu",kernelFunc:jde},Xde=Nn({opType:bt.LOG,cpuKernelImpl:Oue}),Kde={kernelName:Za,backendName:"webgpu",kernelFunc:Xde},Zde=Xn({opSnippet:Vt.LOGICAL_AND,dtype:"bool"}),Yde={kernelName:Ri,backendName:"webgpu",kernelFunc:Zde},Jde=Nn({opType:bt.LOGICAL_NOT}),Qde={kernelName:wu,backendName:"webgpu",kernelFunc:Jde};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 epe={kernelName:Ya,backendName:"webgpu",kernelFunc:jC},tpe=Xn({opSnippet:Vt.MAX,cpuKernelImpl:zue}),npe={kernelName:Ja,backendName:"webgpu",kernelFunc:tpe};function spe(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 $C(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new RC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var rpe={kernelName:Qa,backendName:"webgpu",kernelFunc:spe};function ape(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Ip(r,o,a,"mean",n)}var ope={kernelName:eo,backendName:"webgpu",kernelFunc:ape};function ipe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ip(r,a,o,"min",n)}var lpe={kernelName:to,backendName:"webgpu",kernelFunc:ipe},upe=Xn({opSnippet:Vt.MIN,cpuKernelImpl:Lue}),cpe={kernelName:no,backendName:"webgpu",kernelFunc:upe},dpe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${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=wn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${nt()}
|
|
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}));
|
|
}
|
|
}
|
|
`}},ppe={kernelName:so,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 dpe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function hpe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Wue(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new e0(s.shape,bt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var fpe={kernelName:$i,backendName:"webgpu",kernelFunc:hpe};function mpe(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 gpe={kernelName:Di,backendName:"webgpu",kernelFunc:mpe};function Ape(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 ype={kernelName:Pi,backendName:"webgpu",kernelFunc:Ape};function n0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Sp({inputs:{input:s},backend:n}),a=n0({inputs:{x:r},backend:n}),o=t0({inputs:{input:s},backend:n}),i=n0({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 xpe={kernelName:Qi,backendName:"webgpu",kernelFunc:n0};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=t0({inputs:{input:s},backend:n}),i=n0({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 bpe={kernelName:Fi,backendName:"webgpu",kernelFunc:qC};function vpe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Px({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=Px({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 wpe={kernelName:Mi,backendName:"webgpu",kernelFunc:vpe},kpe=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=wn(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`
|
|
${nt()}
|
|
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 kpe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Spe={kernelName:ao,backendName:"webgpu",kernelFunc:XC},Ipe=Xn({opSnippet:Vt.POW}),Cpe={kernelName:oo,backendName:"webgpu",kernelFunc:Ipe};function Tpe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new TC(Vt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Npe={kernelName:io,backendName:"webgpu",kernelFunc:Tpe};function Epe(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 Rpe={kernelName:zi,backendName:"webgpu",kernelFunc:Epe},$pe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Gue(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},_pe={kernelName:Iu,backendName:"webgpu",kernelFunc:$pe},KC=Xn({opSnippet:Vt.DIV}),Dpe={kernelName:Va,backendName:"webgpu",kernelFunc:KC},Ppe=Nn({opType:bt.RELU}),Fpe={kernelName:lo,backendName:"webgpu",kernelFunc:Ppe},Ope=Nn({opType:bt.RELU6}),Mpe={kernelName:co,backendName:"webgpu",kernelFunc:Ope},zpe=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`
|
|
${nt()}
|
|
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 = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
|
|
|
|
// Compute the four integer indices.
|
|
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
|
|
let sourceCeilRC = vec2<i32>(
|
|
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
|
|
|
|
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
|
|
|
|
let top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
let newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function Lpe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new zpe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var Bpe={kernelName:uo,backendName:"webgpu",kernelFunc:Lpe},Wpe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${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`
|
|
${nt()}
|
|
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 Vpe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new Wpe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Upe={kernelName:Tu,backendName:"webgpu",kernelFunc:Vpe},Gpe=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`
|
|
${nt()}
|
|
|
|
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));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},Hpe={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Gpe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},jpe=Nn({opType:bt.RSQRT,cpuKernelImpl:Hue}),qpe={kernelName:po,backendName:"webgpu",kernelFunc:jpe},Xpe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Xe(e),this.dispatch=Fe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=wn(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}
|
|
|
|
${nt()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getUpdatesCoordsFromFlatIndex(index);
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${t}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${n};
|
|
}
|
|
let updateValue = ${o};
|
|
let flatIndex = getOutputFlatIndex(${r});
|
|
|
|
${i}
|
|
}
|
|
}`}};function Kpe(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 Xpe(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 Zpe={kernelName:Vi,backendName:"webgpu",kernelFunc:Kpe},Ype=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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
let cVal = getC(${e});
|
|
if (cVal >= 1.0) {
|
|
setOutputFlat(index, getA(${t}));
|
|
} else {
|
|
setOutputFlat(index, getB(${t}));
|
|
}
|
|
}
|
|
}
|
|
`}};function Jpe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Ype(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Bn(r.dtype,a.dtype))}var Qpe={kernelName:Ui,backendName:"webgpu",kernelFunc:Jpe},ehe=Nn({opType:bt.SIGMOID}),the={kernelName:fo,backendName:"webgpu",kernelFunc:ehe},nhe=Nn({opType:bt.SIN}),she={kernelName:ho,backendName:"webgpu",kernelFunc:nhe},rhe=Nn({opType:bt.SINH}),ahe={kernelName:Hi,backendName:"webgpu",kernelFunc:rhe},ZC=Xn({opSnippet:Vt.SUB,cpuKernelImpl:Zue,supportsComplex:!0}),ohe={kernelName:xo,backendName:"webgpu",kernelFunc:ZC};function ihe(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=Dx({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 lhe={kernelName:Ao,backendName:"webgpu",kernelFunc:ihe},uhe=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},che={kernelName:ji,backendName:"webgpu",kernelFunc:uhe},dhe=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=wn(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`
|
|
${nt()}
|
|
|
|
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 < ${this.workPerThread}; innerIndex = innerIndex + 1) {
|
|
let curIndex = globalIndex + innerIndex;
|
|
if (curIndex < uniforms.size)
|
|
{
|
|
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
|
|
}
|
|
}
|
|
}
|
|
}`}};function phe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new dhe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=je({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var hhe={kernelName:md,backendName:"webgpu",kernelFunc:phe};function fhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=vc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var mhe={kernelName:qi,backendName:"webgpu",kernelFunc:fhe},ghe=Nn({opType:bt.SQRT}),Ahe={kernelName:mo,backendName:"webgpu",kernelFunc:ghe},yhe={kernelName:$u,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new e0(n.shape,bt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},xhe=Xn({opSnippet:Vt.SQUARED_DIFFERENCE}),bhe={kernelName:yo,backendName:"webgpu",kernelFunc:xhe},vhe=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=wn(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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getX(${t}));
|
|
}
|
|
}
|
|
`}};function whe(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}=Ft.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=Ft.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=Xue(h,N,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let I=new vhe(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 khe={kernelName:Xi,backendName:"webgpu",kernelFunc:whe};function She(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]=Kue(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Ihe={kernelName:gd,backendName:"webgpu",kernelFunc:She},Che=Nn({opType:bt.TANH}),The={kernelName:bo,backendName:"webgpu",kernelFunc:Che},Nhe=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=Ehe(this.rank,"uniforms.");return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function Ehe(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 Rhe(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=Yue(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Nhe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var $he={kernelName:Yr,backendName:"webgpu",kernelFunc:Rhe},_he=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`
|
|
${nt()}
|
|
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));
|
|
}
|
|
}
|
|
}
|
|
`}},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; k : i32;",this.shaderKey="merge"}getUserCode(){return`
|
|
${nt()}
|
|
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 Phe(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]=Jue(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 _he(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 Dhe([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 Fhe={kernelName:Zi,backendName:"webgpu",kernelFunc:Phe},Ohe=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;
|
|
}
|
|
|
|
${nt()}
|
|
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 Mhe(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 Ohe(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 zhe={kernelName:Yi,backendName:"webgpu",kernelFunc:Mhe};function Lhe(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 Bhe={kernelName:Ji,backendName:"webgpu",kernelFunc:Lhe},Whe=[yue,tce,sce,oce,pce,fce,gce,yce,kce,Tce,Ece,Dce,wue,Mce,Wce,Hce,qce,Kce,Jce,tde,sde,lde,cde,pde,fde,hde,gde,yde,bde,Cde,wde,Sde,Ede,$de,Dde,Ode,Lde,Wde,Ude,vue,Fce,Hde,qde,Kde,Yde,Qde,epe,npe,rpe,ope,lpe,cpe,ppe,rde,fpe,gpe,ype,Sce,bpe,wpe,Spe,Npe,Rpe,Cpe,_pe,Ice,Dpe,Fpe,Mpe,gue,Bpe,Upe,Hpe,qpe,Zpe,Qpe,the,she,ahe,vce,khe,Ihe,lhe,che,mhe,hhe,Ahe,yhe,bhe,ohe,ode,The,$he,Fhe,zhe,cce,Bhe,xpe];for(let e of Whe)cr(e);var Vhe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=JC(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=JC(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function JC(e,t){return`${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>"};
|
|
|
|
${nt()}
|
|
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}}},Uhe=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}}},Ghe=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),e6=class extends su{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,!Tx())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 Vhe(this.device),this.tensorMap=new td(this,rs()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().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)*Cx(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)*Cx(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 Uhe),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),Z().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=Ide(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 Z().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=Ghe){return Z().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)}},Fx=e6;Fx.nextDataId=0;var t6={};Oe(t6,{WebGPUBackend:()=>Fx,webgpu_util:()=>bC});Pu.isBrowser()&&Tx()&&ul("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().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 Fx(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 Hhe(e){n6=e.wasm.cwrap(ko,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jhe(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 qhe={kernelName:ko,backendName:"wasm",setupFunc:Hhe,kernelFunc:jhe};function En(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 Xhe=En(fi);function Kn(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} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Khe=!0,Zhe=Kn(Kr,Khe),s6;function Yhe(e){s6=e.wasm.cwrap($a,null,["array","number","number","number"])}function Jhe(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 Qhe={kernelName:$a,backendName:"wasm",setupFunc:Yhe,kernelFunc:Jhe};function s0(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 efe={kernelName:Ka,backendName:"wasm",kernelFunc:s0},r6;function tfe(e){r6=e.wasm.cwrap(vo,null,["number","array","number","number","number","array","number"])}function Ic(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=sfe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=nfe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=s0({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 nfe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function sfe(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 rfe={kernelName:vo,backendName:"wasm",kernelFunc:Ic,setupFunc:tfe};function Zo(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 afe(e){a6=e.wasm.cwrap(lu,null,["number, number, number"])}function ofe(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}=Zo(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 ife={kernelName:lu,backendName:"wasm",setupFunc:afe,kernelFunc:ofe},o6;function lfe(e){o6=e.wasm.cwrap(uu,null,["number, number, number"])}function ufe(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}=Zo(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 cfe={kernelName:uu,backendName:"wasm",setupFunc:lfe,kernelFunc:ufe},i6;function dfe(e){i6=e.wasm.cwrap(_a,null,["number","number","number","number","number"])}function pfe(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}=Zo(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 hfe={kernelName:_a,backendName:"wasm",kernelFunc:pfe,setupFunc:dfe},l6;function ffe(e){l6=e.wasm.cwrap(Da,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mfe(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 gfe={kernelName:Da,backendName:"wasm",setupFunc:ffe,kernelFunc:mfe};function us(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: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Afe={kernelName:Li,backendName:"wasm",kernelFunc:us},u6;function yfe(e){u6=e.wasm.cwrap(Pa,null,["number","array","number","number","array","number","number","number","number"])}function xfe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${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=us({inputs:{x:r},backend:n,attrs:{shape:b}}),I=us({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 bfe={kernelName:Pa,backendName:"wasm",setupFunc:yfe,kernelFunc:xfe};function Tp(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Ft.parseSliceParams(t,n,s),i=Ft.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=Ft.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=Nm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)vfe(l,u[0],p,a,o);else if(h===3)wfe(l,u[0],u[1],p,a,o);else if(h===4)kfe(l,u[0],u[1],u[2],p,a,o);else{let f=Nm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function vfe(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 wfe(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 kfe(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 Sfe={kernelName:Gi,backendName:"wasm",kernelFunc:Tp};function Ife(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=us({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ic({inputs:{x:h},backend:n,attrs:{perm:c}}),m=us({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 Cfe={kernelName:mi,backendName:"wasm",kernelFunc:Ife};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 Tfe={kernelName:Fa,backendName:"wasm",kernelFunc:Np},Nfe=En(Oa),c6;function Efe(e){c6=e.wasm.cwrap(Zr,null,["number","number","number","number"])}function Rfe(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:Efe,kernelFunc:Rfe};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 s0({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 us({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=Gy(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 _fe={kernelName:gi,backendName:"wasm",kernelFunc:d6},p6;function Dfe(e){p6=e.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pfe(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 Ffe={kernelName:Ma,backendName:"wasm",setupFunc:Dfe,kernelFunc:Pfe},h6;function Ofe(e){h6=e.wasm.cwrap(za,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 Mfe(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],Y=_?P[2]:1,J=_?1:P[1],ne=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,Y,J,ne,re,G,se,pe),oe}var zfe={kernelName:za,backendName:"wasm",setupFunc:Ofe,kernelFunc:Mfe},Lfe=En(La),Bfe=En(Ba),Ox;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Ox||(Ox={}));var f6;function Wfe(e){f6=e.wasm.cwrap(yi,null,["number","number","number","number","array","number","number","number","number","number"])}function Vfe(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,Ox[r],a,b),m!=null&&t.disposeData(m.dataId),y}var Ufe={kernelName:yi,backendName:"wasm",setupFunc:Wfe,kernelFunc:Vfe},m6;function Gfe(e){m6=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number"])}function Hfe(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 jfe={kernelName:Ai,backendName:"wasm",setupFunc:Gfe,kernelFunc:Hfe},g6;function qfe(e){g6=e.wasm.cwrap(xi,null,["number","number","number","array","number","array","array","number","number"])}function Xfe(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 Kfe={kernelName:xi,backendName:"wasm",setupFunc:qfe,kernelFunc:Xfe},A6;function Zfe(e){A6=e.wasm.cwrap(Wa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yfe(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 Jfe={kernelName:Wa,backendName:"wasm",setupFunc:Zfe,kernelFunc:Yfe},Qfe=En(Ua),eme=!1,tme=Kn(bi,eme,"bool"),nme=En(Ga,"float32");function Mx(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)}, ${o}]`),l=o+a+1),i.splice(l,0,1),us({inputs:{x:r},backend:s,attrs:{shape:i}})}var sme={kernelName:vi,backendName:"wasm",kernelFunc:Mx};function y6(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var rme={kernelName:Au,backendName:"wasm",kernelFunc:y6},x6;function ame(e){x6=e.wasm.cwrap(ki,null,["number","number","number","number","number","number"])}function ome(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return x6(a,i,l,c,u,o),r}var ime={kernelName:ki,backendName:"wasm",kernelFunc:ome,setupFunc:ame},lme=En(Ha),ume=!1,cme=Kn(ja,ume),b6;function dme(e){b6=e.wasm.cwrap(qa,null,["number","number","number","number","number","number","number"])}function pme(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return b6(u,d,p,h,f,r,g),m}var hme={kernelName:qa,backendName:"wasm",setupFunc:dme,kernelFunc:pme},v6;function fme(e){v6=e.wasm.cwrap(So,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=Cp[h];if(g==null)throw new Error(`${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 (${y})`);b=G.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,O=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,q=m.padInfo.type==="SAME"?1:0,z=m.batchSize,K=m.inHeight,Y=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(J.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return v6(A,z,K,Y,x,w,k,b,I,N,R,O,q,_,P,T,F,U,y,g,re,f||0,ne),J}var gme={kernelName:So,backendName:"wasm",setupFunc:fme,kernelFunc:mme},w6;function Ame(e){w6=e.wasm.cwrap(Io,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 yme(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 (${y})`);b=G.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,O=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,q=m.padInfo.type==="SAME"?1:0,z=m.batchSize,K=m.inHeight,Y=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(J.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return w6(A,z,K,Y,x,w,k,b,I,N,R,O,q,_,P,T,F,U,y,g,re,f||0,ne),J}var xme={kernelName:Io,backendName:"wasm",setupFunc:Ame,kernelFunc:yme},k6;function bme(e){k6=e.wasm.cwrap(Ii,null,["number","number","number","number","number","number","array","number"])}function vme(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=L2.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 wme={kernelName:Ii,backendName:"wasm",setupFunc:bme,kernelFunc:vme},S6;function kme(e){S6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Sme(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=us({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=us({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 Ime={kernelName:Si,backendName:"wasm",setupFunc:kme,kernelFunc:Sme},Cme=!1,Tme=Kn(Ci,Cme,"bool"),Nme=!1,Eme=Kn(Xa,Nme,"bool"),I6;function Rme(e){I6=e.wasm.cwrap(Ti,null,["number","number","number","number"])}function $me(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;I6(r,Qt[t.dtype],n,o)}return a}var _me={kernelName:Ti,backendName:"wasm",setupFunc:Rme,kernelFunc:$me},Dme=!1,Pme=Kn(Ni,Dme,"bool"),Fme=!1,Ome=Kn(Ei,Fme,"bool"),Mme=En(Za),zme=!1,Lme=Kn(Ri,zme,"bool"),C6;function Bme(e){C6=e.wasm.cwrap(Ya,null,["number","number","number","number"])}function Wme(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}=Zo(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 Vme={kernelName:Ya,backendName:"wasm",setupFunc:Bme,kernelFunc:Wme},Ume=!1,Gme=Kn(Ja,Ume),T6;function Hme(e){T6=e.wasm.cwrap(Qa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jme(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 qme={kernelName:Qa,backendName:"wasm",setupFunc:Hme,kernelFunc:jme},N6;function Xme(e){N6=e.wasm.cwrap(eo,null,["number, number, number"])}function Kme(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}=Zo(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 Zme={kernelName:eo,backendName:"wasm",setupFunc:Xme,kernelFunc:Kme},E6;function Yme(e){E6=e.wasm.cwrap(to,null,["number","number","number","number"])}function Jme(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}=Zo(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 Qme={kernelName:to,backendName:"wasm",setupFunc:Yme,kernelFunc:Jme},e0e=!1,t0e=Kn(no,e0e),zx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(zx||(zx={}));var R6;function n0e(e){R6=e.wasm.cwrap(so,null,["number","array","number","number","array","array","number","number"])}function s0e(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,zx[r],l),i}var r0e={kernelName:so,backendName:"wasm",kernelFunc:s0e,setupFunc:n0e},a0e=!0,o0e=Kn(ro,a0e),i0e=En($i);function Lx(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 l0e(e){$6=e.wasm.cwrap(Di,"number",["number","number","number","number","number"])}function u0e(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}=Lx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var c0e={kernelName:Di,backendName:"wasm",setupFunc:l0e,kernelFunc:u0e},_6;function d0e(e){_6=e.wasm.cwrap(Su,"number",["number","number","number","number","number","bool"])}function p0e(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}=Lx(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[A,x]}var h0e={kernelName:Su,backendName:"wasm",setupFunc:d0e,kernelFunc:p0e},D6;function f0e(e){D6=e.wasm.cwrap(Pi,"number",["number","number","number","number","number","number"])}function m0e(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}=Lx(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[A,x]}var g0e={kernelName:Pi,backendName:"wasm",setupFunc:f0e,kernelFunc:m0e},A0e=!1,y0e=Kn(_i,A0e,"bool"),P6;function x0e(e){P6=e.wasm.cwrap(Oi,null,["number","number","number","number","number"])}function b0e(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 v0e={kernelName:Oi,backendName:"wasm",setupFunc:x0e,kernelFunc:b0e};function w0e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var k0e={kernelName:Fi,backendName:"wasm",kernelFunc:w0e};function S0e(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Mx({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=Mx({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 I0e={kernelName:Mi,backendName:"wasm",kernelFunc:S0e},F6;function C0e(e){F6=e.wasm.cwrap(ao,null,["number","array","number","number","array","array","number","number"])}function T0e(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:ao,backendName:"wasm",kernelFunc:T0e,setupFunc:C0e},N0e=!1,E0e=Kn(oo,N0e),M6;function R0e(e){M6=e.wasm.cwrap(io,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 _0e={kernelName:io,backendName:"wasm",setupFunc:R0e,kernelFunc:$0e},z6;function D0e(e){z6=e.wasm.cwrap(zi,null,["number","number","number","number"])}function P0e(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}=Zo(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 F0e={kernelName:zi,backendName:"wasm",setupFunc:D0e,kernelFunc:P0e},O0e=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=qy(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},M0e={kernelName:Iu,backendName:"wasm",kernelFunc:O0e},z0e=!0,L0e=Kn(Va,z0e),B0e=En(lo),W0e=En(co),L6;function V0e(e){L6=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number","number","number","number"])}function U0e(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 G0e={kernelName:uo,backendName:"wasm",setupFunc:V0e,kernelFunc:U0e},B6;function H0e(e){B6=e.wasm.cwrap(Bi,null,["number","array","number","array","number","number"])}function j0e(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 s0({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=us({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),p}var q0e={kernelName:Bi,backendName:"wasm",kernelFunc:j0e,setupFunc:H0e},W6;function X0e(e){W6=e.wasm.cwrap(el,null,["number","number","number","number","number","number","number","number","array","number","number"])}function K0e(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 Z0e={kernelName:el,backendName:"wasm",kernelFunc:K0e,setupFunc:X0e},Y0e=En(Wi),J0e=En(po),V6;function Q0e(e){V6=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","array","number","number"])}function ege(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}=B2.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 tge={kernelName:Vi,backendName:"wasm",setupFunc:Q0e,kernelFunc:ege},U6;function nge(e){U6=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function sge(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 rge={kernelName:Ui,backendName:"wasm",kernelFunc:sge,setupFunc:nge},G6;function age(e){G6=e.wasm.cwrap(fo,null,["number","number"])}function oge(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 ige={kernelName:"Sigmoid",backendName:"wasm",setupFunc:age,kernelFunc:oge},lge=En(ho),H6;function uge(e){H6=e.wasm.cwrap(Ao,null,["number","number","number","number"])}function cge(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 dge={kernelName:Ao,backendName:"wasm",setupFunc:uge,kernelFunc:cge};function pge(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=us({inputs:{x:c},backend:n,attrs:{shape:u}}),x=Ic({inputs:{x:m},backend:n,attrs:{perm:d}}),w=us({inputs:{x},backend:n,attrs:{shape:p}});return n.disposeData(c.dataId),n.disposeData(m.dataId),n.disposeData(x.dataId),w}var hge={kernelName:ji,backendName:"wasm",kernelFunc:pge};function fge(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 mge={kernelName:qi,backendName:"wasm",kernelFunc:fge},gge=En(mo),Age=En($u),yge=!0,xge=Kn(yo,yge),j6;function bge(e){j6=e.wasm.cwrap(wo,null,["number","number","number","number"])}function vge(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 wge={kernelName:wo,backendName:"wasm",setupFunc:bge,kernelFunc:vge},q6;function kge(e){q6=e.wasm.cwrap(Xi,null,["number","array","number","array","array","array","array","array","number","number"])}function Sge(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}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=us({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: ${r.shape.length}`);let k=Ft.computeOutShape(x,y,b),I=Tp({inputs:{x:r},backend:t,attrs:{begin:x,size:k}});w=us({inputs:{x:I},backend:t,attrs:{shape:f}}),t.disposeData(I.dataId)}else{let k=t.makeOutput(h,"float32"),I=t.dataIdMap.get(r.dataId).id,N=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),R=new Uint8Array(new Int32Array(x).buffer),O=new Uint8Array(new Int32Array(y).buffer),_=new Uint8Array(new Int32Array(b).buffer),P=new Uint8Array(new Int32Array(h).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(h)).buffer),F=t.dataIdMap.get(k.dataId).id;q6(I,N,r.shape.length,R,O,_,P,T,h.length,F),w=us({inputs:{x:k},backend:t,attrs:{shape:f}}),t.disposeData(k.dataId)}return w}var Ige={kernelName:Xi,backendName:"wasm",setupFunc:kge,kernelFunc:Sge},Cge=!0,Tge=Kn(xo,Cge),X6;function Nge(e){X6=e.wasm.cwrap(go,null,["number","number","number","number"])}function Ege(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Zo(o,r,t),f=d;if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;X6(l,A,Qt[x.dtype],y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var Rge={kernelName:go,backendName:"wasm",setupFunc:Nge,kernelFunc:Ege},$ge=En(Ki),_ge=En(bo),K6;function Dge(e){K6=e.wasm.cwrap(Yr,null,["number","array","number","array","number","number"])}function Pge(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 Fge={kernelName:Yr,backendName:"wasm",setupFunc:Dge,kernelFunc:Pge},Z6;function Oge(e){Z6=e.wasm.cwrap(Zi,null,["number","array","number","number","number","bool","number","number"])}var Mge=({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]},zge={kernelName:Zi,backendName:"wasm",setupFunc:Oge,kernelFunc:Mge},Y6;function Lge(e){Y6=e.wasm.cwrap(Yi,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Bge(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 Wge={kernelName:Yi,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge};function Vge(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 Uge={kernelName:Ji,backendName:"wasm",kernelFunc:Vge};function Gge(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var Hge={kernelName:Qi,backendName:"wasm",kernelFunc:Gge},jge=[Xhe,Zhe,Qhe,ife,cfe,hfe,gfe,bfe,Cfe,Tfe,Nfe,$fe,_fe,Ffe,zfe,Lfe,Bfe,Ufe,jfe,Kfe,Jfe,Qfe,tme,nme,sme,rme,ime,lme,cme,qhe,hme,gme,xme,wme,Ime,Tme,Eme,efe,_me,Pme,Ome,Mme,Lme,Vme,Gme,qme,Zme,Qme,t0e,r0e,o0e,i0e,c0e,h0e,g0e,y0e,v0e,k0e,I0e,O6,E0e,_0e,F0e,M0e,L0e,B0e,W0e,Afe,G0e,q0e,Z0e,J0e,Y0e,tge,rge,ige,lge,Sfe,dge,hge,mge,gge,Age,xge,wge,Ige,Tge,Rge,$ge,_ge,Fge,zge,Wge,rfe,Uge,Hge];for(let e of jge)cr(e);var Bx=Z();Bx.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])));Bx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Bx.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(WE()),qge='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()}}}}',Xge=di(VE()),Q6=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(n8),Vx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new td(this,rs())}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 Yge(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 ${t}`)}}};function Kge(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function e8(e,t,n){if(r0!=null)return r0;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 Zge(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=qge,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?e8(e,t,Ep!=null?Ep:l):l+i},Wx&&(r.instantiateWasm=Kge(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&&r0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+J6.default.toString()],{type:"text/javascript"}),o=(0,J6.default)(r)):o=(0,Xge.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 Yge(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 ${t}`)}}var Jge=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],r0=null,Ep=null,Rp={},$p=!1,Wx=!1;function Qge(e,t=!1){if(q2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),$p)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");r0=e,Wx=t}function t8(e,t=!1){if($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=Jge.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.`)}Wx=t}var n8=-1,Vx=-1;function e2e(e){n8=e}function t2e(){if(Vx===-1)throw new Error("WASM backend not initialized.");return Vx}var n2e="0.0.0",s2e=2;ul("wasm",async()=>{let{wasm:e}=await Zge();return new Q6(e)},s2e);var Yo="3.11.0-20211110",s8={tfjs:Yo,"tfjs-core":Yo,"tfjs-data":Yo,"tfjs-layers":Yo,"tfjs-converter":Yo,"tfjs-backend-cpu":Yo,"tfjs-backend-webgl":Yo,"tfjs-backend-wasm":Yo},_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 Ux=(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);if(this.gl.shaderSource(s,t),this.gl.compileShader(s),!this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS))throw new Error(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`);return s});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(),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))throw new Error(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);this.gl.useProgram(this.id),Ux(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);Ux(n,"uniform",this.uniform),Ux(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=Zn(100,100),c={},u={INTERMEDIATE:1},d=l.getContext("webgl");if(!d)throw new Error("filter: cannot get webgl context");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;i=new c8(d,r8,x);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);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);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);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);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 a0(e){let t=e.shape.length===4?ot(e):e,n=Ht(t,3,2),s=[Po(n[0]),Po(n[1]),Po(n[2])],r=[An(n[0]),An(n[1]),An(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=[ge(n[0],s[0]),ge(n[1],s[1]),ge(n[2],s[2])],l=[ge(r[0],s[0]),ge(r[1],s[1]),ge(r[2],s[2])],c=[me(o,l[0]),me(o,l[1]),me(o,l[2])],u=[L(i[0],c[0]),L(i[1],c[1]),L(i[2],c[2])],d=yn([u[0],u[1],u[2]],2),p=H(d,[1,t.shape[0],t.shape[1],3]);return te([...n,...s,...r,...i,...l,...c,...u,d,t]),p}var o0=2048,it=null,ln=null,Cc=null,_t,da={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function Zn(e,t){let n;if(de.browser)if(de.worker)n=new OffscreenCanvas(e,t);else{if(typeof document=="undefined")throw new Error("attempted to run in web worker but offscreenCanvas is not supported");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 Gx(e,t){let n=t||Zn(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&&ee("input is missing"),{tensor:null,canvas:null};if(!(e instanceof Je)&&!(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 type is not recognized");if(e instanceof Je){let s=null;if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(!e.shape)throw new Error("input tensor has no shape");if(e.shape.length===3){if(e.shape[2]===3)s=Kt(e,0);else if(e.shape[2]===4){let r=fl(e,[0,0,0],[-1,-1,3]);s=Kt(r,0),te(r)}}else e.shape.length===4&&(e.shape[3]===3?s=Wn(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(`could not process input tensor with shape: ${e.shape}`);if(s.dtype==="int32"){let r=fe(s,"float32");te(s),s=r}return{tensor:s,canvas:t.filter.return?ln:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&ee("input stream is not ready"),{tensor:null,canvas:it};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&&ee("cannot determine input dimensions"),{tensor:null,canvas:it};let a=s,o=r;if(a>o0&&(a=o0,o=Math.trunc(a*r/s)),o>o0&&(o=o0,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 cannot determine dimension");(!it||(it==null?void 0:it.width)!==a||(it==null?void 0:it.height)!==o)&&(it=Zn(a,o));let i=it.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,it==null?void 0:it.width,it==null?void 0:it.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,it==null?void 0:it.width,it==null?void 0:it.height),(!ln||it.width!==ln.width||(it==null?void 0:it.height)!==(ln==null?void 0:ln.height))&&(ln=Zn(it.width,it.height)),t.filter.enabled&&de.webgl.supported){if(_t||(_t=de.browser?new d8:null),de.filter=!!_t,!_t)return{tensor:null,canvas:it};_t.reset(),t.filter.brightness!==0&&_t.add("brightness",t.filter.brightness),t.filter.contrast!==0&&_t.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&_t.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&_t.add("blur",t.filter.blur),t.filter.saturation!==0&&_t.add("saturation",t.filter.saturation),t.filter.hue!==0&&_t.add("hue",t.filter.hue),t.filter.negative&&_t.add("negative"),t.filter.sepia&&_t.add("sepia"),t.filter.vintage&&_t.add("brownie"),t.filter.sepia&&_t.add("sepia"),t.filter.kodachrome&&_t.add("kodachrome"),t.filter.technicolor&&_t.add("technicolor"),t.filter.polaroid&&_t.add("polaroid"),t.filter.pixelate!==0&&_t.add("pixelate",t.filter.pixelate),_t.get()>0?ln=_t.apply(it):ln=_t.draw(it)}else Gx(it,ln),_t&&(_t=null),de.filter=!!_t;if(!n)return{tensor:null,canvas:ln};if(!ln)throw new Error("cannot create output canvas");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=ut(p,[e.height,e.width,c],"int32")}else if((!Cc||ln.width!==Cc.width||ln.height!==Cc.height)&&(Cc=Zn(ln.width,ln.height)),Ks&&de.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=Ks.fromPixels(ln):(Cc=Gx(ln),l=Ks.fromPixels(Cc));else{let f=Gx(ln).getContext("2d").getImageData(0,0,a,o);c=f.data.length/a/o;let m=new Uint8Array(f.data.buffer);l=ut(m,[a,o,c])}if(c===4){let p=fl(l,[0,0,0],[-1,-1,3]);te(l),l=p}if(!l)throw new Error("cannot create tensor from input");let u=fe(l,"float32"),d=t.filter.equalization?await a0(u):Kt(u,0);return te([l,u]),{tensor:d,canvas:t.filter.return?ln:null}}}async function p8(e,t){let n=!1;if(e.cacheSensitivity===0)return n;if(!da.inputTensor)da.inputTensor=Wn(t);else if(da.inputTensor.shape[1]!==t.shape[1]||da.inputTensor.shape[2]!==t.shape[2])te(da.inputTensor),da.inputTensor=Wn(t);else{let s={};s.diff=ge(t,da.inputTensor),s.squared=L(s.diff,s.diff),s.sum=Se(s.squared);let a=(await s.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;te([da.inputTensor,s.diff,s.squared,s.sum]),da.inputTensor=Wn(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||ee("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||ee("input tensors must be of shape [1, height, width, 3]:",t.shape,n.shape),0;s.input1=Wn(t),s.input2=t.shape[1]!==n.shape[1]||t.shape[2]!==n.shape[2]?Ie.resizeBilinear(n,[t.shape[1],t.shape[2]]):Wn(n),s.diff=ge(s.input1,s.input2),s.squared=L(s.diff,s.diff),s.sum=Se(s.squared);let a=(await s.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return te([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(rs().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 Z().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Zn(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"),this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name),this.kernels=Jr(Rs()).map(s=>s.kernelName.toLowerCase())}async updateCPU(){let t={model:"",flags:[]};if(this.node&&this.platform.startsWith("linux")){let n=Ca("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 Hx="2.5.1";var cs,jx=[],a2e=["white","black","asian","indian","other"],o2e=[15,23,28,35.5,45.5,55.5,65],m8=0,g8=0,qx=Number.MAX_SAFE_INTEGER;async function A8(e){return de.initial&&(cs=null),cs?e.debug&&ee("cached model:",cs.modelUrl):(cs=await Be(We(e.modelBasePath,e.face.gear.modelPath)),!cs||!cs.modelUrl?ee("load model failed:",e.face.gear.modelPath):e.debug&&ee("load model:",cs.modelUrl)),cs}async function Xx(e,t,n,s){var o,i;if(!cs)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=qx<(((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&&jx[n]?(qx++,jx[n]):(qx=0,new Promise(async l=>{var A,x;if(!(cs==null?void 0:cs.inputs[0].shape))return;let c={},u=[[0,.1,.9,.9]];c.resize=Ie.cropAndResize(e,u,[0],[cs.inputs[0].shape[2],cs.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]=cs.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:a2e[y]});d.race.sort((y,b)=>b.score-y.score);let m=Array.from(await c.age.data()).map((y,b)=>[o2e[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=>te(c[y])),jx[n]=d,m8=s,g8=ie(),l(d)}))}var Pn,i0=[],y8=0,x8=0,Kx=Number.MAX_SAFE_INTEGER;async function b8(e){return de.initial&&(Pn=null),Pn?e.debug&&ee("cached model:",Pn.modelUrl):(Pn=await Be(We(e.modelBasePath,e.face.ssrnet.modelPathAge)),!Pn||!Pn.modelUrl?ee("load model failed:",e.face.ssrnet.modelPathAge):e.debug&&ee("load model:",Pn.modelUrl)),Pn}async function Zx(e,t,n,s){var o,i,l,c;if(!Pn)return{age:0};let r=Kx<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ie()-x8;return t.skipAllowed&&r&&a&&y8===s&&((l=i0[n])==null?void 0:l.age)&&((c=i0[n])==null?void 0:c.age)>0?(Kx++,i0[n]):(Kx=0,new Promise(async u=>{if(!(Pn==null?void 0:Pn.inputs)||!Pn.inputs[0]||!Pn.inputs[0].shape)return;let d={};d.resize=Ie.resizeBilinear(e,[Pn.inputs[0].shape[2],Pn.inputs[0].shape[1]],!1),d.enhance=L(d.resize,255);let p={age:0};if(t.face.ssrnet.enabled&&(d.age=Pn.execute(d.enhance)),d.age){let h=await d.age.data();p.age=Math.trunc(10*h[0])/10}Object.keys(d).forEach(h=>te(d[h])),i0[n]=p,y8=s,x8=ie(),u(p)}))}var ds,l0=[],v8=0,w8=0,Yx=Number.MAX_SAFE_INTEGER,Jx=[.2989,.587,.114];async function k8(e){return de.initial&&(ds=null),ds?e.debug&&ee("cached model:",ds.modelUrl):(ds=await Be(We(e.modelBasePath,e.face.ssrnet.modelPathGender)),!ds||!ds.modelUrl?ee("load model failed:",e.face.ssrnet.modelPathGender):e.debug&&ee("load model:",ds.modelUrl)),ds}async function Qx(e,t,n,s){var o,i,l,c;if(!ds)return{gender:"unknown",genderScore:0};let r=Yx<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ie()-w8;return t.skipAllowed&&r&&a&&v8===s&&((l=l0[n])==null?void 0:l.gender)&&((c=l0[n])==null?void 0:c.genderScore)>0?(Yx++,l0[n]):(Yx=0,new Promise(async u=>{if(!(ds==null?void 0:ds.inputs[0].shape))return;let d={};d.resize=Ie.resizeBilinear(e,[ds.inputs[0].shape[2],ds.inputs[0].shape[1]],!1),d.enhance=X(()=>{let[f,m,g]=Ht(d.resize,3,3),A=L(f,Jx[0]),x=L(m,Jx[1]),y=L(g,Jx[2]),b=Mu([A,x,y]);return L(ge(b,.5),2)});let p={gender:"",genderScore:0};t.face.ssrnet.enabled&&(d.gender=ds.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=>te(d[f])),l0[n]=p,v8=s,w8=ie(),u(p)}))}var un,u0=[],eb=Number.MAX_SAFE_INTEGER,S8=0,I8=0;async function C8(e){var t,n;return de.initial&&(un=null),un?e.debug&&ee("cached model:",un.modelUrl):(un=await Be(We(e.modelBasePath,((t=e.face.antispoof)==null?void 0:t.modelPath)||"")),!un||!un.modelUrl?ee("load model failed:",(n=e.face.antispoof)==null?void 0:n.modelPath):e.debug&&ee("load model:",un.modelUrl)),un}async function tb(e,t,n,s){var o,i;if(!un)return 0;let r=(((o=t.face.antispoof)==null?void 0:o.skipTime)||0)>ie()-I8,a=eb<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&S8===s&&u0[n]?(eb++,u0[n]):(eb=0,new Promise(async l=>{let c=Ie.resizeBilinear(e,[(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[2]:0,(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[1]:0],!1),u=un==null?void 0:un.execute(c),d=(await u.data())[0];u0[n]=Math.round(100*d)/100,S8=s,I8=ie(),te([c,u]),l(u0[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]},nb={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]},sb=[{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 i2e=[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],l2e=[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],u2e=[33,133,362,263,1,78,308],H1e=i2e.map(e=>Pp[e]),j1e=l2e.map(e=>Pp[e]),q1e=u2e.map(e=>Pp[e]);var Fp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],c0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],rb=(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],ab=(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],T8=(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}},ob=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=Ie.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n),o=me(a,255);return te(a),o},Op=(e,t)=>{let n=c0(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=c0(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}},d0=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}},ib=[[1,0,0],[0,1,0],[0,0,1]],c2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),d2e=(e,t)=>c2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var N8=(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},p2e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},E8=(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],p2e(t,a)))}return n},R8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=N8(t[0],t[1]),o=E8(a,r),i=N8(-t[0],-t[1]);return E8(o,i)},h2e=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]]},f2e=(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 _8(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?R8(n,[0,0]):ib,c=i?o.map(p=>[...f2e(p,l),p[2]]):o,u=i?h2e(s):ib,d=[...c0({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 lb(e,t,n,s){let r=t.landmarks.length>=nb.count?nb.symmetryLine:Dp.symmetryLine,a=0,o=ib,i;if(e&&de.kernels.includes("rotatewithoffset"))if(a=d2e(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=c0({startPoint:t.startPoint,endPoint:t.endPoint}),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=Ie.rotateWithOffset(n,a,0,u);o=R8(-a,c),i=ob(t,d,[s,s]),te(d)}else i=ob(t,n,[s,s]);else i=ob(t,n,[s,s]);return[a,o,i]}var D8=6,Ws,P8=[],F8=null,Vs=0,p0=()=>Vs;async function O8(e){var t,n;return de.initial&&(Ws=null),Ws?e.debug&&ee("cached model:",Ws.modelUrl):(Ws=await Be(We(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Ws||!Ws.modelUrl?ee("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&ee("load model:",Ws.modelUrl)),Vs=Ws.inputs[0].shape?Ws.inputs[0].shape[2]:0,Vs===-1&&(Vs=64),P8=$8(Vs),F8=fr(P8),Ws}function m2e(e){let t={};t.boxStarts=De(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,F8),t.boxSizes=De(e,[0,3],[-1,2]),t.boxSizesNormalized=me(t.boxSizes,Vs),t.centersNormalized=me(t.centers,Vs),t.halfBoxSize=me(t.boxSizesNormalized,2),t.starts=ge(t.centersNormalized,t.halfBoxSize),t.ends=ue(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Vs),t.endNormalized=L(t.ends,Vs);let n=Wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>te(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=Ie.resizeBilinear(e,[Vs,Vs]),n.div=me(n.resized,127.5),n.normalized=ge(n.div,.5);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=wt([d[0],d[2]],2),n.concat512=wt([d[1],d[3]],2),n.concat=wt([n.concat512,n.concat384],1),n.batch=ot(n.concat,0)}else n.batch=ot(s);te(s),n.boxes=m2e(n.batch),n.logits=De(n.batch,[0,0],[-1,1]),n.sigmoid=fs(n.logits),n.scores=ot(n.sigmoid),n.nms=await Ie.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=De(n.boxes,[r[d],0],[1,-1]),h.slice=De(n.batch,[r[d],D8-1],[1,-1]),h.squeeze=ot(h.slice),h.landmarks=H(h.squeeze,[D8,-1]),h.startPoint=De(h.bbox,[0,0],[-1,2]),h.endPoint=De(h.bbox,[0,2],[-1,2]),a.push({box:{startPoint:await h.startPoint.data(),endPoint:await h.endPoint.data()},landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(f=>te(h[f]))}}return Object.keys(n).forEach(d=>te(n[d])),{boxes:a,scaleFactor:[e.shape[2]/Vs,e.shape[1]/Vs]}}var db={};Yc(db,{connected:()=>cb,kpt:()=>ub});var ub=["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"],cb={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},cn=[null,null],Jo=[[0,0],[0,0]],pb=Number.MAX_SAFE_INTEGER,hb,h0=null,Qo=[[0,0],[0,0],[0,0],[0,0]],L8=0;async function B8(e){var t,n,s;if(z8.initial&&(cn[0]=null),!cn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){cn[0]=await Be(We(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(cn[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,!cn[0]||!cn[0].modelUrl?ee("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&ee("load model:",cn[0].modelUrl)}else e.debug&&cn[0]&&ee("cached model:",cn[0].modelUrl);return cn[0]}async function W8(e){var t;if(z8.initial&&(cn[1]=null),cn[1])e.debug&&ee("cached model:",cn[1].modelUrl);else{cn[1]=await Be(We(e.modelBasePath,e.body.modelPath||""));let n=Object.values(cn[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"))?hb=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:hb=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!cn[1]||!cn[1].modelUrl?ee("load model failed:",e.body.modelPath):e.debug&&ee("load model:",cn[1].modelUrl)}return cn[1]}function g2e(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 A2e(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=Ie.resizeBilinear(t.pad,[Jo[1][0],Jo[1][1]]);let n=me(t.resize,255);return Object.keys(t).forEach(s=>te(t[s])),n}function y2e(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 x2e(e,t,n){var h;let s={};s.input=await A2e(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=cn[1])==null?void 0:h.execute(s.input,hb);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:ub[f],positionRaw:x,position:y,score:A})}if(a<(t.body.minConfidence||0))return null;let c=y2e(i,n),u=g2e(c,[n[0],n[1]]);Object.keys(s).forEach(f=>te(s[f]));let d={};for(let[f,m]of Object.entries(cb)){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 fb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ie()-L8,r=pb<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&h0!==null?pb++:(h0=await x2e(e,t,n),L8=ie(),pb=0),h0?[h0]:[]}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,mb=[],U8=0,gb=Number.MAX_SAFE_INTEGER;async function G8(e){if(de.initial&&(ar=null),ar)e.debug&&ee("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?ee("load model failed:",e.object.modelPath):e.debug&&ee("load model:",ar.modelUrl)}return ar}async function b2e(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=ot(e);te(e);let o=Ht(a,6,1);te(a);let i=yn([o[1],o[0],o[3],o[2]],1),l=ot(i);te(i);let c=ot(o[4]),u=ot(o[5]);o.forEach(f=>te(f));let d=await Ie.nonMaxSuppressionAsync(l,c,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);te(l),te(c),te(u);let p=await d.data();te(d);let h=0;for(let f of p){let m=Math.trunc(100*r[0][f][4])/100,g=r[0][f][5],A=Nc[g].label,[x,y]=[r[0][f][0]/Bl,r[0][f][1]/Bl],b=[x,y,r[0][f][2]/Bl-x,r[0][f][3]/Bl-y],w=[Math.trunc(b[0]*t[0]),Math.trunc(b[1]*t[1]),Math.trunc(b[2]*t[0]),Math.trunc(b[3]*t[1])];s.push({id:h++,score:m,class:g,label:A,box:w,boxRaw:b})}return s}async function Ab(e,t){let n=(t.object.skipTime||0)>ie()-U8,s=gb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&mb.length>0?(gb++,mb):(gb=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Ie.resizeBilinear(e,[Bl,Bl]),i=t.object.enabled?ar==null?void 0:ar.execute(o,["tower_0/detections"]):null;U8=ie(),te(o);let l=await b2e(i,a,t);mb=l,r(l)}))}var bb={};Yc(bb,{connected:()=>xb,kpt:()=>yb});var yb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],xb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var dn,H8=0,Yn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},vb=Number.MAX_SAFE_INTEGER;async function j8(e){return de.initial&&(dn=null),dn?e.debug&&ee("cached model:",dn.modelUrl):(dn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!dn||!dn.modelUrl?ee("load model failed:",e.body.modelPath):e.debug&&ee("load model:",dn.modelUrl)),dn}function v2e(e,t){let[n,s]=e.shape;return X(()=>{let r=H(e,[s*n]),a=An(r,0).dataSync()[0];if(a>t){let o=Zs(r,0),i=Md(o,n).dataSync()[0],l=me(o,$e(n,"int32")).dataSync()[0];return[i,l,a]}return[0,0,a]})}async function wb(e,t){let n=(t.body.skipTime||0)>ie()-H8,s=vb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(Yn.keypoints).length>0?(vb++,[Yn]):(vb=0,new Promise(async r=>{var d;let a=X(()=>{if(!(dn==null?void 0:dn.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[dn.inputs[0].shape[2],dn.inputs[0].shape[1]],!1);return L(p,2).sub(1)}),o;if(t.body.enabled&&(o=dn==null?void 0:dn.execute(a)),H8=ie(),te(a),o){Yn.keypoints.length=0;let p=o.squeeze();te(o);let h=p.unstack(2);te(p);for(let f=0;f<h.length;f++){let[m,g,A]=v2e(h[f],t.body.minConfidence);A>(((d=t.body)==null?void 0:d.minConfidence)||0)&&Yn.keypoints.push({score:Math.round(100*A)/100,part:yb[f],positionRaw:[m/dn.inputs[0].shape[2],g/dn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/dn.inputs[0].shape[2]),Math.round(e.shape[1]*g/dn.inputs[0].shape[1])]})}h.forEach(f=>te(f))}Yn.score=Yn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Yn.keypoints.map(p=>p.position[0]),l=Yn.keypoints.map(p=>p.position[1]);Yn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Yn.keypoints.map(p=>p.positionRaw[0]),u=Yn.keypoints.map(p=>p.positionRaw[1]);Yn.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(xb)){let f=[];for(let m=0;m<h.length-1;m++){let g=Yn.keypoints.find(x=>x.part===h[m]),A=Yn.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])}Yn.annotations[p]=f}r([Yn])}))}var w2e=["angry","disgust","fear","happy","sad","surprise","neutral"],Jn,f0=[],q8=0,X8=0,kb=Number.MAX_SAFE_INTEGER,Sb=[.2989,.587,.114];async function K8(e){var t,n;return de.initial&&(Jn=null),Jn?e.debug&&ee("cached model:",Jn.modelUrl):(Jn=await Be(We(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Jn||!Jn.modelUrl?ee("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&ee("load model:",Jn.modelUrl)),Jn}async function Ib(e,t,n,s){var o,i;if(!Jn)return[];let r=kb<(((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&&f0[n]&&f0[n].length>0?(kb++,f0[n]):(kb=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Jn==null?void 0:Jn.inputs[0].shape)?Jn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[h,h],!1),[p.red,p.green,p.blue]=Ht(p.resize,3,3),p.redNorm=L(p.red,Sb[0]),p.greenNorm=L(p.green,Sb[1]),p.blueNorm=L(p.blue,Sb[2]),p.grayscale=Mu([p.redNorm,p.greenNorm,p.blueNorm]),p.grayscaleSub=ge(p.grayscale,.5),p.grayscaleMul=L(p.grayscaleSub,2),p.emotion=Jn==null?void 0:Jn.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:w2e[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>te(p[m]))}f0[n]=c,q8=s,l(c)}))}var ws,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&&(ws=null),ws?e.debug&&ee("cached model:",t):(ws=await Be(t),ws?e.debug&&ee("load model:",t):ee("load model failed:",e.face.mobilefacenet.modelPath)),ws}async function Tb(e,t,n,s){var o,i;if(!ws)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)&&(ws==null?void 0:ws.inputs[0].shape)){let d={};d.crop=Ie.resizeBilinear(e,[ws.inputs[0].shape[2],ws.inputs[0].shape[1]],!1),d.data=ws==null?void 0:ws.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,k2e=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&&ee("cached model:",or.modelUrl):(or=await Be(We(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!or||!or.modelUrl?ee("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&ee("load model:",or.modelUrl)),ei=or.inputs[0].shape?or.inputs[0].shape[2]:0,ei===-1&&(ei=64),or}function m0(e,t,n,s){for(let r=0;r<sb.length;r++){let{key:a,indices:o}=sb[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 S2e=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(d0([e[n],e[s]]),k2e)),i=Fp(o),l=Ie.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=Ie.flipLeftRight(l);te(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&&ee("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=wt([o,c]);te(o),te(c);let d=or.execute(u);te(u);let p=await d.data();te(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=S2e(e);Math.abs(y)<30?(m0(e,f,"left",null),m0(e,A,"right",null)):y<1?m0(e,f,"left",["EyeUpper0","EyeLower0"]):m0(e,A,"right",["EyeUpper0","EyeLower0"]);let b=sT(e,m,"left"),w=sT(e,x,"right");return e.concat(b).concat(w)}var $c=[],ir=null,pa=0,Rb=Number.MAX_SAFE_INTEGER,aT=0;async function oT(e,t){var i,l,c,u,d,p,h,f,m,g,A,x;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ie()-aT,s=Rb<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||$c.length===0){let y=await M8(e,t);aT=ie(),$c=[];for(let b of y.boxes){let w={startPoint:b.box.startPoint,endPoint:b.box.endPoint,landmarks:b.landmarks,confidence:b.confidence};$c.push(Mp(Op(T8(w,y.scaleFactor),Math.sqrt(((c=t.face.detector)==null?void 0:c.cropFactor)||1.6))))}Rb=0}else Rb++;let r=[],a=[],o=0;for(let y=0;y<$c.length;y++){let b=$c[y],w=0,k,I={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([w,k,I.tensor]=lb(!((u=t.face.mesh)==null?void 0:u.enabled)&&((d=t.face.detector)==null?void 0:d.rotation),b,e,((p=t.face.mesh)==null?void 0:p.enabled)?pa:p0()),(h=t==null?void 0:t.filter)==null?void 0:h.equalization){let N=await a0(I.tensor);te(I.tensor),I.tensor=N}if(I.boxScore=Math.round(100*b.confidence)/100,(f=t.face.mesh)==null?void 0:f.enabled)if(!ir)t.debug&&ee("face mesh detection requested, but model is not loaded");else{let[N,R,O]=ir.execute(I.tensor),_=await R.data();I.faceScore=Math.round(100*_[0])/100;let P=H(O,[-1,3]),T=await P.array();if(te([O,P,R,N]),I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1))b.confidence=I.faceScore;else{((g=t.face.iris)==null?void 0:g.enabled)&&(T=await rT(T,I.tensor,t,pa)),I.mesh=_8(T,b,w,k,pa),I.meshRaw=I.mesh.map(F=>[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/pa]);for(let F of Object.keys(rr))I.annotations[F]=rr[F].map(U=>I.mesh[U]);b=Mp({...Op(d0(I.mesh),((A=t.face.detector)==null?void 0:A.cropFactor)||1.6),confidence:b.confidence}),I.box=rb(b,e),I.boxRaw=ab(b,e),I.score=I.faceScore,a.push(b),te(I.tensor),[w,k,I.tensor]=lb((x=t.face.detector)==null?void 0:x.rotation,b,e,pa)}}else{I.box=rb(b,e),I.boxRaw=ab(b,e),I.score=I.boxScore,I.mesh=b.landmarks.map(N=>[(b.startPoint[0]+b.endPoint[0])/2+(b.endPoint[0]+b.startPoint[0])*N[0]/p0(),(b.startPoint[1]+b.endPoint[1])/2+(b.endPoint[1]+b.startPoint[1])*N[1]/p0()]),I.meshRaw=I.mesh.map(N=>[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/pa]);for(let N of Object.keys(Dp))I.annotations[N]=[I.mesh[Dp[N]]]}r.push(I)}return $c=[...a],r}async function iT(e){var t,n;return de.initial&&(ir=null),ir?e.debug&&ee("cached model:",ir.modelUrl):(ir=await Be(We(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!ir||!ir.modelUrl?ee("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&ee("load model:",ir.modelUrl)),pa=ir.inputs[0].shape?ir.inputs[0].shape[2]:0,pa===-1&&(pa=64),ir}var lT=zl,uT=Pp;var ks,g0=[],cT=0,dT=0,$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&&(ks=null),ks?e.debug&&ee("cached model:",t):(ks=await Be(t),ks?e.debug&&ee("load model:",t):ee("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),ks}function _b(e){let t=e.image||e.tensor||e;if(!(ks==null?void 0:ks.inputs[0].shape))return t;let n=Ie.resizeBilinear(t,[ks.inputs[0].shape[2],ks.inputs[0].shape[1]],!1),s=L(n,255);return te(n),s}async function Db(e,t,n,s){var o,i,l,c;if(!ks)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let r=$b<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ie()-cT;return t.skipAllowed&&r&&a&&dT===s&&((l=g0[n])==null?void 0:l.age)&&((c=g0[n])==null?void 0:c.age)>0?($b++,g0[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=ks==null?void 0:ks.execute(f);cT=ie(),te(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];te(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=>te(R))}g0[n]=d,dT=s,u(d)}))}function A0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function zp(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 Ie.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 y0(e,t=1.5){let n=zp(e),s=A0(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 x0(e){let t=zp(e),n=A0(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 I2e(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 I2e(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 C2e(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],C2e(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=Zt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Zt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=De(t,[0,0],[-1,2]),n.boxSizes=De(t,[0,2],[-1,2]),n.div=me(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=ue(n.div,this.anchorsTensor),n.halfBoxSizes=me(n.boxSizes,this.doubleInputSizeTensor),n.sub=ge(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=>te(n[r])),s}normalizeLandmarks(t,n){let s={};s.reshape=H(t,[-1,7,2]),s.div=me(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=>te(s[a])),r}async predict(t,n){let s={};s.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=me(s.resize,127.5),s.image=ge(s.div,1),s.batched=this.model.execute(s.image),s.predictions=ot(s.batched),s.slice=De(s.predictions,[0,0],[-1,1]),s.sigmoid=fs(s.slice),s.scores=ot(s.sigmoid);let r=await s.scores.data();s.boxes=De(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Ie.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=De(s.norm,[i,0],[1,-1]),l.slice=De(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=>te(l[m]))}return Object.keys(s).forEach(i=>te(s[i])),o}};var T2e=5,bT=1.65,vT=[0,5,9,13,17,1,2],N2e=0,E2e=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 y0(x0(r),T2e)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=y0(x0(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=A0(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=[...zp(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[N2e],c.palmLandmarks[E2e]):0,d=zp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&de.kernels.includes("rotatewithoffset")?Ie.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=me(g,255);te(g),te(h);let[x,y]=this.handPoseModel.execute(A);wT=ie(),te(A);let b=(await x.data())[0];if(te(x),b>=n.hand.minConfidence/4){let w=H(y,[-1,3]),k=await w.array();te(y),te(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;te(y)}else{let u=y0(x0(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 Qn={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=>Qn.nameMapping[e],getPoints:e=>Qn.pointsMapping[e]},ni={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>ni.nameMapping[e]},zt={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=>zt.nameMapping[e]},Wl=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:ha,middle:fa,ring:Vl,pinky:Ul}=Qn,{none:Ir,half:R2e,full:Cr}=ni,{verticalUp:_c,verticalDown:oye,horizontalLeft:zb,horizontalRight:$2e,diagonalUpRight:_2e,diagonalUpLeft:Dc,diagonalDownRight:iye,diagonalDownLeft:lye}=zt,si=new Wl("thumbs up");si.curl(Sr,Ir,1);si.direction(Sr,_c,1);si.direction(Sr,Dc,.25);si.direction(Sr,_2e,.25);for(let e of[Qn.index,Qn.middle,Qn.ring,Qn.pinky])si.curl(e,Cr,1),si.direction(e,zb,1),si.direction(e,$2e,1);var en=new Wl("victory");en.curl(Sr,R2e,.5);en.curl(Sr,Ir,.5);en.direction(Sr,_c,1);en.direction(Sr,Dc,1);en.curl(ha,Ir,1);en.direction(ha,_c,.75);en.direction(ha,Dc,1);en.curl(fa,Ir,1);en.direction(fa,_c,1);en.direction(fa,Dc,.75);en.curl(Vl,Cr,1);en.direction(Vl,_c,.2);en.direction(Vl,Dc,1);en.direction(Vl,zb,.2);en.curl(Ul,Cr,1);en.direction(Ul,_c,.2);en.direction(Ul,Dc,1);en.direction(Ul,zb,.2);en.weight(ha,2);en.weight(fa,2);var ri=new Wl("point");ri.curl(Sr,Cr,1);ri.curl(ha,Ir,.5);ri.curl(fa,Cr,.5);ri.curl(Vl,Cr,.5);ri.curl(Ul,Cr,.5);ri.weight(ha,2);ri.weight(fa,2);var ai=new Wl("middle finger");ai.curl(Sr,Ir,1);ai.curl(ha,Cr,.5);ai.curl(fa,Cr,.5);ai.curl(Vl,Cr,.5);ai.curl(Ul,Cr,.5);ai.weight(ha,2);ai.weight(fa,2);var Pc=new Wl("open palm");Pc.curl(Sr,Ir,.75);Pc.curl(ha,Ir,.75);Pc.curl(fa,Ir,.75);Pc.curl(Vl,Ir,.75);Pc.curl(Ul,Ir,.75);var kT=[si,en,ri,ai,Pc];var D2e=.7,Gl={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 P2e(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>Gl.NO_CURL_START_LIMIT?A=ni.none:g>Gl.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=zt.horizontalLeft:r=zt.horizontalRight:s===Math.abs(t)?t>0?r=zt.horizontalLeft:r=zt.horizontalRight:n>0?r=zt.horizontalLeft:r=zt.horizontalRight,r}function NT(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=zt.verticalDown:r=zt.verticalUp:s===Math.abs(t)?t<0?r=zt.verticalDown:r=zt.verticalUp:n<0?r=zt.verticalDown:r=zt.verticalUp,r}function F2e(e,t,n,s,r,a,o,i){let l,c=NT(e,t,n,s),u=TT(r,a,o,i);return c===zt.verticalUp?u===zt.horizontalLeft?l=zt.diagonalUpLeft:l=zt.diagonalUpRight:u===zt.horizontalLeft?l=zt.diagonalDownLeft:l=zt.diagonalDownRight,l}function O2e(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+=Gl.DISTANCE_VOTE_POWER:m>.66?h+=Gl.DISTANCE_VOTE_POWER:f+=Gl.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,Gl.TOTAL_ANGLE_VOTE_POWER);p+=_[0],h+=_[1],f+=_[2];for(let T of s){let F=CT(T,Gl.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=F2e(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 Qn.all){let o=Qn.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 Qn.all){let o=a===Qn.thumb?1:0,i=Qn.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=P2e(l,c,u),p=O2e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function b0(e){if(!e||e.length===0)return null;let t=ET(e),n={};for(let s of Qn.all)n[Qn.getName(s)]={curl:ni.getName(t.curls[s]),direction:zt.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>=D2e&&t.push({name:s.name,confidence:r})}return t}var $T={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ma,ga,_T;async function Lb(e,t){let n=await _T.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys($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=b0(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&&(ma=null,ga=null),!ma||!ga?([ma,ga]=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&&(!ma||!ma.modelUrl?ee("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&ee("load model:",ma.modelUrl),!ga||!ga.modelUrl?ee("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&ee("load model:",ga.modelUrl))):(e.debug&&ee("cached model:",ma.modelUrl),e.debug&&ee("cached model:",ga.modelUrl));let t=new Ob(ma);return _T=new Mb(t,ga),[ma,ga]}function Hl(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 v0(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 It=[null,null],M2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oi=[[0,0],[0,0]],z2e=["hand","fist","pinch","point","face","tip","pinchtip"],PT=4,FT=1.6,L2e=512,B2e=1.4,w0=Number.MAX_SAFE_INTEGER,Vb=0,Aa=[0,0],qt={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&&(It[0]=null),It[0])e.debug&&ee("cached model:",It[0].modelUrl);else{k0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),It[0]=await Be(We(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(It[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,!It[0]||!It[0].modelUrl?ee("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&ee("load model:",It[0].modelUrl)}return It[0]}async function zT(e){var t,n;if(de.initial&&(It[1]=null),It[1])e.debug&&ee("cached model:",It[1].modelUrl);else{It[1]=await Be(We(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(It[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,!It[1]||!It[1].modelUrl?ee("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&ee("load model:",It[1].modelUrl)}return It[1]}async function W2e(e,t){let n=[];if(!e||!It[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,L2e),o=Math.round(a*r/8)*8;s.resize=Ie.resizeBilinear(e,[a,o]),s.cast=fe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await It[0].executeAsync(s.cast,M2e),s.boxes=ot(s.rawBoxes,[0,2]),s.scores=ot(s.rawScores,[0]);let i=as(s.scores,1);te(i[PT]),i.splice(PT,1),s.filtered=yn(i,1),te(i),s.max=An(s.filtered,1),s.argmax=Zs(s.filtered,1);let l=0;s.nms=await Ie.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=De(s.boxes,p,1),f=await h.data();te(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=v0(m,B2e),A=Wb(g),x=[Math.trunc(m[0]*Aa[0]),Math.trunc(m[1]*Aa[1]),Math.trunc(m[2]*Aa[0]),Math.trunc(m[3]*Aa[1])],y=u[p],b=z2e[d[p]],w={id:l++,score:y,box:x,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>te(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&&It[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=Ie.cropAndResize(e,[t.boxCrop],[0],[oi[1][0],oi[1][1]],"bilinear"),r.cast=fe(r.crop,"float32"),r.div=me(r.cast,255),[r.score,r.keypoints]=It[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=>[Aa[0]*(u[0]+t.boxRaw[0]),Aa[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=b0(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=>te(r[i]))}return s}async function Gb(e,t){var r,a;if(!It[0]||!It[1]||!((r=It[0])==null?void 0:r.inputs[0].shape)||!((a=It[1])==null?void 0:a.inputs[0].shape))return[];Aa=[e.shape[2]||0,e.shape[1]||0],w0++;let n=(t.hand.skipTime||0)>ie()-Vb,s=w0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?qt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ie()-Vb,l=w0<3*(t.hand.skipFrames||0);t.skipAllowed&&qt.hands.length===t.hand.maxDetected?qt.hands=await Promise.all(qt.boxes.map(u=>Ub(e,u,t))):t.skipAllowed&&i&&l&&qt.hands.length>0?qt.hands=await Promise.all(qt.boxes.map(u=>Ub(e,u,t))):(qt.boxes=await W2e(e,t),Vb=ie(),qt.hands=await Promise.all(qt.boxes.map(u=>Ub(e,u,t))),w0=0);let c=[...qt.boxes];if(qt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<qt.hands.length;u++){let d=DT(qt.hands[u].keypoints,Aa);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&qt.hands[u].fingerScore&&qt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=v0(d.box,FT),h=v0(d.boxRaw,FT),f=Wb(h);qt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<qt.hands.length;u++){let d=Hl(qt.hands[u].keypoints,Aa);qt.hands[u].box=d.box,qt.hands[u].boxRaw=d.boxRaw}o(qt.hands)})}var pn,S0=[],Hb=Number.MAX_SAFE_INTEGER,LT=0,BT=0;async function WT(e){var t,n;return de.initial&&(pn=null),pn?e.debug&&ee("cached model:",pn.modelUrl):(pn=await Be(We(e.modelBasePath,((t=e.face.liveness)==null?void 0:t.modelPath)||"")),!pn||!pn.modelUrl?ee("load model failed:",(n=e.face.liveness)==null?void 0:n.modelPath):e.debug&&ee("load model:",pn.modelUrl)),pn}async function jb(e,t,n,s){var o,i;if(!pn)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&&S0[n]?(Hb++,S0[n]):(Hb=0,new Promise(async l=>{let c=Ie.resizeBilinear(e,[(pn==null?void 0:pn.inputs[0].shape)?pn.inputs[0].shape[2]:0,(pn==null?void 0:pn.inputs[0].shape)?pn.inputs[0].shape[1]:0],!1),u=pn==null?void 0:pn.execute(c),d=(await u.data())[0];S0[n]=Math.round(100*d)/100,LT=s,BT=ie(),te([c,u]),l(S0[n])}))}var Zb={};Yc(Zb,{connected:()=>C0,horizontal:()=>qb,kpt:()=>I0,relative:()=>Kb,vertical:()=>Xb});var I0=["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"]]],C0={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,Ss={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]&&Ss.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-Ss.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-Ss.keypoints[t].positionRaw[1])];n[0]<VT&&n[1]<VT?e[t]=Ss.keypoints[t]:Ss.keypoints[t]=e[t]}else Ss.keypoints[t]=e[t];return e}function GT(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Ss.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,Ss.padding),n.resize=Ie.resizeBilinear(n.pad,[t,t]);let s=fe(n.resize,"int32");return Object.keys(n).forEach(r=>te(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]+Ss.padding[2][0]+Ss.padding[2][1])/t[0]-Ss.padding[2][0],s.position[1]*(t[1]+Ss.padding[1][0]+Ss.padding[1][1])/t[1]-Ss.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Hl(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Fn,T0=0,Jb=Number.MAX_SAFE_INTEGER,jl={boxes:[],bodies:[],last:0};async function jT(e){return de.initial&&(Fn=null),Fn?e.debug&&ee("cached model:",Fn.modelUrl):(k0(["size"],e),Fn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!Fn||!Fn.modelUrl?ee("load model failed:",e.body.modelPath):e.debug&&ee("load model:",Fn.modelUrl)),T0=Fn.inputs[0].shape?Fn.inputs[0].shape[2]:0,T0===-1&&(T0=256),Fn}async function V2e(e,t,n,s){let r=e[0][0],a=[],o=0;for(let d=0;d<r.length;d++)if(o=r[d][2],o>t.body.minConfidence){let p=[(s[3]-s[1])*r[d][1]+s[1],(s[2]-s[0])*r[d][0]+s[0]];a.push({score:Math.round(100*o)/100,part:I0[d],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}o=a.reduce((d,p)=>p.score>d?p.score:d,0);let i=[],l=Hl(a.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(C0)){let h=[];for(let f=0;f<p.length-1;f++){let m=a.find(A=>A.part===p[f]),g=a.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:0,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:a,annotations:c};return Yb(u),i.push(u),i}async function U2e(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){let l=[];for(let p=0;p<17;p++){let h=o[3*p+2];if(h>t.body.minConfidence){let f=[(s[3]-s[1])*o[3*p+1]+s[1],(s[2]-s[0])*o[3*p+0]+s[0]];l.push({part:I0[p],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((n.shape[2]||0)*f[0]),Math.round((n.shape[1]||0)*f[1])]})}}let c=Hl(l.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,h]of Object.entries(C0)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(x=>x.part===h[m]),A=l.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])}u[p]=f}let d={id:a,score:i,box:c.box,boxRaw:c.boxRaw,keypoints:[...l],annotations:u};Yb(d),r.push(d)}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function Qb(e,t){if(!Fn||!(Fn==null?void 0:Fn.inputs[0].shape))return[];t.skipAllowed||(jl.boxes.length=0),Jb++;let n=(t.body.skipTime||0)>ie()-jl.last,s=Jb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?jl.bodies:new Promise(async r=>{let a={};Jb=0,a.input=GT(e,T0),a.res=Fn==null?void 0:Fn.execute(a.input),jl.last=ie();let o=await a.res.array();jl.bodies=a.res.shape[2]===17?await V2e(o,t,e,[0,0,1,1]):await U2e(o,t,e,[0,0,1,1]);for(let i of jl.bodies)HT(i,[e.shape[2]||1,e.shape[1]||1]),UT(i.keypoints);Object.keys(a).forEach(i=>te(a[i])),r(jl.bodies)})}var Is,N0=[],qT=0,e5=Number.MAX_SAFE_INTEGER,E0=2.5;async function XT(e){if(!Is||de.initial){Is=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Is.modelSignature.inputs);if(Is.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Is.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!Is||!Is.modelUrl?ee("load model failed:",e.object.modelPath):e.debug&&ee("load model:",Is.modelUrl)}else e.debug&&ee("cached model:",Is.modelUrl);return Is}async function G2e(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-E0/c*I[0],k-E0/c*I[1]],[O,_]=[w+E0/c*I[2]-N,k+E0/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=>te(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 Ie.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),te(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()-qT,s=e5<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&N0.length>0?(e5++,N0):(e5=0,!de.kernels.includes("mod")||!de.kernels.includes("sparsetodense")?N0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Ie.resizeBilinear(e,[Is.inputSize,Is.inputSize],!1),i=me(o,255),l=i.transpose([0,3,1,2]);te(i),te(o);let c;t.object.enabled&&(c=Is.execute(l)),qT=ie(),te(l);let u=await G2e(c,Is.inputSize,a,t);N0=u,r(u)}))}var Lp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],H2e=Lp.length,Bp=Lp.reduce((e,t,n)=>(e[t]=n,e),{}),j2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Kye=j2e.map(([e,t])=>[Bp[e],Bp[t]]),KT=[["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 ZT(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 YT(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+H2e)}}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 JT(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,q2e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],R0=1,Fc=16,X2e=50**2;function QT(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:Lp[n],score:g}}function K2e(e,t,n,s,r){let a=KT.map(([p,h])=>[Bp[p],Bp[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:Lp[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]=QT(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]=QT(p,u[h],f,t,n,s))}return u}function Z2e(e,t,n,s,r){let[a,o]=r.shape,i=!0,l=Math.max(n-R0,0),c=Math.min(n+R0+1,a);for(let u=l;u<c;++u){let d=Math.max(s-R0,0),p=Math.min(s+R0+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 Y2e(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||Z2e(l,c,o,i,t)&&a.enqueue({score:c,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function eN(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?JT(n,t,a.y,a.x)<=X2e:!1})}function J2e(e,t){return t.reduce((s,{position:r,score:a},o)=>(eN(e,r,o)||(s+=a),s),0)/t.length}function Q2e(e,t,n,s,r,a){let o=[],i=Y2e(a,t);for(;o.length<r&&!i.empty();){let l=i.dequeue(),c=r5(l.part,Fc,e);if(eN(o,c,l.part.id))continue;let u=K2e(l,t,e,n,s);u=u.filter(h=>h.score>a);let d=J2e(o,u),p=ZT(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=Ie.resizeBilinear(e,[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]),i=ge(me(fe(o,"float32"),127.5),1),c=Cs.execute(i,q2e).map(u=>ot(u,[0]));return c[1]=c[1].sigmoid(),c}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)te(o);let r=await Q2e(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Cs.inputs[0].shape?YT(r,[e.shape[1],e.shape[2]],[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]):[]}async function tN(e){return!Cs||de.initial?(Cs=await Be(We(e.modelBasePath,e.body.modelPath||"")),!Cs||!Cs.modelUrl?ee("load model failed:",e.body.modelPath):e.debug&&ee("load model:",Cs.modelUrl)):e.debug&&ee("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?ee("load model failed:",e.segmentation.modelPath):e.debug&&ee("load model:",Us.modelUrl)):e.debug&&ee("cached model:",Us.modelUrl),Us}async function nN(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=Ie.resizeBilinear(s.tensor,[Us.inputs[0].shape?Us.inputs[0].shape[1]:0,Us.inputs[0].shape?Us.inputs[0].shape[2]:0],!1),te(s.tensor),o.norm=me(o.resize,255),o.res=Us.execute(o.norm),o.squeeze=ot(o.res,0),o.squeeze.shape[2]===2?(o.softmax=Xu(o.squeeze),[o.bg,o.fg]=as(o.softmax,2),o.expand=Kt(o.fg,2),o.pad=Kt(o.expand,0),o.crop=Ie.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=ot(o.crop,0)):o.data=Ie.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(de.node&&!de.Canvas&&typeof ImageData=="undefined")return n.debug&&ee("canvas support missing"),Object.keys(o).forEach(A=>te(o[A])),{data:i,canvas:null,alpha:null};let l=Zn(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=Zn(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=Zn(r,a);let A=await Tc(t,n);te(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=>te(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 sN(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=jT(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=tN(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=C8(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=b8(e.config)),e.config.face.enabled&&((N=e.config.face.ssrnet)==null?void 0:N.enabled)&&!e.models.ssrnetgender&&(e.models.ssrnetgender=k8(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=XT(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 rN(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&&ee("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&&ee("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&&ee("model validation:",n,i)}}}var Ct={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 e1e(){let e=Ct.gl;!e||(Ct.extensions=e.getSupportedExtensions())}async function aN(e){var t;if(e.config.backend==="humangl"&&(Ct.name in rs().registry&&(!Ct.gl||!Ct.gl.getParameter(Ct.gl.VERSION))&&(ee("error: humangl backend invalid context"),d5(e)),!X2(Ct.name))){try{Ct.canvas=await Zn(100,100)}catch(s){ee("error: cannot create canvas:",s);return}try{if(Ct.gl=(t=Ct.canvas)==null?void 0:t.getContext("webgl2",Ct.webGLattr),!Ct.gl.getParameter(Ct.gl.VERSION).includes("2.0")){ee("override: using fallback webgl backend as webgl 2.0 is not detected"),e.config.backend="webgl";return}Ct.canvas&&(Ct.canvas.addEventListener("webglcontextlost",async r=>{throw ee("error: humangl:",r.type),ee("possible browser memory leak using webgl or conflict with multiple backend registrations"),e.emit("error"),new Error("browser webgl error")}),Ct.canvas.addEventListener("webglcontextrestored",r=>{ee("error: humangl context restored:",r)}),Ct.canvas.addEventListener("webglcontextcreationerror",r=>{ee("error: humangl context create:",r)}))}catch(s){ee("error: cannot get WebGL context:",s);return}try{$m(2,Ct.gl)}catch(s){ee("error: cannot set WebGL context:",s);return}try{let s=new Bm(Ct.gl);ul(Ct.name,()=>new yp(s),Ct.priority)}catch(s){ee("error: cannot register WebGL backend:",s);return}try{Jr("webgl").forEach(r=>{let a={...r,backendName:Ct.name};cr(a)})}catch(s){ee("error: cannot update WebGL backend registration:",s);return}let n=Dr().getGPGPUContext?Dr().getGPGPUContext().gl:null;if(n)ee(`humangl webgl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`);else{ee("error: no current gl context:",n,Ct.gl);return}try{Rr.set("WEBGL_VERSION",2)}catch(s){ee("error: cannot set WebGL backend flags:",s);return}e1e(),ee("backend registered:",Ct.name)}}function t1e(){if(!de.kernels.includes("mod")){let e={kernelName:"Mod",backendName:Rs(),kernelFunc:t=>X(()=>ge(t.inputs.a,L(me(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(()=>uf(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&&ee("running inside web worker"),de.browser&&e.config.backend==="tensorflow"&&(e.config.debug&&ee("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&&ee(`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")ee("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&&ee("enumerated webgpu adapter:",r)}e.config.backend==="humangl"&&await aN(e);let s=Object.keys(rs().registryFactory);if(e.config.debug&&ee("available backends:",s),s.includes(e.config.backend)||(ee(`error: backend ${e.config.backend} not found in registry`),e.config.backend=de.node?"tensorflow":"webgl",e.config.debug&&ee(`override: setting backend ${e.config.backend}`)),e.config.debug&&ee("setting backend:",e.config.backend),e.config.backend==="wasm"){if(e.config.debug&&ee("wasm path:",e.config.wasmPath),typeof(Ml==null?void 0:Ml.setWasmPaths)!="undefined")await t8(e.config.wasmPath);else throw new Error("wasm backend is not loaded");let r=await Z().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&ee(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),e.config.debug&&!r&&ee("warning: wasm simd support is not enabled")}try{await J3(e.config.backend),await lf()}catch(r){return ee("error: cannot set backend:",e.config.backend,r),!1}}if(Rs()==="humangl"&&(Rr.set("CHECK_COMPUTATION_FOR_ERRORS",!1),Rr.set("WEBGL_CPU_FORWARD",!0),Rr.set("WEBGL_USE_SHAPES_UNIFORMS",!0),Rr.set("CPU_HANDOFF_SIZE_THRESHOLD",256),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(ee("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),Rr.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),Dr().getGPGPUContext)){let s=await Dr().getGPGPUContext().gl;e.config.debug&&ee(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}Rs()==="webgpu",Y3(),await lf(),e.performance.initBackend=Math.trunc(ie()-n),e.config.backend=Rs(),await de.updateBackend(),t1e()}return!0}function k0(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&ee("kernelFunc",n,t.backend)}};cr(s)}de.kernels=Jr(Rs()).map(n=>n.kernelName.toLowerCase())}var ya={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1},p5=0,ql=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("invalid canvas")},Oc=e=>Math.round(e*180/Math.PI);function h5(e,t,n,s,r){s=s||0,e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Wp(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function oN(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function n1e(e,t,n){if(!(t.length<2)){if(!n.useCurves||t.length<=2){oN(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function iN(e,t,n,s=5){let r,a,o;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.moveTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),e.closePath(),e.stroke(),e.fill()}async function f5(e,t,n){let s=$n(ya,n);if(!(!t||!e)&&s.drawGestures){let r=ql(e);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(ya,n);if(!t||!e)return;let r=ql(e);for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Wp(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]);oN(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]} ${u.box[1]+u.box[3]/2}
|
|
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];iN(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];iN(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function g5(e,t,n){var a;let s=$n(ya,n);if(!t||!e)return;let r=ql(e);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&&(Wp(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} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)n1e(r,l,s)}}async function A5(e,t,n){let s=$n(ya,n);if(!t||!e)return;let r=ql(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Wp(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(ya,n);if(!t||!e)return;let r=ql(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Wp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function lN(e,t,n){let s=$n(ya,n);if(!t||!e)return;let r=ql(e);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,Wp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function uN(e,t){if(!e||!t)return;ql(t).drawImage(e,0,0)}async function cN(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ie(),r=$n(ya,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 s1e(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 dN(e){if(!e.tensor)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++)s1e(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 te(s),a}var r1e=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}},pN=(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?r1e(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,Y,J,ne,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){ee("Face object is disposed:",p[G].tensor);continue}if((h=e.config.face.detector)==null?void 0:h.mask){let we=await dN(p[G]);te(p[G].tensor),p[G].tensor=we}let se=p[G].mesh&&p[G].mesh.length>200?pN(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||ut([]),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||ut([]),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)?tb(p[G].tensor||ut([]),e.config,G,p.length):null:(e.state="run:antispoof",n=ie(),l=((A=e.config.face.antispoof)==null?void 0:A.enabled)?await tb(p[G].tensor||ut([]),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||ut([]),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||ut([]),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)?Xx(p[G].tensor||ut([]),e.config,G,p.length):{}:(e.state="run:gear",n=ie(),r=((w=e.config.face.gear)==null?void 0:w.enabled)?await Xx(p[G].tensor||ut([]),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)?Zx(p[G].tensor||ut([]),e.config,G,p.length):{},a=((I=e.config.face.ssrnet)==null?void 0:I.enabled)?Qx(p[G].tensor||ut([]),e.config,G,p.length):{}):(e.state="run:ssrnet",n=ie(),s=((N=e.config.face.ssrnet)==null?void 0:N.enabled)?await Zx(p[G].tensor||ut([]),e.config,G,p.length):{},a=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await Qx(p[G].tensor||ut([]),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||ut([]),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||ut([]),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||ut([]),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||ut([]),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)&&((Y=(K=p[G])==null?void 0:K.annotations)==null?void 0:Y.leftEyeIris)&&((ne=(J=p[G])==null?void 0:J.annotations)==null?void 0:ne.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)?ot(p[G].tensor):null;te(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 hN=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},fN=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 ${l<0?"up":"down"}`})}return t},mN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},gN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${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 Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0},v5=0;function AN(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};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(Pe.canvas=e.canvas,!Pe.body||e.body.length!==Pe.body.length)Pe.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)*Pe.body[z].box[se]+G)/r),Y=e.body[z].boxRaw.map((G,se)=>((r-1)*Pe.body[z].boxRaw[se]+G)/r),J=e.body[z].keypoints.map((G,se)=>({score:G.score,part:G.part,position:[Pe.body[z].keypoints[se]?((r-1)*Pe.body[z].keypoints[se].position[0]+G.position[0])/r:G.position[0],Pe.body[z].keypoints[se]?((r-1)*Pe.body[z].keypoints[se].position[1]+G.position[1])/r:G.position[1]],positionRaw:[Pe.body[z].keypoints[se]?((r-1)*Pe.body[z].keypoints[se].positionRaw[0]+G.positionRaw[0])/r:G.position[0],Pe.body[z].keypoints[se]?((r-1)*Pe.body[z].keypoints[se].positionRaw[1]+G.positionRaw[1])/r:G.position[1]]})),ne={},re={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?re=bb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?re=db:((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=J.find(Te=>Te.part===se[pe]),we=J.find(Te=>Te.part===se[pe+1]);ye&&we&&ye.score>(t.body.minConfidence||0)&&we.score>(t.body.minConfidence||0)&&oe.push([ye.position,we.position])}ne[G]=oe}Pe.body[z]={...e.body[z],box:K,boxRaw:Y,keypoints:J,annotations:ne}}if(!Pe.hand||e.hand.length!==Pe.hand.length)Pe.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)*Pe.hand[z].box[G]+re)/r),Y=e.hand[z].boxRaw.map((re,G)=>((r-1)*Pe.hand[z].boxRaw[G]+re)/r);Pe.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Pe.hand[z].keypoints=e.hand[z].keypoints);let J=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((re,G)=>re.map((se,oe)=>((r-1)*(Pe.hand[z].keypoints[G][oe]||1)+(se||0))/r)):[],ne={};if(Object.keys(Pe.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Pe.hand[z].annotations=e.hand[z].annotations,ne=Pe.hand[z].annotations;else if(e.hand[z].annotations)for(let re of Object.keys(e.hand[z].annotations))ne[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)*Pe.hand[z].annotations[re][se][pe]+oe)/r)):null;Pe.hand[z]={...e.hand[z],box:K,boxRaw:Y,keypoints:J,annotations:ne}}if(!Pe.face||e.face.length!==Pe.face.length)Pe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let K=e.face[z].box.map((J,ne)=>((r-1)*Pe.face[z].box[ne]+J)/r),Y=e.face[z].boxRaw.map((J,ne)=>((r-1)*Pe.face[z].boxRaw[ne]+J)/r);if(e.face[z].rotation){let J={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};J.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,J.angle={roll:((r-1)*(((f=(h=Pe.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=Pe.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=Pe.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},J.gaze={bearing:((r-1)*(((O=(R=Pe.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=Pe.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},Pe.face[z]={...e.face[z],rotation:J,box:K,boxRaw:Y}}Pe.face[z]={...e.face[z],box:K,boxRaw:Y}}if(!Pe.object||e.object.length!==Pe.object.length)Pe.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let K=e.object[z].box.map((J,ne)=>((r-1)*Pe.object[z].box[ne]+J)/r),Y=e.object[z].boxRaw.map((J,ne)=>((r-1)*Pe.object[z].boxRaw[ne]+J)/r);Pe.object[z]={...e.object[z],box:K,boxRaw:Y}}if(e.persons){let z=e.persons;if(!Pe.persons||z.length!==Pe.persons.length)Pe.persons=JSON.parse(JSON.stringify(z));else for(let K=0;K<z.length;K++)Pe.persons[K].box=z[K].box.map((Y,J)=>((r-1)*Pe.persons[K].box[J]+Y)/r)}e.gesture&&(Pe.gesture=e.gesture);let a=ie();return v5=de.perfadd?v5+Math.round(a-n):Math.round(a-n),e.performance&&(Pe.performance={...e.performance,interpolate:v5}),Pe}function _0(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 yN=(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 xN(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=_0(e,t,n);return yN(s,n.order||2,n.min||0,n.max||1)}function bN(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=_0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=yN(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function vN(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 D0=`
|
|
/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==`,P0=`
|
|
/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 a1e(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(D0);break;case"body":case"full":n=await t(P0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function o1e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+D0;break;case"full":case"body":n="data:image/jpeg;base64,"+P0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:de.Image&&(s=new de.Image),s.onload=async()=>{let r=Zn(s.naturalWidth,s.naturalHeight);if(!r)ee("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function i1e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(D0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(P0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ee("Warmup tfjs-node not loaded");return s}async function wN(e,t){let n=ie();if(e.state="warmup",t&&(e.config=$n(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await a1e(e):typeof Image!="undefined"||de.Canvas!==void 0?s=await o1e(e):s=await i1e(e);let a=ie();e.config.debug&&ee("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var zc,Vp,Up,F0,SN=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,Vp,void 0);Qc(this,Up,void 0);he(this,"gl");he(this,"analyze",(...t)=>{if(!Jc(this,Vp))return;let n=this.tf.engine().state.numTensors,s=Jc(this,zc);ed(this,zc,n);let r=n-s;r!==0&&ee(...t,r)});Qc(this,F0,t=>{if(!Jc(this,Up))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Je))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});he(this,"similarity",xN);he(this,"distance",_0);he(this,"match",bN);he(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=de,Ta.wasmPath=_p.includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${_p}/dist/`,Ta.modelBasePath=de.browser?"../models/":"file://models/",Ta.backend=de.browser?"humangl":"tensorflow",this.version=Hx,Object.defineProperty(this,"version",{value:Hx}),this.config=JSON.parse(JSON.stringify(Ta)),Object.seal(this.config),t&&(this.config=$n(this.config,t)),this.tf=Ml,this.state="idle",ed(this,zc,0),ed(this,Vp,!1),ed(this,Up,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new c5,this.draw={options:ya,canvas:(n,s)=>uN(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)=>lN(n,s,r),all:(n,s,r)=>cN(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=lT,this.faceUVMap=uT,this.gl=Ct,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ta)),this.config.backend=t}validate(t){return a2(Ta,t||this.config)}now(){return ie()}image(t,n=!0){return Tc(t,this.config,n)}async segmentation(t,n){return nN(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&&ee(`version: ${this.version}`),this.config.debug&&ee(`tfjs version: ${this.tf.version_core}`),await $0(this)||ee("error: backend check failed"),await lf(),this.env.browser&&(this.config.debug&&ee("configuration:",this.config),this.config.debug&&ee("environment:",this.env),this.config.debug&&ee("tf flags:",this.tf.ENV.flags))),await sN(this),this.env.initial&&this.config.debug&&ee("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await rN(this),this.emit("load"));let a=Math.trunc(ie()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return AN(t,this.config)}async warmup(t){let n=ie(),s=await wN(this,t),r=ie();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,x,y,b,w,k,I,N,R,O,_,P,T,F,U,q,z,K,Y,J,ne;this.state="config";let r;this.config=$n(this.config,n),this.state="check";let a=Jc(this,F0).call(this,t);a&&(ee(a,t),s({error:a}));let o=ie();await $0(this),await this.load(),r=ie(),this.state="image";let i=await Tc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&ee("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ie(),this.config.skipAllowed=await p8(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?b5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ie(),l=this.config.face.enabled?await b5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?$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?fb(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?wb(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 fb(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await wb(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?Ab(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):[]:((Y=this.config.object.modelPath)==null?void 0:Y.includes("centernet"))&&(d=this.config.object.enabled?await Ab(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=[...fN(l),...hN(c),...gN(u),...mN(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=((ne=(J=this.process)==null?void 0:J.tensor)==null?void 0:ne.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return vN(l,c,u,f,m)}},te(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};zc=new WeakMap,Vp=new WeakMap,Up=new WeakMap,F0=new WeakMap;return l1e;})();
|
|
/**
|
|
* @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. */
|