mirror of https://github.com/vladmandic/human
5546 lines
1.4 MiB
5546 lines
1.4 MiB
/*
|
|
Human
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
|
|
var Human=(()=>{var pg=Object.defineProperty;var kS=(e,t,n)=>t in e?pg(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var IS=e=>pg(e,"__esModule",{value:!0});var Ma=(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 Yx=(e,t)=>{IS(e);for(var n in t)pg(e,n,{get:t[n],enumerable:!0})};var xe=(e,t,n)=>(kS(e,typeof t!="symbol"?t+"":t,n),n),Jx=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var Yu=(e,t,n)=>(Jx(e,t,"read from private field"),n?n.call(e):t.get(e)),Ju=(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)},Qu=(e,t,n,s)=>(Jx(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);var nue={};Yx(nue,{Human:()=>$I,Models:()=>Ud,default:()=>$I,defaults:()=>ea,env:()=>le});function ct(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: ${r} expecting json file`);return r}function re(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var et=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function hg(e,t,n="config",s=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")hg(e[r],t[r],r,s);else{let a=e&&typeof e[r]!="undefined";a||s.push({reason:"unknown property",where:`${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&&re("invalid configuration",s),s}function Vt(...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]=Vt(a,o):n[r]=o}),n),{})}var ea={backend:"",modelBasePath:"",wasmPath:"",debug:!0,async:!0,warmup:"full",cacheSensitivity:.75,skipFrame:!1,filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:15,skipFrames:15,minConfidence:.2,iouThreshold:.1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:11,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:17,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.2,skipFrames:1},hand:{enabled:!0,rotation:!0,skipFrames:18,minConfidence:.8,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:19},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var vi={};Yx(vi,{Abs:()=>Bi,Acos:()=>Wi,Acosh:()=>Vi,AdadeltaOptimizer:()=>Yh,AdagradOptimizer:()=>Jh,AdamOptimizer:()=>Qh,AdamaxOptimizer:()=>ef,Add:()=>sa,AddN:()=>Wa,All:()=>Ui,Any:()=>Hi,ArgMax:()=>Va,ArgMin:()=>rc,Asin:()=>Gi,Asinh:()=>ji,Atan:()=>qi,Atan2:()=>Ki,Atanh:()=>Xi,AvgPool:()=>Ua,AvgPool3D:()=>ac,AvgPool3DGrad:()=>kp,AvgPoolGrad:()=>wp,BackendWasm:()=>l8,BatchMatMul:()=>Ha,BatchToSpaceND:()=>Zi,Bincount:()=>Ip,BroadcastArgs:()=>bg,BroadcastTo:()=>A5,Callback:()=>aw,CallbackList:()=>Y3,Cast:()=>Ga,Ceil:()=>ja,ClipByValue:()=>ra,Complex:()=>Sp,ComplexAbs:()=>oc,Concat:()=>Yi,Conv2D:()=>qa,Conv2DBackpropFilter:()=>Cp,Conv2DBackpropInput:()=>Xa,Conv3D:()=>ic,Conv3DBackpropFilterV2:()=>Tp,Conv3DBackpropInputV2:()=>Np,Cos:()=>Ka,Cosh:()=>Za,CropAndResize:()=>Ji,Cumsum:()=>Ya,CustomCallback:()=>Q3,DataStorage:()=>Ap,DenseBincount:()=>Ep,DepthToSpace:()=>Qi,DepthwiseConv2dNative:()=>Ja,DepthwiseConv2dNativeBackpropFilter:()=>Rp,DepthwiseConv2dNativeBackpropInput:()=>Dp,Diag:()=>_p,Dilation2D:()=>lc,Dilation2DBackpropFilter:()=>$p,Dilation2DBackpropInput:()=>Fp,ENV:()=>sr,EarlyStopping:()=>iw,Einsum:()=>Op,Elu:()=>eo,EluGrad:()=>Pp,Environment:()=>m5,Equal:()=>tl,Erf:()=>el,Exp:()=>to,ExpandDims:()=>nl,Expm1:()=>sl,FFT:()=>Mp,Fill:()=>uc,FlipLeftRight:()=>rl,Floor:()=>no,FloorDiv:()=>so,FromPixels:()=>rh,FusedBatchNorm:()=>ro,FusedConv2D:()=>Lo,FusedDepthwiseConv2D:()=>Bo,GPGPUContext:()=>nm,GatherNd:()=>ol,GatherV2:()=>al,GraphModel:()=>Lw,Greater:()=>il,GreaterEqual:()=>ao,History:()=>J3,IFFT:()=>zp,Identity:()=>oo,Imag:()=>Lp,InputSpec:()=>jt,IsFinite:()=>ll,IsInf:()=>ul,IsNan:()=>cl,KernelBackend:()=>tc,LRN:()=>pc,LRNGrad:()=>Wp,LayerVariable:()=>j3,LayersModel:()=>Mr,LeakyRelu:()=>io,Less:()=>dl,LessEqual:()=>pl,LinSpace:()=>Bp,Log:()=>lo,Log1p:()=>hl,LogSoftmax:()=>y5,LogicalAnd:()=>fl,LogicalNot:()=>cc,LogicalOr:()=>dc,MathBackendWebGL:()=>Tu,Max:()=>uo,MaxPool:()=>po,MaxPool3D:()=>hc,MaxPool3DGrad:()=>Up,MaxPoolGrad:()=>Vp,MaxPoolWithArgmax:()=>Hp,Maximum:()=>co,Mean:()=>ho,Min:()=>fo,Minimum:()=>mo,MirrorPad:()=>go,Mod:()=>ml,MomentumOptimizer:()=>tf,Multinomial:()=>Gp,Multiply:()=>Ao,Neg:()=>gl,NonMaxSuppressionV3:()=>yl,NonMaxSuppressionV4:()=>xl,NonMaxSuppressionV5:()=>bl,NotEqual:()=>Al,OP_SCOPE_SUFFIX:()=>F5,OneHot:()=>yo,OnesLike:()=>vl,Optimizer:()=>$r,Pack:()=>wl,PadV2:()=>xo,Pool:()=>bC,Pow:()=>bo,Prelu:()=>vo,Prod:()=>kl,RMSPropOptimizer:()=>nf,RNN:()=>mr,Range:()=>fc,Rank:()=>Sg,Real:()=>jp,RealDiv:()=>Qa,Reciprocal:()=>Il,Reduction:()=>En,Relu:()=>wo,Relu6:()=>Io,Reshape:()=>Sl,ResizeBilinear:()=>ko,ResizeBilinearGrad:()=>Xp,ResizeNearestNeighbor:()=>mc,ResizeNearestNeighborGrad:()=>qp,Reverse:()=>So,RotateWithOffset:()=>Bl,Round:()=>Co,Rsqrt:()=>To,SGDOptimizer:()=>qc,ScatterNd:()=>Cl,Select:()=>Tl,Selu:()=>Nl,Sequential:()=>hu,Sigmoid:()=>Eo,Sign:()=>Dl,Sin:()=>No,Sinh:()=>Rl,Slice:()=>El,Softmax:()=>_o,Softplus:()=>_l,SpaceToBatchND:()=>Fl,SparseFillEmptyRows:()=>Kp,SparseReshape:()=>Zp,SparseSegmentMean:()=>Yp,SparseSegmentSum:()=>Jp,SparseToDense:()=>Qp,SplitV:()=>$l,Sqrt:()=>Ro,Square:()=>gc,SquaredDifference:()=>Fo,Step:()=>oa,StridedSlice:()=>Ol,StringNGrams:()=>eh,StringSplit:()=>th,StringToHashBucketFast:()=>nh,Sub:()=>$o,Sum:()=>Do,SymbolicTensor:()=>Ks,Tan:()=>Oo,Tanh:()=>Po,Tensor:()=>Ge,TensorBuffer:()=>Yt,Tile:()=>aa,TopK:()=>Pl,Transform:()=>Ml,Transpose:()=>Mo,Unique:()=>sh,Unpack:()=>zl,UnsortedSegmentSum:()=>Ac,Variable:()=>Sc,ZerosLike:()=>Ll,_FusedMatMul:()=>zo,abs:()=>Ut,acos:()=>Qg,acosh:()=>eA,add:()=>ie,addN:()=>xh,all:()=>bh,any:()=>Rc,argMax:()=>Vs,argMin:()=>tA,asin:()=>nA,asinh:()=>sA,atan:()=>rA,atan2:()=>aA,atanh:()=>oA,avgPool:()=>_c,avgPool3d:()=>uA,backend:()=>Er,backend_util:()=>_,basicLSTMCell:()=>Q9,batchNorm:()=>Ko,batchNorm2d:()=>vb,batchNorm3d:()=>wb,batchNorm4d:()=>kb,batchToSpaceND:()=>Fc,bincount:()=>cA,booleanMaskAsync:()=>oD,broadcastArgs:()=>Ib,broadcastTo:()=>Zl,browser:()=>_s,buffer:()=>je,callbacks:()=>bL,cast:()=>pe,ceil:()=>dA,clipByValue:()=>Un,clone:()=>Ws,complex:()=>ua,concat:()=>gt,concat1d:()=>Sb,concat2d:()=>Yl,concat3d:()=>Cb,concat4d:()=>Tb,constraints:()=>S3,conv1d:()=>wh,conv2d:()=>Rr,conv2dTranspose:()=>kh,conv3d:()=>hA,conv3dTranspose:()=>Eb,copyRegisteredKernels:()=>kC,cos:()=>$c,cosh:()=>Ih,cosineWindow:()=>BA,cumsum:()=>Sh,customGrad:()=>lr,data:()=>Bw,denseBincount:()=>Rb,deprecationWarn:()=>Yg,depthToSpace:()=>fA,depthwiseConv2d:()=>Jl,deregisterOp:()=>wL,device_util:()=>Tc,diag:()=>EN,dilation2d:()=>mA,disableDeprecationWarnings:()=>m9,dispose:()=>Z,disposeVariables:()=>g9,div:()=>he,divNoNan:()=>gA,dot:()=>Db,dropout:()=>Jb,einsum:()=>_b,elu:()=>Ql,enableDebugMode:()=>f9,enableProdMode:()=>gb,enclosingPowerOfTwo:()=>Qb,engine:()=>es,env:()=>Y,equal:()=>ts,erf:()=>AA,exp:()=>ns,expandDims:()=>Lt,expm1:()=>yA,eye:()=>xA,fft:()=>Hc,fill:()=>eu,findBackend:()=>Jg,findBackendFactory:()=>b9,floor:()=>tu,floorDiv:()=>yh,forceHalfFloat:()=>h4,fused:()=>ga,gather:()=>Zo,gatherND:()=>Yb,gather_util:()=>Hg,getBackend:()=>or,getGradient:()=>vg,getKernel:()=>ah,getKernelsForBackend:()=>Tr,gpgpu_util:()=>W6,grad:()=>aE,grads:()=>oE,greater:()=>Hn,greaterEqual:()=>fa,ifft:()=>au,imag:()=>Ch,image:()=>De,inTopKAsync:()=>AD,initializers:()=>_3,input:()=>wv,io:()=>Wn,irfft:()=>Wh,isFinite:()=>Fb,isInf:()=>$b,isNaN:()=>bA,keep:()=>un,kernel_impls:()=>cr,layers:()=>U3,leakyRelu:()=>Oc,less:()=>Th,lessEqual:()=>ma,linalg:()=>d3,linspace:()=>Ob,loadGraphModel:()=>ot,loadLayersModel:()=>RM,localResponseNormalization:()=>vA,log:()=>ss,log1p:()=>Pc,logSigmoid:()=>Mb,logSoftmax:()=>Eh,logSumExp:()=>IA,logicalAnd:()=>Fs,logicalNot:()=>Mc,logicalOr:()=>Rh,logicalXor:()=>Wb,losses:()=>Q_,matMul:()=>Ue,math:()=>Y5,max:()=>rs,maxPool:()=>zc,maxPool3d:()=>SA,maxPoolWithArgmax:()=>Vb,maximum:()=>ur,mean:()=>_t,memory:()=>gh,meshgrid:()=>NE,metrics:()=>nw,min:()=>Lc,minimum:()=>nu,mirrorPad:()=>CA,mod:()=>TA,model:()=>NM,models:()=>sw,moments:()=>Dh,movingAverage:()=>uD,mul:()=>z,multiRNNCell:()=>PE,multinomial:()=>Ub,neg:()=>Ct,nextFrame:()=>sf,norm:()=>Gh,notEqual:()=>Qo,oneHot:()=>jl,ones:()=>as,onesLike:()=>os,op:()=>W,outerProduct:()=>WE,pad:()=>Dr,pad1d:()=>HE,pad2d:()=>jE,pad3d:()=>XE,pad4d:()=>ZE,pool:()=>Hb,pow:()=>_r,prelu:()=>Wc,print:()=>G5,prod:()=>_h,profile:()=>A9,rand:()=>aR,randomGamma:()=>uR,randomNormal:()=>Gb,randomUniform:()=>su,range:()=>ru,ready:()=>Ah,real:()=>Vc,reciprocal:()=>RA,registerBackend:()=>Xl,registerCallbackConstructor:()=>DM,registerGradient:()=>x5,registerKernel:()=>ia,registerOp:()=>vL,regularizers:()=>rw,relu:()=>Us,relu6:()=>Fh,removeBackend:()=>x9,reshape:()=>V,reverse:()=>is,reverse1d:()=>yR,reverse2d:()=>bR,reverse3d:()=>wR,reverse4d:()=>IR,rfft:()=>Gc,round:()=>$h,rsqrt:()=>Oh,scalar:()=>Te,scatterND:()=>Zb,scatter_util:()=>Gg,selu:()=>Ph,separableConv2d:()=>DA,sequential:()=>EM,serialization:()=>ue,setBackend:()=>Ab,setPlatform:()=>v9,setWasmPath:()=>ele,setWasmPaths:()=>c8,setWebGLContext:()=>qf,setdiff1dAsync:()=>jb,sigmoid:()=>Vn,sign:()=>_A,signal:()=>J_,sin:()=>Mh,sinh:()=>zh,slice:()=>_e,slice1d:()=>Lh,slice2d:()=>FA,slice3d:()=>Bh,slice4d:()=>Uc,slice_util:()=>Tn,softmax:()=>ei,softplus:()=>Yo,spaceToBatchND:()=>Bc,sparse:()=>jc,sparseToDense:()=>LA,spectral:()=>Y_,split:()=>Ht,sqrt:()=>gn,square:()=>ft,squaredDifference:()=>Vh,squeeze:()=>st,stack:()=>An,step:()=>ou,stridedSlice:()=>$A,string:()=>Zh,sub:()=>ye,sum:()=>ke,sumOutType:()=>ch,tan:()=>OA,tanh:()=>Xo,tensor:()=>ln,tensor1d:()=>Gt,tensor2d:()=>Hs,tensor3d:()=>hh,tensor4d:()=>ZR,tensor5d:()=>YR,tensor6d:()=>JR,tensor_util:()=>Ls,test_util:()=>hb,tidy:()=>H,tile:()=>bs,time:()=>y9,topk:()=>PA,train:()=>ni,transpose:()=>Ze,truncatedNormal:()=>Uh,unique:()=>Hh,unregisterGradient:()=>wC,unregisterKernel:()=>vC,unsortedSegmentSum:()=>MA,unstack:()=>Nn,upcastType:()=>Ds,util:()=>w,valueAndGrad:()=>iE,valueAndGrads:()=>lE,variable:()=>qb,variableGrads:()=>Pb,version:()=>dle,version_converter:()=>SB,version_core:()=>mh,version_layers:()=>v1,version_wasm:()=>tle,version_webgl:()=>MK,webgl:()=>zK,webgl_util:()=>d6,where:()=>wn,whereAsync:()=>zA,zeros:()=>Mt,zerosLike:()=>Ye});var SS=Object.create,gp=Object.defineProperty,CS=Object.getOwnPropertyDescriptor,TS=Object.getOwnPropertyNames,NS=Object.getPrototypeOf,ES=Object.prototype.hasOwnProperty,Qx=e=>gp(e,"__esModule",{value:!0}),Mi=(e=>typeof Ma!="undefined"?Ma:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof Ma!="undefined"?Ma:t)[n]}):e)(function(e){if(typeof Ma!="undefined")return Ma.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),St=(e,t)=>function(){return t||(0,e[Object.keys(e)[0]])((t={exports:{}}).exports,t),t.exports},Le=(e,t)=>{Qx(e);for(var n in t)gp(e,n,{get:t[n],enumerable:!0})},RS=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of TS(t))!ES.call(e,s)&&s!=="default"&&gp(e,s,{get:()=>t[s],enumerable:!(n=CS(t,s))||n.enumerable});return e},za=e=>RS(Qx(gp(e!=null?SS(NS(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),DS=St({"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(R){}function s(R,T,P){this.low=R|0,this.high=T|0,this.unsigned=!!P}s.prototype.__isLong__,Object.defineProperty(s.prototype,"__isLong__",{value:!0});function r(R){return(R&&R.__isLong__)===!0}s.isLong=r;var a={},o={};function i(R,T){var P,U,j;return T?(R>>>=0,(j=0<=R&&R<256)&&(U=o[R],U)?U:(P=u(R,(R|0)<0?-1:0,!0),j&&(o[R]=P),P)):(R|=0,(j=-128<=R&&R<128)&&(U=a[R],U)?U:(P=u(R,R<0?-1:0,!1),j&&(a[R]=P),P))}s.fromInt=i;function l(R,T){if(isNaN(R))return T?b:x;if(T){if(R<0)return b;if(R>=g)return D}else{if(R<=-A)return O;if(R+1>=A)return C}return R<0?l(-R,T).neg():u(R%m|0,R/m|0,T)}s.fromNumber=l;function u(R,T,P){return new s(R,T,P)}s.fromBits=u;var c=Math.pow;function d(R,T,P){if(R.length===0)throw Error("empty string");if(R==="NaN"||R==="Infinity"||R==="+Infinity"||R==="-Infinity")return x;if(typeof T=="number"?(P=T,T=!1):T=!!T,P=P||10,P<2||36<P)throw RangeError("radix");var U;if((U=R.indexOf("-"))>0)throw Error("interior hyphen");if(U===0)return d(R.substring(1),T,P).neg();for(var j=l(c(P,8)),q=x,X=0;X<R.length;X+=8){var te=Math.min(8,R.length-X),ne=parseInt(R.substring(X,X+te),P);if(te<8){var se=l(c(P,te));q=q.mul(se).add(l(ne))}else q=q.mul(j),q=q.add(l(ne))}return q.unsigned=T,q}s.fromString=d;function p(R,T){return typeof R=="number"?l(R,T):typeof R=="string"?d(R,T):u(R.low,R.high,typeof T=="boolean"?T:R.unsigned)}s.fromValue=p;var h=1<<16,f=1<<24,m=h*h,g=m*m,A=g/2,y=i(f),x=i(0);s.ZERO=x;var b=i(0,!0);s.UZERO=b;var v=i(1);s.ONE=v;var k=i(1,!0);s.UONE=k;var S=i(-1);s.NEG_ONE=S;var C=u(4294967295|0,2147483647|0,!1);s.MAX_VALUE=C;var D=u(4294967295|0,4294967295|0,!0);s.MAX_UNSIGNED_VALUE=D;var O=u(0,2147483648|0,!1);s.MIN_VALUE=O;var E=s.prototype;E.toInt=function(){return this.unsigned?this.low>>>0:this.low},E.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},E.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 P=l(T),U=this.div(P),j=U.mul(P).sub(this);return U.toString(T)+j.toInt().toString(T)}else return"-"+this.neg().toString(T);for(var q=l(c(T,6),this.unsigned),X=this,te="";;){var ne=X.div(q),se=X.sub(ne.mul(q)).toInt()>>>0,ae=se.toString(T);if(X=ne,X.isZero())return ae+te;for(;ae.length<6;)ae="0"+ae;te=""+ae+te}},E.getHighBits=function(){return this.high},E.getHighBitsUnsigned=function(){return this.high>>>0},E.getLowBits=function(){return this.low},E.getLowBitsUnsigned=function(){return this.low>>>0},E.getNumBitsAbs=function(){if(this.isNegative())return this.eq(O)?64:this.neg().getNumBitsAbs();for(var T=this.high!=0?this.high:this.low,P=31;P>0&&(T&1<<P)==0;P--);return this.high!=0?P+33:P+1},E.isZero=function(){return this.high===0&&this.low===0},E.eqz=E.isZero,E.isNegative=function(){return!this.unsigned&&this.high<0},E.isPositive=function(){return this.unsigned||this.high>=0},E.isOdd=function(){return(this.low&1)==1},E.isEven=function(){return(this.low&1)==0},E.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},E.eq=E.equals,E.notEquals=function(T){return!this.eq(T)},E.neq=E.notEquals,E.ne=E.notEquals,E.lessThan=function(T){return this.comp(T)<0},E.lt=E.lessThan,E.lessThanOrEqual=function(T){return this.comp(T)<=0},E.lte=E.lessThanOrEqual,E.le=E.lessThanOrEqual,E.greaterThan=function(T){return this.comp(T)>0},E.gt=E.greaterThan,E.greaterThanOrEqual=function(T){return this.comp(T)>=0},E.gte=E.greaterThanOrEqual,E.ge=E.greaterThanOrEqual,E.compare=function(T){if(r(T)||(T=p(T)),this.eq(T))return 0;var P=this.isNegative(),U=T.isNegative();return P&&!U?-1:!P&&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},E.comp=E.compare,E.negate=function(){return!this.unsigned&&this.eq(O)?O:this.not().add(v)},E.neg=E.negate,E.add=function(T){r(T)||(T=p(T));var P=this.high>>>16,U=this.high&65535,j=this.low>>>16,q=this.low&65535,X=T.high>>>16,te=T.high&65535,ne=T.low>>>16,se=T.low&65535,ae=0,Q=0,ce=0,de=0;return de+=q+se,ce+=de>>>16,de&=65535,ce+=j+ne,Q+=ce>>>16,ce&=65535,Q+=U+te,ae+=Q>>>16,Q&=65535,ae+=P+X,ae&=65535,u(ce<<16|de,ae<<16|Q,this.unsigned)},E.subtract=function(T){return r(T)||(T=p(T)),this.add(T.neg())},E.sub=E.subtract,E.multiply=function(T){if(this.isZero())return x;if(r(T)||(T=p(T)),n){var P=n.mul(this.low,this.high,T.low,T.high);return u(P,n.get_high(),this.unsigned)}if(T.isZero())return x;if(this.eq(O))return T.isOdd()?O:x;if(T.eq(O))return this.isOdd()?O:x;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(y)&&T.lt(y))return l(this.toNumber()*T.toNumber(),this.unsigned);var U=this.high>>>16,j=this.high&65535,q=this.low>>>16,X=this.low&65535,te=T.high>>>16,ne=T.high&65535,se=T.low>>>16,ae=T.low&65535,Q=0,ce=0,de=0,fe=0;return fe+=X*ae,de+=fe>>>16,fe&=65535,de+=q*ae,ce+=de>>>16,de&=65535,de+=X*se,ce+=de>>>16,de&=65535,ce+=j*ae,Q+=ce>>>16,ce&=65535,ce+=q*se,Q+=ce>>>16,ce&=65535,ce+=X*ne,Q+=ce>>>16,ce&=65535,Q+=U*ae+j*se+q*ne+X*te,Q&=65535,u(de<<16|fe,Q<<16|ce,this.unsigned)},E.mul=E.multiply,E.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 P=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,T.low,T.high);return u(P,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:x;var U,j,q;if(this.unsigned){if(T.unsigned||(T=T.toUnsigned()),T.gt(this))return b;if(T.gt(this.shru(1)))return k;q=b}else{if(this.eq(O)){if(T.eq(v)||T.eq(S))return O;if(T.eq(O))return v;var X=this.shr(1);return U=X.div(T).shl(1),U.eq(x)?T.isNegative()?v:S:(j=this.sub(T.mul(U)),q=U.add(j.div(T)),q)}else if(T.eq(O))return this.unsigned?b:x;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();q=x}for(j=this;j.gte(T);){U=Math.max(1,Math.floor(j.toNumber()/T.toNumber()));for(var te=Math.ceil(Math.log(U)/Math.LN2),ne=te<=48?1:c(2,te-48),se=l(U),ae=se.mul(T);ae.isNegative()||ae.gt(j);)U-=ne,se=l(U,this.unsigned),ae=se.mul(T);se.isZero()&&(se=v),q=q.add(se),j=j.sub(ae)}return q},E.div=E.divide,E.modulo=function(T){if(r(T)||(T=p(T)),n){var P=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,T.low,T.high);return u(P,n.get_high(),this.unsigned)}return this.sub(this.div(T).mul(T))},E.mod=E.modulo,E.rem=E.modulo,E.not=function(){return u(~this.low,~this.high,this.unsigned)},E.and=function(T){return r(T)||(T=p(T)),u(this.low&T.low,this.high&T.high,this.unsigned)},E.or=function(T){return r(T)||(T=p(T)),u(this.low|T.low,this.high|T.high,this.unsigned)},E.xor=function(T){return r(T)||(T=p(T)),u(this.low^T.low,this.high^T.high,this.unsigned)},E.shiftLeft=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?u(this.low<<T,this.high<<T|this.low>>>32-T,this.unsigned):u(0,this.low<<T-32,this.unsigned)},E.shl=E.shiftLeft,E.shiftRight=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?u(this.low>>>T|this.high<<32-T,this.high>>T,this.unsigned):u(this.high>>T-32,this.high>=0?0:-1,this.unsigned)},E.shr=E.shiftRight,E.shiftRightUnsigned=function(T){if(r(T)&&(T=T.toInt()),T&=63,T===0)return this;var P=this.high;if(T<32){var U=this.low;return u(U>>>T|P<<32-T,P>>>T,this.unsigned)}else return T===32?u(P,0,this.unsigned):u(P>>>T-32,0,this.unsigned)},E.shru=E.shiftRightUnsigned,E.shr_u=E.shiftRightUnsigned,E.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},E.toUnsigned=function(){return this.unsigned?this:u(this.low,this.high,!0)},E.toBytes=function(T){return T?this.toBytesLE():this.toBytesBE()},E.toBytesLE=function(){var T=this.high,P=this.low;return[P&255,P>>>8&255,P>>>16&255,P>>>24,T&255,T>>>8&255,T>>>16&255,T>>>24]},E.toBytesBE=function(){var T=this.high,P=this.low;return[T>>>24,T>>>16&255,T>>>8&255,T&255,P>>>24,P>>>16&255,P>>>8&255,P&255]},s.fromBytes=function(T,P,U){return U?s.fromBytesLE(T,P):s.fromBytesBE(T,P)},s.fromBytesLE=function(T,P){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,P)},s.fromBytesBE=function(T,P){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],P)}}}),_S=St({"(disabled):node_modules/.pnpm/node-fetch@2.6.5/node_modules/node-fetch/browser.js"(){}}),FS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(u){var c=this,d=l();c.next=function(){var p=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=p-(c.c=p|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),p=c&&c.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 u=4022871197,c=function(d){d=d.toString();for(var p=0;p<d.length;p++){u+=d.charCodeAt(p);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return c}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)}}),$S=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var p=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^p^p>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),OS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var p=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(p^p<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),PS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.x,p=u.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,u.i=p+1&7,f};function c(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()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(d.x&&o(d,c),p.state=function(){return o(c,{})}),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)}}),MS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.w,p=u.X,h=u.i,f,m;return u.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,u.i=h,m+(d^d>>>16)|0};function c(d,p){var h,f,m,g,A,y=[],x=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,x=Math.max(x,p.length)),m=0,g=-32;g<x;++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=y[g&127]^=f+A,m=h==0?m+1:0);for(m>=128&&(y[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=y[m+34&127],h=y[m=m+1&127],f^=f<<13,h^=h<<17,f^=f>>>15,h^=h>>>12,y[m]=f^h;d.w=A,d.X=y,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(d.X&&o(d,c),p.state=function(){return o(c,{})}),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)}}),zS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var p=u.b,h=u.c,f=u.d,m=u.a;return p=p<<25^p>>>7^h,h=h-f|0,f=f<<24^f>>>8^m,m=m-p|0,u.b=p=p<<20^p>>>12^h,u.c=h=h-f|0,u.d=f<<16^h>>>16^m,u.a=m-p|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),e5=St({"(disabled):crypto"(){}}),LS=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s){var r=this,a=256,o=6,i=52,l="random",u=s.pow(a,o),c=s.pow(2,i),d=c*2,p=a-1,h;function f(v,k,S){var C=[];k=k==!0?{entropy:!0}:k||{};var D=y(A(k.entropy?[v,b(n)]:v==null?x():v,3),C),O=new m(C),E=function(){for(var R=O.g(o),T=u,P=0;R<c;)R=(R+P)*a,T*=a,P=O.g(1);for(;R>=d;)R/=2,T/=2,P>>>=1;return(R+P)/T};return E.int32=function(){return O.g(4)|0},E.quick=function(){return O.g(4)/4294967296},E.double=E,y(b(O.S),n),(k.pass||S||function(R,T,P,U){return U&&(U.S&&g(U,O),R.state=function(){return g(O,{})}),P?(s[l]=R,T):R})(E,D,"global"in k?k.global:this==s,k.state)}s["seed"+l]=f;function m(v){var k,S=v.length,C=this,D=0,O=C.i=C.j=0,E=C.S=[];for(S||(v=[S++]);D<a;)E[D]=D++;for(D=0;D<a;D++)E[D]=E[O=p&O+v[D%S]+(k=E[D])],E[O]=k;(C.g=function(R){for(var T,P=0,U=C.i,j=C.j,q=C.S;R--;)T=q[U=p&U+1],P=P*a+q[p&(q[U]=q[j=p&j+T])+(q[j]=T)];return C.i=U,C.j=j,P})(a)}function g(v,k){return k.i=v.i,k.j=v.j,k.S=v.S.slice(),k}function A(v,k){var S=[],C=typeof v,D;if(k&&C=="object")for(D in v)try{S.push(A(v[D],k-1))}catch(O){}return S.length?S:C=="string"?v:v+"\0"}function y(v,k){for(var S=v+"",C,D=0;D<S.length;)k[p&D]=p&(C^=k[p&D]*19)+S.charCodeAt(D++);return b(k)}function x(){try{var v;return h&&(v=h.randomBytes)?v=v(a):(v=new Uint8Array(a),(r.crypto||r.msCrypto).getRandomValues(v)),b(v)}catch(C){var k=r.navigator,S=k&&k.plugins;return[+new Date,r,S,r.screen,b(n)]}}function b(v){return String.fromCharCode.apply(0,v)}if(y(s.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{h=e5()}catch(v){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}}),t5=St({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(e,t){var n=FS(),s=$S(),r=OS(),a=PS(),o=MS(),i=zS(),l=LS();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),BS=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(u){var c=this,d=l();c.next=function(){var p=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=p-(c.c=p|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),p=c&&c.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 u=4022871197,c=function(d){d=String(d);for(var p=0;p<d.length;p++){u+=d.charCodeAt(p);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return c}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)}}),WS=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var p=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^p^p>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),VS=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var p=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(p^p<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),US=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.x,p=u.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,u.i=p+1&7,f};function c(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()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(d.x&&o(d,c),p.state=function(){return o(c,{})}),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)}}),HS=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.w,p=u.X,h=u.i,f,m;return u.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,u.i=h,m+(d^d>>>16)|0};function c(d,p){var h,f,m,g,A,y=[],x=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,x=Math.max(x,p.length)),m=0,g=-32;g<x;++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=y[g&127]^=f+A,m=h==0?m+1:0);for(m>=128&&(y[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=y[m+34&127],h=y[m=m+1&127],f^=f<<13,h^=h<<17,f^=f>>>15,h^=h>>>12,y[m]=f^h;d.w=A,d.X=y,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(d.X&&o(d,c),p.state=function(){return o(c,{})}),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)}}),GS=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var p=u.b,h=u.c,f=u.d,m=u.a;return p=p<<25^p>>>7^h,h=h-f|0,f=f<<24^f>>>8^m,m=m-p|0,u.b=p=p<<20^p>>>12^h,u.c=h=h-f|0,u.d=f<<16^h>>>16^m,u.a=m-p|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,p=function(){return(c.next()>>>0)/4294967296};return p.double=function(){do var h=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=c.next,p.quick=p,d&&(typeof d=="object"&&o(d,c),p.state=function(){return o(c,{})}),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)}}),jS=St({"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",u=r.pow(a,o),c=r.pow(2,i),d=c*2,p=a-1,h;function f(v,k,S){var C=[];k=k==!0?{entropy:!0}:k||{};var D=y(A(k.entropy?[v,b(s)]:v==null?x():v,3),C),O=new m(C),E=function(){for(var R=O.g(o),T=u,P=0;R<c;)R=(R+P)*a,T*=a,P=O.g(1);for(;R>=d;)R/=2,T/=2,P>>>=1;return(R+P)/T};return E.int32=function(){return O.g(4)|0},E.quick=function(){return O.g(4)/4294967296},E.double=E,y(b(O.S),s),(k.pass||S||function(R,T,P,U){return U&&(U.S&&g(U,O),R.state=function(){return g(O,{})}),P?(r[l]=R,T):R})(E,D,"global"in k?k.global:this==r,k.state)}function m(v){var k,S=v.length,C=this,D=0,O=C.i=C.j=0,E=C.S=[];for(S||(v=[S++]);D<a;)E[D]=D++;for(D=0;D<a;D++)E[D]=E[O=p&O+v[D%S]+(k=E[D])],E[O]=k;(C.g=function(R){for(var T,P=0,U=C.i,j=C.j,q=C.S;R--;)T=q[U=p&U+1],P=P*a+q[p&(q[U]=q[j=p&j+T])+(q[j]=T)];return C.i=U,C.j=j,P})(a)}function g(v,k){return k.i=v.i,k.j=v.j,k.S=v.S.slice(),k}function A(v,k){var S=[],C=typeof v,D;if(k&&C=="object")for(D in v)try{S.push(A(v[D],k-1))}catch(O){}return S.length?S:C=="string"?v:v+"\0"}function y(v,k){for(var S=v+"",C,D=0;D<S.length;)k[p&D]=p&(C^=k[p&D]*19)+S.charCodeAt(D++);return b(k)}function x(){try{var v;return h&&(v=h.randomBytes)?v=v(a):(v=new Uint8Array(a),(n.crypto||n.msCrypto).getRandomValues(v)),b(v)}catch(C){var k=n.navigator,S=k&&k.plugins;return[+new Date,n,S,n.screen,b(s)]}}function b(v){return String.fromCharCode.apply(0,v)}if(y(r.random(),s),typeof t=="object"&&t.exports){t.exports=f;try{h=e5()}catch(v){}}else typeof define=="function"&&define.amd?define(function(){return f}):r["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),n5=St({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=BS(),s=WS(),r=VS(),a=US(),o=HS(),i=GS(),l=jS();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),s5=St({"(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"(){}}),ec=St({"(disabled):path"(){}}),qS=St({"(disabled):worker_threads"(){}}),XS=St({"(disabled):perf_hooks"(){}}),KS=St({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.9.0_@tensorflow+tfjs-core@3.9.0/node_modules/@tensorflow/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 Q.buffer!=Xe&&fn(Q.buffer),zn}function o(){return Q.buffer!=Xe&&fn(Q.buffer),Rt}function i(){return Q.buffer!=Xe&&fn(Q.buffer),Ns}function l(){return Q.buffer!=Xe&&fn(Q.buffer),kn}function u(){return Q.buffer!=Xe&&fn(Q.buffer),gs}var c=typeof r!="undefined"?r:{},d,p;c.ready=new Promise(function(N,$){d=N,p=$});var h={},f;for(f in c)c.hasOwnProperty(f)&&(h[f]=c[f]);var m=[],g="./this.program",A=function(N,$){throw $},y=!1,x=!1,b=!1,v=!1;y=typeof window=="object",x=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!y&&!b&&!x;var k=c.ENVIRONMENT_IS_PTHREAD||!1;k&&(Xe=c.buffer);var S="";function C(N){return c.locateFile?c.locateFile(N,S):S+N}var D,O,E,R,T,P;if(b){x?S=ec().dirname(S)+"/":S=__dirname+"/",D=function($,B){return T||(T=Mi("fs")),P||(P=ec()),$=P.normalize($),T.readFileSync($,B?null:"utf8")},E=function($){var B=D($,!0);return B.buffer||(B=new Uint8Array(B)),be(B.buffer),B},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(N){if(!(N instanceof Zu))throw N}),process.on("unhandledRejection",kr),A=function(N){process.exit(N)},c.inspect=function(){return"[Emscripten Module object]"};var U;try{U=qS()}catch(N){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),N}global.Worker=U.Worker}else v?(typeof read!="undefined"&&(D=function($){return read($)}),E=function($){var B;return typeof readbuffer=="function"?new Uint8Array(readbuffer($)):(B=read($,"binary"),be(typeof B=="object"),B)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(A=function(N){quit(N)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(y||x)&&(x?S=self.location.href:typeof document!="undefined"&&document.currentScript&&(S=document.currentScript.src),typeof s!="undefined"&&s&&(S=s),S.indexOf("blob:")!==0?S=S.substr(0,S.lastIndexOf("/")+1):S="",b?(D=function($,B){return T||(T=Mi("fs")),P||(P=ec()),$=P.normalize($),T.readFileSync($,B?null:"utf8")},E=function($){var B=D($,!0);return B.buffer||(B=new Uint8Array(B)),be(B.buffer),B}):(D=function(N){var $=new XMLHttpRequest;return $.open("GET",N,!1),$.send(null),$.responseText},x&&(E=function(N){var $=new XMLHttpRequest;return $.open("GET",N,!1),$.responseType="arraybuffer",$.send(null),new Uint8Array($.response)}),O=function(N,$,B){var K=new XMLHttpRequest;K.open("GET",N,!0),K.responseType="arraybuffer",K.onload=function(){if(K.status==200||K.status==0&&K.response){$(K.response);return}B()},K.onerror=B,K.send(null)}),R=function(N){document.title=N});b&&typeof performance=="undefined"&&(global.performance=XS().performance);var j=c.print||console.log.bind(console),q=c.printErr||console.warn.bind(console);for(f in h)h.hasOwnProperty(f)&&(c[f]=h[f]);h=null,c.arguments&&(m=c.arguments),c.thisProgram&&(g=c.thisProgram),c.quit&&(A=c.quit);var X=Atomics.load,te=Atomics.store,ne=Atomics.compareExchange,se;c.wasmBinary&&(se=c.wasmBinary);var ae=c.noExitRuntime||!0;typeof WebAssembly!="object"&&kr("no native wasm support detected");var Q,ce,de=!1,fe;function be(N,$){N||kr("Assertion failed: "+$)}function Ee(N){var $=c["_"+N];return be($,"Cannot call unknown function "+N+", make sure it is exported"),$}function Re(N,$,B,K,Ae){var me={string:function(In){var Pi=0;if(In!=null&&In!==0){var Zx=(In.length<<2)+1;Pi=Fi(Zx),it(In,Pi,Zx)}return Pi},array:function(In){var Pi=Fi(In.length);return rt(In,Pi),Pi}};function ge(In){return $==="string"?Me(In):$==="boolean"?Boolean(In):In}var Se=Ee(N),ut=[],rn=0;if(K)for(var Zt=0;Zt<K.length;Zt++){var Qr=me[B[Zt]];Qr?(rn===0&&(rn=Ku()),ut[Zt]=Qr(K[Zt])):ut[Zt]=K[Zt]}var Oi=Se.apply(null,ut);return Oi=ge(Oi),rn!==0&&_i(rn),Oi}function Pe(N,$,B,K){B=B||[];var Ae=B.every(function(ge){return ge==="number"}),me=$!=="string";return me&&Ae&&!K?Ee(N):function(){return Re(N,$,B,arguments,K)}}function Be(N,$,B){for(var K=$+B,Ae="";!($>=K);){var me=N[$++];if(!me)return Ae;if(!(me&128)){Ae+=String.fromCharCode(me);continue}var ge=N[$++]&63;if((me&224)==192){Ae+=String.fromCharCode((me&31)<<6|ge);continue}var Se=N[$++]&63;if((me&240)==224?me=(me&15)<<12|ge<<6|Se:me=(me&7)<<18|ge<<12|Se<<6|N[$++]&63,me<65536)Ae+=String.fromCharCode(me);else{var ut=me-65536;Ae+=String.fromCharCode(55296|ut>>10,56320|ut&1023)}}return Ae}function Me(N,$){return N?Be(o(),N,$):""}function mt(N,$,B,K){if(!(K>0))return 0;for(var Ae=B,me=B+K-1,ge=0;ge<N.length;++ge){var Se=N.charCodeAt(ge);if(Se>=55296&&Se<=57343){var ut=N.charCodeAt(++ge);Se=65536+((Se&1023)<<10)|ut&1023}if(Se<=127){if(B>=me)break;$[B++]=Se}else if(Se<=2047){if(B+1>=me)break;$[B++]=192|Se>>6,$[B++]=128|Se&63}else if(Se<=65535){if(B+2>=me)break;$[B++]=224|Se>>12,$[B++]=128|Se>>6&63,$[B++]=128|Se&63}else{if(B+3>=me)break;$[B++]=240|Se>>18,$[B++]=128|Se>>12&63,$[B++]=128|Se>>6&63,$[B++]=128|Se&63}}return $[B]=0,B-Ae}function it(N,$,B){return mt(N,o(),$,B)}function lt(N){for(var $=0,B=0;B<N.length;++B){var K=N.charCodeAt(B);K>=55296&&K<=57343&&(K=65536+((K&1023)<<10)|N.charCodeAt(++B)&1023),K<=127?++$:K<=2047?$+=2:K<=65535?$+=3:$+=4}return $}function rt(N,$){a().set(N,$)}function ht(N,$){return N%$>0&&(N+=$-N%$),N}var Xe,zn,Rt,Yn,hn,Ns,kn,ms,gs;function fn(N){Xe=N,c.HEAP8=zn=new Int8Array(N),c.HEAP16=Yn=new Int16Array(N),c.HEAP32=Ns=new Int32Array(N),c.HEAPU8=Rt=new Uint8Array(N),c.HEAPU16=hn=new Uint16Array(N),c.HEAPU32=kn=new Uint32Array(N),c.HEAPF32=ms=new Float32Array(N),c.HEAPF64=gs=new Float64Array(N)}var As=c.INITIAL_MEMORY||16777216;if(k)Q=c.wasmMemory,Xe=c.buffer;else if(c.wasmMemory)Q=c.wasmMemory;else if(Q=new WebAssembly.Memory({initial:As/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw q("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(Xe=Q.buffer),As=Xe.byteLength,fn(Xe);var ys,Jn=[],er=[],vr=[],Xr=[],Ci=[],tr=!1,Xd=!1;k||er.push({func:function(){up()}});function Bm(){if(!k){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)Zd(c.preRun.shift());Ni(Jn)}}function Bu(){tr=!0,!k&&Ni(er)}function Wm(){k||Ni(vr)}function Kd(){k||(Xd=!0)}function Ln(){if(!k){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)Vm(c.postRun.shift());Ni(Ci)}}function Zd(N){Jn.unshift(N)}function Vm(N){Ci.unshift(N)}var wr=0,Kr=null,$a=null;function Um(N){be(!k,"addRunDependency cannot be used in a pthread worker"),wr++,c.monitorRunDependencies&&c.monitorRunDependencies(wr)}function Hm(N){if(wr--,c.monitorRunDependencies&&c.monitorRunDependencies(wr),wr==0&&(Kr!==null&&(clearInterval(Kr),Kr=null),$a)){var $=$a;$a=null,$()}}c.preloadedImages={},c.preloadedAudios={};function kr(N){c.onAbort&&c.onAbort(N),k&&console.error("Pthread aborting at "+new Error().stack),N+="",q(N),de=!0,fe=1,N="abort("+N+"). Build with -s ASSERTIONS=1 for more info.";var $=new WebAssembly.RuntimeError(N);throw p($),$}function Yd(N,$){return String.prototype.startsWith?N.startsWith($):N.indexOf($)===0}var Ti="data:application/octet-stream;base64,";function Jd(N){return Yd(N,Ti)}var Gm="file://";function Qd(N){return Yd(N,Gm)}var Bn="tfjs-backend-wasm-threaded-simd.wasm";Jd(Bn)||(Bn=C(Bn));function ep(N){try{if(N==Bn&&se)return new Uint8Array(se);if(E)return E(N);throw"both async and sync fetching of the wasm failed"}catch($){kr($)}}function jm(){if(!se&&(y||x)){if(typeof fetch=="function"&&!Qd(Bn))return fetch(Bn,{credentials:"same-origin"}).then(function(N){if(!N.ok)throw"failed to load wasm binary file at '"+Bn+"'";return N.arrayBuffer()}).catch(function(){return ep(Bn)});if(O)return new Promise(function(N,$){O(Bn,function(B){N(new Uint8Array(B))},$)})}return Promise.resolve().then(function(){return ep(Bn)})}function qm(){var N={a:M0};function $(ge,Se){var ut=ge.exports;if(c.asm=ut,ys=c.asm.F,ce=Se,!k){var rn=Ne.unusedWorkers.length;Ne.unusedWorkers.forEach(function(Zt){Ne.loadWasmModuleToWorker(Zt,function(){--rn||Hm("wasm-instantiate")})})}}k||Um("wasm-instantiate");function B(ge){$(ge.instance,ge.module)}function K(ge){return jm().then(function(Se){return WebAssembly.instantiate(Se,N)}).then(ge,function(Se){q("failed to asynchronously prepare wasm: "+Se),kr(Se)})}function Ae(){return!se&&typeof WebAssembly.instantiateStreaming=="function"&&!Jd(Bn)&&!Qd(Bn)&&typeof fetch=="function"?fetch(Bn,{credentials:"same-origin"}).then(function(ge){var Se=WebAssembly.instantiateStreaming(ge,N);return Se.then(B,function(ut){return q("wasm streaming compile failed: "+ut),q("falling back to ArrayBuffer instantiation"),K(B)})}):K(B)}if(c.instantiateWasm)try{var me=c.instantiateWasm(N,$);return me}catch(ge){return q("Module.instantiateWasm callback failed with error: "+ge),!1}return Ae().catch(p),{}}var Xm={10024:function(){throw"Canceled!"},10042:function(N,$){setTimeout(function(){Hx(N,$)},0)}};function tp(){Ne.initRuntime()}function Ni(N){for(;N.length>0;){var $=N.shift();if(typeof $=="function"){$(c);continue}var B=$.func;typeof B=="number"?$.arg===void 0?ys.get(B)():ys.get(B)($.arg):B($.arg===void 0?null:$.arg)}}function Wu(N,$){if(N<=0||N>a().length||N&!0||$<0)return-28;if($==0)return 0;$>=2147483647&&($=1/0);var B=Atomics.load(i(),$i>>2),K=0;if(B==N){var Ae=Atomics.compareExchange(i(),$i>>2,B,0);if(Ae==B&&(--$,K=1,$<=0))return 1}var me=Atomics.notify(i(),N>>2,$);if(me>=0)return me+K;throw"Atomics.notify returned an unexpected value "+me}c._emscripten_futex_wake=Wu;function Km(N){if(k)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in killThread!";i()[N+12>>2]=0;var $=Ne.pthreads[N];$.worker.terminate(),Ne.freeThreadData($),Ne.runningWorkers.splice(Ne.runningWorkers.indexOf($.worker),1),$.worker.pthread=void 0}function Zm(N){if(k)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cancelThread!";var $=Ne.pthreads[N];$.worker.postMessage({cmd:"cancel"})}function Ym(N){if(k)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cleanupThread!";var $=Ne.pthreads[N];if($){i()[N+12>>2]=0;var B=$.worker;Ne.returnWorkerToPool(B)}}var Ne={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var N=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),$=0;$<N;++$)Ne.allocateUnusedWorker()},initRuntime:function(){for(var N=Pa(228),$=0;$<228/4;++$)l()[N/4+$]=0;i()[N+12>>2]=N;var B=N+152;i()[B>>2]=B;for(var K=Pa(512),$=0;$<128;++$)l()[K/4+$]=0;Atomics.store(l(),N+100>>2,K),Atomics.store(l(),N+40>>2,N),cg(N,!x,1),Ux(N)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ne.threadExitHandlers.length>0;)Ne.threadExitHandlers.pop()();k&&Di()&&Vx()},runExitHandlersAndDeinitThread:function(N,$){Atomics.store(l(),N+56>>2,1),Atomics.store(l(),N+60>>2,0),Ne.runExitHandlers(),Atomics.store(l(),N+4>>2,$),Atomics.store(l(),N+0>>2,1),Wu(N+0,2147483647),cg(0,0,0)},threadExit:function(N){var $=Di();$&&(Ne.runExitHandlersAndDeinitThread($,N),k&&postMessage({cmd:"exit"}))},threadCancel:function(){Ne.runExitHandlersAndDeinitThread(Di(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var N in Ne.pthreads){var $=Ne.pthreads[N];$&&$.worker&&Ne.returnWorkerToPool($.worker)}Ne.pthreads={};for(var B=0;B<Ne.unusedWorkers.length;++B){var K=Ne.unusedWorkers[B];K.terminate()}Ne.unusedWorkers=[];for(var B=0;B<Ne.runningWorkers.length;++B){var K=Ne.runningWorkers[B],$=K.pthread;Ne.freeThreadData($),K.terminate()}Ne.runningWorkers=[]},freeThreadData:function(N){if(!!N){if(N.threadInfoStruct){var $=i()[N.threadInfoStruct+100>>2];i()[N.threadInfoStruct+100>>2]=0,Xu($),Xu(N.threadInfoStruct)}N.threadInfoStruct=0,N.allocatedOwnStack&&N.stackBase&&Xu(N.stackBase),N.stackBase=0,N.worker&&(N.worker.pthread=null)}},returnWorkerToPool:function(N){Ne.runWithoutMainThreadQueuedCalls(function(){delete Ne.pthreads[N.pthread.threadInfoStruct],Ne.unusedWorkers.push(N),Ne.runningWorkers.splice(Ne.runningWorkers.indexOf(N),1),Ne.freeThreadData(N.pthread),N.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(N){i()[Kx>>2]=0;try{N()}finally{i()[Kx>>2]=1}},receiveObjectTransfer:function(N){},loadWasmModuleToWorker:function(N,$){N.onmessage=function(B){var K=B.data,Ae=K.cmd;if(N.pthread&&(Ne.currentProxiedOperationCallerThread=N.pthread.threadInfoStruct),K.targetThread&&K.targetThread!=Di()){var me=Ne.pthreads[K.targetThread];me?me.worker.postMessage(B.data,K.transferList):console.error('Internal error! Worker sent a message "'+Ae+'" to target pthread '+K.targetThread+", but that thread no longer exists!"),Ne.currentProxiedOperationCallerThread=void 0;return}if(Ae==="processQueuedMainThreadWork")lg();else if(Ae==="spawnThread")ip(B.data);else if(Ae==="cleanupThread")Ym(K.thread);else if(Ae==="killThread")Km(K.thread);else if(Ae==="cancelThread")Zm(K.thread);else if(Ae==="loaded")N.loaded=!0,$&&$(N),N.runPthread&&(N.runPthread(),delete N.runPthread);else if(Ae==="print")j("Thread "+K.threadId+": "+K.text);else if(Ae==="printErr")q("Thread "+K.threadId+": "+K.text);else if(Ae==="alert")alert("Thread "+K.threadId+": "+K.text);else if(Ae==="exit"){var ge=N.pthread&&Atomics.load(l(),N.pthread.threadInfoStruct+64>>2);ge&&Ne.returnWorkerToPool(N)}else if(Ae==="exitProcess")try{wS(K.returnCode)}catch(Se){if(Se instanceof Zu)return;throw Se}else Ae==="cancelDone"?Ne.returnWorkerToPool(N):Ae==="objectTransfer"?Ne.receiveObjectTransfer(B.data):B.data.target==="setimmediate"?N.postMessage(B.data):q("worker sent an unknown command "+Ae);Ne.currentProxiedOperationCallerThread=void 0},N.onerror=function(B){q("pthread sent an error! "+B.filename+":"+B.lineno+": "+B.message)},b&&(N.on("message",function(B){N.onmessage({data:B})}),N.on("error",function(B){N.onerror(B)}),N.on("exit",function(B){})),N.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||s,wasmMemory:Q,wasmModule:ce})},allocateUnusedWorker:function(){var N=C("tfjs-backend-wasm-threaded-simd.worker.js");Ne.unusedWorkers.push(new Worker(N))},getNewWorker:function(){return Ne.unusedWorkers.length==0&&(Ne.allocateUnusedWorker(),Ne.loadWasmModuleToWorker(Ne.unusedWorkers[0])),Ne.unusedWorkers.length>0?Ne.unusedWorkers.pop():null},busySpinWait:function(N){for(var $=performance.now()+N;performance.now()<$;);}};function Jm(N,$){qx(N,$),_i(N)}c.establishStackSpace=Jm;function Qm(){return ae}c.getNoExitRuntime=Qm;function e0(N,$){return ys.get(N)($)}c.invokeEntryPoint=e0;function t0(N,$,B,K){kr("Assertion failed: "+Me(N)+", at: "+[$?Me($):"unknown filename",B,K?Me(K):"unknown function"])}function n0(N,$){var B=_main(N,$)}var Oa;b?Oa=function(){var N=process.hrtime();return N[0]*1e3+N[1]/1e6}:k?Oa=function(){return performance.now()-c.__performance_now_clock_drift}:typeof dateNow!="undefined"?Oa=dateNow:Oa=function(){return performance.now()};function s0(N){return i()[Bx()>>2]=N,N}function r0(N,$){if(k)return Zr(1,1,N,$)}function a0(N,$){if(N==$)postMessage({cmd:"processQueuedMainThreadWork"});else if(k)postMessage({targetThread:N,cmd:"processThreadQueue"});else{var B=Ne.pthreads[N],K=B&&B.worker;if(!K)return;K.postMessage({cmd:"processThreadQueue"})}return 1}function o0(){kr()}function i0(N,$,B){var K=p0($,B);return Xm[N].apply(null,K)}function l0(N,$){}function u0(N,$,B){if(N<=0||N>a().length||N&!0)return-28;if(y){if(Atomics.load(i(),N>>2)!=$)return-6;for(var Ae=performance.now(),me=Ae+B,ge=Atomics.exchange(i(),$i>>2,N);;){if(Ae=performance.now(),Ae>me)return ge=Atomics.exchange(i(),$i>>2,0),-73;if(ge=Atomics.exchange(i(),$i>>2,0),ge==0)break;if(lg(),Atomics.load(i(),N>>2)!=$)return-6;ge=Atomics.exchange(i(),$i>>2,N)}return 0}else{var K=Atomics.wait(i(),N>>2,$,B);if(K==="timed-out")return-73;if(K==="not-equal")return-6;if(K==="ok")return 0;throw"Atomics.wait returned an unexpected value "+K}}function c0(N,$,B){o().copyWithin(N,$,$+B)}function d0(){return b?Mi("os").cpus().length:navigator.hardwareConcurrency}function Zr(N,$){for(var B=arguments.length-2,K=Ku(),Ae=B,me=Fi(Ae*8),ge=me>>3,Se=0;Se<B;Se++){var ut=arguments[2+Se];u()[ge+Se]=ut}var rn=jx(N,Ae,me,$);return _i(K),rn}var Vu=[],Uu=[];function p0(N,$){Uu.length=0;var B;for($>>=2;B=o()[N++];){var K=B<105;K&&$&1&&$++,Uu.push(K?u()[$++>>1]:i()[$]),++$}return Uu}function h0(N,$,B){Vu.length=$;for(var K=B>>3,Ae=0;Ae<$;Ae++)Vu[Ae]=u()[K+Ae];var me=N<0,ge=me?Xm[-N-1]:P0[N];return ge.apply(null,Vu)}function f0(){return o().length}function m0(N){try{return Q.grow(N-Xe.byteLength+65535>>>16),fn(Q.buffer),1}catch($){}}function g0(N){var $=f0();if(N<=$)return!1;var B=2147483648;if(N>B)return!1;for(var K=1;K<=4;K*=2){var Ae=$*(1+.2/K);Ae=Math.min(Ae,N+100663296);var me=Math.min(B,ht(Math.max(N,Ae),65536)),ge=m0(me);if(ge)return!0}return!1}var Ve={inEventHandler:0,removeAllEventListeners:function(){for(var N=Ve.eventHandlers.length-1;N>=0;--N)Ve._removeHandler(N);Ve.eventHandlers=[],Ve.deferredCalls=[]},registerRemoveEventListeners:function(){Ve.removeEventListenersRegistered||(Xr.push(Ve.removeAllEventListeners),Ve.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(N,$,B){function K(ge,Se){if(ge.length!=Se.length)return!1;for(var ut in ge)if(ge[ut]!=Se[ut])return!1;return!0}for(var Ae in Ve.deferredCalls){var me=Ve.deferredCalls[Ae];if(me.targetFunction==N&&K(me.argsList,B))return}Ve.deferredCalls.push({targetFunction:N,precedence:$,argsList:B}),Ve.deferredCalls.sort(function(ge,Se){return ge.precedence<Se.precedence})},removeDeferredCalls:function(N){for(var $=0;$<Ve.deferredCalls.length;++$)Ve.deferredCalls[$].targetFunction==N&&(Ve.deferredCalls.splice($,1),--$)},canPerformEventHandlerRequests:function(){return Ve.inEventHandler&&Ve.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!Ve.canPerformEventHandlerRequests())for(var N=0;N<Ve.deferredCalls.length;++N){var $=Ve.deferredCalls[N];Ve.deferredCalls.splice(N,1),--N,$.targetFunction.apply(null,$.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(N,$){for(var B=0;B<Ve.eventHandlers.length;++B)Ve.eventHandlers[B].target==N&&(!$||$==Ve.eventHandlers[B].eventTypeString)&&Ve._removeHandler(B--)},_removeHandler:function(N){var $=Ve.eventHandlers[N];$.target.removeEventListener($.eventTypeString,$.eventListenerFunc,$.useCapture),Ve.eventHandlers.splice(N,1)},registerOrRemoveHandler:function(N){var $=function(Ae){++Ve.inEventHandler,Ve.currentEventHandler=N,Ve.runDeferredCalls(),N.handlerFunc(Ae),Ve.runDeferredCalls(),--Ve.inEventHandler};if(N.callbackfunc)N.eventListenerFunc=$,N.target.addEventListener(N.eventTypeString,$,N.useCapture),Ve.eventHandlers.push(N),Ve.registerRemoveEventListeners();else for(var B=0;B<Ve.eventHandlers.length;++B)Ve.eventHandlers[B].target==N.target&&Ve.eventHandlers[B].eventTypeString==N.eventTypeString&&Ve._removeHandler(B--)},queueEventHandlerOnThread_iiii:function(N,$,B,K,Ae){var me=Ku(),ge=Fi(12);i()[ge>>2]=B,i()[ge+4>>2]=K,i()[ge+8>>2]=Ae,ug(0,N,637534208,$,K,ge),_i(me)},getTargetThreadForEventCallback:function(N){switch(N){case 1:return 0;case 2:return Ne.currentProxiedOperationCallerThread;default:return N}},getNodeNameForTarget:function(N){return N?N==window?"#window":N==screen?"#screen":N&&N.nodeName?N.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function A0(N){var $=lt(N)+1,B=Pa($);return it(N,B,$),B}function y0(N,$,B,K){var Ae=Ku(),me=Fi(12),ge=0;$&&(ge=A0($)),i()[me>>2]=ge,i()[me+4>>2]=B,i()[me+8>>2]=K,ug(0,N,657457152,0,ge,me),_i(Ae)}function x0(N,$,B,K){$=$?Me($):"",y0(N,$,B,K)}function b0(N){return N>2?Me(N):N}var v0=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function w0(N){N=b0(N);var $=v0[N]||(typeof document!="undefined"?document.querySelector(N):void 0);return $}function Hu(N){return w0(N)}function np(N,$,B){var K=Hu(N);if(!K)return-4;if(K.canvasSharedPtr&&(i()[K.canvasSharedPtr>>2]=$,i()[K.canvasSharedPtr+4>>2]=B),K.offscreenCanvas||!K.controlTransferredOffscreen){K.offscreenCanvas&&(K=K.offscreenCanvas);var Ae=!1;if(K.GLctxObject&&K.GLctxObject.GLctx){var me=K.GLctxObject.GLctx.getParameter(2978);Ae=me[0]===0&&me[1]===0&&me[2]===K.width&&me[3]===K.height}K.width=$,K.height=B,Ae&&K.GLctxObject.GLctx.viewport(0,0,$,B)}else if(K.canvasSharedPtr){var ge=i()[K.canvasSharedPtr+8>>2];return x0(ge,N,$,B),1}else return-4;return 0}function sp(N,$,B){return k?Zr(2,1,N,$,B):np(N,$,B)}function k0(N,$,B){var K=Hu(N);return K?np(N,$,B):sp(N,$,B)}function I0(N){}function S0(N,$){}function C0(N){var $=N.getExtension("ANGLE_instanced_arrays");if($)return N.vertexAttribDivisor=function(B,K){$.vertexAttribDivisorANGLE(B,K)},N.drawArraysInstanced=function(B,K,Ae,me){$.drawArraysInstancedANGLE(B,K,Ae,me)},N.drawElementsInstanced=function(B,K,Ae,me,ge){$.drawElementsInstancedANGLE(B,K,Ae,me,ge)},1}function T0(N){var $=N.getExtension("OES_vertex_array_object");if($)return N.createVertexArray=function(){return $.createVertexArrayOES()},N.deleteVertexArray=function(B){$.deleteVertexArrayOES(B)},N.bindVertexArray=function(B){$.bindVertexArrayOES(B)},N.isVertexArray=function(B){return $.isVertexArrayOES(B)},1}function N0(N){var $=N.getExtension("WEBGL_draw_buffers");if($)return N.drawBuffers=function(B,K){$.drawBuffersWEBGL(B,K)},1}function E0(N){return!!(N.multiDrawWebgl=N.getExtension("WEBGL_multi_draw"))}var at={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function($){at.lastError||(at.lastError=$)},getNewId:function(N){for(var $=at.counter++,B=N.length;B<$;B++)N[B]=null;return $},getSource:function(N,$,B,K){for(var Ae="",me=0;me<$;++me){var ge=K?i()[K+me*4>>2]:-1;Ae+=Me(i()[B+me*4>>2],ge<0?void 0:ge)}return Ae},createContext:function(N,$){var B=N.getContext("webgl",$);if(!B)return 0;var K=at.registerContext(B,$);return K},registerContext:function(N,$){var B=Pa(8);i()[B+4>>2]=Di();var K={handle:B,attributes:$,version:$.majorVersion,GLctx:N};return N.canvas&&(N.canvas.GLctxObject=K),at.contexts[B]=K,(typeof $.enableExtensionsByDefault=="undefined"||$.enableExtensionsByDefault)&&at.initExtensions(K),B},makeContextCurrent:function(N){return at.currentContext=at.contexts[N],c.ctx=Yr=at.currentContext&&at.currentContext.GLctx,!(N&&!Yr)},getContext:function(N){return at.contexts[N]},deleteContext:function(N){at.currentContext===at.contexts[N]&&(at.currentContext=null),typeof Ve=="object"&&Ve.removeAllHandlersOnTarget(at.contexts[N].GLctx.canvas),at.contexts[N]&&at.contexts[N].GLctx.canvas&&(at.contexts[N].GLctx.canvas.GLctxObject=void 0),Xu(at.contexts[N].handle),at.contexts[N]=null},initExtensions:function(N){if(N||(N=at.currentContext),!N.initExtensionsDone){N.initExtensionsDone=!0;var $=N.GLctx;C0($),T0($),N0($),$.disjointTimerQueryExt=$.getExtension("EXT_disjoint_timer_query"),E0($);var B=$.getSupportedExtensions()||[];B.forEach(function(K){K.indexOf("lose_context")<0&&K.indexOf("debug")<0&&$.getExtension(K)})}},populateUniformTable:function(N){for(var $=at.programs[N],B=at.programInfos[N]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},K=B.uniforms,Ae=Yr.getProgramParameter($,35718),me=0;me<Ae;++me){var ge=Yr.getActiveUniform($,me),Se=ge.name;B.maxUniformLength=Math.max(B.maxUniformLength,Se.length+1),Se.slice(-1)=="]"&&(Se=Se.slice(0,Se.lastIndexOf("[")));var ut=Yr.getUniformLocation($,Se);if(ut){var rn=at.getNewId(at.uniforms);K[Se]=[ge.size,rn],at.uniforms[rn]=ut;for(var Zt=1;Zt<ge.size;++Zt){var Qr=Se+"["+Zt+"]";ut=Yr.getUniformLocation($,Qr),rn=at.getNewId(at.uniforms),at.uniforms[rn]=ut}}}}},R0=["default","low-power","high-performance"];function D0(N,$){var B=$>>2,K=i()[B+(24>>2)],Ae={alpha:!!i()[B+(0>>2)],depth:!!i()[B+(4>>2)],stencil:!!i()[B+(8>>2)],antialias:!!i()[B+(12>>2)],premultipliedAlpha:!!i()[B+(16>>2)],preserveDrawingBuffer:!!i()[B+(20>>2)],powerPreference:R0[K],failIfMajorPerformanceCaveat:!!i()[B+(28>>2)],majorVersion:i()[B+(32>>2)],minorVersion:i()[B+(36>>2)],enableExtensionsByDefault:i()[B+(40>>2)],explicitSwapControl:i()[B+(44>>2)],proxyContextToMainThread:i()[B+(48>>2)],renderViaOffscreenBackBuffer:i()[B+(52>>2)]},me=Hu(N);if(!me||Ae.explicitSwapControl)return 0;var ge=at.createContext(me,Ae);return ge}function _0(N,$){return D0(N,$)}var Ei={mappings:{},buffers:[null,[],[]],printChar:function(N,$){var B=Ei.buffers[N];$===0||$===10?((N===1?j:q)(Be(B,0)),B.length=0):B.push($)},varargs:void 0,get:function(){Ei.varargs+=4;var N=i()[Ei.varargs-4>>2];return N},getStr:function(N){var $=Me(N);return $},get64:function(N,$){return N}};function rp(N){return k?Zr(3,1,N):0}function ap(N,$,B,K,Ae){if(k)return Zr(4,1,N,$,B,K,Ae)}function op(N,$,B,K){if(k)return Zr(5,1,N,$,B,K);for(var Ae=0,me=0;me<B;me++){for(var ge=i()[$+me*8>>2],Se=i()[$+(me*8+4)>>2],ut=0;ut<Se;ut++)Ei.printChar(N,o()[ge+ut]);Ae+=Se}return i()[K>>2]=Ae,0}function F0(N){var $=Ne.threadExitHandlers.pop();N&&$()}function $0(N,$){Ne.threadExitHandlers.push(function(){ys.get(N)($)})}function ip(N){if(k)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var $=Ne.getNewWorker();if($.pthread!==void 0)throw"Internal error!";if(!N.pthread_ptr)throw"Internal error, no pthread ptr!";Ne.runningWorkers.push($);for(var B=Pa(128*4),K=0;K<128;++K)i()[B+K*4>>2]=0;var Ae=N.stackBase+N.stackSize,me=Ne.pthreads[N.pthread_ptr]={worker:$,stackBase:N.stackBase,stackSize:N.stackSize,allocatedOwnStack:N.allocatedOwnStack,threadInfoStruct:N.pthread_ptr},ge=me.threadInfoStruct>>2;Atomics.store(l(),ge+(64>>2),N.detached),Atomics.store(l(),ge+(100>>2),B),Atomics.store(l(),ge+(40>>2),me.threadInfoStruct),Atomics.store(l(),ge+(80>>2),N.stackSize),Atomics.store(l(),ge+(76>>2),Ae),Atomics.store(l(),ge+(104>>2),N.stackSize),Atomics.store(l(),ge+(104+8>>2),Ae),Atomics.store(l(),ge+(104+12>>2),N.detached);var Se=Wx(),ut=Se+40;Atomics.store(l(),ge+(172>>2),ut),$.pthread=me;var rn={cmd:"run",start_routine:N.startRoutine,arg:N.arg,threadInfoStruct:N.pthread_ptr,stackBase:N.stackBase,stackSize:N.stackSize};$.runPthread=function(){rn.time=performance.now(),$.postMessage(rn,N.transferList)},$.loaded&&($.runPthread(),delete $.runPthread)}function O0(N,$,B,K){if(typeof SharedArrayBuffer=="undefined")return q("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!N)return q("pthread_create called with a null thread pointer!"),28;var Ae=[],me=0;if(k&&(Ae.length===0||me))return Gx(687865856,N,$,B,K);if(me)return me;var ge=0,Se=0,ut=0;$&&$!=-1?(ge=i()[$>>2],ge+=81920,Se=i()[$+8>>2],ut=i()[$+12>>2]!==0):ge=2097152;var rn=Se==0;rn?Se=Xx(16,ge):(Se-=ge,be(Se>0));for(var Zt=Pa(228),Qr=0;Qr<228>>2;++Qr)l()[(Zt>>2)+Qr]=0;i()[N>>2]=Zt,i()[Zt+12>>2]=Zt;var Oi=Zt+152;i()[Oi>>2]=Oi;var In={stackBase:Se,stackSize:ge,allocatedOwnStack:rn,detached:ut,startRoutine:B,pthread_ptr:Zt,arg:K,transferList:Ae};return k?(In.cmd="spawnThread",postMessage(In,Ae)):ip(In),0}function lp(N){if(k)return Zr(6,1,N);switch(N){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 s0(28),-1}k||Ne.initMainThreadBlock();var Yr,P0=[null,r0,sp,rp,ap,op,lp],M0={e:t0,r:n0,x:a0,b:o0,y:i0,j:l0,c:u0,d:Wu,f:Oa,p:c0,z:d0,u:h0,q:g0,v:k0,i:I0,t:S0,w:_0,m:rp,n:ap,g:op,o:tp,a:Q||c.wasmMemory,k:F0,l:$0,h:O0,s:lp},Lx=qm(),up=c.___wasm_call_ctors=function(){return(up=c.___wasm_call_ctors=c.asm.A).apply(null,arguments)},z0=c._init=function(){return(z0=c._init=c.asm.B).apply(null,arguments)},L0=c._register_tensor=function(){return(L0=c._register_tensor=c.asm.C).apply(null,arguments)},B0=c._dispose_data=function(){return(B0=c._dispose_data=c.asm.D).apply(null,arguments)},W0=c._dispose=function(){return(W0=c._dispose=c.asm.E).apply(null,arguments)},V0=c._Abs=function(){return(V0=c._Abs=c.asm.G).apply(null,arguments)},U0=c._Add=function(){return(U0=c._Add=c.asm.H).apply(null,arguments)},H0=c._AddN=function(){return(H0=c._AddN=c.asm.I).apply(null,arguments)},G0=c._All=function(){return(G0=c._All=c.asm.J).apply(null,arguments)},j0=c._Any=function(){return(j0=c._Any=c.asm.K).apply(null,arguments)},q0=c._ArgMax=function(){return(q0=c._ArgMax=c.asm.L).apply(null,arguments)},X0=c._AvgPool=function(){return(X0=c._AvgPool=c.asm.M).apply(null,arguments)},K0=c._BatchMatMul=function(){return(K0=c._BatchMatMul=c.asm.N).apply(null,arguments)},Z0=c._Ceil=function(){return(Z0=c._Ceil=c.asm.O).apply(null,arguments)},Y0=c._ClipByValue=function(){return(Y0=c._ClipByValue=c.asm.P).apply(null,arguments)},J0=c._Conv2D=function(){return(J0=c._Conv2D=c.asm.Q).apply(null,arguments)},Q0=c._Conv2DBackpropInput=function(){return(Q0=c._Conv2DBackpropInput=c.asm.R).apply(null,arguments)},eg=c._Cos=function(){return(eg=c._Cos=c.asm.S).apply(null,arguments)},tg=c._Cosh=function(){return(tg=c._Cosh=c.asm.T).apply(null,arguments)},ng=c._CropAndResize=function(){return(ng=c._CropAndResize=c.asm.U).apply(null,arguments)},sg=c._Cumsum=function(){return(sg=c._Cumsum=c.asm.V).apply(null,arguments)},rg=c._DepthToSpace=function(){return(rg=c._DepthToSpace=c.asm.W).apply(null,arguments)},ag=c._DepthwiseConv2dNative=function(){return(ag=c._DepthwiseConv2dNative=c.asm.X).apply(null,arguments)},og=c._Elu=function(){return(og=c._Elu=c.asm.Y).apply(null,arguments)},cp=c._Equal=function(){return(cp=c._Equal=c.asm.Z).apply(null,arguments)},dp=c._Exp=function(){return(dp=c._Exp=c.asm._).apply(null,arguments)},pp=c._FlipLeftRight=function(){return(pp=c._FlipLeftRight=c.asm.$).apply(null,arguments)},Gu=c._Floor=function(){return(Gu=c._Floor=c.asm.aa).apply(null,arguments)},Ri=c._FloorDiv=function(){return(Ri=c._FloorDiv=c.asm.ba).apply(null,arguments)},ig=c._FusedBatchNorm=function(){return(ig=c._FusedBatchNorm=c.asm.ca).apply(null,arguments)},ju=c._FusedConv2D=function(){return(ju=c._FusedConv2D=c.asm.da).apply(null,arguments)},J=c._FusedDepthwiseConv2D=function(){return(J=c._FusedDepthwiseConv2D=c.asm.ea).apply(null,arguments)},oe=c._Gather=function(){return(oe=c._Gather=c.asm.fa).apply(null,arguments)},we=c._GatherNd=function(){return(we=c._GatherNd=c.asm.ga).apply(null,arguments)},nt=c._Greater=function(){return(nt=c._Greater=c.asm.ha).apply(null,arguments)},Ot=c._GreaterEqual=function(){return(Ot=c._GreaterEqual=c.asm.ia).apply(null,arguments)},It=c._LeakyRelu=function(){return(It=c._LeakyRelu=c.asm.ja).apply(null,arguments)},Ke=c._Less=function(){return(Ke=c._Less=c.asm.ka).apply(null,arguments)},Je=c._LessEqual=function(){return(Je=c._LessEqual=c.asm.la).apply(null,arguments)},mn=c._Log=function(){return(mn=c._Log=c.asm.ma).apply(null,arguments)},Ir=c._LogicalAnd=function(){return(Ir=c._LogicalAnd=c.asm.na).apply(null,arguments)},Sr=c._Max=function(){return(Sr=c._Max=c.asm.oa).apply(null,arguments)},hp=c._MaxPool=function(){return(hp=c._MaxPool=c.asm.pa).apply(null,arguments)},qu=c._Maximum=function(){return(qu=c._Maximum=c.asm.qa).apply(null,arguments)},Qn=c._Mean=function(){return(Qn=c._Mean=c.asm.ra).apply(null,arguments)},Jr=c._Min=function(){return(Jr=c._Min=c.asm.sa).apply(null,arguments)},fp=c._Minimum=function(){return(fp=c._Minimum=c.asm.ta).apply(null,arguments)},OI=c._MirrorPad=function(){return(OI=c._MirrorPad=c.asm.ua).apply(null,arguments)},PI=c._Multiply=function(){return(PI=c._Multiply=c.asm.va).apply(null,arguments)},MI=c._Neg=function(){return(MI=c._Neg=c.asm.wa).apply(null,arguments)},zI=c._NonMaxSuppressionV3=function(){return(zI=c._NonMaxSuppressionV3=c.asm.xa).apply(null,arguments)},LI=c._NonMaxSuppressionV4=function(){return(LI=c._NonMaxSuppressionV4=c.asm.ya).apply(null,arguments)},BI=c._NonMaxSuppressionV5=function(){return(BI=c._NonMaxSuppressionV5=c.asm.za).apply(null,arguments)},WI=c._NotEqual=function(){return(WI=c._NotEqual=c.asm.Aa).apply(null,arguments)},VI=c._OneHot=function(){return(VI=c._OneHot=c.asm.Ba).apply(null,arguments)},UI=c._PadV2=function(){return(UI=c._PadV2=c.asm.Ca).apply(null,arguments)},HI=c._Pow=function(){return(HI=c._Pow=c.asm.Da).apply(null,arguments)},GI=c._Prelu=function(){return(GI=c._Prelu=c.asm.Ea).apply(null,arguments)},jI=c._Prod=function(){return(jI=c._Prod=c.asm.Fa).apply(null,arguments)},qI=c._RealDiv=function(){return(qI=c._RealDiv=c.asm.Ga).apply(null,arguments)},XI=c._Relu=function(){return(XI=c._Relu=c.asm.Ha).apply(null,arguments)},KI=c._Relu6=function(){return(KI=c._Relu6=c.asm.Ia).apply(null,arguments)},ZI=c._ResizeBilinear=function(){return(ZI=c._ResizeBilinear=c.asm.Ja).apply(null,arguments)},YI=c._Reverse=function(){return(YI=c._Reverse=c.asm.Ka).apply(null,arguments)},JI=c._RotateWithOffset=function(){return(JI=c._RotateWithOffset=c.asm.La).apply(null,arguments)},QI=c._Round=function(){return(QI=c._Round=c.asm.Ma).apply(null,arguments)},eS=c._Rsqrt=function(){return(eS=c._Rsqrt=c.asm.Na).apply(null,arguments)},tS=c._ScatterNd=function(){return(tS=c._ScatterNd=c.asm.Oa).apply(null,arguments)},nS=c._SelectV2=function(){return(nS=c._SelectV2=c.asm.Pa).apply(null,arguments)},sS=c._Sigmoid=function(){return(sS=c._Sigmoid=c.asm.Qa).apply(null,arguments)},rS=c._Sin=function(){return(rS=c._Sin=c.asm.Ra).apply(null,arguments)},aS=c._Softmax=function(){return(aS=c._Softmax=c.asm.Sa).apply(null,arguments)},oS=c._Sqrt=function(){return(oS=c._Sqrt=c.asm.Ta).apply(null,arguments)},iS=c._Square=function(){return(iS=c._Square=c.asm.Ua).apply(null,arguments)},lS=c._SquaredDifference=function(){return(lS=c._SquaredDifference=c.asm.Va).apply(null,arguments)},uS=c._Step=function(){return(uS=c._Step=c.asm.Wa).apply(null,arguments)},cS=c._StridedSlice=function(){return(cS=c._StridedSlice=c.asm.Xa).apply(null,arguments)},dS=c._Sub=function(){return(dS=c._Sub=c.asm.Ya).apply(null,arguments)},pS=c._Sum=function(){return(pS=c._Sum=c.asm.Za).apply(null,arguments)},hS=c._Tan=function(){return(hS=c._Tan=c.asm._a).apply(null,arguments)},fS=c._Tanh=function(){return(fS=c._Tanh=c.asm.$a).apply(null,arguments)},mS=c._Tile=function(){return(mS=c._Tile=c.asm.ab).apply(null,arguments)},gS=c._TopK=function(){return(gS=c._TopK=c.asm.bb).apply(null,arguments)},AS=c._Transform=function(){return(AS=c._Transform=c.asm.cb).apply(null,arguments)},yS=c._Transpose=function(){return(yS=c._Transpose=c.asm.db).apply(null,arguments)},xS=c.__FusedMatMul=function(){return(xS=c.__FusedMatMul=c.asm.eb).apply(null,arguments)},Pa=c._malloc=function(){return(Pa=c._malloc=c.asm.fb).apply(null,arguments)},Xu=c._free=function(){return(Xu=c._free=c.asm.gb).apply(null,arguments)},Bx=c.___errno_location=function(){return(Bx=c.___errno_location=c.asm.hb).apply(null,arguments)},Wx=c._emscripten_get_global_libc=function(){return(Wx=c._emscripten_get_global_libc=c.asm.ib).apply(null,arguments)},Di=c._pthread_self=function(){return(Di=c._pthread_self=c.asm.jb).apply(null,arguments)},Vx=c.___pthread_tsd_run_dtors=function(){return(Vx=c.___pthread_tsd_run_dtors=c.asm.kb).apply(null,arguments)},lg=c._emscripten_main_thread_process_queued_calls=function(){return(lg=c._emscripten_main_thread_process_queued_calls=c.asm.lb).apply(null,arguments)},bS=c._emscripten_current_thread_process_queued_calls=function(){return(bS=c._emscripten_current_thread_process_queued_calls=c.asm.mb).apply(null,arguments)},Ux=c._emscripten_register_main_browser_thread_id=function(){return(Ux=c._emscripten_register_main_browser_thread_id=c.asm.nb).apply(null,arguments)},Hx=c.__emscripten_do_dispatch_to_thread=function(){return(Hx=c.__emscripten_do_dispatch_to_thread=c.asm.ob).apply(null,arguments)},Gx=c._emscripten_sync_run_in_main_thread_4=function(){return(Gx=c._emscripten_sync_run_in_main_thread_4=c.asm.pb).apply(null,arguments)},jx=c._emscripten_run_in_main_runtime_thread_js=function(){return(jx=c._emscripten_run_in_main_runtime_thread_js=c.asm.qb).apply(null,arguments)},ug=c.__emscripten_call_on_thread=function(){return(ug=c.__emscripten_call_on_thread=c.asm.rb).apply(null,arguments)},vS=c._emscripten_tls_init=function(){return(vS=c._emscripten_tls_init=c.asm.sb).apply(null,arguments)},cg=c.__emscripten_thread_init=function(){return(cg=c.__emscripten_thread_init=c.asm.tb).apply(null,arguments)},Ku=c.stackSave=function(){return(Ku=c.stackSave=c.asm.ub).apply(null,arguments)},_i=c.stackRestore=function(){return(_i=c.stackRestore=c.asm.vb).apply(null,arguments)},Fi=c.stackAlloc=function(){return(Fi=c.stackAlloc=c.asm.wb).apply(null,arguments)},qx=c._emscripten_stack_set_limits=function(){return(qx=c._emscripten_stack_set_limits=c.asm.xb).apply(null,arguments)},Xx=c._memalign=function(){return(Xx=c._memalign=c.asm.yb).apply(null,arguments)},Kx=c.__emscripten_allow_main_runtime_queued_calls=10016,$i=c.__emscripten_main_thread_futex=11652;c.cwrap=Pe,c.PThread=Ne,c.PThread=Ne,c.wasmMemory=Q,c.ExitStatus=Zu;var mp;function Zu(N){this.name="ExitStatus",this.message="Program terminated with exit("+N+")",this.status=N}$a=function N(){mp||dg(),mp||($a=N)};function dg(N){if(N=N||m,wr>0)return;if(k){d(c),Bu(),postMessage({cmd:"loaded"});return}if(Bm(),wr>0)return;function $(){mp||(mp=!0,c.calledRun=!0,!de&&(Bu(),Wm(),d(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),Ln()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),$()},1)):$()}c.run=dg;function wS(N,$){if(!($&&ae&&N===0)){if(!$&&k)throw postMessage({cmd:"exitProcess",returnCode:N}),new Zu(N);ae||(Ne.terminateAllThreads(),fe=N,Kd(),c.onExit&&c.onExit(N),de=!0),A(N,new Zu(N))}}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();return k&&(ae=!1,Ne.initWorker()),dg(),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)}}),ZS=St({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.9.0_@tensorflow+tfjs-core@3.9.0/node_modules/@tensorflow/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(J,oe){o=J,i=oe});var l={},u;for(u in a)a.hasOwnProperty(u)&&(l[u]=a[u]);var c=[],d="./this.program",p=function(J,oe){throw oe},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 y(J){return a.locateFile?a.locateFile(J,A):A+J}var x,b,v,k,S,C;m?(f?A=ec().dirname(A)+"/":A=__dirname+"/",x=function(oe,we){return S||(S=Mi("fs")),C||(C=ec()),oe=C.normalize(oe),S.readFileSync(oe,we?null:"utf8")},v=function(oe){var we=x(oe,!0);return we.buffer||(we=new Uint8Array(we)),j(we.buffer),we},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(J){if(!(J instanceof ig))throw J}),process.on("unhandledRejection",tr),p=function(J){process.exit(J)},a.inspect=function(){return"[Emscripten Module object]"}):g?(typeof read!="undefined"&&(x=function(oe){return read(oe)}),v=function(oe){var we;return typeof readbuffer=="function"?new Uint8Array(readbuffer(oe)):(we=read(oe,"binary"),j(typeof we=="object"),we)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(p=function(J){quit(J)}),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="",x=function(J){var oe=new XMLHttpRequest;return oe.open("GET",J,!1),oe.send(null),oe.responseText},f&&(v=function(J){var oe=new XMLHttpRequest;return oe.open("GET",J,!1),oe.responseType="arraybuffer",oe.send(null),new Uint8Array(oe.response)}),b=function(J,oe,we){var nt=new XMLHttpRequest;nt.open("GET",J,!0),nt.responseType="arraybuffer",nt.onload=function(){if(nt.status==200||nt.status==0&&nt.response){oe(nt.response);return}we()},nt.onerror=we,nt.send(null)},k=function(J){document.title=J});var D=a.print||console.log.bind(console),O=a.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(a[u]=l[u]);l=null,a.arguments&&(c=a.arguments),a.thisProgram&&(d=a.thisProgram),a.quit&&(p=a.quit);var E;a.wasmBinary&&(E=a.wasmBinary);var R=a.noExitRuntime||!0;typeof WebAssembly!="object"&&tr("no native wasm support detected");var T,P=!1,U;function j(J,oe){J||tr("Assertion failed: "+oe)}function q(J){var oe=a["_"+J];return j(oe,"Cannot call unknown function "+J+", make sure it is exported"),oe}function X(J,oe,we,nt,Ot){var It={string:function(Qn){var Jr=0;if(Qn!=null&&Qn!==0){var fp=(Qn.length<<2)+1;Jr=Gu(fp),ce(Qn,Jr,fp)}return Jr},array:function(Qn){var Jr=Gu(Qn.length);return de(Qn,Jr),Jr}};function Ke(Qn){return oe==="string"?ae(Qn):oe==="boolean"?Boolean(Qn):Qn}var Je=q(J),mn=[],Ir=0;if(nt)for(var Sr=0;Sr<nt.length;Sr++){var hp=It[we[Sr]];hp?(Ir===0&&(Ir=dp()),mn[Sr]=hp(nt[Sr])):mn[Sr]=nt[Sr]}var qu=Je.apply(null,mn);return qu=Ke(qu),Ir!==0&&pp(Ir),qu}function te(J,oe,we,nt){we=we||[];var Ot=we.every(function(Ke){return Ke==="number"}),It=oe!=="string";return It&&Ot&&!nt?q(J):function(){return X(J,oe,we,arguments,nt)}}var ne=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function se(J,oe,we){for(var nt=oe+we,Ot=oe;J[Ot]&&!(Ot>=nt);)++Ot;if(Ot-oe>16&&J.subarray&&ne)return ne.decode(J.subarray(oe,Ot));for(var It="";oe<Ot;){var Ke=J[oe++];if(!(Ke&128)){It+=String.fromCharCode(Ke);continue}var Je=J[oe++]&63;if((Ke&224)==192){It+=String.fromCharCode((Ke&31)<<6|Je);continue}var mn=J[oe++]&63;if((Ke&240)==224?Ke=(Ke&15)<<12|Je<<6|mn:Ke=(Ke&7)<<18|Je<<12|mn<<6|J[oe++]&63,Ke<65536)It+=String.fromCharCode(Ke);else{var Ir=Ke-65536;It+=String.fromCharCode(55296|Ir>>10,56320|Ir&1023)}}return It}function ae(J,oe){return J?se(Re,J,oe):""}function Q(J,oe,we,nt){if(!(nt>0))return 0;for(var Ot=we,It=we+nt-1,Ke=0;Ke<J.length;++Ke){var Je=J.charCodeAt(Ke);if(Je>=55296&&Je<=57343){var mn=J.charCodeAt(++Ke);Je=65536+((Je&1023)<<10)|mn&1023}if(Je<=127){if(we>=It)break;oe[we++]=Je}else if(Je<=2047){if(we+1>=It)break;oe[we++]=192|Je>>6,oe[we++]=128|Je&63}else if(Je<=65535){if(we+2>=It)break;oe[we++]=224|Je>>12,oe[we++]=128|Je>>6&63,oe[we++]=128|Je&63}else{if(we+3>=It)break;oe[we++]=240|Je>>18,oe[we++]=128|Je>>12&63,oe[we++]=128|Je>>6&63,oe[we++]=128|Je&63}}return oe[we]=0,we-Ot}function ce(J,oe,we){return Q(J,Re,oe,we)}function de(J,oe){Ee.set(J,oe)}function fe(J,oe){return J%oe>0&&(J+=oe-J%oe),J}var be,Ee,Re,Pe,Be,Me,mt,it,lt;function rt(J){be=J,a.HEAP8=Ee=new Int8Array(J),a.HEAP16=Pe=new Int16Array(J),a.HEAP32=Me=new Int32Array(J),a.HEAPU8=Re=new Uint8Array(J),a.HEAPU16=Be=new Uint16Array(J),a.HEAPU32=mt=new Uint32Array(J),a.HEAPF32=it=new Float32Array(J),a.HEAPF64=lt=new Float64Array(J)}var ht=a.INITIAL_MEMORY||16777216,Xe,zn=[],Rt=[],Yn=[],hn=[],Ns=!1;Rt.push({func:function(){tp()}});function kn(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)As(a.preRun.shift());Kr(zn)}function ms(){Ns=!0,Kr(Rt)}function gs(){Kr(Yn)}function fn(){if(a.postRun)for(typeof a.postRun=="function"&&(a.postRun=[a.postRun]);a.postRun.length;)ys(a.postRun.shift());Kr(hn)}function As(J){zn.unshift(J)}function ys(J){hn.unshift(J)}var Jn=0,er=null,vr=null;function Xr(J){Jn++,a.monitorRunDependencies&&a.monitorRunDependencies(Jn)}function Ci(J){if(Jn--,a.monitorRunDependencies&&a.monitorRunDependencies(Jn),Jn==0&&(er!==null&&(clearInterval(er),er=null),vr)){var oe=vr;vr=null,oe()}}a.preloadedImages={},a.preloadedAudios={};function tr(J){a.onAbort&&a.onAbort(J),J+="",O(J),P=!0,U=1,J="abort("+J+"). Build with -s ASSERTIONS=1 for more info.";var oe=new WebAssembly.RuntimeError(J);throw i(oe),oe}function Xd(J,oe){return String.prototype.startsWith?J.startsWith(oe):J.indexOf(oe)===0}var Bm="data:application/octet-stream;base64,";function Bu(J){return Xd(J,Bm)}var Wm="file://";function Kd(J){return Xd(J,Wm)}var Ln="tfjs-backend-wasm.wasm";Bu(Ln)||(Ln=y(Ln));function Zd(J){try{if(J==Ln&&E)return new Uint8Array(E);if(v)return v(J);throw"both async and sync fetching of the wasm failed"}catch(oe){tr(oe)}}function Vm(){if(!E&&(h||f)){if(typeof fetch=="function"&&!Kd(Ln))return fetch(Ln,{credentials:"same-origin"}).then(function(J){if(!J.ok)throw"failed to load wasm binary file at '"+Ln+"'";return J.arrayBuffer()}).catch(function(){return Zd(Ln)});if(b)return new Promise(function(J,oe){b(Ln,function(we){J(new Uint8Array(we))},oe)})}return Promise.resolve().then(function(){return Zd(Ln)})}function wr(){var J={a:qm};function oe(Ke,Je){var mn=Ke.exports;a.asm=mn,T=a.asm.i,rt(T.buffer),Xe=a.asm.o,Ci("wasm-instantiate")}Xr("wasm-instantiate");function we(Ke){oe(Ke.instance)}function nt(Ke){return Vm().then(function(Je){return WebAssembly.instantiate(Je,J)}).then(Ke,function(Je){O("failed to asynchronously prepare wasm: "+Je),tr(Je)})}function Ot(){return!E&&typeof WebAssembly.instantiateStreaming=="function"&&!Bu(Ln)&&!Kd(Ln)&&typeof fetch=="function"?fetch(Ln,{credentials:"same-origin"}).then(function(Ke){var Je=WebAssembly.instantiateStreaming(Ke,J);return Je.then(we,function(mn){return O("wasm streaming compile failed: "+mn),O("falling back to ArrayBuffer instantiation"),nt(we)})}):nt(we)}if(a.instantiateWasm)try{var It=a.instantiateWasm(J,oe);return It}catch(Ke){return O("Module.instantiateWasm callback failed with error: "+Ke),!1}return Ot().catch(i),{}}function Kr(J){for(;J.length>0;){var oe=J.shift();if(typeof oe=="function"){oe(a);continue}var we=oe.func;typeof we=="number"?oe.arg===void 0?Xe.get(we)():Xe.get(we)(oe.arg):we(oe.arg===void 0?null:oe.arg)}}function $a(){tr()}function Um(J,oe,we){Re.copyWithin(J,oe,oe+we)}function Hm(){return Re.length}function kr(J){try{return T.grow(J-be.byteLength+65535>>>16),rt(T.buffer),1}catch(oe){}}function Yd(J){var oe=Hm(),we=2147483648;if(J>we)return!1;for(var nt=1;nt<=4;nt*=2){var Ot=oe*(1+.2/nt);Ot=Math.min(Ot,J+100663296);var It=Math.min(we,fe(Math.max(J,Ot),65536)),Ke=kr(It);if(Ke)return!0}return!1}var Ti={mappings:{},buffers:[null,[],[]],printChar:function(J,oe){var we=Ti.buffers[J];oe===0||oe===10?((J===1?D:O)(se(we,0)),we.length=0):we.push(oe)},varargs:void 0,get:function(){Ti.varargs+=4;var J=Me[Ti.varargs-4>>2];return J},getStr:function(J){var oe=ae(J);return oe},get64:function(J,oe){return J}};function Jd(J){return 0}function Gm(J,oe,we,nt,Ot){}function Qd(J,oe,we,nt){for(var Ot=0,It=0;It<we;It++){for(var Ke=Me[oe+It*8>>2],Je=Me[oe+(It*8+4)>>2],mn=0;mn<Je;mn++)Ti.printChar(J,Re[Ke+mn]);Ot+=Je}return Me[nt>>2]=Ot,0}function Bn(){return 6}function ep(J){return Me[cp()>>2]=J,J}function jm(J){switch(J){case 30:return 16384;case 85:var oe=2147483648;return oe/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 ep(28),-1}var qm={a:$a,d:Um,e:Yd,f:Jd,c:Gm,b:Qd,g:Bn,h:jm},Xm=wr(),tp=a.___wasm_call_ctors=function(){return(tp=a.___wasm_call_ctors=a.asm.j).apply(null,arguments)},Ni=a._init=function(){return(Ni=a._init=a.asm.k).apply(null,arguments)},Wu=a._register_tensor=function(){return(Wu=a._register_tensor=a.asm.l).apply(null,arguments)},Km=a._dispose_data=function(){return(Km=a._dispose_data=a.asm.m).apply(null,arguments)},Zm=a._dispose=function(){return(Zm=a._dispose=a.asm.n).apply(null,arguments)},Ym=a._Abs=function(){return(Ym=a._Abs=a.asm.p).apply(null,arguments)},Ne=a._Add=function(){return(Ne=a._Add=a.asm.q).apply(null,arguments)},Jm=a._AddN=function(){return(Jm=a._AddN=a.asm.r).apply(null,arguments)},Qm=a._All=function(){return(Qm=a._All=a.asm.s).apply(null,arguments)},e0=a._Any=function(){return(e0=a._Any=a.asm.t).apply(null,arguments)},t0=a._ArgMax=function(){return(t0=a._ArgMax=a.asm.u).apply(null,arguments)},n0=a._AvgPool=function(){return(n0=a._AvgPool=a.asm.v).apply(null,arguments)},Oa=a._BatchMatMul=function(){return(Oa=a._BatchMatMul=a.asm.w).apply(null,arguments)},s0=a._Ceil=function(){return(s0=a._Ceil=a.asm.x).apply(null,arguments)},r0=a._ClipByValue=function(){return(r0=a._ClipByValue=a.asm.y).apply(null,arguments)},a0=a._Conv2D=function(){return(a0=a._Conv2D=a.asm.z).apply(null,arguments)},o0=a._Conv2DBackpropInput=function(){return(o0=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},i0=a._Cos=function(){return(i0=a._Cos=a.asm.B).apply(null,arguments)},l0=a._Cosh=function(){return(l0=a._Cosh=a.asm.C).apply(null,arguments)},u0=a._CropAndResize=function(){return(u0=a._CropAndResize=a.asm.D).apply(null,arguments)},c0=a._Cumsum=function(){return(c0=a._Cumsum=a.asm.E).apply(null,arguments)},d0=a._DepthToSpace=function(){return(d0=a._DepthToSpace=a.asm.F).apply(null,arguments)},Zr=a._DepthwiseConv2dNative=function(){return(Zr=a._DepthwiseConv2dNative=a.asm.G).apply(null,arguments)},Vu=a._Elu=function(){return(Vu=a._Elu=a.asm.H).apply(null,arguments)},Uu=a._Equal=function(){return(Uu=a._Equal=a.asm.I).apply(null,arguments)},p0=a._Exp=function(){return(p0=a._Exp=a.asm.J).apply(null,arguments)},h0=a._FlipLeftRight=function(){return(h0=a._FlipLeftRight=a.asm.K).apply(null,arguments)},f0=a._Floor=function(){return(f0=a._Floor=a.asm.L).apply(null,arguments)},m0=a._FloorDiv=function(){return(m0=a._FloorDiv=a.asm.M).apply(null,arguments)},g0=a._FusedBatchNorm=function(){return(g0=a._FusedBatchNorm=a.asm.N).apply(null,arguments)},Ve=a._FusedConv2D=function(){return(Ve=a._FusedConv2D=a.asm.O).apply(null,arguments)},A0=a._FusedDepthwiseConv2D=function(){return(A0=a._FusedDepthwiseConv2D=a.asm.P).apply(null,arguments)},y0=a._Gather=function(){return(y0=a._Gather=a.asm.Q).apply(null,arguments)},x0=a._GatherNd=function(){return(x0=a._GatherNd=a.asm.R).apply(null,arguments)},b0=a._Greater=function(){return(b0=a._Greater=a.asm.S).apply(null,arguments)},v0=a._GreaterEqual=function(){return(v0=a._GreaterEqual=a.asm.T).apply(null,arguments)},w0=a._LeakyRelu=function(){return(w0=a._LeakyRelu=a.asm.U).apply(null,arguments)},Hu=a._Less=function(){return(Hu=a._Less=a.asm.V).apply(null,arguments)},np=a._LessEqual=function(){return(np=a._LessEqual=a.asm.W).apply(null,arguments)},sp=a._Log=function(){return(sp=a._Log=a.asm.X).apply(null,arguments)},k0=a._LogicalAnd=function(){return(k0=a._LogicalAnd=a.asm.Y).apply(null,arguments)},I0=a._Max=function(){return(I0=a._Max=a.asm.Z).apply(null,arguments)},S0=a._MaxPool=function(){return(S0=a._MaxPool=a.asm._).apply(null,arguments)},C0=a._Maximum=function(){return(C0=a._Maximum=a.asm.$).apply(null,arguments)},T0=a._Mean=function(){return(T0=a._Mean=a.asm.aa).apply(null,arguments)},N0=a._Min=function(){return(N0=a._Min=a.asm.ba).apply(null,arguments)},E0=a._Minimum=function(){return(E0=a._Minimum=a.asm.ca).apply(null,arguments)},at=a._MirrorPad=function(){return(at=a._MirrorPad=a.asm.da).apply(null,arguments)},R0=a._Multiply=function(){return(R0=a._Multiply=a.asm.ea).apply(null,arguments)},D0=a._Neg=function(){return(D0=a._Neg=a.asm.fa).apply(null,arguments)},_0=a._NonMaxSuppressionV3=function(){return(_0=a._NonMaxSuppressionV3=a.asm.ga).apply(null,arguments)},Ei=a._NonMaxSuppressionV4=function(){return(Ei=a._NonMaxSuppressionV4=a.asm.ha).apply(null,arguments)},rp=a._NonMaxSuppressionV5=function(){return(rp=a._NonMaxSuppressionV5=a.asm.ia).apply(null,arguments)},ap=a._NotEqual=function(){return(ap=a._NotEqual=a.asm.ja).apply(null,arguments)},op=a._OneHot=function(){return(op=a._OneHot=a.asm.ka).apply(null,arguments)},F0=a._PadV2=function(){return(F0=a._PadV2=a.asm.la).apply(null,arguments)},$0=a._Pow=function(){return($0=a._Pow=a.asm.ma).apply(null,arguments)},ip=a._Prelu=function(){return(ip=a._Prelu=a.asm.na).apply(null,arguments)},O0=a._Prod=function(){return(O0=a._Prod=a.asm.oa).apply(null,arguments)},lp=a._RealDiv=function(){return(lp=a._RealDiv=a.asm.pa).apply(null,arguments)},Yr=a._Relu=function(){return(Yr=a._Relu=a.asm.qa).apply(null,arguments)},P0=a._Relu6=function(){return(P0=a._Relu6=a.asm.ra).apply(null,arguments)},M0=a._ResizeBilinear=function(){return(M0=a._ResizeBilinear=a.asm.sa).apply(null,arguments)},Lx=a._Reverse=function(){return(Lx=a._Reverse=a.asm.ta).apply(null,arguments)},up=a._RotateWithOffset=function(){return(up=a._RotateWithOffset=a.asm.ua).apply(null,arguments)},z0=a._Round=function(){return(z0=a._Round=a.asm.va).apply(null,arguments)},L0=a._Rsqrt=function(){return(L0=a._Rsqrt=a.asm.wa).apply(null,arguments)},B0=a._ScatterNd=function(){return(B0=a._ScatterNd=a.asm.xa).apply(null,arguments)},W0=a._SelectV2=function(){return(W0=a._SelectV2=a.asm.ya).apply(null,arguments)},V0=a._Sigmoid=function(){return(V0=a._Sigmoid=a.asm.za).apply(null,arguments)},U0=a._Sin=function(){return(U0=a._Sin=a.asm.Aa).apply(null,arguments)},H0=a._Softmax=function(){return(H0=a._Softmax=a.asm.Ba).apply(null,arguments)},G0=a._Sqrt=function(){return(G0=a._Sqrt=a.asm.Ca).apply(null,arguments)},j0=a._Square=function(){return(j0=a._Square=a.asm.Da).apply(null,arguments)},q0=a._SquaredDifference=function(){return(q0=a._SquaredDifference=a.asm.Ea).apply(null,arguments)},X0=a._Step=function(){return(X0=a._Step=a.asm.Fa).apply(null,arguments)},K0=a._StridedSlice=function(){return(K0=a._StridedSlice=a.asm.Ga).apply(null,arguments)},Z0=a._Sub=function(){return(Z0=a._Sub=a.asm.Ha).apply(null,arguments)},Y0=a._Sum=function(){return(Y0=a._Sum=a.asm.Ia).apply(null,arguments)},J0=a._Tan=function(){return(J0=a._Tan=a.asm.Ja).apply(null,arguments)},Q0=a._Tanh=function(){return(Q0=a._Tanh=a.asm.Ka).apply(null,arguments)},eg=a._Tile=function(){return(eg=a._Tile=a.asm.La).apply(null,arguments)},tg=a._TopK=function(){return(tg=a._TopK=a.asm.Ma).apply(null,arguments)},ng=a._Transform=function(){return(ng=a._Transform=a.asm.Na).apply(null,arguments)},sg=a._Transpose=function(){return(sg=a._Transpose=a.asm.Oa).apply(null,arguments)},rg=a.__FusedMatMul=function(){return(rg=a.__FusedMatMul=a.asm.Pa).apply(null,arguments)},ag=a._malloc=function(){return(ag=a._malloc=a.asm.Qa).apply(null,arguments)},og=a._free=function(){return(og=a._free=a.asm.Ra).apply(null,arguments)},cp=a.___errno_location=function(){return(cp=a.___errno_location=a.asm.Sa).apply(null,arguments)},dp=a.stackSave=function(){return(dp=a.stackSave=a.asm.Ta).apply(null,arguments)},pp=a.stackRestore=function(){return(pp=a.stackRestore=a.asm.Ua).apply(null,arguments)},Gu=a.stackAlloc=function(){return(Gu=a.stackAlloc=a.asm.Va).apply(null,arguments)};a.cwrap=te;var Ri;function ig(J){this.name="ExitStatus",this.message="Program terminated with exit("+J+")",this.status=J}vr=function J(){Ri||ju(),Ri||(vr=J)};function ju(J){if(J=J||c,Jn>0||(kn(),Jn>0))return;function oe(){Ri||(Ri=!0,a.calledRun=!0,!P&&(ms(),gs(),o(a),a.onRuntimeInitialized&&a.onRuntimeInitialized(),fn()))}a.setStatus?(a.setStatus("Running..."),setTimeout(function(){setTimeout(function(){a.setStatus("")},1),oe()},1)):oe()}if(a.run=ju,a.preInit)for(typeof a.preInit=="function"&&(a.preInit=[a.preInit]);a.preInit.length>0;)a.preInit.pop()();return ju(),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)}}),YS=1e-7,JS=1e-4,Ap=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}},tc=class{refCount(e){return Es("refCount")}incRef(e){return Es("incRef")}timerAvailable(){return!0}time(e){return Es("time")}read(e){return Es("read")}readSync(e){return Es("readSync")}numDataIds(){return Es("numDataIds")}disposeData(e,t){return Es("disposeData")}write(e,t,n){return Es("write")}move(e,t,n,s,r){return Es("move")}memory(){return Es("memory")}floatPrecision(){return Es("floatPrecision")}epsilon(){return this.floatPrecision()===32?YS:JS}dispose(){return Es("dispose")}};function Es(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 r5(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,yp(e,t,n)}function QS(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--,yp(e,n,s),yp(t,n,s)}function nc(e,t,n){return Math.max(e,Math.min(t,n))}function eC(e){return e%2==0?e:e+1}function yp(e,t,n){let s=e[t];e[t]=e[n],e[n]=s}function tC(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function nC(e,t){let n=Math.random();return t*n+(1-n)*e}function sC(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 Sn(e,t,n=""){M(Cr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function La(e){M(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Ba(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||vn(e)&&!n)for(let s=0;s<e.length;++s)Ba(e[s],t,n);else t.push(e);return t}function zt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function rC(e){return e.length===0}function Cr(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 an(e){return e%1==0}function aC(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 oC(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function iC(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return r5(t),t}function sc(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function lC(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 uC(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} / ${n}`);let r=e.slice();return r[s]=t/n,r}function Rs(e,t){let n=t.length;return e=e==null?t.map((s,r)=>r):[].concat(e),M(e.every(s=>s>=-n&&s<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),M(e.every(s=>an(s)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(s=>s<0?n+s:s)}function a5(e,t){let n=[],s=[],r=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||r?null:Rs(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 o5(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 i5(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 l5(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 cC(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function vn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function fg(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function c5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function ta(e){return typeof e=="string"||e instanceof String}function d5(e){return typeof e=="boolean"}function p5(e){return typeof e=="number"}function xp(e){return Array.isArray(e)?xp(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":p5(e)?"float32":ta(e)?"string":d5(e)?"bool":"float32"}function na(e){return!!(e&&e.constructor&&e.call&&e.apply)}function bp(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function zi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let s=t-3;s>=0;--s)n[s]=n[s+1]*e[s+1];return n}function h5(e,t,n,s=!1){let r=new Array;if(t.length===1){let a=t[0]*(s?2:1);for(let o=0;o<a;o++)r[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,u)=>l*u)*(s?2:1);for(let l=0;l<a;l++)r[l]=h5(e+l*i,o,n,s)}return r}function Li(e,t,n=!1){if(e.length===0)return t[0];let s=e.reduce((r,a)=>r*a)*(n?2:1);if(s===0)return[];if(s!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return h5(0,e,t,n)}function mg(e,t){let n=vp(e,t);for(let s=0;s<n.length;s++)n[s]=1;return n}function vp(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 dC(e,t){let n=e.reduce((s,r)=>s*r,1);if(t==null||t==="float32")return Li(e,new Float32Array(n));if(t==="int32")return Li(e,new Int32Array(n));if(t==="bool")return Li(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function gg(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function pC(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 hC(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 Ag(e){return e&&e.then&&typeof e.then=="function"}function nr(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.warn(...e)}function fC(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.log(...e)}var f5="tfjsflags",m5=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=mC,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&nr(`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];nr(`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(Ag(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);f5 in e&&e[f5].split(",").forEach(n=>{let[s,r]=n.split(":");this.urlFlags[s]=AC(s,r)})}};function mC(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...s)=>(gC(t,s[0],s[1]),s.join("="))),t}function gC(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function AC(e,t){if(t=t.toLowerCase(),t==="true"||t==="false")return t==="true";if(`${+t}`===t)return+t;throw new Error(`Could not parse value flag value ${t} for flag ${e}.`)}function Y(){return sr}var sr=null;function yC(e){sr=e}var yg;function g5(){if(yg==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");yg=e}return yg}function xC(){let e=g5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function xg(e,t){let n=xC();if(n.has(e))return n.get(e);{let s=t();return n.set(e,s),n.get(e)}}var Bi="Abs",Wi="Acos",Vi="Acosh",sa="Add",Wa="AddN",Ui="All",Hi="Any",Va="ArgMax",rc="ArgMin",Gi="Asin",ji="Asinh",qi="Atan",Xi="Atanh",Ki="Atan2",Ua="AvgPool",wp="AvgPoolGrad",ac="AvgPool3D",kp="AvgPool3DGrad",Ha="BatchMatMul",Zi="BatchToSpaceND",Ip="Bincount",A5="BroadcastTo",bg="BroadcastArgs",Ga="Cast",ja="Ceil",ra="ClipByValue",Sp="Complex",oc="ComplexAbs",Yi="Concat",qa="Conv2D",Cp="Conv2DBackpropFilter",Xa="Conv2DBackpropInput",ic="Conv3D",Tp="Conv3DBackpropFilterV2",Np="Conv3DBackpropInputV2",Ka="Cos",Za="Cosh",Ya="Cumsum",Ji="CropAndResize",Ep="DenseBincount",Qi="DepthToSpace",Ja="DepthwiseConv2dNative",Rp="DepthwiseConv2dNativeBackpropFilter",Dp="DepthwiseConv2dNativeBackpropInput",_p="Diag",lc="Dilation2D",Fp="Dilation2DBackpropInput",$p="Dilation2DBackpropFilter",Qa="RealDiv",Op="Einsum",eo="Elu",Pp="EluGrad",el="Erf",tl="Equal",to="Exp",nl="ExpandDims",sl="Expm1",Mp="FFT",uc="Fill",rl="FlipLeftRight",no="Floor",so="FloorDiv",ro="FusedBatchNorm",al="GatherV2",ol="GatherNd",il="Greater",ao="GreaterEqual",oo="Identity",zp="IFFT",Lp="Imag",ll="IsFinite",ul="IsInf",cl="IsNan",io="LeakyRelu",dl="Less",pl="LessEqual",Bp="LinSpace",lo="Log",hl="Log1p",fl="LogicalAnd",cc="LogicalNot",dc="LogicalOr",y5="LogSoftmax",pc="LRN",Wp="LRNGrad",uo="Max",co="Maximum",po="MaxPool",Vp="MaxPoolGrad",hc="MaxPool3D",Up="MaxPool3DGrad",Hp="MaxPoolWithArgmax",ho="Mean",fo="Min",mo="Minimum",go="MirrorPad",ml="Mod",Gp="Multinomial",Ao="Multiply",gl="Neg",Al="NotEqual",yl="NonMaxSuppressionV3",xl="NonMaxSuppressionV4",bl="NonMaxSuppressionV5",vl="OnesLike",yo="OneHot",wl="Pack",xo="PadV2",bC="Pool",bo="Pow",vo="Prelu",kl="Prod",fc="Range",jp="Real",Il="Reciprocal",wo="Relu",Sl="Reshape",mc="ResizeNearestNeighbor",qp="ResizeNearestNeighborGrad",ko="ResizeBilinear",Xp="ResizeBilinearGrad",Io="Relu6",So="Reverse",Co="Round",To="Rsqrt",Cl="ScatterNd",Tl="Select",Nl="Selu",El="Slice",No="Sin",Rl="Sinh",Dl="Sign",Eo="Sigmoid",_l="Softplus",Ro="Sqrt",Do="Sum",Fl="SpaceToBatchND",$l="SplitV",_o="Softmax",Kp="SparseFillEmptyRows",Zp="SparseReshape",Yp="SparseSegmentMean",Jp="SparseSegmentSum",Qp="SparseToDense",Fo="SquaredDifference",gc="Square",Ol="StridedSlice",eh="StringNGrams",th="StringSplit",nh="StringToHashBucketFast",$o="Sub",Oo="Tan",Po="Tanh",aa="Tile",Pl="TopK",Ml="Transform",Mo="Transpose",sh="Unique",zl="Unpack",Ac="UnsortedSegmentSum",Ll="ZerosLike",oa="Step",rh="FromPixels",Bl="RotateWithOffset",zo="_FusedMatMul",Lo="FusedConv2D",Bo="FusedDepthwiseConv2D",Wl=xg("kernelRegistry",()=>new Map),yc=xg("gradRegistry",()=>new Map);function ah(e,t){let n=wg(e,t);return Wl.get(n)}function vg(e){return yc.get(e)}function Tr(e){let t=Wl.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 ia(e){let{kernelName:t,backendName:n}=e,s=wg(t,n);Wl.has(s)&&nr(`The kernel '${t}' for backend '${n}' is already registered`),Wl.set(s,e)}function x5(e){let{kernelName:t}=e;yc.has(t)&&Y().getBool("DEBUG")&&nr(`Overriding the gradient for '${t}'`),yc.set(t,e)}function vC(e,t){let n=wg(e,t);if(!Wl.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Wl.delete(n)}function wC(e){if(!yc.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);yc.delete(e)}function kC(e,t){Tr(e).forEach(s=>{let r=Object.assign({},s,{backendName:t});ia(r)})}function wg(e,t){return`${t}_${e}`}var w={};Le(w,{arraysEqual:()=>Cr,assert:()=>M,assertNonNegativeIntegerDimensions:()=>gg,assertNonNull:()=>La,assertShapesMatch:()=>Sn,bytesFromStringArray:()=>c5,bytesPerElement:()=>fg,checkConversionForErrors:()=>l5,clamp:()=>nc,computeStrides:()=>zi,createScalarValue:()=>EC,createShuffledIndices:()=>iC,decodeString:()=>lh,distSquared:()=>sC,encodeString:()=>vc,fetch:()=>DC,fingerPrint64:()=>NC,flatten:()=>Ba,getArrayFromDType:()=>i5,getTypedArrayFromDType:()=>o5,hasEncodingLoss:()=>cC,hexToLong:()=>xc,indexToLoc:()=>hC,inferDtype:()=>xp,inferFromImplicitShape:()=>uC,isBoolean:()=>d5,isFunction:()=>na,isInt:()=>an,isNumber:()=>p5,isPromise:()=>Ag,isScalarShape:()=>rC,isString:()=>ta,isTypedArray:()=>vn,isValidDtype:()=>u5,locToIndex:()=>pC,makeOnesTypedArray:()=>mg,makeZerosNestedTypedArray:()=>dC,makeZerosTypedArray:()=>vp,nearestDivisor:()=>bp,nearestLargerEven:()=>eC,now:()=>bc,parseAxisParam:()=>Rs,randUniform:()=>nC,repeatedTry:()=>lC,rightPad:()=>sc,shuffle:()=>r5,shuffleCombo:()=>QS,sizeFromShape:()=>zt,sizeToSquarishShape:()=>oC,squeezeShape:()=>a5,sum:()=>tC,swap:()=>yp,tanh:()=>aC,toNestedArray:()=>Li,toTypedArray:()=>ih});var b5=za(DS()),Wo=b5.default||b5;function xc(e){return Wo.fromString(e,!0,16)}var v5=xc("c3a5c85c97cb3127"),Vo=xc("b492b66fbe98f273"),Cn=xc("9ae16a3b2f90404f");function kg(e){return e.xor(e.shru(47))}function w5(e,t,n){let s=e.slice(t,t+n);return Wo.fromBytes(Array.from(s),!0,!0)}function xt(e,t){return w5(e,t,8)}function k5(e,t){return w5(e,t,4)}function on(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function la(e,t,n=xc("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 IC(e,t,n,s,r,a){r=r.add(e),a=on(a.add(r).add(s),21);let o=r;return r=r.add(t),r=r.add(n),a=a.add(on(r,44)),[r.add(s),a.add(o)]}function oh(e,t,n,s){return IC(xt(e,t),xt(e,t+8),xt(e,t+16),xt(e,t+24),n,s)}function SC(e,t=e.length){if(t>=8){let n=Cn.add(t*2),s=xt(e,0).add(Cn),r=xt(e,t-8),a=on(r,37).mul(n).add(s),o=on(s,25).add(r).mul(n);return la(a,o,n)}if(t>=4){let n=Cn.add(t*2),s=k5(e,0);return la(s.shl(3).add(t),k5(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 kg(Cn.mul(a).xor(v5.mul(o))).mul(Cn)}return Cn}function CC(e,t=e.length){let n=Cn.add(t*2),s=xt(e,0).mul(Vo),r=xt(e,8),a=xt(e,t-8).mul(n),o=xt(e,t-16).mul(Cn);return la(on(s.add(r),43).add(on(a,30)).add(o),s.add(on(r.add(Cn),18)).add(a),n)}function TC(e,t=e.length){let n=Cn.add(t*2),s=xt(e,0).mul(Cn),r=xt(e,8),a=xt(e,t-8).mul(n),o=xt(e,t-16).mul(Cn),i=on(s.add(r),43).add(on(a,30)).add(o),l=la(i,s.add(on(r.add(Cn),18)).add(a),n),u=xt(e,16).mul(n),c=xt(e,24),d=i.add(xt(e,t-32)).mul(n),p=l.add(xt(e,t-24)).mul(n);return la(on(u.add(c),43).add(on(d,30)).add(p),u.add(on(c.add(s),18)).add(d),n)}function NC(e,t=e.length){let n=Wo.fromNumber(81,!0);if(t<=32)return t<=16?SC(e,t):CC(e,t);if(t<=64)return TC(e,t);let s=n,r=n.mul(Vo).add(113),a=kg(r.mul(Cn).add(113)).mul(Cn),o=[Wo.UZERO,Wo.UZERO],i=[Wo.UZERO,Wo.UZERO];s=s.mul(Cn).add(xt(e,0));let l=0,u=(t-1>>6)*64,c=u+(t-1&63)-63;do s=on(s.add(r).add(o[0]).add(xt(e,l+8)),37).mul(Vo),r=on(r.add(o[1]).add(xt(e,l+48)),42).mul(Vo),s=s.xor(i[1]),r=r.add(o[0]).add(xt(e,l+40)),a=on(a.add(i[0]),33).mul(Vo),o=oh(e,l,o[1].mul(Vo),s.add(i[0])),i=oh(e,l+32,a.add(i[1]),r.add(xt(e,l+16))),[a,s]=[s,a],l+=64;while(l!==u);let d=Vo.add(a.and(255).shl(1));return l=c,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),s=on(s.add(r).add(o[0]).add(xt(e,l+8)),37).mul(d),r=on(r.add(o[1]).add(xt(e,l+48)),42).mul(d),s=s.xor(i[1].mul(9)),r=r.add(o[0].mul(9).add(xt(e,l+40))),a=on(a.add(i[0]),33).mul(d),o=oh(e,l,o[1].mul(d),s.add(i[0])),i=oh(e,l+32,a.add(i[1]),r.add(xt(e,l+16))),[a,s]=[s,a],la(la(o[0],i[0],d).add(kg(r).mul(v5)).add(a),la(o[1],i[1],d).add(s),d)}function EC(e,t){return t==="string"?vc(e):ih([e],t)}function RC(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function ih(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Ba(e)),Y().getBool("DEBUG")&&l5(e,t),RC(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 bc(){return Y().platform.now()}function DC(e,t){return Y().platform.fetch(e,t)}function vc(e,t="utf-8"){return t=t||"utf-8",Y().platform.encode(e,t)}function lh(e,t="utf-8"){return t=t||"utf-8",Y().platform.decode(e,t)}var _C=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new $C)}profileKernel(e,t,n){let s,r=()=>{s=n()},a,o=bc();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(r);else{r();for(let l of s)l.dataSync();a=Promise.resolve({kernelMs:bc()-o})}if(Y().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<s.length;l++){let u=s[l];u.data().then(c=>{FC(c,u.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 FC(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 $C=class{logKernelProfile(e,t,n,s,r,a){let o=typeof s=="number"?sc(`${s}ms`,9):s.error,i=sc(e,25),l=t.rank,u=t.size,c=sc(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 ${c} %c${u} %c${d} %c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function OC(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 u=e[l],c=u.inputs;for(let d in c){let p=c[d],h=!1;for(let f=0;f<t.length;f++)if(s[p.id]){u.outputs.forEach(m=>s[m.id]=!0),h=!0,r[u.id]=!0;break}if(h)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let d=0;d<u.outputs.length;d++)if(a[u.outputs[d].id]){for(let p in c)a[c[p].id]=!0,o[u.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let u=e[l];if(r[u.id]&&o[u.id]){let c={};for(let p in u.inputs){let h=u.inputs[p];s[h.id]&&(c[p]=h)}let d=Object.assign({},u);d.inputs=c,d.outputs=u.outputs,i.push(d)}}return i}function PC(e,t,n,s){for(let r=t.length-1;r>=0;r--){let a=t[r],o=[];if(a.outputs.forEach(l=>{let u=e[l.id];u!=null?o.push(u):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 u=n(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=a.inputs[l];if(!Cr(u.shape,c.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let d=e[c.id];e[c.id]=s(d,u),d.dispose()}}}}var I5=20,wc=3,Ig=7;function MC(e,t,n,s){let r=zi(t),a=zC(e,t,n,r),o=t.length,i=uh(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(u=>" "+u).join(`
|
|
`)),l.join(`
|
|
`)}function zC(e,t,n,s){let r=zt(t),a=s[s.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?Ic(e):e;if(i>1)for(let u=0;u<r/a;u++){let c=u*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],kc(l[c+d],0,n).length)}return o}function kc(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(Ig))} + ${parseFloat(e[1].toFixed(Ig))}j`:ta(e)?s=`'${e}'`:n==="bool"?s=S5(e):s=parseFloat(e.toFixed(Ig)).toString(),sc(s,t)}function S5(e){return e===0?"false":"true"}function uh(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=Ic(e);return[kc(m[0],0,n)]}return n==="bool"?[S5(e[0])]:[e[0].toString()]}if(l===1){if(i>I5){let g=wc*o,A=Array.from(e.slice(0,g)),y=Array.from(e.slice((i-wc)*o,i*o));return n==="complex64"&&(A=Ic(A),y=Ic(y)),["["+A.map((x,b)=>kc(x,r[b],n)).join(", ")+", ..., "+y.map((x,b)=>kc(x,r[i-wc+b],n)).join(", ")+"]"]}let m=n==="complex64"?Ic(e):Array.from(e);return["["+m.map((g,A)=>kc(g,r[A],n)).join(", ")+"]"]}let u=t.slice(1),c=s.slice(1),d=s[0]*o,p=[];if(i>I5){for(let m=0;m<wc;m++){let g=m*d,A=g+d;p.push(...uh(e.slice(g,A),u,n,c,r,!1))}p.push("...");for(let m=i-wc;m<i;m++){let g=m*d,A=g+d;p.push(...uh(e.slice(g,A),u,n,c,r,m===i-1))}}else for(let m=0;m<i;m++){let g=m*d,A=g+d;p.push(...uh(e.slice(g,A),u,n,c,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 Ic(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Yt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=zt(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||i5(t,this.size),this.strides=zi(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 rr().makeTensor(this.values,this.shape,this.dtype)}},rr=null,Vl=null,LC=null;function BC(e){rr=e}function WC(e){Vl=e}function VC(e){LC=e}var Ge=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=zt(e),this.strides=zi(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 Vl.buffer(this.shape,this.dtype,e)}bufferSync(){return Vl.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Li(this.shape,e,this.dtype==="complex64")}arraySync(){return Li(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=rr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>lh(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=rr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>lh(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 rr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(rr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Vl.print(this,e)}clone(){return this.throwIfDisposed(),Vl.clone(this)}toString(e=!1){let t=this.dataSync();return MC(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Vl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),rr().makeVariable(this,e,t,n)}};Object.defineProperty(Ge,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function ee(){return xg("Tensor",()=>Ge)}ee();var Sc=class extends Ge{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(!Cr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);rr().disposeTensor(this),this.dataId=e.dataId,rr().incRef(this,null)}dispose(){rr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Sc,Symbol.hasInstance,{value:e=>e instanceof Ge&&e.assign!=null&&e.assign instanceof Function});var Ls={};Le(Ls,{assertTypesMatch:()=>C5,getTensorsInContainer:()=>Rg,isTensorInList:()=>HC,makeTypesMatch:()=>Dt});var Sg;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Sg||(Sg={}));var Cg;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Cg||(Cg={}));var Tg;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Tg||(Tg={}));var Ng;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Ng||(Ng={}));var Eg;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Eg||(Eg={}));var UC={float32:Ng,int32:Cg,bool:Tg,complex64:Eg};function Ds(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return UC[e][t]}function ch(e){return Ds(e,"int32")}function Dt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Ds(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function C5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function HC(e,t){return t.some(n=>n.id===e.id)}function Rg(e){let t=[],n=new Set;return T5(e,t,n),t}function T5(e,t,n){if(e==null)return;if(e instanceof Ge){t.push(e);return}if(!GC(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),T5(a,t,n))}}function GC(e){return Array.isArray(e)||typeof e=="object"}function Dg(e){return e.kernelName!=null}var N5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Cc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new N5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(nr(`${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 _C(this.backendInstance),!0}setupRegisteredKernels(){Tr(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Tr(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 tc)&&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,nr(`Initialization of backend ${e} failed`),nr(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 nr(`Initialization of backend ${e} failed`),nr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 Cc.nextTensorId++}nextVariableId(){return Cc.nextVariableId++}clone(e){let t=L.runKernel(oo,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return L.runKernel(Ga,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,!(ah(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 '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=Dg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Dg(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=ah(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,y);let x=y.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:S}=b;return this.makeTensorFromDataId(v,k,S)});if(s){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}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:u,attrs:c}=e,d=Dg(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,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,u,t,d,n,c),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(u).map(h=>u[h]!=null?u[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=vg(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,u)=>a[u]);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"&&ta(e[0])&&(r=e.map(i=>vc(i)));let a=s.write(r,t,n),o=new Ge(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=c5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ge(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 Sc(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*fg(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 Sc||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*fg(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=vg(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let d=n[c],p=vp(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return u}),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=Rg(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 Ge,()=>"The result y returned by f() must be a tensor.");let a=OC(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?jC(r.shape):n,PC(o,a,l=>this.tidy(l),qC);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return M(na(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(o=>o instanceof Ge),()=>"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 Ge,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(na(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),u=Array.isArray(l)?l:[l];M(u.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(u.every(d=>d instanceof Ge),()=>"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 c={};return u.forEach((d,p)=>{c[p]=()=>d}),c};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=bc(),n=await this.backend.time(e);return n.wallMs=bc()-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 N5;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}};Cc.nextTensorId=0;Cc.nextVariableId=0;function jC(e){let t=mg(zt(e),"float32");return L.makeTensor(t,e,"float32")}function E5(){let e=g5();if(e._tfengine==null){let t=new m5(e);e._tfengine=new Cc(t)}return yC(e._tfengine.ENV),BC(()=>e._tfengine),e._tfengine}var L=E5();function qC(e,t){let n={a:e,b:t};return L.runKernel(sa,n)}var Tc={};Le(Tc,{isBrowser:()=>R5,isMobile:()=>KC});function XC(){return typeof navigator!="undefined"&&navigator!=null}function KC(e){if(e||XC()){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 R5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Bs=Y();Bs.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.")});Bs.registerFlag("IS_BROWSER",()=>R5());Bs.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Bs.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Bs.registerFlag("PROD",()=>!1);Bs.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Bs.getBool("DEBUG"));Bs.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Bs.registerFlag("IS_TEST",()=>!1);Bs.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Bs.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function ar(e,t){let n=e;if(vn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||vn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&Y().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&D5(e,s,[]),s}function D5(e,t,n){if(n=n||[],!Array.isArray(e)&&!vn(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 ${e.length} elements`);let s=t.slice(1);for(let r=0;r<e.length;++r)D5(e[r],s,n.concat(r))}function _5(e,t,n,s){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}}function F(e,t,n,s="numeric"){if(e instanceof Ge)return _5(s,e.dtype,t,n),e;let r=xp(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),_5(s,r,t,n),e==null||!vn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=ar(e,r);!vn(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?ih(e,r):Ba(e,[],!0);return L.makeTensor(i,a,r)}function Nc(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>F(a,`${t}[${o}]`,n,s))}var F5="__op";function W(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+F5;let r=(...a)=>{L.startScope(n);try{let o=s(...a);return Ag(o)&&console.error("Cannot return a Promise inside of tidy."),L.endScope(o),o}catch(o){throw L.endScope(null),o}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function ZC(e,t){let n=F(e,"real","complex"),s=F(t,"imag","complex");Sn(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);let r={real:n,imag:s};return L.runKernel(Sp,r)}var ua=W({complex_:ZC});function ca(e,t,n,s){if(s==null&&(s=xp(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!vn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){gg(t);let r=zt(t),a=zt(n);M(r===a,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==zt(t.slice(o)):!0;M(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!vn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?ih(e,s):Ba(e,[],!0),L.makeTensor(e,t,s)}function ln(e,t,n){let s=ar(e,n);return ca(e,t,s,n)}var _g={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},dh=4;async function YC(e,t){let n=[],s=[],r=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<r.length;++o){let i=r[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let u={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let c=new Promise(async d=>{let p=await l.bytes(),h=p.reduce((g,A)=>g+A.length,0)+dh*p.length,f=new Uint8Array(h),m=0;for(let g=0;g<p.length;g++){let A=p[g],y=new Uint8Array(new Uint32Array([A.length]).buffer);f.set(y,m),m+=dh,f.set(A,m),m+=A.length}d(f)});s.push(c)}else s.push(l.data());t!=null&&(u.group=t),n.push(u)}let a=await Promise.all(s);return{data:JC(a),specs:n}}function $5(e,t){let n={},s,r=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,u=zt(l),c;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=_g[d.dtype],h=e.slice(r,r+u*p),f=d.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=g*d.scale+d.min}}else if(d.dtype==="float16")s===void 0&&(s=rT()),c=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 ${d.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=u*p}else if(i==="string"){let d=zt(a.shape);c=[];for(let p=0;p<d;p++){let h=new Uint32Array(e.slice(r,r+dh))[0];r+=dh;let f=new Uint8Array(e.slice(r,r+h));c.push(f),r+=h}}else{let d=_g[i],p=e.slice(r,r+u*d);if(i==="float32")c=new Float32Array(p);else if(i==="int32")c=new Int32Array(p);else if(i==="bool")c=new Uint8Array(p);else if(i==="complex64"){c=new Float32Array(p);let h=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let A=0;A<h.length;A++)h[A]=c[A*2],f[A]=c[A*2+1];let m=ln(h,l,"float32"),g=ln(f,l,"float32");n[o]=ua(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=u*d}i!=="complex64"&&(n[o]=ln(c,l,i))}return n}function JC(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 Fg=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function O5(e){return Fg?Buffer.byteLength(e):new Blob([e]).size}function QC(e){if(Fg)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 eT(e){if(Fg){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 $g(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 P5(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 M5(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 Og(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 Ec(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:O5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:O5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function tT(){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 nT(){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 sT(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function rT(){let e=tT(),t=nT(),n=sT();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 Pt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Pt.instance==null&&(Pt.instance=new Pt),Pt.instance}static registerSaveRouter(e){Pt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Pt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Pt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Pt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let s=[];return(t==="load"?Pt.getInstance().loadRouters:Pt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&s.push(o)}),s}},aT=e=>Pt.registerSaveRouter(e),oT=e=>Pt.registerLoadRouter(e),iT=e=>Pt.getSaveHandlers(e),lT=(e,t)=>Pt.getLoadHandlers(e,t),Pg="tensorflowjs",Mg=1,Uo="models_store",da="model_info_store";function z5(){if(!Y().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function zg(e){let t=e.result;t.createObjectStore(Uo,{keyPath:"modelPath"}),t.createObjectStore(da,{keyPath:"modelPath"})}var Ho=class{constructor(e){if(this.indexedDB=z5(),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(Pg,Mg);r.onupgradeneeded=()=>zg(r),r.onsuccess=()=>{let a=r.result;if(t==null){let o=a.transaction(Uo,"readonly"),l=o.objectStore(Uo).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=u=>(a.close(),s(l.error)),o.oncomplete=()=>a.close()}else{let o=Ec(t),i=a.transaction(da,"readwrite"),l=i.objectStore(da),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),c;u.onsuccess=()=>{c=a.transaction(Uo,"readwrite");let p=c.objectStore(Uo).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});p.onsuccess=()=>n({modelArtifactsInfo:o}),p.onerror=h=>{l=i.objectStore(da);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),s(p.error)),f.onerror=m=>(a.close(),s(p.error))}},u.onerror=d=>(a.close(),s(u.error)),i.oncomplete=()=>{c==null?a.close():c.oncomplete=()=>a.close()}}},r.onerror=a=>s(r.error)})}};Ho.URL_SCHEME="indexeddb://";var L5=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ho.URL_SCHEME)?uT(e.slice(Ho.URL_SCHEME.length)):null;Pt.registerSaveRouter(L5);Pt.registerLoadRouter(L5);function uT(e){return new Ho(e)}function cT(e){return e.startsWith(Ho.URL_SCHEME)?e.slice(Ho.URL_SCHEME.length):e}var dT=class{constructor(){this.indexedDB=z5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Pg,Mg);n.onupgradeneeded=()=>zg(n),n.onsuccess=()=>{let s=n.result,r=s.transaction(da,"readonly"),o=r.objectStore(da).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=cT(e),new Promise((t,n)=>{let s=this.indexedDB.open(Pg,Mg);s.onupgradeneeded=()=>zg(s),s.onsuccess=()=>{let r=s.result,a=r.transaction(da,"readwrite"),o=a.objectStore(da),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 u=o.delete(e),c=()=>{l=r.transaction(Uo,"readwrite");let p=l.objectStore(Uo).delete(e);p.onsuccess=()=>t(i.result.modelArtifactsInfo),p.onerror=h=>n(i.error)};u.onsuccess=c,u.onerror=d=>(c(),r.close(),n(i.error))}},i.onerror=u=>(r.close(),n(i.error)),a.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},s.onerror=r=>n(s.error)})}},Nr="/",Ul="tensorflowjs_models",B5="info",pT="model_topology",hT="weight_specs",fT="weight_data",mT="model_metadata";function W5(e){return{info:[Ul,e,B5].join(Nr),topology:[Ul,e,pT].join(Nr),weightSpecs:[Ul,e,hT].join(Nr),weightData:[Ul,e,fT].join(Nr),modelMetadata:[Ul,e,mT].join(Nr)}}function V5(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function gT(e){let t=e.split(Nr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Nr)}function AT(e){return e.startsWith(Go.URL_SCHEME)?e.slice(Go.URL_SCHEME.length):e}var Go=class{constructor(e){if(!Y().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=W5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Ec(e);try{this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,QC(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:s}}catch(r){throw V5(this.keys),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=eT(a),t}};Go.URL_SCHEME="localstorage://";var U5=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Go.URL_SCHEME)?yT(e.slice(Go.URL_SCHEME.length)):null;Pt.registerSaveRouter(U5);Pt.registerLoadRouter(U5);function yT(e){return new Go(e)}var xT=class{constructor(){M(Y().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Ul+Nr,n=Nr+B5;for(let s=0;s<this.LS.length;++s){let r=this.LS.key(s);if(r.startsWith(t)&&r.endsWith(n)){let a=gT(r);e[a]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=AT(e);let t=W5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return V5(t),n}},Hl="://",xs=class{constructor(){this.managers={}}static getInstance(){return xs.instance==null&&(xs.instance=new xs),xs.instance}static registerManager(e,t){M(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Hl)&&(e=e.slice(0,e.indexOf(Hl))),M(e.length>0,()=>"scheme must not be an empty string.");let n=xs.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 ph(e){if(e.indexOf(Hl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${xs.getSchemes().join(",")}`);return{scheme:e.split(Hl)[0],path:e.split(Hl)[1]}}async function H5(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let s=Pt.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=Pt.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=ph(e).scheme,l=ph(e).path,u=i===ph(e).scheme,c=await r.load();n&&u&&await xs.getManager(i).removeModel(l);let d=await o.save(c);return n&&!u&&await xs.getManager(i).removeModel(l),d.modelArtifactsInfo}async function bT(){let e=xs.getSchemes(),t={};for(let n of e){let s=await xs.getManager(n).listModels();for(let r in s){let a=n+Hl+r;t[a]=s[r]}}return t}async function vT(e){let t=ph(e);return xs.getManager(t.scheme).removeModel(t.path)}async function wT(e,t){return H5(e,t,!1)}async function kT(e,t){return H5(e,t,!0)}var IT=class{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(Y().get("IS_BROWSER")){Y().setPlatform("browser",new IT);try{xs.registerManager(Go.URL_SCHEME,new xT)}catch(e){}try{xs.registerManager(Ho.URL_SCHEME,new dT)}catch(e){}}var ST={importFetch:()=>_S()},Lg,CT=class{constructor(){this.util=Mi("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Y().global.fetch!=null?Y().global.fetch(e,t):(Lg==null&&(Lg=ST.importFetch()),Lg(e,t))}now(){let e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};Y().get("IS_NODE")&&Y().setPlatform("node",new CT);function je(e,t="float32",n){return t=t||"float32",gg(e),new Yt(e,t,n)}function TT(e,t){let n=F(e,"x","cast");if(!u5(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let s={x:n},r={dtype:t};return L.runKernel(Ga,s,r)}var pe=W({cast_:TT});function NT(e){let n={x:F(e,"x","clone","string_or_numeric")};return L.runKernel(oo,n)}var Ws=W({clone_:NT});function G5(e,t=!1){console.log(e.toString(t))}E5();var ET={buffer:je,cast:pe,clone:Ws,print:G5};WC(ET);var Wn={};Le(Wn,{browserFiles:()=>PT,browserHTTPRequest:()=>WT,concatenateArrayBuffers:()=>$g,copyModel:()=>wT,decodeWeights:()=>$5,encodeWeights:()=>YC,fromMemory:()=>UT,getLoadHandlers:()=>lT,getModelArtifactsForJSON:()=>Og,getModelArtifactsInfoForJSON:()=>Ec,getSaveHandlers:()=>iT,http:()=>Vg,isHTTPScheme:()=>Wg,listModels:()=>bT,loadWeights:()=>MT,moveModel:()=>kT,registerLoadRouter:()=>oT,registerSaveRouter:()=>aT,removeModel:()=>vT,weightsLoaderFactory:()=>K5,withSaveHandler:()=>HT});var RT="model",DT=".json",_T=".weights.bin";function j5(e){return new Promise(t=>setTimeout(t)).then(e)}var Gl=class{constructor(e){if(!Y().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Gl.URL_SCHEME)&&(e=e.slice(Gl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=RT),this.modelJsonFileName=e+DT,this.weightDataFileName=e+_T}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=M5(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 j5(()=>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 j5(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ec(e)}}}};Gl.URL_SCHEME="downloads://";var FT=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 ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let i=Og(r,l=>this.loadWeights(l));e(i)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${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,$g(a)])}loadWeightsFile(e,t){return new Promise((n,s)=>{let r=new FileReader;r.onload=a=>{let o=a.target.result;n(o)},r.onerror=a=>s(`Failed to weights data from file of path '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>P5(r.name)),s={};for(let r of e)r.paths.forEach(a=>{let o=P5(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}},$T=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Gl.URL_SCHEME)?OT(e.slice(Gl.URL_SCHEME.length)):null;Pt.registerSaveRouter($T);function OT(e="model"){return new Gl(e)}function PT(e){return new FT(e)}function q5(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(u=>{let c=n+ ++r/e.length*(s-n);return t(c),u}),l);function o(l){M(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,u){M(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),M(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),M(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(a))}async function X5(e,t){t==null&&(t={});let n=t.fetchFunc==null?Y().platform.fetch:t.fetchFunc,s=e.map(d=>n(d,t.requestInit,{isBinary:!0})),r=0,a=.5,i=(t.onProgress==null?await Promise.all(s):await q5(s,t.onProgress,r,a)).map(d=>d.arrayBuffer()),l=.5,u=1;return t.onProgress==null?await Promise.all(i):await q5(i,t.onProgress,l,u)}async function MT(e,t="",n,s){return K5(o=>X5(o,{requestInit:s}))(e,t,n)}function K5(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,y=_g[A]*zt(g.shape),x=()=>{r[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:g,groupOffset:m,sizeBytes:y})};s!=null?s.forEach((b,v)=>{b===g.name&&(x(),o[v]=!0)}):x(),i.push(g.name),m+=y})}),!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),[]),u=[];l.forEach(h=>{t[h].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),d={},p=0;return l.forEach(h=>{let f=t[h].paths.length,m=0;for(let b=0;b<f;b++)m+=c[p+b].byteLength;let g=new ArrayBuffer(m),A=new Uint8Array(g),y=0;for(let b=0;b<f;b++){let v=new Uint8Array(c[p+b]);A.set(v,y),y+=v.byteLength}a[h].forEach(b=>{let v=g.slice(b.groupOffset,b.groupOffset+b.sizeBytes),k=$5(v,[b.manifestEntry]);for(let S in k)d[S]=k[S]}),p+=f}),d}}var zT="application/octet-stream",LT="application/json",Bg=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(M(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=Y().platform.fetch,M(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&M(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],s=M5(e,n);t.body.append("model.json",new Blob([JSON.stringify(s)],{type:LT}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:zT}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Ec(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 ${this.path} contains neither model topology or manifest for weights.`);return Og(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,s]=BT(t),r=this.weightPathPrefix||n,a=[];for(let u of e)a.push(...u.weights);let o=[],i=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(c)):o.push(r+c+s);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await X5(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,$g(l)]}};Bg.URL_SCHEME_REGEX=/^https?:\/\//;function BT(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),s=e.substring(0,t),r=n>t?e.substring(n):"";return[s+"/",r]}function Wg(e){return e.match(Bg.URL_SCHEME_REGEX)!=null}var Z5=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(s=>Wg(s)):n=Wg(e),n)return Vg(e,t)}return null};Pt.registerSaveRouter(Z5);Pt.registerLoadRouter(Z5);function Vg(e,t){return new Bg(e,t)}function WT(e,t){return Vg(e,t)}var Ug=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},VT=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function UT(e,t,n,s){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Ug(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 Ug({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 Ug({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:s}))}function HT(e){return new VT(e)}var Y5={};Le(Y5,{confusionMatrix:()=>KT});function GT(e,t,n=!1,s=!1){let r=F(e,"a","matMul"),a=F(t,"b","matMul");[r,a]=Dt(r,a);let o={a:r,b:a},i={transposeA:n,transposeB:s};return L.runKernel(Ha,o,i)}var Ue=W({matMul_:GT});function jT(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:F(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:s};return L.runKernel(yo,a,o)}var jl=W({oneHot_:jT});function qT(e,t){let n=F(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 L.runKernel(Mo,s,r)}var Ze=W({transpose_:qT});function XT(e,t,n){let s=F(e,"labels","confusionMatrix"),r=F(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=jl(pe(s,"int32"),n),o=jl(pe(r,"int32"),n),i=Ze(a),l=Ue(i,o);return pe(l,"int32")}var KT=W({confusionMatrix_:XT}),_s={};Le(_s,{fromPixels:()=>n9,fromPixelsAsync:()=>e9,toPixels:()=>t9});function hh(e,t,n){if(La(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let s=ar(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 ca(e,t,s,n)}var ql;function J5(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(ah(rh,L.backendName)!=null){let f={pixels:e},m={numChannels:t};return L.runKernel(rh,f,m)}let[u,c]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;o?d=e.getContext("2d").getImageData(0,0,u,c).data:s||n?d=e.data:(a||r||i)&&(ql==null&&(ql=document.createElement("canvas").getContext("2d")),ql.canvas.width=u,ql.canvas.height=c,ql.drawImage(e,0,0,u,c),d=ql.getImageData(0,0,u,c).data);let p;if(t===4)p=new Int32Array(d);else{let f=u*c;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 hh(p,[c,u,t],"int32")}function ZT(e){return e!=null&&e.data instanceof Uint8Array}function YT(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function JT(e){return e!=null&&e.width!==0&&e.height!==0}function QT(e){return YT()&&!(e instanceof ImageBitmap)&&JT(e)&&!ZT(e)}async function e9(e,t=3){let n=null;if(Y().getBool("WRAP_TO_IMAGEBITMAP")&&QT(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 J5(n,t)}async function t9(e,t){let n=F(e,"img","toPixels");if(!(e instanceof Ge)){let u=n;n=pe(u,"int32"),u.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 u=0;u<s*r;++u){let c=[0,0,0,255];for(let p=0;p<a;p++){let h=o[u*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?(c[0]=h*i,c[1]=h*i,c[2]=h*i):c[p]=h*i}let d=u*4;l[d+0]=Math.round(c[0]),l[d+1]=Math.round(c[1]),l[d+2]=Math.round(c[2]),l[d+3]=Math.round(c[3])}if(t!=null){t.width=r,t.height=s;let u=t.getContext("2d"),c=new ImageData(l,r,s);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var n9=W({fromPixels_:J5}),Hg={};Le(Hg,{prepareAndValidate:()=>Q5});function Q5(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(zt(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 u=1;for(let d=a;d<n;++d)u*=i[d],l.push(i[d]);let c=[...zi(e.shape).map(d=>d/u),1].slice(0,a);return[l,o,u,c]}var Gg={};Le(Gg,{calculateShapes:()=>eb,validateInput:()=>qg,validateUpdateShape:()=>jg});function jg(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 qg(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: ${e.shape}`)}jg(n,t,e)}function eb(e,t,n){let s=t.shape.length,r=s>1?t.shape[s-1]:1,a=n.length,o=1;for(let d=r;d<a;++d)o*=n[d];let i=r<1?1:r,l=zt(t.shape)/i,u=[...zi(n.slice(0,r)),1],c=zt(n);return{sliceRank:r,numUpdates:l,sliceSize:o,strides:u,outputSize:c}}var Tn={};Le(Tn,{assertParamsValid:()=>s9,computeFlatOffset:()=>a9,computeOutShape:()=>tb,getNormalizedAxes:()=>ab,isSliceContinous:()=>r9,maskToAxes:()=>fh,parseSliceParams:()=>db,sliceInfo:()=>o9,startForAxis:()=>ub,startIndicesWithElidedDims:()=>ob,stopForAxis:()=>cb,stopIndicesWithElidedDims:()=>ib,stridesForAxis:()=>lb,stridesWithElidedDims:()=>nb});function s9(e,t,n){let s=e.shape.length;M(s===t.length,()=>`Error in slice${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 fh(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function tb(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 nb(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 sb(e,t,n){return n<=e?n:n-(t-1)}function rb(e,t){let n=[];for(let s=0;s<e;s++)n.push(t+s);return n}function ab(e,t,n,s,r,a,o,i,l){let u=e.length,c=new Array(u),d=new Array(u),p=new Array(u);if(t.length&&n>0){let h=t[0],f=n+1;c=ob(o,h,f,s,e),d=ib(i,h,f,r,e),p=nb(a,h,f,e)}else for(let h=0;h<u;h++)c[h]=ub(o,s,a,e,h,l),d[h]=cb(i,r,a,e,h,l),p[h]=lb(a,h,l);return{begin:c,end:d,strides:p}}function ob(e,t,n,s,r){let a=[...r],o=rb(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=sb(t,n,i),u=s[l];e&1<<l&&(u=0),a[i]=u}return a}function ib(e,t,n,s,r){let a=[...r],o=rb(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=sb(t,n,i),u=s[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),a[i]=u}for(let i=0;i<a.length;i++){let l=r[i];a[i]<0&&(a[i]+=l),a[i]=nc(0,a[i],r[i])}return a}function lb(e,t,n){let s=e[t];return(n&1<<t||s==null)&&(s=1),s}function ub(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=nc(0,o,l-1),o}function cb(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=nc(0,o,l):o=nc(-1,o,l-1),o}function r9(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 a9(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 db(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 ${i}.`),e.shape[i]-s[i])),[s,a]}function o9(e,t,n,s,r,a,o,i,l){let u=t.slice(),c=n.slice(),d=s;s==null&&(d=new Array(u.length));let p=fh(o);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-u.length,f=fh(i),m=e.slice();f.forEach(S=>{u[S]=0,c[S]=1,m.splice(S,0,1)});let{begin:g,end:A,strides:y}=ab(m,p,h,u,c,d,r,a,o);u=g,c=A,d=y;let x=fh(l);x.forEach(S=>{c[S]=u[S]+1,d[S]=1});let b=tb(u,c,d),v=b.filter((S,C)=>x.indexOf(C)===-1);return{nonStrided:d.every(S=>S===1),$begin:u,$end:c,$strides:d,size:b,newShape:m,outShape:v}}var ue={};Le(ue,{Serializable:()=>pb,SerializationMap:()=>jo,registerClass:()=>pa});var pb=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},jo=class{constructor(){this.classNameMap={}}static getMap(){return jo.instance==null&&(jo.instance=new jo),jo.instance}static register(e){jo.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function pa(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."),jo.register(e)}var hb={};Le(hb,{TEST_EPSILON_FLOAT16:()=>fb,encodeStrings:()=>mb,expectArrayBuffersEqual:()=>h9,expectArraysClose:()=>l9,expectArraysEqual:()=>c9,expectNumbersClose:()=>d9,expectPromiseToFail:()=>u9,expectValuesInRange:()=>p9,testEpsilon:()=>Xg});var i9=.001,fb=.1;function l9(e,t,n){return n==null&&(n=Xg()),Kg(e,t,(s,r)=>Zg(s,r,n))}function Xg(){return L.backend.floatPrecision()===32?i9:fb}function Kg(e,t,n){let s=!0;if((vn(e)||vn(t))&&(s=!1),vn(e)&&vn(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: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=ar(e),i=ar(t);if(!Cr(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let r=vn(e)?e:Ba(e),a=vn(t)?t:Ba(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 u9(e,t){e().then(()=>t.fail(),()=>t())}function c9(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return ta(e)||ta(e[0])||ta(t)||ta(t[0])?Kg(e,n,(s,r)=>s==r):Kg(e,t,(s,r)=>Zg(s,r,0))}function d9(e,t,n){if(n==null&&(n=Xg()),!Zg(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Zg(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function p9(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function h9(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function mb(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?mb(n):e[t]=vc(n)}return e}var mh="3.9.0";function gb(){Y().set("PROD",!0)}function f9(){Y().set("DEBUG",!0)}function m9(){Y().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Yg(e){Y().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}VC(Yg);function g9(){L.disposeVariables()}function es(){return L}function gh(){return L.memory()}function A9(e){return L.profile(e)}function H(e,t){return L.tidy(e,t)}function Z(e){Rg(e).forEach(n=>n.dispose())}function un(e){return L.keep(e)}function y9(e){return L.time(e)}function Ab(e){return L.setBackend(e)}function Ah(){return L.ready()}function or(){return L.backendName}function x9(e){L.removeBackend(e)}function Jg(e){return L.findBackend(e)}function b9(e){return L.findBackendFactory(e)}function Xl(e,t,n=1){return L.registerBackend(e,t,n)}function Er(){return L.backend}function v9(e,t){Y().setPlatform(e,t)}function w9(e,t){let n=F(e,"a","add"),s=F(t,"b","add");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(sa,r)}var ie=W({add_:w9});function k9(e,t){let n=F(e,"a","floorDiv"),s=F(t,"b","floorDiv");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(so,r)}var yh=W({floorDiv_:k9});function I9(e,t){let n=F(e,"a","div"),s=F(t,"b","div");if([n,s]=Dt(n,s),n.dtype==="int32"&&s.dtype==="int32")return yh(n,s);let r={a:n,b:s},a={};return L.runKernel(Qa,r,a)}var he=W({div_:I9});function S9(e,t){let n=F(e,"a","mul"),s=F(t,"b","mul");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(Ao,r)}var z=W({mul_:S9});function C9(e){let t=F(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return L.runKernel(oc,n)}else{let n={x:t};return L.runKernel(Bi,n)}}var Ut=W({abs_:C9});function T9(e){let n={x:F(e,"x","acos")};return L.runKernel(Wi,n)}var Qg=W({acos_:T9});function N9(e){let n={x:F(e,"x","acosh")};return L.runKernel(Vi,n)}var eA=W({acosh_:N9});function E9(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 ${e.length}`);let t=e.map((r,a)=>F(r,`tensors${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(!Cr(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let s=t;return L.runKernel(Wa,s)}var xh=W({addN_:E9});function R9(e,t=null,n=!1){let r={x:F(e,"x","all","bool")},a={axis:t,keepDims:n};return L.runKernel(Ui,r,a)}var bh=W({all_:R9});function D9(e,t=null,n=!1){let r={x:F(e,"x","any","bool")},a={axis:t,keepDims:n};return L.runKernel(Hi,r,a)}var Rc=W({any_:D9});function _9(e,t=0){let s={x:F(e,"x","argMax")},r={axis:t};return L.runKernel(Va,s,r)}var Vs=W({argMax_:_9});function F9(e,t=0){let s={x:F(e,"x","argMin")},r={axis:t};return L.runKernel(rc,s,r)}var tA=W({argMin_:F9});function $9(e){let n={x:F(e,"x","asin")};return L.runKernel(Gi,n)}var nA=W({asin_:$9});function O9(e){let n={x:F(e,"x","asinh")};return L.runKernel(ji,n)}var sA=W({asinh_:O9});function P9(e){let n={x:F(e,"x","atan")};return L.runKernel(qi,n)}var rA=W({atan_:P9});function M9(e,t){let n=F(e,"a","atan2"),s=F(t,"b","atan2");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(Ki,r)}var aA=W({atan2_:M9});function z9(e){let n={x:F(e,"x","atanh")};return L.runKernel(Xi,n)}var oA=W({atanh_:z9});function L9(e,t,n,s,r="NHWC",a){let o=e[3],i=[...t,o],l=bb(r);return Dc(e,i,n,a,s,null,null,l)}function yb(e,t,n,s,r,a,o="channelsLast"){let[i,l]=vh(t),u;if(o==="channelsLast")u=[i,l,e[3],e[3]];else if(o==="channelsFirst")u=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Dc(e,u,n,s,r,a,!1,o)}function B9(e,t,n,s,r,a,o="NDHWC"){let[i,l,u]=lA(t),c,d;if(o==="NDHWC")d="channelsLast",c=[i,l,u,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",c=[i,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return xb(e,c,n,s,r,!1,d,a)}function Dc(e,t,n,s,r,a,o=!1,i="channelsLast"){let[l,u,c,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,d]=e;else if(i==="channelsFirst")[l,d,u,c]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,h,,f]=t,[m,g]=vh(n),[A,y]=vh(s),x=Kl(p,A),b=Kl(h,y),{padInfo:v,outHeight:k,outWidth:S}=U9(r,u,c,m,g,x,b,a,i),C=o?f*d:f,D;return i==="channelsFirst"?D=[l,C,k,S]:i==="channelsLast"&&(D=[l,k,S,C]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:d,outHeight:k,outWidth:S,outChannels:C,padInfo:v,strideHeight:m,strideWidth:g,filterHeight:p,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:A,dilationWidth:y,inShape:e,outShape:D,filterShape:t}}function xb(e,t,n,s,r,a=!1,o="channelsLast",i){let[l,u,c,d,p]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,d,p]=e;else if(o==="channelsFirst")[l,p,u,c,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[h,f,m,,g]=t,[A,y,x]=lA(n),[b,v,k]=lA(s),S=Kl(h,b),C=Kl(f,v),D=Kl(m,k),{padInfo:O,outDepth:E,outHeight:R,outWidth:T}=H9(r,u,c,d,A,y,x,S,C,D,i),P=a?g*p:g,U;return o==="channelsFirst"?U=[l,P,E,R,T]:o==="channelsLast"&&(U=[l,E,R,T,P]),{batchSize:l,dataFormat:o,inDepth:u,inHeight:c,inWidth:d,inChannels:p,outDepth:E,outHeight:R,outWidth:T,outChannels:P,padInfo:O,strideDepth:A,strideHeight:y,strideWidth:x,filterDepth:h,filterHeight:f,filterWidth:m,effectiveFilterDepth:S,effectiveFilterHeight:C,effectiveFilterWidth:D,dilationDepth:b,dilationHeight:v,dilationWidth:k,inShape:e,outShape:U,filterShape:t}}function W9(e,t,n,s,r){s==null&&(s=iA(e,t,n));let a=e[0],o=e[1],i=qo((a-t+2*s)/n+1,r),l=qo((o-t+2*s)/n+1,r);return[i,l]}function V9(e,t,n,s,r,a){r==null&&(r=iA(e,t,s));let o=e[0],i=e[1],l=e[2],u=qo((o-t+2*r)/s+1,a),c=qo((i-t+2*r)/s+1,a),d=qo((l-t+2*r)/s+1,a);return[u,c,d,n]}function iA(e,t,n,s=1){let r=Kl(t,s);return Math.floor((e[0]*(n-1)-n+r)/2)}function vh(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function lA(e){return typeof e=="number"?[e,e,e]:e}function Kl(e,t){return t<=1?e:e+(e-1)*(t-1)}function U9(e,t,n,s,r,a,o,i,l){let u,c,d;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let h=W9([t,n],a,s,e,i);c=h[0],d=h[1]}else if(e==="same"){c=Math.ceil(t/s),d=Math.ceil(n/r);let p=Math.max(0,(c-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;u={top:f,bottom:m,left:g,right:A,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=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];u={top:p,bottom:h,left:f,right:m,type:p===0&&h===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=qo((t-a+p+h)/s+1,i),d=qo((n-o+f+m)/r+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:d}}function H9(e,t,n,s,r,a,o,i,l,u,c){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=V9([t,n,s,1],i,1,r,e,c);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+u-s,y=Math.floor(m/2),x=m-y,b=Math.floor(g/2),v=g-b,k=Math.floor(A/2),S=A-k;d={top:b,bottom:v,left:k,right:S,front:y,back:x,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-u+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:p,outHeight:h,outWidth:f}}function qo(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 ha(e){let[t,n,s]=vh(e);return t===1&&n===1&&s===1}function ir(e,t){return ha(e)||ha(t)}function bb(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function G9(e,t){let s={x:F(e,"x","reshape","string_or_numeric")},r={shape:t};return L.runKernel(Sl,s,r)}var V=W({reshape_:G9});function j9(e,t,n,s,r){let a=F(e,"x","avgPool","float32"),o=1;M(ir(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=V(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(an(s),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=L.runKernel(Ua,u,c);return d=pe(d,a.dtype),l?V(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var _c=W({avgPool_:j9});function q9(e,t,n,s,r,a="NDHWC"){let o=F(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=V(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(an(s),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=L.runKernel(ac,u,c);return d=pe(d,i.dtype),l?V(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var uA=W({avgPool3d_:q9});function X9(e,t=0){M(e.length>=1,()=>"Pass at least one tensor to concat");let n=Nc(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 Ws(n[0]);let s=n,r={axis:t};return L.runKernel(Yi,s,r)}var gt=W({concat_:X9});function K9(e){let n={x:F(e,"x","sigmoid")};return L.runKernel(Eo,n)}var Vn=W({sigmoid_:K9});function Z9(e,t,n){let s=F(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 L.runKernel(El,r,a)}var _e=W({slice_:Z9});function Y9(e){let n={x:F(e,"x","tanh")};return L.runKernel(Po,n)}var Xo=W({tanh_:Y9});function J9(e,t,n,s,r,a){let o=F(e,"forgetBias","basicLSTMCell"),i=F(t,"lstmKernel","basicLSTMCell"),l=F(n,"lstmBias","basicLSTMCell"),u=F(s,"data","basicLSTMCell"),c=F(r,"c","basicLSTMCell"),d=F(a,"h","basicLSTMCell"),p=gt([u,d],1),h=Ue(p,i),f=ie(h,l),m=f.shape[0],g=f.shape[1]/4,A=[m,g],y=_e(f,[0,0],A),x=_e(f,[0,g],A),b=_e(f,[0,g*2],A),v=_e(f,[0,g*3],A),k=ie(z(Vn(y),Xo(x)),z(c,Vn(ie(o,b)))),S=z(Xo(k),Vn(v));return[k,S]}var Q9=W({basicLSTMCell_:J9});function eN(e,t,n){let s=F(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 L.runKernel(Zi,a,o)}var Fc=W({batchToSpaceND_:eN});function tN(e){let t;return e.rank===0||e.rank===1?t=V(e,[1,1,1,e.size]):e.rank===2?t=V(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=V(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function nN(e,t,n,s,r,a){a==null&&(a=.001);let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;s!=null&&(c=F(s,"offset","batchNorm")),M(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),M(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),M(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:tN(o),scale:u,offset:c,mean:i,variance:l},h={varianceEpsilon:a},f=L.runKernel(ro,p,h);return V(f,o.shape)}var Ko=W({batchNorm_:nN});function sN(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(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}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Ko(o,i,l,c,u,a)}var vb=W({batchNorm2d_:sN});function rN(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(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}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Ko(o,i,l,c,u,a)}var wb=W({batchNorm3d_:rN});function aN(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(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}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Ko(o,i,l,c,u,a)}var kb=W({batchNorm4d_:aN});function oN(e,t,n){let s=F(e,"x","bincount"),r=F(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 L.runKernel(Ip,a,o)}var cA=W({bincount_:oN});function iN(e,t){let n=F(e,"s0","broadcastArgs","int32"),s=F(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 ${s.rank}`);let r={s0:n,s1:s};return L.runKernel(bg,r)}var Ib=W({broadcastArgs_:iN});function lN(e,t){let n=F(e,"broadcastTo","x"),s=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=V(n,u)}let r=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(r[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ws(n);let i={x:n},l={reps:a};return L.runKernel(aa,i,l)}var Zl=W({broadcastTo_:lN});function uN(e){let n={x:F(e,"x","ceil")};return L.runKernel(ja,n)}var dA=W({ceil_:uN});function cN(e,t,n){let s=F(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 L.runKernel(ra,r,a)}var Un=W({clipByValue_:cN});function dN(e){return gt(e,0)}var Sb=W({concat1d_:dN});function pN(e,t){return gt(e,t)}var Yl=W({concat2d_:pN});function hN(e,t){return gt(e,t)}var Cb=W({concat3d_:hN});function fN(e,t){return gt(e,t)}var Tb=W({concat4d_:fN});function mN(e,t,n,s,r="NHWC",a=[1,1],o){let i=F(e,"x","conv2d"),l=F(t,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&M(an(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d=r==="NHWC"?u.shape[3]:u.shape[1];M(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),M(ir(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let p={x:u,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=L.runKernel(qa,p,h);return c?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Rr=W({conv2d_:mN});function gN(e,t,n,s,r="NWC",a=1,o){let i=F(e,"x","conv1d"),l=F(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1]])),M(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&M(an(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(ir(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 ${r} but only NWC is currently supported.`);let d=V(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=V(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Rr(p,d,[1,n],s,"NHWC",[1,a],o);return c?V(g,[g.shape[2],g.shape[3]]):V(g,[g.shape[0],g.shape[2],g.shape[3]])}var wh=W({conv1d_:gN});function AN(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=V(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 ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) 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(an(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=L.runKernel(Xa,p,h);return u?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var pA=W({conv2DBackpropInput_:AN});function yN(e,t,n,s,r,a){let o=F(e,"x","conv2dTranspose"),i=F(t,"filter","conv2dTranspose");return pA(n,o,i,s,r,"NHWC",a)}var kh=W({conv2dTranspose_:yN});function xN(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=F(e,"x","conv3d"),i=F(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=V(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(ir(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 ${r} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=L.runKernel(ic,c,d);return u?V(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var hA=W({conv3d_:xN});function bN(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=V(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],u=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 ${n.shape[3]}.`),M(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=L.runKernel(Np,c,d);return i?V(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Nb=W({conv3DBackpropInput_:bN});function vN(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return Nb(n,a,o,s,r)}var Eb=W({conv3dTranspose_:vN});function wN(e){let n={x:F(e,"x","cos")};return L.runKernel(Ka,n)}var $c=W({cos_:wN});function kN(e){let n={x:F(e,"x","cosh")};return L.runKernel(Za,n)}var Ih=W({cosh_:kN});function IN(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return L.runKernel(Ya,a,o)}var Sh=W({cumsum_:IN});function SN(e,t,n,s=!1){let r=F(e,"x","denseBincount"),a=F(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 L.runKernel(Ep,o,i)}var Rb=W({denseBincount_:SN});function CN(e,t,n="NHWC"){let s=F(e,"x","depthToSpace"),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(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 L.runKernel(Qi,i,l)}var fA=W({depthToSpace_:CN});function TN(e,t,n,s,r="NHWC",a=[1,1],o){let i=F(e,"x","depthwiseConv2d"),l=F(t,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),M(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&M(an(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:u,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=L.runKernel(Ja,d,p);return c?V(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Jl=W({depthwiseConv2d_:TN});function NN(e){let n={x:F(e,"x","diag")};return L.runKernel(_p,n)}var EN=W({diag_:NN});function RN(e,t,n,s,r=[1,1],a="NHWC"){let o=F(e,"x","dilation2d"),i=F(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,u=!1;o.rank===3&&(l=V(o,[1,o.shape[0],o.shape[1],o.shape[2]]),u=!0);let c={x:l,filter:i},d={strides:n,pad:s,dilations:r},p=L.runKernel(lc,c,d);return u?V(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var mA=W({dilation2d_:RN});function DN(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 Jt(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 bt(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}function _N(e,t){let n=F(e,"a","equal","string_or_numeric"),s=F(t,"b","equal","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(tl,r)}var ts=W({equal_:_N});function FN(e,t,n){let s=F(t,"a","where"),r=F(n,"b","where"),a=F(e,"condition","where","bool"),o=bt(bt(a.shape,s.shape),r.shape),i=Zl(a,o),l=Zl(s,o),u=Zl(r,o),c={condition:i,t:l,e:u};return L.runKernel(Tl,c)}var wn=W({where_:FN});function $N(e){let n={x:F(e,"x","zerosLike")};return L.runKernel(Ll,n)}var Ye=W({zerosLike_:$N});function ON(e,t){let n=F(e,"a","div"),s=F(t,"b","div");[n,s]=Dt(n,s);let r=he(n,s),a=Ye(r),o=ts(s,a);return wn(o,a,r)}var gA=W({divNoNan_:ON});function PN(e,t){let n=F(e,"t1","dot"),s=F(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 ${a}.`),n.rank===1&&s.rank===1){let o=V(n,[1,-1]),i=V(s,[-1,1]),l=Ue(o,i);return V(l,[])}else if(n.rank===1&&s.rank===2){let o=V(n,[1,-1]),i=V(s,[s.shape[0],s.shape[1]]),l=Ue(o,i);return V(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=V(s,[-1,1]),i=Ue(n,o);return V(i,[i.size])}else{let o=V(s,[s.shape[0],s.shape[1]]);return Ue(n,o)}}var Db=W({dot_:PN});function MN(e,...t){let n=t.map((r,a)=>F(r,`tensors${a}`,"einsum")),s={equation:e};return L.runKernel(Op,n,s)}var _b=W({einsum_:MN});function zN(e){let n={x:F(e,"x","elu")};return L.runKernel(eo,n)}var Ql=W({elu_:zN});function LN(e){let t=F(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=pe(t,"float32"));let n={x:t};return L.runKernel(el,n)}var AA=W({erf_:LN});function BN(e){let n={x:F(e,"x","exp")};return L.runKernel(to,n)}var ns=W({exp_:BN});function WN(e,t=0){let n=F(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 L.runKernel(nl,s,r)}var Lt=W({expandDims_:WN});function VN(e){let n={x:F(e,"x","expm1")};return L.runKernel(sl,n)}var yA=W({expm1_:VN});function UN(e,t){let n=F(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 L.runKernel(aa,s,r)}var bs=W({tile_:UN});function HN(e,t,n,s="float32"){t==null&&(t=e);let r=je([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=V(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return bs(Lt(o,0),[n[0],1,1]);if(n.length===2)return bs(Lt(Lt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return bs(Lt(Lt(Lt(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 xA=W({eye_:HN});function eu(e,t,n){let s={shape:e,value:t,dtype:n};return L.runKernel(uc,{},s)}function GN(e){let n={x:F(e,"x","floor")};return L.runKernel(no,n)}var tu=W({floor_:GN});function jN(e,t,n=0,s=0){let r=F(e,"x","gather"),a=F(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return L.runKernel(al,o,i)}var Zo=W({gather_:jN});function qN(e,t){let n=F(e,"a","greater","string_or_numeric"),s=F(t,"b","greater","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(il,r)}var Hn=W({greater_:qN});function XN(e,t){let n=F(e,"a","greaterEqual","string_or_numeric"),s=F(t,"b","greaterEqual","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(ao,r)}var fa=W({greaterEqual_:XN});function KN(e){let n={input:F(e,"input","imag")};return L.runKernel(Lp,n)}var Ch=W({imag_:KN});function ZN(e){let n={x:F(e,"x","isFinite")};return L.runKernel(ll,n)}var Fb=W({isFinite_:ZN});function YN(e){let n={x:F(e,"x","isInf")};return L.runKernel(ul,n)}var $b=W({isInf_:YN});function JN(e){let n={x:F(e,"x","isNaN")};return L.runKernel(cl,n)}var bA=W({isNaN_:JN});function QN(e,t=.2){let s={x:F(e,"x","leakyRelu")},r={alpha:t};return L.runKernel(io,s,r)}var Oc=W({leakyRelu_:QN});function eE(e,t){let n=F(e,"a","less","string_or_numeric"),s=F(t,"b","less","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(dl,r)}var Th=W({less_:eE});function tE(e,t){let n=F(e,"a","lessEqual","string_or_numeric"),s=F(t,"b","lessEqual","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(pl,r)}var ma=W({lessEqual_:tE});function Ob(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 L.runKernel(Bp,{},s)}function nE(e,t=5,n=1,s=1,r=.5){let a=F(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(an(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=V(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},u={depthRadius:t,bias:n,alpha:s,beta:r},c=L.runKernel(pc,l,u);return i?V(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var vA=W({localResponseNormalization_:nE});function sE(e){let n={x:F(e,"x","log")};return L.runKernel(lo,n)}var ss=W({log_:sE});function rE(e){let n={x:F(e,"x","log1p")};return L.runKernel(hl,n)}var Pc=W({log1p_:rE});function aE(e){return M(na(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=F(t,"x","tf.grad","string_or_numeric"),r=n!=null?F(n,"dy","tf.grad"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(s),[s],r);return r!=null&&Sn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Nh(o),o[0]})}}function oE(e){return M(na(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=Nc(t,"args","tf.grads","string_or_numeric"),r=n!=null?F(n,"dy","tf.grads"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(...s),s,r);return r!=null&&Sn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Nh(o),o})}}function iE(e){return M(na(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Ge,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Ge,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=L.gradients(()=>e(t),[t],n);return Nh(s),{grad:s[0],value:r}}}function lE(e){return M(na(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof Ge),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Ge,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=L.gradients(()=>e(...t),t,n);return n!=null&&Sn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Nh(s.grads),s}}function Pb(e,t){M(na(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(u=>u instanceof Sc),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in L.registeredVariables)t.push(L.registeredVariables[u])}let s=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=L.gradients(e,t,null,a);M(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),s!=null&&s.forEach(u=>l[u.name]=null),{value:o,grads:l}}function lr(e){return L.customGrad(e)}function Nh(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 uE(e){let n={x:F(e,"x","neg")};return L.runKernel(gl,n)}var Ct=W({neg_:uE});function cE(e){let n={x:F(e,"x","softplus")};return L.runKernel(_l,n)}var Yo=W({softplus_:cE});function dE(e){let t=F(e,"x","logSigmoid");return lr(s=>({value:Ct(Yo(Ct(s))),gradFunc:o=>z(o,Vn(Ct(s)))}))(t)}var Mb=W({logSigmoid_:dE});function pE(e,t=null,n=!1){let r={x:F(e,"x","max")},a={reductionIndices:t,keepDims:n};return L.runKernel(uo,r,a)}var rs=W({max_:pE});function hE(e,t){let n=F(e,"a","sub"),s=F(t,"b","sub");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel($o,r)}var ye=W({sub_:hE});function fE(e,t=null,n=!1){let s=F(e,"x","sum");s.dtype==="bool"&&(s=pe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return L.runKernel(Do,r,a)}var ke=W({sum_:fE});function mE(e,t=-1){let n=F(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return lr((r,a)=>{let o=!0,i=rs(r,t,!0),l=ye(r,i),u=ye(pe(l,"float32"),ss(ke(ns(l),t,o)));return a([u]),{value:u,gradFunc:(d,p)=>{let[h]=p,f=!0,m=ns(h);return ye(d,z(ke(d,t,f),m))}}})(n)}var Eh=W({logSoftmax_:mE});function wA(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function zb(e,t,n){let s=e.length+t.length,r=[],a=0,o=0;for(let i=0;i<s;i++)n.indexOf(i)===-1?r.push(e[a++]):r.push(t[o++]);return r}function Lb(e,t){let n=[],s=e.length;for(let a=0;a<s;a++)t.indexOf(a)===-1&&n.push(e[a]);let r=t.map(a=>e[a]);return[n,r]}function Jo(e,t){let n=t.map(s=>1);return zb(e,n,t)}function gE(e,t,n){M(wA(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Bb(e,t){if(wA(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 kA(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function AE(e,t){let n=[];for(let s=t-e;s<t;++s)n.push(s);return n}function yE(e,t=null,n=!1){let s=F(e,"x","logSumExp"),r=Rs(t,s.shape),a=rs(s,r,!0),o=ye(s,a),i=ns(o),l=ke(i,r),u=ss(l),c=ie(V(a,u.shape),u);if(n){let d=Jo(c.shape,r);return V(c,d)}return c}var IA=W({logSumExp_:yE});function xE(e,t){let n=F(e,"a","logicalAnd","bool"),s=F(t,"b","logicalAnd","bool");bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(fl,r)}var Fs=W({logicalAnd_:xE});function bE(e){let n={x:F(e,"x","logicalNot","bool")};return L.runKernel(cc,n)}var Mc=W({logicalNot_:bE});function vE(e,t){let n=F(e,"a","logicalOr","bool"),s=F(t,"b","logicalOr","bool");bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(dc,r)}var Rh=W({logicalOr_:vE});function wE(e,t){let n=F(e,"a","logicalXor","bool"),s=F(t,"b","logicalXor","bool");return bt(n.shape,s.shape),Fs(Rh(e,t),Mc(Fs(e,t)))}var Wb=W({logicalXor_:wE});function kE(e,t,n,s,r){let a=F(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=V(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(ir(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),r!=null&&M(an(s),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=L.runKernel(po,u,c);return l?V(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var zc=W({maxPool_:kE});function IE(e,t=[1,1,1],n,s,r,a="NDHWC"){let o=F(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=V(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(an(s),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=L.runKernel(hc,u,c);return l?V(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var SA=W({maxPool3d_:IE});function SE(e,t,n,s,r=!1){let o={x:F(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:s,includeBatchInIndex:r},l=L.runKernel(Hp,o,i);return{result:l[0],indexes:l[1]}}var Vb=W({maxPoolWithArgmax_:SE});function CE(e,t){let n=F(e,"a","maximum"),s=F(t,"b","maximum");[n,s]=Dt(n,s),n.dtype==="bool"&&(n=pe(n,"int32"),s=pe(s,"int32")),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(co,r)}var ur=W({maximum_:CE});function TE(e,t=null,n=!1){let r={x:F(e,"x","mean")},a={axis:t,keepDims:n};return L.runKernel(ho,r,a)}var _t=W({mean_:TE});function Mt(e,t="float32"){if(t==="complex64"){let s=Mt(e,"float32"),r=Mt(e,"float32");return ua(s,r)}let n=vp(zt(e),t);return L.makeTensor(n,e,t)}function as(e,t="float32"){if(t==="complex64"){let s=as(e,"float32"),r=Mt(e,"float32");return ua(s,r)}let n=mg(zt(e),t);return L.makeTensor(n,e,t)}function NE(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let s=F(e,"x","meshgrid",e instanceof Ge?e.dtype:"float32");if(t===void 0)return[s];let r=F(t,"y","meshgrid",t instanceof Ge?t.dtype:"float32"),a=zt(s.shape),o=zt(r.shape);return n==="xy"?(s=V(s,[1,-1]),r=V(r,[-1,1]),[Ue(as([o,1],s.dtype),s),Ue(r,as([1,a],r.dtype))]):(s=V(s,[-1,1]),r=V(r,[1,-1]),[Ue(s,as([1,o],s.dtype)),Ue(as([a,1],r.dtype),r)])}function EE(e,t=null,n=!1){let r={x:F(e,"x","min")},a={axis:t,keepDims:n};return L.runKernel(fo,r,a)}var Lc=W({min_:EE});function RE(e,t){let n=F(e,"a","minimum"),s=F(t,"b","minimum");[n,s]=Dt(n,s),n.dtype==="bool"&&(n=pe(n,"int32"),s=pe(s,"int32")),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(mo,r)}var nu=W({minimum_:RE});function DE(e,t,n){M(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let s=F(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 L.runKernel(go,o,a)}var CA=W({mirrorPad_:DE});function _E(e,t){let n=F(e,"a","mod"),s=F(t,"b","mod");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(ml,r)}var TA=W({mod_:_E});function FE(e){let t=F(e,"x","square"),n={};return L.runKernel("Square",{x:t},n)}var ft=W({square_:FE});function $E(e,t=null,n=!1){e=F(e,"x","moments");let s=Rs(t,e.shape),r=_t(e,s,n),a=r.shape;n||(a=Jo(r.shape,s));let o=ft(ye(pe(e,"float32"),V(r,a))),i=_t(o,s,n);return{mean:r,variance:i}}var Dh=W({moments_:$E});function OE(e,t,n,s){let r=F(t,"data","multiRNNCell"),a=Nc(n,"c","multiRNNCell"),o=Nc(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 u=[],c=[];for(let d=0;d<l.length;d+=2)u.push(l[d]),c.push(l[d+1]);return[u,c]}var PE=W({multiRNNCell_:OE});function ME(e,t,n,s=!1){let r=F(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?V(r,[1,-1]):r},u={numSamples:t,seed:n,normalized:s},c=L.runKernel(Gp,l,u);return o===1?V(c,[c.size]):c}var Ub=W({multinomial_:ME});function zE(e,t){let n=F(e,"a","notEqual","string_or_numeric"),s=F(t,"b","notEqual","string_or_numeric");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Al,r)}var Qo=W({notEqual_:zE});function LE(e){let n={x:F(e,"x","onesLike")};return L.runKernel(vl,n)}var os=W({onesLike_:LE});function BE(e,t){let n=F(e,"v1","outerProduct"),s=F(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=V(n,[-1,1]),a=V(s,[1,-1]);return Ue(r,a)}var WE=W({outerProduct_:BE});function VE(e,t,n=0){let s=F(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 L.runKernel(xo,a,r)}var Dr=W({pad_:VE});function UE(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Dr(e,[t],n)}var HE=W({pad1d_:UE});function GE(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."),Dr(e,t,n)}var jE=W({pad2d_:GE});function qE(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."),Dr(e,t,n)}var XE=W({pad3d_:qE});function KE(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."),Dr(e,t,n)}var ZE=W({pad4d_:KE});function YE(e,t,n){let s=F(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 L.runKernel(Fl,r,a)}var Bc=W({spaceToBatchND_:YE});function JE(e,t,n,s,r,a){r==null&&(r=[1,1]),a==null&&(a=1),s===0&&(s="valid");let o=F(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=V(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(ir(a,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);let u=yb(i.shape,t,a,r,s),c=[u.dilationHeight,u.dilationWidth],d;s==="same"?d=eR([u.filterHeight,u.filterWidth],c):d=[[0,0],[0,0]];let p=c[0]===1&&c[1]===1,[h,f]=QE([u.inHeight,u.inWidth],c,d),m=p?s:"valid",g=p?i:Bc(i,c,h),y=(n==="avg"?()=>_c(g,t,a,m):()=>zc(g,t,a,m))(),x=p?y:Fc(y,c,f);return l?V(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function QE(e,t,n){let s=n.map(c=>c[0]),r=n.map(c=>c[1]),a=e.concat(s,r),o=t.map((c,d)=>(c-a[d]%c)%c),i=r.map((c,d)=>c+o[d]),l=t.map((c,d)=>[s[d],i[d]]),u=t.map((c,d)=>[0,o[d]]);return[l,u]}function eR(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 Hb=W({pool_:JE});function tR(e,t){let n=F(e,"base","pow"),s=F(t,"exp","pow");[n,s]=Dt(n,s);let r={a:n,b:s};return L.runKernel(bo,r)}var _r=W({pow_:tR});function nR(e,t){let n=F(e,"x","prelu"),s=F(t,"alpha","prelu"),r={x:n,alpha:s};return L.runKernel(vo,r)}var Wc=W({prelu_:nR});function sR(e,t=null,n=!1){let s=F(e,"x","prod");s.dtype==="bool"&&(s=pe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return L.runKernel(kl,r,a)}var _h=W({prod_:sR});function rR(e,t,n){let s=zt(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 L.makeTensor(r,e,n)}var aR=W({rand_:rR}),NA=za(t5()),EA=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=NA.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}},oR=class{constructor(e,t,n,s){this.alpha=e,this.beta=1/t,this.dtype=n;let r=s||Math.random();this.randu=NA.alea(r.toString()),this.randn=new EA(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)}},iR=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=NA.alea(s)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function lR(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 oR(t,n,s,r),o=je(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var uR=W({randomGamma_:lR});function cR(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error(`Unsupported data type ${s}`);let a=new EA(t,n,s,!1,r),o=je(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Gb=W({randomNormal_:cR});function dR(e,t=0,n=1,s="float32",r){let a=je(e,s),o=new iR(t,n,null,r);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var su=W({randomUniform_:dR});function ru(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 L.runKernel(fc,{},r)}function pR(e){let n={input:F(e,"input","real")};return L.runKernel(jp,n)}var Vc=W({real_:pR});function hR(e){let n={x:F(e,"x","reciprocal")};return L.runKernel(Il,n)}var RA=W({reciprocal_:hR});function fR(e){let n={x:F(e,"x","relu")};return L.runKernel(wo,n)}var Us=W({relu_:fR});function mR(e){let n={x:F(e,"x","relu6")};return L.runKernel(Io,n)}var Fh=W({relu6_:mR});function gR(e,t){let s={x:F(e,"x","reverse")},r={dims:t};return L.runKernel(So,s,r)}var is=W({reverse_:gR});function AR(e){let t=F(e,"x","reverse");return M(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),is(t,0)}var yR=W({reverse1d_:AR});function xR(e,t){let n=F(e,"x","reverse");return M(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),is(n,t)}var bR=W({reverse2d_:xR});function vR(e,t){let n=F(e,"x","reverse");return M(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),is(n,t)}var wR=W({reverse3d_:vR});function kR(e,t){let n=F(e,"x","reverse");return M(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),is(n,t)}var IR=W({reverse4d_:kR});function SR(e){let n={x:F(e,"x","round")};return L.runKernel(Co,n)}var $h=W({round_:SR});function CR(e){let n={x:F(e,"x","rsqrt")};return L.runKernel(To,n)}var Oh=W({rsqrt_:CR});function Te(e,t){if((vn(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"&&vn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return ca(e,[],[],t)}function TR(e){let n={x:F(e,"x","selu")};return L.runKernel(Nl,n)}var Ph=W({selu_:TR});function NR(e,t,n,s,r,a=[1,1],o="NHWC"){let i=F(e,"x","separableConv2d"),l=F(t,"depthwiseFilter","separableConv2d"),u=F(n,"pointwiseFilter","separableConv2d"),c=i,d=!1;if(i.rank===3&&(d=!0,c=V(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(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),M(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),M(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),M(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let p=l.shape[2],h=l.shape[3];M(u.shape[2]===p*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${p*h}, but got ${u.shape[2]}.`);let f=Jl(c,l,s,r,o,a),g=Rr(f,u,1,"valid",o);return d?V(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var DA=W({separableConv2d_:NR});async function ER(e,t){let n=F(e,"x","setdiff1d"),s=F(t,"y","setdiff1d");M(n.dtype===s.dtype,()=>`x and y should have the same dtype, but got x (${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 (${s.shape}).`);let r=await n.data(),a=await s.data(),o=new Set(a),i=0;for(let c=0;c<r.length;c++)o.has(r[c])||i++;let l=new Yt([i],n.dtype),u=new Yt([i],"int32");for(let c=0,d=0;c<r.length;c++)o.has(r[c])||(l.values[d]=r[c],u.values[d]=c,d++);return[l.toTensor(),u.toTensor()]}var jb=ER;function RR(e){let n={x:F(e,"x","sign")};return L.runKernel(Dl,n)}var _A=W({sign_:RR});function DR(e){let n={x:F(e,"x","sin")};return L.runKernel(No,n)}var Mh=W({sin_:DR});function _R(e){let n={x:F(e,"x","sinh")};return L.runKernel(Rl,n)}var zh=W({sinh_:_R});function FR(e,t,n){let s=F(e,"x","slice1d");return M(s.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`),_e(s,[t],[n])}var Lh=W({slice1d_:FR});function $R(e,t,n){let s=F(e,"x","slice2d");return M(s.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var FA=W({slice2d_:$R});function OR(e,t,n){let s=F(e,"x","slice3d");return M(s.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var Bh=W({slice3d_:OR});function PR(e,t,n){let s=F(e,"x","slice4d");return M(s.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var Uc=W({slice4d_:PR});function MR(e,t=-1){let n=F(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 L.runKernel(_o,s,r)}var ei=W({softmax_:MR});function zR(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(Mp,t)}var Hc=W({fft_:zR});function LR(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(zp,t)}var au=W({ifft_:LR});function BR(e){let t=e.shape[e.shape.length-1],n=e.size/t,s;if(t<=2){let r=V(e,[n,t]);s=au(r)}else{let r=[n,2*(t-1)],a=V(Vc(e),[n,t]),o=V(Ch(e),[n,t]),i=is(_e(a,[0,1],[n,t-2]),1),l=z(is(_e(o,[0,1],[n,t-2]),1),Te(-1)),u=gt([a,i],1),c=gt([o,l],1),d=V(ua(u,c),[r[0],r[1]]);s=au(d)}if(s=Vc(s),e.rank===3&&e.shape[0]!==0){let r=s,a=e.shape[0];s=V(s,[a,s.shape[0]/a,s.shape[1]]),r.dispose()}return s}var Wh=W({irfft_:BR});function WR(e,t,n=0){let r={x:F(e,"x","split")},a={numOrSizeSplits:t,axis:n};return L.runKernel($l,r,a)}var Ht=W({split_:WR});function VR(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=_e(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=gt([e,Mt(f)],e.shape.length-1),n=t}else r=e;let a=Ye(r),o=V(ua(r,a),[s,n]),i=Hc(o),l=Math.floor(n/2)+1,u=Vc(i),c=Ch(i),d=Ht(u,[l,n-l],u.shape.length-1),p=Ht(c,[l,n-l],c.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,V(ua(d[0],p[0]),h)}var Gc=W({rfft_:VR});function UR(e){let n={x:F(e,"x","sqrt")};return L.runKernel(Ro,n)}var gn=W({sqrt_:UR});function HR(e,t){let n=F(e,"a","squaredDifference"),s=F(t,"b","squaredDifference");[n,s]=Dt(n,s),bt(n.shape,s.shape);let r={a:n,b:s},a={};return L.runKernel(Fo,r,a)}var Vh=W({squaredDifference_:HR});function GR(e,t){let n=F(e,"x","squeeze");return V(n,a5(n.shape,t).newShape)}var st=W({squeeze_:GR});function jR(e,t=0){let n=Nc(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 L.runKernel(wl,s,r)}var An=W({stack_:jR});function qR(e,t=0){let s={x:F(e,"x","step")},r={alpha:t};return L.runKernel(oa,s,r)}var ou=W({step_:qR});function XR(e,t,n,s,r=0,a=0,o=0,i=0,l=0){let c={x:F(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:s,beginMask:r,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return L.runKernel(Ol,c,d)}var $A=W({stridedSlice_:XR});function KR(e){let n={x:F(e,"x","tan")};return L.runKernel(Oo,n)}var OA=W({tan_:KR});function Gt(e,t){La(e);let n=ar(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return ca(e,null,n,t)}function Hs(e,t,n){if(La(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let s=ar(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 ca(e,t,s,n)}function ZR(e,t,n){if(La(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let s=ar(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 ca(e,t,s,n)}function YR(e,t,n){if(La(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let s=ar(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 ca(e,t,s,n)}function JR(e,t,n){if(La(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let s=ar(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,ca(e,t,s,n)}function QR(e,t=1,n=!0){let s=F(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]=L.runKernel(Pl,a,o);return{values:i,indices:l}}var PA=W({topk_:QR});function eD(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new EA(t,n,s,!0,r),o=je(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Uh=W({truncatedNormal_:eD});function tD(e,t=0){let n=F(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]=L.runKernel(sh,s,r);return{values:a,indices:o}}var Hh=W({unique_:tD});function nD(e,t,n){let s=F(e,"x","unsortedSegmentSum"),r=F(t,"segmentIds","unsortedSegmentSum","int32");M(an(n),()=>"numSegments must be of dtype int");let a={x:s,segmentIds:r},o={numSegments:n};return L.runKernel(Ac,a,o)}var MA=W({unsortedSegmentSum_:nD});function sD(e,t=0){let n=F(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 L.runKernel(zl,s,r)}var Nn=W({unstack_:sD});function qb(e,t=!0,n,s){return L.makeVariable(e,t,n,s)}function Xb(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let s=je(e,"int32"),r=je([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 rD(e){let t=F(e,"condition","whereAsync","bool"),n=await t.data(),s=Xb(t.shape,n);return e!==t&&t.dispose(),s}var zA=rD;async function aD(e,t,n){let s=F(e,"tensor","boolMask"),r=F(t,"mask","boolMask","bool"),a=n==null?0:n,o=r.rank,i=s.shape;M(o>0,()=>"mask cannot be scalar"),Sn(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 u=i.slice(0,a).concat([l],i.slice(a+o)),c=V(s,u),d=V(r,[-1]),p=await zA(d),h=st(p,[1]),f=Zo(c,h,a);return e!==s&&s.dispose(),t!==r&&r.dispose(),h.dispose(),c.dispose(),d.dispose(),p.dispose(),f}var oD=aD;function iD(e,t="euclidean",n=null,s=!1){e=F(e,"x","norm");let r=Kb(e,t,n),a=r.shape;if(s){let o=Rs(n,e.shape);a=Jo(r.shape,o)}return V(r,a)}function Kb(e,t,n=null){if(e.rank===0)return Ut(e);if(e.rank!==1&&n===null)return Kb(V(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return ke(Ut(e),n);if(t===1/0)return rs(Ut(e),n);if(t===-1/0)return Lc(Ut(e),n);if(t==="euclidean"||t===2)return gn(ke(_r(Ut(e),Te(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return rs(ke(Ut(e),n[0]),n[1]-1);if(t===1/0)return rs(ke(Ut(e),n[1]),n[0]);if(t===-1/0)return Lc(ke(Ut(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return gn(ke(ft(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Gh=W({norm_:iD});function lD(e,t,n,s,r=!0){let a=F(e,"v","movingAverage"),o=F(t,"x","movingAverage"),i=F(n,"decay","movingAverage");C5(a,o),M(Cr(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=Te(1),u=ye(l,i),c=z(ye(o,a),u);if(r){M(s!=null,()=>"When using zeroDebias: true, step is required.");let d=F(s,"step","movingAverage");c=he(c,ye(l,_r(i,d)))}return ie(a,c)}var uD=W({movingAverage_:lD});function cD(e,t,n){let s=F(e,"indices","scatterND","int32"),r=F(t,"updates","scatterND");qg(r,s,n);let a={indices:s,updates:r},o={shape:n};return L.runKernel(Cl,a,o)}var Zb=W({scatterND_:cD});function dD(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 pD(e,t,n,s=0){let r=F(e,"sparseIndices","sparseToDense","int32"),a=F(t,"sparseValues","sparseToDense"),o=F(s,"defaultValue","sparseToDense",a.dtype);dD(r,a,n,o);let i={sparseIndices:r,sparseValues:a,defaultValue:o},l={outputShape:n};return L.runKernel(Qp,i,l)}var LA=W({sparseToDense_:pD});function hD(e,t){let n=F(t,"indices","gatherND","int32"),r={params:F(e,"x","gatherND","string_or_numeric"),indices:n};return L.runKernel(ol,r)}var Yb=W({gatherND_:hD});function fD(e,t){if(t==null)return e.shape.slice();if(Cr(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 mD(e,t,n,s){let r=F(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 Ge?r.clone():r;let a=fD(r,n),o=1-t,i=he(tu(ie(su(a,0,1,"float32",s),o)),o);return z(r,i)}var Jb=W({dropout_:mD});function Qb(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function BA(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 Gt(r,"float32")}async function gD(e,t,n=1){let s=F(e,"predictions","inTopK"),r=F(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 ${r.rank}`),Sn(s.shape.slice(0,s.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=s.shape[s.shape.length-1];M(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);let o=await s.data(),i=await r.data(),[l,u]=[o.length/a,a],c=o5("bool",l);for(let d=0;d<l;d++){let p=d*u,h=o.subarray(p,p+u),f=[];for(let m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort((m,g)=>g.value-m.value),c[d]=0;for(let m=0;m<n;m++)if(f[m].index===i[d]){c[d]=1;break}}return e!==s&&s.dispose(),t!==r&&r.dispose(),ln(c,r.shape,"bool")}var AD=gD,ga={};Le(ga,{conv2d:()=>bD,depthwiseConv2d:()=>ID,matMul:()=>CD});function yD(e,t,n,s,r,a="NHWC",o){let i=e;e.rank===3&&(i=V(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=V(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 u=a==="NHWC"?i.shape[3]:i.shape[1],c=a==="NHWC"?l.shape[3]:l.shape[1];M(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),M(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),o!=null&&M(an(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 L.runKernel(Cp,d,p)}var WA=W({conv2DBackpropFilter_:yD});function jh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,ou(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function qh(e,t){let n=t,s=Jt(e.shape,t.shape);return s.length>0&&(n=ke(n,s)),V(n,e.shape)}function Xh(e,t,n,s){if(t==="linear")return e;if(t==="relu")return Us(e);if(t==="elu")return Ql(e);if(t==="relu6")return Fh(e);if(t==="prelu")return Wc(e,n);if(t==="leakyrelu")return Oc(e,s);if(t==="sigmoid")return Vn(e);throw new Error(`Unknown fused activation ${t}.`)}var Kh=(e,t)=>!(e>0)||t==="linear";function xD({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Kh(L.state.gradientDepth,l)===!1){let v=Rr(e,t,n,s,r,a,o);return i!=null&&(v=ie(v,i)),Xh(v,l,u,c)}let d=F(e,"x","conv2d"),p=F(t,"filter","conv2d"),h=d,f=!1;d.rank===3&&(f=!0,h=V(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(an(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(ir(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 ${r} but only NHWC is currently supported.`);let m=Dc(h.shape,p.shape,n,a,s,o),g;i!=null&&(g=F(i,"bias","fused conv2d"),[g]=Dt(g,d),bt(m.outShape,g.shape));let A;u!=null&&(A=F(u,"prelu weights","fused conv2d"));let y=(v,k)=>{let[S,C,D,O]=k,E=jh(v,D,l);M(ha(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let R=pA(C.shape,E,S,n,s),T=WA(C,E,S.shape,n,s),P=[R,T];if(O!=null){let U=qh(O,E);P.push(U)}return P},x={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?lr((k,S,C)=>{let D=L.runKernel(Lo,x,b);return C([S,k,D]),f&&(D=V(D,[D.shape[1],D.shape[2],D.shape[3]])),{value:D,gradFunc:y}})(h,p):lr((k,S,C,D)=>{let O=L.runKernel(Lo,x,b);return D([S,k,O,C]),f&&(O=V(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:y}})(h,p,g)}var bD=W({fusedConv2d_:xD});function vD(e,t,n,s,r,a=[1,1],o){let i=e;e.rank===3&&(i=V(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=V(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:i,dy:l},c={strides:s,pad:r,dimRoundingMode:o,dilations:a,filterShape:n};return L.runKernel(Rp,u,c)}var e3=W({depthwiseConv2dNativeBackpropFilter_:vD});function wD(e,t,n,s,r,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=V(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:i,filter:n},c={strides:s,pad:r,dimRoundingMode:o,dilations:a,inputShape:e},d=L.runKernel(Dp,u,c);return l?V(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var t3=W({depthwiseConv2dNativeBackpropInput_:wD});function kD({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(Kh(L.state.gradientDepth,l)===!1){let v=Jl(e,t,n,s,r,a,o);return i!=null&&(v=ie(v,i)),Xh(v,l,u,c)}let d=F(e,"x","depthwiseConv2d"),p=F(t,"filter","depthwiseConv2d"),h=d,f=!1;d.rank===3&&(f=!0,h=V(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(ir(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&M(an(s),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${s}.`);let m=Dc(h.shape,p.shape,n,a,s,o,!0),g;i!=null&&(g=F(i,"bias","fused conv2d"),[g]=Dt(g,d),bt(m.outShape,g.shape));let A;u!=null&&(A=F(u,"prelu weights","fused depthwiseConv2d"));let y=(v,k)=>{M(ha(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[S,C,D,O]=k,E=jh(v,D,l),R=t3(C.shape,E,S,n,s,a,o),T=e3(C,E,S.shape,n,s,a,o);if(O!=null){let P=qh(g,E);return[R,T,P]}return[R,T]},x={x:h,filter:p,bias:g,preluActivationWeights:A},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?lr((k,S,C)=>{let D=L.runKernel(Bo,x,b);return C([S,k,D]),f&&(D=V(D,[D.shape[1],D.shape[2],D.shape[3]])),{value:D,gradFunc:y}})(h,p):lr((k,S,C,D)=>{let O=L.runKernel(Bo,x,b);return D([S,k,O,C]),f&&(O=V(O,[O.shape[1],O.shape[2],O.shape[3]])),{value:O,gradFunc:y}})(h,p,g)}var ID=W({fusedDepthwiseConv2d_:kD});function SD({a:e,b:t,transposeA:n=!1,transposeB:s=!1,bias:r,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(Kh(L.state.gradientDepth,a)===!1){let O=Ue(e,t,n,s);return r!=null&&(O=ie(O,r)),Xh(O,a,o,i)}let l=F(e,"a","fused matMul"),u=F(t,"b","fused matMul");[l,u]=Dt(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=s?u.shape[u.rank-1]:u.shape[u.rank-2],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=s?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),g=zt(f),A=zt(m);M(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),M(Cr(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),M(c===d,()=>`Error in fused matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${s} must match.`);let y=l.shape.slice(0,-2).concat([p,h]),x=n?V(l,[g,c,p]):V(l,[g,p,c]),b=s?V(u,[A,h,d]):V(u,[A,d,h]),v;r!=null&&(v=F(r,"bias","fused matMul"),[v]=Dt(v,l),bt(y,v.shape));let k;o!=null&&(k=F(o,"prelu weights","fused matMul"));let S=(O,E)=>{let[R,T,P,U]=E,j=jh(V(O,P.shape),P,a),q,X;if(!n&&!s?(q=Ue(j,T,!1,!0),X=Ue(R,j,!0,!1)):!n&&s?(q=Ue(j,T,!1,!1),X=Ue(j,R,!0,!1)):n&&!s?(q=Ue(T,j,!1,!0),X=Ue(R,j,!1,!1)):(q=Ue(T,j,!0,!0),X=Ue(j,R,!0,!0)),r!=null){let te=qh(U,j);return[q,X,te]}else return[q,X]},C={a:x,b,bias:v,preluActivationWeights:k},D={transposeA:n,transposeB:s,activation:a,leakyreluAlpha:i};return r==null?lr((E,R,T)=>{let P=L.runKernel(zo,C,D);return T([E,R,P]),{value:V(P,y),gradFunc:S}})(x,b):lr((E,R,T,P)=>{let U=L.runKernel(zo,C,D);return P([E,R,U,T]),{value:V(U,y),gradFunc:S}})(x,b,v)}var CD=W({fusedMatMul_:SD});function TD(e){return BA(e,.54,.46)}var ND=W({hammingWindow_:TD});function ED(e){return BA(e,.5,.5)}var n3=W({hannWindow_:ED});function RD(e,t,n,s=!1,r=0){let a=0,o=[];for(;a+t<=e.size;)o.push(_e(e,a,t)),a+=n;if(s)for(;a<e.size;){let i=a+t-e.size,l=gt([_e(e,a,t-i),eu([i],r)]);o.push(l),a+=n}return o.length===0?Hs([],[0,t]):V(gt(o),[o.length,t])}var s3=W({frame_:RD});function DD(e,t,n,s,r=n3){s==null&&(s=Qb(t));let a=s3(e,t,n),o=z(a,r(t));return Gc(o,s)}var _D=W({stft_:DD});function FD(e,t,n,s,r="bilinear",a=0){let o=F(e,"image","cropAndResize"),i=F(t,"boxes","cropAndResize","float32"),l=F(n,"boxInd","cropAndResize","int32"),u=i.shape[0];M(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),M(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),M(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] 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 c={image:o,boxes:i,boxInd:l},d={method:r,extrapolationValue:a,cropSize:s};return L.runKernel(Ji,c,d)}var $D=W({cropAndResize_:FD});function OD(e){let t=F(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 L.runKernel(rl,n,{})}var PD=W({flipLeftRight_:OD});function MD(e){let t=F(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,bs(t,r)}var zD=W({grayscaleToRGB_:MD});function LD(e,t,n=0,s=.5){let r=F(e,"image","rotateWithOffset","float32");M(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let a={image:r},o={radians:t,fillValue:n,center:s};return L.runKernel(Bl,a,o)}var BD=W({rotateWithOffset_:LD});function iu(e,t,n,s,r,a){s==null&&(s=.5),r==null&&(r=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),M(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${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 WD(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=F(e,"boxes","nonMaxSuppression"),o=F(t,"scores","nonMaxSuppression"),i=iu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:s,scoreThreshold:r};return L.runKernel(yl,{boxes:a,scores:o},l)}var VD=W({nonMaxSuppression_:WD});function UD(e,t,n){let s=HD(e,t,n),r=s<0?-(s+1):s;e.splice(r,0,t)}function HD(e,t,n){return jD(e,t,n||GD)}function GD(e,t){return e>t?1:e<t?-1:0}function jD(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 r3(e,t,n,s,r){return VA(e,t,n,s,r,0)}function a3(e,t,n,s,r,a){return VA(e,t,n,s,r,0,!1,a,!0)}function o3(e,t,n,s,r,a){return VA(e,t,n,s,r,a,!0)}function VA(e,t,n,s,r,a,o=!1,i=!1,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>r&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(i3);let c=a>0?-.5/a:0,d=[],p=[];for(;d.length<n&&u.length>0;){let g=u.pop(),{score:A,boxIndex:y,suppressBeginIndex:x}=g;if(A<r)break;let b=!1;for(let v=d.length-1;v>=x;--v){let k=qD(e,y,d[v]);if(k>=s){b=!0;break}if(g.score=g.score*XD(s,c,k),g.score<=r)break}g.suppressBeginIndex=d.length,b||(g.score===A?(d.push(y),p.push(g.score)):g.score>r&&UD(u,g,i3))}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 qD(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]),u=Math.min(r[0],r[2]),c=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-u)*(p-c);if(h<=0||f<=0)return 0;let m=Math.max(a,u),g=Math.max(o,c),A=Math.min(i,d),y=Math.min(l,p),x=Math.max(A-m,0)*Math.max(y-g,0);return x/(h+f-x)}function XD(e,t,n){let s=Math.exp(t*n*n);return n<=e?s:0}function i3(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function KD(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=F(e,"boxes","nonMaxSuppressionAsync"),o=F(t,"scores","nonMaxSuppressionAsync"),i=iu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),u=l[0],c=l[1],{selectedIndices:d}=r3(u,c,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Gt(d,"int32")}var ZD=KD;function YD(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=F(e,"boxes","nonMaxSuppression"),i=F(t,"scores","nonMaxSuppression"),l=iu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let u={boxes:o,scores:i},c={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=L.runKernel(bl,u,c);return{selectedIndices:d[0],selectedScores:d[1]}}var JD=W({nonMaxSuppressionWithScore_:YD});async function QD(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=F(e,"boxes","nonMaxSuppressionAsync"),i=F(t,"scores","nonMaxSuppressionAsync"),l=iu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let u=await Promise.all([o.data(),i.data()]),c=u[0],d=u[1],{selectedIndices:p,selectedScores:h}=o3(c,d,n,s,r,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Gt(p,"int32"),selectedScores:Gt(h)}}var e_=QD;function t_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=F(e,"boxes","nonMaxSuppression"),i=F(t,"scores","nonMaxSuppression"),l=iu(o,i,n,s,r,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,p={boxes:o,scores:i},h={maxOutputSize:u,iouThreshold:c,scoreThreshold:d,padToMaxOutputSize:a},f=L.runKernel(xl,p,h);return{selectedIndices:f[0],validOutputs:f[1]}}var n_=W({nonMaxSuppressionPadded_:t_});async function s_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=F(e,"boxes","nonMaxSuppressionAsync"),i=F(t,"scores","nonMaxSuppressionAsync"),l=iu(o,i,n,s,r,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,[p,h]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=a3(p,h,u,c,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Gt(f,"int32"),validOutputs:Te(m,"int32")}}var r_=s_;function a_(e,t,n=!1,s=!1){let r=F(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=V(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},u=L.runKernel(ko,i,l);return o?V(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var l3=W({resizeBilinear_:a_});function o_(e,t,n=!1,s=!1){let r=F(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=V(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},u=L.runKernel(mc,i,l);return o?V(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var u3=W({resizeNearestNeighbor_:o_});function i_(e,t="binary",n=!1,s=.5){let r=F(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],u=z(Gt([s]),255),c,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 ${t}`),r.shape[2]===3){[c,d,p]=Ht(r,[1,1,1],-1);let g=z(c,a),A=z(d,o),y=z(p,i);h=ie(ie(g,A),y)}else h=e;if(t==="otsu"){let g=cA(pe($h(h),"int32"),ln([]),256);u=l_(g,l)}let f=n?ma(h,u):Hn(h,u);return pe(z(f,255),"int32")}function l_(e,t){let n=Gt([-1]),s=Gt([0]),r=Gt([0]),a,o,i,l,u,c;for(let d=0;d<e.size-1;d++){a=_e(e,0,d+1),o=_e(e,d+1),u=he(ke(a),t),c=he(ke(o),t);let p=ke(z(a,ru(0,a.size)));i=he(p,ke(a));let h=eu(o.shape,a.size),f=ie(ru(0,o.size),h),m=z(o,f);l=he(ke(m),ke(o));let g=ye(i,l),A=ye(i,l),y=z(u,c);r=z(z(y,g),A);let x=Hn(r,s);s=wn(x,r,s),n=wn(x,Gt([d]),n)}return n}var u_=W({threshold_:i_});function c_(e,t,n="nearest",s="constant",r=0,a){let o=F(e,"image","transform","float32"),i=F(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},u={interpolation:n,fillMode:s,fillValue:r,outputShape:a};return L.runKernel(Ml,l,u)}var d_=W({transform_:c_});function p_(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=F(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 (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=V(ru(0,a,1,"int32"),[-1,1]),l=ru(0,o,1,"int32"),u=ye(i,l),c=Fs(ma(u,Te(+t,"int32")),fa(u,Te(-n,"int32"))),d=Mt([a,o],s.dtype);return V(An(Nn(V(s,[-1,a,o])).map(p=>wn(c,p,d))),r)}var h_=W({bandPart_:p_});function f_(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=Ht(e,e.shape[0],0).map(r=>st(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(L.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=z(ke(z(n[o],a)),n[o]);a=ye(a,i)}return he(a,Gh(a,"euclidean"))}));return t?An(n,0):n}var m_=W({gramSchmidt_:f_});function g_(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 c3(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),s=Nn(V(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[u,c]=c3(l,t);r.push(u),a.push(c)});let o=V(An(r,0),e.shape),i=V(An(a,0),e.shape);return[o,i]}}function c3(e,t=!1){return L.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=xA(n),a=Ws(e),o=Hs([[1]],[1,1]),i=Ws(o),l=n>=s?s:n;for(let u=0;u<l;++u){let c=a,d=i,p=r;[i,a,r]=L.tidy(()=>{let h=_e(a,[u,u],[n-u,1]),f=Gh(h),m=_e(a,[u,u],[1,1]),g=wn(Hn(m,0),Hs([[-1]]),Hs([[1]])),A=ye(m,z(g,f)),y=he(h,A);y.shape[0]===1?i=Ws(o):i=gt([o,_e(y,[1,0],[y.shape[0]-1,y.shape[1]])],0);let x=Ct(he(Ue(g,A),f)),b=_e(a,[u,0],[n-u,s]),v=z(x,i),k=Ze(i);if(u===0)a=ye(b,Ue(v,Ue(k,b)));else{let D=ye(b,Ue(v,Ue(k,b)));a=gt([_e(a,[0,0],[u,s]),D],0)}let S=Ze(v),C=_e(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=ye(C,Ue(Ue(C,i),S));else{let D=ye(C,Ue(Ue(C,i),S));r=gt([_e(r,[0,0],[n,u]),D],1)}return[i,a,r]}),Z([c,d,p])}return!t&&n>s&&(r=_e(r,[0,0],[n,s]),a=_e(a,[0,0],[s,s])),[r,a]})}var A_=W({qr_:g_}),En;(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"})(En||(En={}));function y_(e,t,n=En.SUM_BY_NONZERO_WEIGHTS){let s=F(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=F(t,"weights","computeWeightedLoss"));let a=r==null?s:z(s,r);if(n===En.NONE)return a;if(n===En.SUM)return ke(a);if(n===En.MEAN){if(r==null)return _t(a);{let o=s.size/r.size,i=he(ke(a),ke(r));return o>1?he(i,Te(o)):i}}if(n===En.SUM_BY_NONZERO_WEIGHTS){if(r==null)return he(ke(a),Te(s.size));{let o=z(r,as(s.shape)),i=pe(ke(Qo(o,Te(0))),"float32");return he(ke(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Fr=W({computeWeightedLoss_:y_});function x_(e,t,n,s=En.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),Sn(r.shape,a.shape,"Error in absoluteDifference: ");let i=Ut(ye(r,a));return Fr(i,o,s)}var b_=W({absoluteDifference_:x_});function v_(e,t,n,s,r=En.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),Sn(a.shape,o.shape,"Error in cosineDistance: ");let l=Te(1),u=ye(l,ke(z(a,o),n,!0));return Fr(u,i,r)}var w_=W({cosineDistance_:v_});function k_(e,t,n,s=En.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),Sn(r.shape,a.shape,"Error in hingeLoss: ");let i=Te(1);r=ye(z(Te(2),r),i);let l=Us(ye(i,z(r,a)));return Fr(l,o,s)}var I_=W({hingeLoss_:k_});function S_(e,t,n,s=1,r=En.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),Sn(a.shape,o.shape,"Error in huberLoss: ");let l=Te(s),u=Ut(ye(o,a)),c=nu(u,l),d=ye(u,c),p=ie(z(Te(.5),ft(c)),z(l,d));return Fr(p,i,r)}var C_=W({huberLoss_:S_});function T_(e,t,n,s=1e-7,r=En.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),Sn(a.shape,o.shape,"Error in logLoss: ");let l=Te(1),u=Te(s),c=Ct(z(a,ss(ie(o,u)))),d=z(ye(l,a),ss(ie(ye(l,o),u))),p=ye(c,d);return Fr(p,i,r)}var N_=W({logLoss_:T_});function E_(e,t,n,s=En.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),Sn(r.shape,a.shape,"Error in meanSquaredError: ");let i=Vh(r,a);return Fr(i,o,s)}var R_=W({meanSquaredError_:E_});function D_(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");Sn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Us(s),a=z(s,n),o=Pc(ns(Ct(Ut(s))));return ie(ye(r,a),o)}function __(e,t,n,s=0,r=En.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),Sn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let u=Te(s),c=Te(1),d=Te(.5);a=ie(z(a,ye(c,u)),z(d,u))}let l=D_(a,o);return Fr(l,i,r)}var F_=W({sigmoidCrossEntropy_:__});function $_(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return lr((r,a,o)=>{let l=IA(a,[n],!0),u=ye(pe(a,"float32"),l);o([r,u]);let c=Ct(z(u,r));return{value:ke(c,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=Jo(h.shape,[n]);return[z(V(h,A),ye(pe(m,"float32"),ns(g))),z(V(h,A),ye(ns(g),pe(m,"float32")))]}}})(e,t)}function O_(e,t,n,s=0,r=En.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"onehotLabels","softmaxCrossEntropy"),o=F(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","softmaxCrossEntropy")),Sn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let u=Te(s),c=Te(1),d=Te(a.shape[1]);a=ie(z(a,ye(c,u)),he(u,d))}let l=$_(a,o);return Fr(l,i,r)}var P_=W({softmaxCrossEntropy_:O_});function M_(e,t,n,s){let r=F(e,"indices","sparseFillEmptyRows"),a=F(t,"values","sparseFillEmptyRows"),o=F(n,"denseShape","sparseFillEmptyRows"),i=F(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},u=L.runKernel(Kp,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var z_=W({sparseFillEmptyRows_:M_});function L_(e,t,n){let s=F(e,"inputIndices","sparseReshape"),r=F(t,"inputShape","sparseReshape"),a=F(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=L.runKernel(Zp,o);return{outputIndices:i[0],outputShape:i[1]}}var B_=W({sparseReshape_:L_});function W_(e,t,n){let s=F(e,"data","sparseSegmentMean"),r=F(t,"indices","sparseSegmentMean"),a=F(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 L.runKernel(Yp,o)}var V_=W({sparseSegmentMean_:W_});function U_(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum"),a=F(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 L.runKernel(Jp,o)}var H_=W({sparseSegmentSum_:U_});function G_(e,t,n,s,r,a,o,i){let l=F(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 u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},p=L.runKernel(eh,d,c);return{nGrams:p[0],nGramsSplits:p[1]}}var j_=W({stringNGrams_:G_});function q_(e,t,n=!0){let s=F(e,"input","stringSplit","string"),r=F(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 ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=L.runKernel(th,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var X_=W({stringSplit_:q_});function K_(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(nh,r,s)}var Z_=W({stringToHashBucketFast_:K_}),Y_={fft:Hc,ifft:au,rfft:Gc,irfft:Wh},J_={hammingWindow:ND,hannWindow:n3,frame:s3,stft:_D},De={flipLeftRight:PD,grayscaleToRGB:zD,resizeNearestNeighbor:u3,resizeBilinear:l3,rotateWithOffset:BD,cropAndResize:$D,nonMaxSuppression:VD,nonMaxSuppressionAsync:ZD,nonMaxSuppressionWithScore:JD,nonMaxSuppressionWithScoreAsync:e_,nonMaxSuppressionPadded:n_,nonMaxSuppressionPaddedAsync:r_,threshold:u_,transform:d_},d3={bandPart:h_,gramSchmidt:m_,qr:A_},Q_={absoluteDifference:b_,computeWeightedLoss:Fr,cosineDistance:w_,hingeLoss:I_,huberLoss:C_,logLoss:N_,meanSquaredError:R_,sigmoidCrossEntropy:F_,softmaxCrossEntropy:P_},jc={sparseFillEmptyRows:z_,sparseReshape:B_,sparseSegmentMean:V_,sparseSegmentSum:H_},Zh={stringNGrams:j_,stringSplit:X_,stringToHashBucketFast:Z_},$r=class extends pb{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 Z(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 Pb(e,t)}dispose(){this.iterations_!=null&&Z(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Te(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty($r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Yh=class extends $r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>Ye(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>Ye(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;H(()=>{let u=ie(z(i,this.rho),z(ft(o),1-this.rho)),c=z(he(gn(ie(l,this.epsilon)),gn(ie(i,this.epsilon))),o),d=ie(z(l,this.rho),z(ft(c),1-this.rho));i.assign(u),l.assign(d);let p=ie(z(c,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Z(this.accumulatedGrads.map(e=>e.variable)),Z(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)}};Yh.className="Adadelta";pa(Yh);var Jh=class extends $r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>eu(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;H(()=>{let i=ie(o,ft(a));o.assign(i);let l=ie(z(he(a,gn(ie(i,L.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Z(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)}};Jh.className="Adagrad";pa(Jh);var Qh=class extends $r{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=Te(t).variable(),this.accBeta2=Te(n).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ye(1,this.accBeta1),s=ye(1,this.accBeta2);t.forEach((r,a)=>{let o=L.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:H(()=>Ye(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:H(()=>Ye(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=ie(z(u,this.beta1),z(l,1-this.beta1)),p=ie(z(c,this.beta2),z(ft(l),1-this.beta2)),h=he(d,n),f=he(p,s);u.assign(d),c.assign(p);let m=ie(z(he(h,ie(gn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Z(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Z(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),H(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(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)}};Qh.className="Adam";pa(Qh);var ef=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Te(0).variable(),this.accBeta1=Te(t).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ye(1,this.accBeta1),s=he(-this.learningRate,ie(z(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=L.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:Ye(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:Ye(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,d=ie(z(u,this.beta1),z(l,1-this.beta1)),p=z(c,this.beta2),h=Ut(l),f=ur(p,h);u.assign(d),c.assign(f);let m=ie(z(he(s,n),he(d,ie(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Z(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Z(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)}};ef.className="Adamax";pa(ef);var qc=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=L.registeredVariables[n];H(()=>{let o=ie(z(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=un(Te(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qc.className="SGD";pa(qc);var tf=class extends qc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Te(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:H(()=>Ye(r).variable(i))}}let a=this.accumulations[s].variable,o=Array.isArray(e)?e[s].tensor:e[n];o!=null&&H(()=>{let i,l=ie(z(this.m,a),o);this.useNesterov?i=ie(z(this.c,ie(o,z(l,this.m))),r):i=ie(z(this.c,l),r),a.assign(l),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Z(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)}};tf.className="Momentum";pa(tf);var nf=class extends $r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:H(()=>Ye(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:H(()=>Ye(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:H(()=>Ye(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;H(()=>{let u=ie(z(i,this.decay),z(ft(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,d=ie(z(c,this.decay),z(o,1-this.decay)),p=he(z(o,this.learningRate),gn(ye(u,ie(ft(d),this.epsilon)))),h=ie(z(l,this.momentum),p);i.assign(u),c.assign(d),l.assign(h);let f=ye(r,h);r.assign(f)}else{let c=ie(z(i,this.decay),z(ft(o),1-this.decay)),d=ie(z(l,this.momentum),he(z(o,this.learningRate),gn(ie(c,this.epsilon))));i.assign(c),l.assign(d);let p=ye(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Z(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Z(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Z(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)}};nf.className="RMSProp";pa(nf);var ti=class{static sgd(e){return new qc(e)}static momentum(e,t,n=!1){return new tf(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new nf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Qh(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Yh(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new ef(e,t,n,s,r)}static adagrad(e,t=.1){return new Jh(e,t)}},ni={sgd:ti.sgd,momentum:ti.momentum,adadelta:ti.adadelta,adagrad:ti.adagrad,rmsprop:ti.rmsprop,adamax:ti.adamax,adam:ti.adam},eF=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function sf(){return new Promise(e=>eF(()=>e()))}var _={};Le(_,{ERF_A1:()=>dF,ERF_A2:()=>pF,ERF_A3:()=>hF,ERF_A4:()=>fF,ERF_A5:()=>mF,ERF_P:()=>cF,PARALLELIZE_THRESHOLD:()=>UA,SELU_SCALE:()=>h3,SELU_SCALEALPHA:()=>p3,applyActivation:()=>Xh,assertAndGetBroadcastShape:()=>bt,assertAxesAreInnerMostDims:()=>gE,assertParamsConsistent:()=>tF,assignToTypedArray:()=>vF,axesAreInnerMostDims:()=>wA,calculateShapes:()=>eb,checkEinsumDimSizes:()=>TF,combineLocations:()=>zb,complexWithEvenIndex:()=>yF,complexWithOddIndex:()=>xF,computeConv2DInfo:()=>Dc,computeConv3DInfo:()=>xb,computeDefaultPad:()=>iA,computeDilation2DInfo:()=>L9,computeOptimalWindowSize:()=>sF,computeOutAndReduceShapes:()=>Lb,computeOutShape:()=>nF,computePool2DInfo:()=>yb,computePool3DInfo:()=>B9,convertConv2DDataFormat:()=>bb,decodeEinsumEquation:()=>SF,eitherStridesOrDilationsAreOne:()=>ir,expandShapeToKeepDim:()=>Jo,exponent:()=>kF,exponents:()=>wF,fromStringArrayToUint8:()=>PF,fromUint8ToStringArray:()=>OF,getAxesPermutation:()=>Bb,getBroadcastDims:()=>DN,getComplexWithIndex:()=>bF,getEinsumComputePath:()=>NF,getEinsumPermutation:()=>CF,getFusedBiasGradient:()=>qh,getFusedDyActivation:()=>jh,getImageCenter:()=>rF,getInnerMostAxes:()=>AE,getPermuted:()=>oF,getReductionAxes:()=>Jt,getReshaped:()=>aF,getReshapedPermuted:()=>iF,getSliceBeginCoords:()=>lF,getSliceSize:()=>uF,getUndoAxesPermutation:()=>kA,isIdentityPermutation:()=>EF,log:()=>fC,mergeRealAndImagArrays:()=>gF,prepareAndValidate:()=>Q5,prepareSplitSize:()=>DF,segment_util:()=>g3,shouldFuse:()=>Kh,slice_util:()=>Tn,splitRealAndImagArrays:()=>AF,tupleValuesAreOne:()=>ha,upcastType:()=>Ds,validateInput:()=>qg,validateUpdateShape:()=>jg,warn:()=>nr});function tF(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 ${a}.`)})}function nF(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var UA=30;function sF(e){return e<=UA?e:bp(e,Math.floor(Math.sqrt(e)))}function rF(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function aF(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function oF(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function iF(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function lF(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function uF(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var p3=1.7580993408473768,h3=1.0507009873554805,cF=.3275911,dF=.254829592,pF=-.284496736,hF=1.421413741,fF=-1.453152027,mF=1.061405429;function gF(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function AF(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function yF(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function xF(e){let t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function bF(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function vF(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function wF(e,t){let n=new Float32Array(e/2),s=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let a=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(a),s[r]=Math.sin(a)}return{real:n,imag:s}}function kF(e,t,n){let s=(n?2:-2)*Math.PI*(e/t),r=Math.cos(s),a=Math.sin(s);return{real:r,imag:a}}var HA="->",IF=/->/g,f3=",",m3="...";function SF(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(IF,"").length)/HA.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${HA}").`);let[s,r]=e.split(HA);M(s.indexOf(m3)===-1,()=>`The ellipsis notation ("${m3}") is not supported yet.`);let a=s.split(f3),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!==f3&&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 u=i.length,c=r.length,d=[];for(let p=c;p<u;++p)d.push(p);return{allDims:i,summedDims:d,idDims:l}}function CF(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 TF(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 ${a[o]}`)}}function NF(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let o=0;o<r;++o)s.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=RF(t,i);for(let u of l)a.indexOf(u)===-1&&(s[o].push(u),a.push(u))}return{path:n,steps:s}}function EF(e){return e.every((t,n)=>t===n)}function RF(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function DF(e,t,n=0){let s=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);M(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}M(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}var g3={};Le(g3,{collectGatherOpShapeInfo:()=>$F,computeOutShape:()=>FF,segOpComputeOptimalWindowSize:()=>_F});function _F(e,t){let n=!1,s;for(e<=UA?(s=e,n=!0):s=bp(e,Math.floor(Math.sqrt(e)));!n;)s>t||s===e?n=!0:s=bp(e,s+1);return s}function FF(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 $F(e,t,n,s){let r=t.shape.length,a=e.shape.length;if(s!==0&&(s<-r||s>r))throw new Error(`Expect batchDims in the range of [-${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,u=1,c=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]),u*=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]),c*=e.shape[d];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:o,outputShape:i}}function OF(e){try{return e.map(t=>lh(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function PF(e){return e.map(t=>vc(t))}var cr={};Le(cr,{nonMaxSuppressionV3Impl:()=>r3,nonMaxSuppressionV4Impl:()=>a3,nonMaxSuppressionV5Impl:()=>o3,whereImpl:()=>Xb});var A3={kernelName:Bi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,ou(pe(n,"float32"),-1))}}},MF={kernelName:Wi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=ft(pe(n,"float32")),r=gn(ye(Te(1),s));return Ct(he(e,r))}}}},zF={kernelName:Vi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=gn(ye(ft(pe(n,"float32")),1));return he(e,s)}}}},LF={kernelName:sa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=e,l=Jt(n.shape,r);return l.length>0&&(i=ke(i,l)),V(i,n.shape)},b:()=>{let i=e,l=Jt(s.shape,r);return l.length>0&&(i=ke(i,l)),V(i,s.shape)}}}},BF={kernelName:Wa,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((s,r)=>{n[r]=()=>e.clone()}),n}},WF={kernelName:Va,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ye(n)}}},VF={kernelName:rc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ye(n)}}},UF={kernelName:Gi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,gn(ye(Te(1),ft(pe(n,"float32")))))}}},HF={kernelName:ji,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=gn(ie(Te(1),ft(pe(n,"float32"))));return he(e,s)}}}},GF={kernelName:Ki,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=ie(ft(n),ft(s)),l=z(e,he(s,i)),u=Jt(n.shape,r);return u.length>0&&(l=ke(l,u)),V(l,n.shape)},b:()=>{let i=ie(ft(n),ft(s)),l=Ct(z(e,he(n,i))),u=Jt(s.shape,r);return u.length>0&&(l=ke(l,u)),V(l,s.shape)}}}},jF={kernelName:qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,ie(ft(pe(n,"float32")),1))}}},qF={kernelName:Xi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,ye(Te(1),ft(pe(n,"float32"))))}}};function XF(e,t,n,s,r,a){let o=F(e,"dy","avgPool3dGrad"),i=F(t,"input","avgPool3dGrad"),l=o,u=i,c=!1;i.rank===4&&(c=!0,l=V(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),u=V(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(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),a!=null&&M(an(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let d={dy:l,input:u},p={filterSize:n,strides:s,pad:r,dimRoundingMode:a},h=L.runKernel(kp,d,p);return c?V(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var KF=W({avgPool3dGrad_:XF}),ZF={kernelName:ac,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>KF(e,s,r,a,o,i)}}};function YF(e,t,n,s,r){let a=F(e,"dy","avgPoolGrad"),o=F(t,"input","avgPoolGrad");M(o.rank===a.rank,()=>`Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);let i=o,l=a,u=!1;o.rank===3&&(u=!0,i=V(o,[1,o.shape[0],o.shape[1],o.shape[2]]),l=V(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 ${i.rank}.`);let c={dy:l,input:i},d={filterSize:n,strides:s,pad:r},p=L.runKernel(wp,c,d);return u?V(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var JF=W({avgPoolGrad_:YF}),QF={kernelName:Ua,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o}=n;return{x:()=>JF(e,s,r,a,o)}}},e$={kernelName:Ha,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[s,r]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>Ue(e,r,!1,!0),b:()=>Ue(s,e,!0,!1)}:!a&&o?{a:()=>Ue(e,r,!1,!1),b:()=>Ue(e,s,!0,!1)}:a&&!o?{a:()=>Ue(r,e,!1,!0),b:()=>Ue(s,e,!1,!1)}:{a:()=>Ue(r,e,!0,!0),b:()=>Ue(e,s,!0,!0)}}},t$={kernelName:Zi,gradFunc:(e,t,n)=>{let{blockShape:s,crops:r}=n;return{x:()=>Bc(e,s,r)}}},n$={kernelName:A5,gradFunc:(e,t,n)=>{let s=n,r=s.inputShape,a=s.shape,o=Array.from(a);for(let l=r.length-1;l>=0;l--)if(r[l]===a[l])o[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>ke(e,i,!0)}}},s$={kernelName:Ga,gradFunc:e=>({x:()=>e.clone()})},r$={kernelName:ja,gradFunc:e=>({x:()=>Ye(e)})},a$={kernelName:ra,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{clipValueMin:r,clipValueMax:a}=n;return{x:()=>wn(Fs(fa(s,r),ma(s,a)),e,Ye(e))}}},o$={kernelName:oc,inputsToSave:["x"],gradFunc:A3.gradFunc},i$={kernelName:Yi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let s=t.map(l=>l.shape),{axis:r}=n,a=Rs(r,t[0].shape)[0],o=s.map(l=>l[a]);return Ht(e,o,a).map(l=>()=>l)}},l$={kernelName:qa,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return M(ha(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>pA(s.shape,e,r,o,i,l),filter:()=>WA(s,e,r.shape,o,i,l)}}},u$={kernelName:Xa,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>Rr(e,r,a,o,i,1,l),filter:()=>WA(e,s,r.shape,a,o,i,l)}}};function c$(e,t,n,s,r){let a=e;e.rank===4&&(a=V(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let o=t;o.rank===4&&(o=V(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 L.runKernel(Tp,i,l)}var d$=W({conv3DBackpropFilter_:c$}),p$={kernelName:ic,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a}=n;M(ha(s),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let[o,i]=t;return{x:()=>Nb(o.shape,e,i,r,a),filter:()=>d$(o,e,i.shape,r,a)}}},h$={kernelName:Ka,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Ct(Mh(pe(n,"float32"))),e)}}},f$={kernelName:Za,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(zh(pe(n,"float32")),e)}}},m$={kernelName:Ya,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r,exclusive:a,reverse:o}=n;return{x:()=>{let i=Bb([r],s.rank),l=Sh(e,r,a,!o);return i!=null&&(l=Ze(l,i)),l}}}},g$={kernelName:Ja,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a,dimRoundingMode:o}=n,i=s==null?[1,1]:s;M(ha(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);let[l,u]=t;return M(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),M(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),M(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),M(ir(r,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${i}'.`),o!=null&&M(an(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>t3(l.shape,e,u,r,a,i,o),filter:()=>e3(l,e,u.shape,r,a,i,o)}}},A$={kernelName:lc,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:()=>L.runKernel(Fp,a,n),filter:()=>L.runKernel($p,o,n)}}},y$={kernelName:eo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,s={dy:e,y:n};return{x:()=>L.runKernel(Pp,s)}}},x$={kernelName:el,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=z(ns(Ct(ft(n))),2/Math.sqrt(Math.PI));return{x:()=>z(e,s)}}},b$={kernelName:to,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,n)}}},v$={kernelName:nl,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>V(e,n.shape)}}},w$={kernelName:sl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,ns(n))}}},k$={kernelName:no,gradFunc:e=>({x:()=>Ye(e)})},I$={kernelName:so,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=he(e,pe(s,"float32")),l=Jt(n.shape,r);return l.length>0?V(ke(i,l),n.shape):i},b:()=>{let i=z(e,pe(n,"float32")),l=Jt(s.shape,r);l.length>0&&(i=V(ke(i,l),s.shape));let u=ft(s);return Ct(he(i,pe(u,"float32")))}}}},S$={kernelName:ro,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:s}=n,[r,a,o,i]=t,l=i==null?Te(1):i,u=Jt(a.shape,r.shape),c=[];if(a.rank===1){for(let b=0;b<r.shape.length-1;++b)c.push(r.shape[b]);c.push(1)}let d=ye(r,a),p=z(e,l),h=Oh(ie(o,Te(s))),f=z(z(z(h,h),h),Te(-.5));return{x:()=>a.rank===1?V(z(z(e,bs(V(h,[1,1,1,a.shape[0]]),c)),l),r.shape):V(z(z(e,h),l),r.shape),mean:()=>{let b=z(z(h,Te(-1)),p);return a.rank===1&&(b=ke(b,u)),V(b,a.shape)},variance:()=>{let b=z(z(f,d),p);return a.rank===1&&(b=ke(b,u)),V(b,a.shape)},scale:()=>{let b=z(d,h),v=z(e,b);return a.rank===1&&(v=ke(v,u)),V(v,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=ke(b,u)),V(b,a.shape)}}}},C$={kernelName:al,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[s,r]=t,{axis:a}=n,o=Rs(a,s.shape)[0];return{x:()=>{let l=s.shape,u=r.size,c=l.slice(0,o),d=c.length,p=l.slice(a,l.length).slice(1),h=p.length,f=y3(0,d),m=y3(d+1,d+1+h),g=x3([c,[u],p]),A=V(e,g),y=V(r,[u]),x=x3([[d],f,m]),b=Ze(A,x),v=MA(b,y,s.shape[o]),k=kA(x);return v=Ze(v,k),v},indices:()=>r}}};function y3(e,t){let n=[];for(let s=e;s<t;++s)n.push(s);return n}function x3(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 T$={kernelName:ao,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>Ye(n),b:()=>Ye(s)}}},N$={kernelName:oo,gradFunc:e=>({x:()=>pe(e,"float32")})},E$={kernelName:ll,gradFunc:e=>({x:()=>Ye(e)})},R$={kernelName:ul,gradFunc:e=>({x:()=>Ye(e)})},D$={kernelName:cl,gradFunc:e=>({x:()=>Ye(e)})},_$={kernelName:io,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{alpha:r}=n,a=Hn(s,0);return{x:()=>wn(a,e,z(e,r))}}},F$={kernelName:hl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,ie(n,1))}}},$$={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,pe(n,"float32"))}}},O$={kernelName:y5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n;return{logits:()=>{let a=!0,o=ns(s);return ye(e,z(ke(e,r,a),o))}}}};function P$(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 L.runKernel(Wp,i,l)}var M$=W({localResponseNormalizationBackprop_:P$}),z$={kernelName:pc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{depthRadius:a,bias:o,alpha:i,beta:l}=n;return{x:()=>M$(s,r,e,a,o,i,l)}}};function b3(e,t,n,s){return t.rank<n.rank&&(t=V(t,Jo(t.shape,s))),e.rank<n.rank&&(e=V(e,Jo(e.shape,s))),{x:()=>z(e,pe(ts(n,t),e.dtype))}}var v3={kernelName:uo,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{reductionIndices:r}=s,a=t[0],o=t[1],i=Rs(r,a.shape),l=b3(e,o,a,i);return{x:()=>l.x()}}},L$={kernelName:co,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>z(e,pe(fa(n,s),"float32")),b:()=>z(e,pe(Th(n,s),"float32"))}}};function B$(e,t,n,s,r,a,o){let i=F(e,"dy","maxPool3dGrad"),l=F(t,"input","maxPool3dGrad"),u=F(n,"output","maxPool3dGrad"),c=i,d=l,p=u,h=!1;l.rank===4&&(h=!0,c=V(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),d=V(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),p=V(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),M(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.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(an(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:c,input:d,output:p},m={filterSize:s,strides:r,pad:a,dimRoundingMode:o},g=L.runKernel(Up,f,m);return h?V(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var W$=W({maxPool3dGrad_:B$}),V$={kernelName:hc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>W$(e,s,r,a,o,i,l)}}};function U$(e,t,n,s,r,a,o){let i=F(e,"dy","maxPoolGrad"),l=F(t,"input","maxPoolGrad"),u=F(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(an(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let c={dy:i,input:l,output:u},d={filterSize:s,strides:r,pad:a,dimRoundingMode:o};return L.runKernel(Vp,c,d)}var H$=W({maxPoolGrad_:U$}),G$={kernelName:po,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>H$(e,s,r,a,o,i)}}},j$={kernelName:ho,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n,a=Rs(r,s.shape),i=Lb(s.shape,a)[1],l=zt(i);return{x:()=>{let c=s.shape.slice();a.forEach(h=>{c[h]=1});let d=V(e,c);return he(z(d,as(s.shape,"float32")),l)}}}},q$={kernelName:fo,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{axis:r}=s,[a,o]=t,i=Rs(r,a.shape),l=b3(e,o,a,i);return{x:()=>l.x()}}},X$={kernelName:mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>z(e,pe(ma(n,s),"float32")),b:()=>z(e,pe(Hn(n,s),"float32"))}}},K$={kernelName:go,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>_e(e,a,s.shape)}}},Z$={kernelName:ml,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=Jt(n.shape,r);return i.length>0?V(ke(e,i),n.shape):e},b:()=>{let i=z(e,Ct(tu(he(n,s)))),l=Jt(s.shape,r);return l.length>0?V(ke(i,l),s.shape):i}}}},Y$={kernelName:Ao,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=z(e,pe(s,"float32")),l=Jt(n.shape,r);return l.length>0?V(ke(i,l),n.shape):i},b:()=>{let i=z(e,pe(n,"float32")),l=Jt(s.shape,r);return l.length>0?V(ke(i,l),s.shape):i}}}},J$={kernelName:gl,gradFunc:e=>({x:()=>Ct(e)})},Q$={kernelName:yo,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Mt(n.shape,"float32")}}},eO={kernelName:vl,gradFunc:e=>({x:()=>Ye(e)})},tO={kernelName:wl,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:s}=n;return Nn(e,s).map(a=>()=>a)}},w3={kernelName:xo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>_e(e,a,s.shape)}}},nO={kernelName:bo,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,s,r]=t,a=n,o=s,i=bt(a.shape,o.shape);return{a:()=>{let c=pe(o,"float32"),d=z(e,z(c,_r(a,ye(c,Te(1))))),p=Jt(a.shape,i);return p.length>0&&(d=ke(d,p)),V(d,a.shape)},b:()=>{let c=Hn(a,0),d=wn(c,ss(a),Ye(a)),p=z(e,z(r,d)),h=Jt(o.shape,i);return h.length>0&&(p=ke(p,h)),V(p,o.shape)}}}},sO={kernelName:vo,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,s]=t,r=Hn(n,0);return{x:()=>wn(r,e,z(e,s)),alpha:()=>{let a=wn(r,Ye(e),z(e,n)),o=Jt(s.shape,e.shape);return o.length>0&&(a=ke(a,o)),V(a,s.shape)}}}},rO={kernelName:Qa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=he(e,pe(s,"float32")),l=Jt(n.shape,r);return l.length>0?V(ke(i,l),n.shape):i},b:()=>{let i=z(e,pe(n,"float32")),l=Jt(s.shape,r);l.length>0&&(i=V(ke(i,l),s.shape));let u=ft(s);return Ct(he(i,pe(u,"float32")))}}}},aO={kernelName:Il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,Ct(ft(n)))}}},oO={kernelName:Io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=z(ma(n,6),ou(n));return{x:()=>z(e,pe(s,"float32"))}}},iO={kernelName:wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,pe(ou(n),"float32"))}}},lO={kernelName:Sl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>V(e,n.shape)}}},uO={kernelName:ko,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>L.runKernel(Xp,r,n)}}},cO={kernelName:mc,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>L.runKernel(qp,r,n)}}},dO={kernelName:So,gradFunc:(e,t,n)=>{let{dims:s}=n,r=Rs(s,e.shape);return{x:()=>is(e,r)}}},pO={kernelName:Co,gradFunc:e=>({x:()=>Ye(e)})},hO={kernelName:To,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ct(he(e,z(_r(n,1.5),2)))}}},fO={kernelName:Tl,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>pe(Ye(n),"float32"),t:()=>z(e,pe(n,e.dtype)),e:()=>z(e,pe(Mc(n),e.dtype))}}},mO={kernelName:Nl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Hn(n,Te(0)),r=Te(p3),a=Te(h3),o=z(e,a),i=z(z(e,r),ns(pe(n,"float32")));return wn(s,o,i)}}}},gO={kernelName:Eo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(n,ye(Te(1),n)))}}},AO={kernelName:Dl,gradFunc:e=>({x:()=>Ye(e)})},yO={kernelName:No,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z($c(pe(n,"float32")),e)}}},xO={kernelName:Rl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Ih(pe(n,"float32")),e)}}},bO={kernelName:El,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{begin:r,size:a}=n,o=s.shape,[i,l]=db(s,r,a),u=[];for(let c=0;c<e.rank;c++)u.push([i[c],o[c]-i[c]-l[c]]);return{x:()=>Dr(e,u)}}},vO={kernelName:_o,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{dim:r}=n,a=!0,o=z(e,s);return{logits:()=>ye(o,z(ke(o,[r],a),s))}}},wO={kernelName:_l,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,Vn(n))}}},k3={kernelName:Fl,gradFunc:(e,t,n)=>{let{blockShape:s,paddings:r}=n;return{x:()=>Fc(e,s,r)}}},I3={kernelName:$l,gradFunc:(e,t,n)=>{let{axis:s}=n;return{x:()=>gt(e,s)}}},kO={kernelName:Ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,z(gn(pe(n,"float32")),2))}}},IO={kernelName:gc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(pe(n,"float32"),2))}}},SO={kernelName:Fo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Te(2);return{a:()=>z(e,z(r,ye(n,s))),b:()=>z(e,z(r,ye(s,n)))}}},CO={kernelName:oa,gradFunc:e=>({x:()=>Ye(e)})},TO={kernelName:$o,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=bt(n.shape,s.shape);return{a:()=>{let i=e,l=Jt(n.shape,r);return l.length>0&&(i=ke(i,l)),V(i,n.shape)},b:()=>{let i=e,l=Jt(s.shape,r);return l.length>0&&(i=ke(i,l)),V(Ct(i),s.shape)}}}},NO={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,r=s.shape.slice(),{axis:a}=n;Rs(a,s.shape).forEach(u=>{r[u]=1});let i=V(e,r),l=z(i,as(s.shape,"float32"));return{x:()=>l}}},EO={kernelName:Oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>he(e,ft($c(n)))}}},RO={kernelName:Po,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(ye(Te(1),ft(n)),e)}}},DO={kernelName:aa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{reps:r}=n;return{x:()=>{let o=Ye(s);if(s.rank===1)for(let i=0;i<r[0];++i)o=ie(o,_e(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=ie(o,_e(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 u=0;u<r[2];++u)o=ie(o,_e(e,[i*s.shape[0],l*s.shape[1],u*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 u=0;u<r[2];++u)for(let c=0;c<r[3];++c)o=ie(o,_e(e,[i*s.shape[0],l*s.shape[1],u*s.shape[2],c*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}}}},_O={kernelName:Mo,gradFunc:(e,t,n)=>{let s=n,{perm:r}=s,a=kA(r);return{x:()=>Ze(e,a)}}},FO={kernelName:zl,gradFunc:(e,t,n)=>{let s=n,{axis:r}=s;return{value:()=>An(e,r)}}},$O={kernelName:Ac,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>OO(e,n)}}};function OO(e,t){let n=ur(t,Ye(t)),s=Zo(e,n),r=fa(t,Te(0,"int32")),a=s.rank-r.rank;for(let i=0;i<a;++i)r=Lt(r,i+1);r=Fs(r,as(s.shape,"bool"));let o=Ye(s);return wn(r,s,o)}var PO={kernelName:Ll,gradFunc:e=>({x:()=>Ye(e)})},MO=[A3,MF,zF,LF,BF,WF,VF,UF,HF,GF,jF,qF,ZF,QF,e$,t$,n$,s$,r$,a$,o$,i$,u$,l$,p$,h$,f$,m$,g$,A$,rO,y$,x$,b$,v$,w$,I$,k$,S$,C$,T$,N$,E$,R$,D$,_$,F$,$$,O$,z$,v3,v3,L$,V$,G$,j$,q$,X$,K$,Z$,Y$,J$,Q$,eO,tO,w3,w3,nO,sO,aO,oO,iO,lO,uO,cO,dO,pO,hO,fO,mO,gO,AO,yO,xO,bO,vO,wO,k3,k3,I3,I3,kO,SO,IO,CO,TO,NO,EO,RO,DO,_O,FO,$O,PO];for(let e of MO)x5(e);ee().prototype.abs=function(){return this.throwIfDisposed(),Ut(this)};ee().prototype.acos=function(){return this.throwIfDisposed(),Qg(this)};ee().prototype.acosh=function(){return this.throwIfDisposed(),eA(this)};ee().prototype.add=function(e){return this.throwIfDisposed(),ie(this,e)};ee().prototype.all=function(e,t){return this.throwIfDisposed(),bh(this,e,t)};ee().prototype.any=function(e,t){return this.throwIfDisposed(),Rc(this,e,t)};ee().prototype.argMax=function(e){return this.throwIfDisposed(),Vs(this,e)};ee().prototype.argMin=function(e){return this.throwIfDisposed(),tA(this,e)};ee().prototype.asScalar=function(){return this.throwIfDisposed(),M(this.size===1,()=>"The array must have only 1 element."),V(this,[])};ee().prototype.asType=function(e){return this.throwIfDisposed(),pe(this,e)};ee().prototype.as1D=function(){return this.throwIfDisposed(),V(this,[this.size])};ee().prototype.as2D=function(e,t){return this.throwIfDisposed(),V(this,[e,t])};ee().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),V(this,[e,t,n])};ee().prototype.as4D=function(e,t,n,s){return this.throwIfDisposed(),V(this,[e,t,n,s])};ee().prototype.as5D=function(e,t,n,s,r){return this.throwIfDisposed(),V(this,[e,t,n,s,r])};ee().prototype.asin=function(){return this.throwIfDisposed(),nA(this)};ee().prototype.asinh=function(){return this.throwIfDisposed(),sA(this)};ee().prototype.atan=function(){return this.throwIfDisposed(),rA(this)};ee().prototype.atan2=function(e){return this.throwIfDisposed(),aA(this,e)};ee().prototype.atanh=function(){return this.throwIfDisposed(),oA(this)};ee().prototype.avgPool=function(e,t,n,s){return this.throwIfDisposed(),_c(this,e,t,n,s)};ee().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Fc(this,e,t)};ee().prototype.batchNorm=function(e,t,n,s,r){return this.throwIfDisposed(),Ko(this,e,t,n,s,r)};ee().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Zl(this,e)};ee().prototype.cast=function(e){return this.throwIfDisposed(),pe(this,e)};ee().prototype.ceil=function(){return this.throwIfDisposed(),dA(this)};ee().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Un(this,e,t)};ee().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ge&&(e=[e]),gt([this,...e],t)};ee().prototype.conv1d=function(e,t,n,s,r,a){return this.throwIfDisposed(),wh(this,e,t,n,s,r,a)};ee().prototype.conv2dTranspose=function(e,t,n,s,r){return this.throwIfDisposed(),kh(this,e,t,n,s,r)};ee().prototype.conv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),Rr(this,e,t,n,s,r,a)};ee().prototype.cos=function(){return this.throwIfDisposed(),$c(this)};ee().prototype.cosh=function(){return this.throwIfDisposed(),Ih(this)};ee().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Sh(this,e,t,n)};ee().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),fA(this,e,t)};ee().prototype.depthwiseConv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),Jl(this,e,t,n,s,r,a)};ee().prototype.dilation2d=function(e,t,n,s,r){return this.throwIfDisposed(),mA(this,e,t,n,s,r)};ee().prototype.divNoNan=function(e){return this.throwIfDisposed(),gA(this,e)};ee().prototype.div=function(e){return this.throwIfDisposed(),he(this,e)};ee().prototype.dot=function(e){return this.throwIfDisposed(),Db(this,e)};ee().prototype.elu=function(){return this.throwIfDisposed(),Ql(this)};ee().prototype.equal=function(e){return this.throwIfDisposed(),ts(this,e)};ee().prototype.erf=function(){return this.throwIfDisposed(),AA(this)};ee().prototype.exp=function(){return this.throwIfDisposed(),ns(this)};ee().prototype.expandDims=function(e){return this.throwIfDisposed(),Lt(this,e)};ee().prototype.expm1=function(){return this.throwIfDisposed(),yA(this)};ee().prototype.fft=function(){return this.throwIfDisposed(),Hc(this)};ee().prototype.flatten=function(){return this.throwIfDisposed(),V(this,[this.size])};ee().prototype.floor=function(){return this.throwIfDisposed(),tu(this)};ee().prototype.floorDiv=function(e){return this.throwIfDisposed(),yh(this,e)};ee().prototype.gather=function(e,t){return this.throwIfDisposed(),Zo(this,e,t)};ee().prototype.greaterEqual=function(e){return this.throwIfDisposed(),fa(this,e)};ee().prototype.greater=function(e){return this.throwIfDisposed(),Hn(this,e)};ee().prototype.ifft=function(){return this.throwIfDisposed(),au(this)};ee().prototype.irfft=function(){return this.throwIfDisposed(),Wh(this)};ee().prototype.isFinite=function(){return this.throwIfDisposed(),Fb(this)};ee().prototype.isInf=function(){return this.throwIfDisposed(),$b(this)};ee().prototype.isNaN=function(){return this.throwIfDisposed(),bA(this)};ee().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Oc(this,e)};ee().prototype.lessEqual=function(e){return this.throwIfDisposed(),ma(this,e)};ee().prototype.less=function(e){return this.throwIfDisposed(),Th(this,e)};ee().prototype.localResponseNormalization=function(e,t,n,s){return this.throwIfDisposed(),vA(this,e,t,n,s)};ee().prototype.logSigmoid=function(){return this.throwIfDisposed(),Mb(this)};ee().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Eh(this,e)};ee().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),IA(this,e,t)};ee().prototype.log=function(){return this.throwIfDisposed(),ss(this)};ee().prototype.log1p=function(){return this.throwIfDisposed(),Pc(this)};ee().prototype.logicalAnd=function(e){return this.throwIfDisposed(),Fs(this,e)};ee().prototype.logicalNot=function(){return this.throwIfDisposed(),Mc(this)};ee().prototype.logicalOr=function(e){return this.throwIfDisposed(),Rh(this,e)};ee().prototype.logicalXor=function(e){return this.throwIfDisposed(),Wb(this,e)};ee().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Ue(this,e,t,n)};ee().prototype.maxPool=function(e,t,n,s){return this.throwIfDisposed(),zc(this,e,t,n,s)};ee().prototype.max=function(e,t){return this.throwIfDisposed(),rs(this,e,t)};ee().prototype.maximum=function(e){return this.throwIfDisposed(),ur(this,e)};ee().prototype.mean=function(e,t){return this.throwIfDisposed(),_t(this,e,t)};ee().prototype.min=function(e,t){return this.throwIfDisposed(),Lc(this,e,t)};ee().prototype.minimum=function(e){return this.throwIfDisposed(),nu(this,e)};ee().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),CA(this,e,t)};ee().prototype.mod=function(e){return this.throwIfDisposed(),TA(this,e)};ee().prototype.mul=function(e){return this.throwIfDisposed(),z(this,e)};ee().prototype.neg=function(){return this.throwIfDisposed(),Ct(this)};ee().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Gh(this,e,t,n)};ee().prototype.notEqual=function(e){return this.throwIfDisposed(),Qo(this,e)};ee().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),jl(this,e,t,n)};ee().prototype.onesLike=function(){return this.throwIfDisposed(),os(this)};ee().prototype.pad=function(e,t){return this.throwIfDisposed(),Dr(this,e,t)};ee().prototype.pool=function(e,t,n,s,r){return this.throwIfDisposed(),Hb(this,e,t,n,s,r)};ee().prototype.pow=function(e){return this.throwIfDisposed(),_r(this,e)};ee().prototype.prelu=function(e){return this.throwIfDisposed(),Wc(this,e)};ee().prototype.prod=function(e,t){return this.throwIfDisposed(),_h(this,e,t)};ee().prototype.reciprocal=function(){return this.throwIfDisposed(),RA(this)};ee().prototype.relu=function(){return this.throwIfDisposed(),Us(this)};ee().prototype.relu6=function(){return this.throwIfDisposed(),Fh(this)};ee().prototype.reshapeAs=function(e){return this.throwIfDisposed(),V(this,e.shape)};ee().prototype.reshape=function(e){return this.throwIfDisposed(),V(this,e)};ee().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),l3(this,e,t,n)};ee().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),u3(this,e,t,n)};ee().prototype.reverse=function(e){return this.throwIfDisposed(),is(this,e)};ee().prototype.rfft=function(){return this.throwIfDisposed(),Gc(this)};ee().prototype.round=function(){return this.throwIfDisposed(),$h(this)};ee().prototype.rsqrt=function(){return this.throwIfDisposed(),Oh(this)};ee().prototype.selu=function(){return this.throwIfDisposed(),Ph(this)};ee().prototype.separableConv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),DA(this,e,t,n,s,r,a)};ee().prototype.sigmoid=function(){return this.throwIfDisposed(),Vn(this)};ee().prototype.sign=function(){return this.throwIfDisposed(),_A(this)};ee().prototype.sin=function(){return this.throwIfDisposed(),Mh(this)};ee().prototype.sinh=function(){return this.throwIfDisposed(),zh(this)};ee().prototype.slice=function(e,t){return this.throwIfDisposed(),_e(this,e,t)};ee().prototype.softmax=function(e){return this.throwIfDisposed(),ei(this,e)};ee().prototype.softplus=function(){return this.throwIfDisposed(),Yo(this)};ee().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Bc(this,e,t)};ee().prototype.split=function(e,t){return this.throwIfDisposed(),Ht(this,e,t)};ee().prototype.sqrt=function(){return this.throwIfDisposed(),gn(this)};ee().prototype.square=function(){return this.throwIfDisposed(),ft(this)};ee().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Vh(this,e)};ee().prototype.squeeze=function(e){return this.throwIfDisposed(),st(this,e)};ee().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ge?[this,e]:[this,...e];return An(n,t)};ee().prototype.step=function(e){return this.throwIfDisposed(),ou(this,e)};ee().prototype.stridedSlice=function(e,t,n,s,r,a,o,i){return this.throwIfDisposed(),$A(this,e,t,n,s,r,a,o,i)};ee().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};ee().prototype.sum=function(e,t){return this.throwIfDisposed(),ke(this,e,t)};ee().prototype.tan=function(){return this.throwIfDisposed(),OA(this)};ee().prototype.tanh=function(){return this.throwIfDisposed(),Xo(this)};ee().prototype.tile=function(e){return this.throwIfDisposed(),bs(this,e)};ee().prototype.toBool=function(){return this.throwIfDisposed(),pe(this,"bool")};ee().prototype.toFloat=function(){return this.throwIfDisposed(),pe(this,"float32")};ee().prototype.toInt=function(){return this.throwIfDisposed(),pe(this,"int32")};ee().prototype.topk=function(e,t){return this.throwIfDisposed(),PA(this,e,t)};ee().prototype.transpose=function(e){return this.throwIfDisposed(),Ze(this,e)};ee().prototype.unique=function(e){return this.throwIfDisposed(),Hh(this,e)};ee().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),MA(this,e,t)};ee().prototype.unstack=function(e){return this.throwIfDisposed(),Nn(this,e)};ee().prototype.where=function(e,t){return this.throwIfDisposed(),wn(e,this,t)};ee().prototype.zerosLike=function(){return this.throwIfDisposed(),Ye(this)};var S3={};Le(S3,{maxNorm:()=>WO,minMaxNorm:()=>HO,nonNeg:()=>UO,unitNorm:()=>VO});var GA;function Qt(){return GA==null&&(GA=Er().epsilon()),GA}function Gs(){return"channelsLast"}var Or=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Or.prototype)}},js=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,js.prototype)}},G=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,G.prototype)}},ze=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ze.prototype)}},C3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,C3.prototype)}};function si(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 dr(e,t){if(!e)throw new C3(t)}function T3(e,t){let n=0;for(let s of e)s===t&&n++;return n}function Gn(e){return e.length===1?e[0]:e}function vt(e){return Array.isArray(e)?e:[e]}function Pr(e){let n=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return n[0]!=="_"?n:"private"+n}function ri(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var $s={};function jA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function qA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>qA(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:qA(s))}}}function Xc(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 $s)o=$s[a];else if(o=t[a],o==null)throw new G(`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 ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return o}else{let a=e;if(a.className==null||a.config==null)throw new G(`${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 $s?[i,l]=$s.className:o in t&&([i,l]=t[o]),i==null)throw new G(`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 ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let h of Object.keys($s))u[h]=$s[h];for(let h of Object.keys(n))u[h]=n[h];let c=a.config;c.customObjects=u;let d=Object.assign({},$s);for(let h of Object.keys(n))$s[h]=n[h];qA(a.config);let p=l(i,a.config,n,r);return $s=Object.assign({},d),p}else{let u=Object.assign({},$s);for(let d of Object.keys(n))$s[d]=n[d];let c=new i(a.config);return $s=Object.assign({},u),c}}}function zO(e,t){return e<t?-1:e>t?1:0}function rf(e,t){return-1*zO(e,t)}function Aa(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function LO(e){if(e==null)throw new G(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function ai(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new G(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function XA(e,t,n=0,s=1/0){return dr(n>=0),dr(s>=n),Array.isArray(e)&&e.length>=n&&e.length<=s&&e.every(r=>typeof r===t)}function cn(e,t){Array.isArray(e)?(w.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,s)=>cn(n,`element ${s+1} of ${t}`))):w.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${N3(e)}.`)}function N3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>N3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function BO(e,t){let n=w.now(),s;return(...a)=>{let o=w.now();return o-n<t||(n=o,s=e(...a)),s}}function E3(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function KA(e,t){return H(()=>gn(ke(z(e,e),t,!0)))}var Kc=class extends ue.Serializable{getConfig(){return{}}},ZA=class extends Kc{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 H(()=>{let t=KA(e,this.axis),n=Un(t,0,this.maxValue);return z(e,he(n,ie(Qt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};ZA.className="MaxNorm";ue.registerClass(ZA);var YA=class extends Kc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return H(()=>he(e,ie(Qt(),KA(e,this.axis))))}getConfig(){return{axis:this.axis}}};YA.className="UnitNorm";ue.registerClass(YA);var JA=class extends Kc{apply(e){return Us(e)}};JA.className="NonNeg";ue.registerClass(JA);var QA=class extends Kc{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 H(()=>{let t=KA(e,this.axis),n=ie(z(this.rate,Un(t,this.minValue,this.maxValue)),z(1-this.rate,t));return z(e,he(n,ie(Qt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};QA.className="MinMaxNorm";ue.registerClass(QA);var R3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function en(e){return jA(e)}function D3(e,t={}){return Xc(e,ue.SerializationMap.getMap().classNameMap,t,"constraint")}function tn(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in R3?R3[e]:e,config:{}};return D3(n)}else return e instanceof Kc?e:D3(e)}function WO(e){return new ZA(e)}function VO(e){return new YA(e)}function UO(){return new JA}function HO(e){return new QA(e)}var _3={};Le(_3,{constant:()=>hP,glorotNormal:()=>bP,glorotUniform:()=>xP,heNormal:()=>vP,heUniform:()=>wP,identity:()=>AP,leCunNormal:()=>kP,leCunUniform:()=>IP,ones:()=>pP,orthogonal:()=>SP,randomNormal:()=>mP,randomUniform:()=>fP,truncatedNormal:()=>gP,varianceScaling:()=>yP,zeros:()=>dP});var GO=["channelsFirst","channelsLast"],jO=["nearest","bilinear"],qO=["valid","same","causal"],XO=["max","avg"],KO=["sum","mul","concat","ave"],lu=new Map;function Bt(e){ai(GO,"DataFormat",e)}function ZO(e){ai(jO,"InterpolationFormat",e)}function vs(e){ai(qO,"PaddingMode",e)}function F3(e){ai(XO,"PoolMode",e)}var Zc=[],$3="/";function oi(e,t){Zc.push(e);try{let n=t();return Zc.pop(),n}catch(n){throw Zc.pop(),n}}function YO(){return Zc.length===0?"":Zc.join($3)+$3}function O3(e){if(!M3(e))throw new Error("Not a valid tensor name: '"+e+"'");return YO()+e}function P3(e){if(!M3(e))throw new Error("Not a valid tensor name: '"+e+"'");lu.has(e)||lu.set(e,0);let t=lu.get(e);if(lu.set(e,lu.get(e)+1),t>0){let n=`${e}_${t}`;return lu.set(n,1),n}else return e}var JO=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function M3(e){return!!e.match(JO)}function QO(e){return e===parseInt(e.toString(),10)}function ya(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 uu(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 xa(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 qs(e,t){if(t<e)throw new G(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let s=e;s<t;++s)n.push(s);return n}function af(e,t){return pe(e,t)}function Yc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),V(e,n)}function eP(e,t){return H(()=>{if(e.shape.length!==2)throw new G(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Yc(e,1);return n1(n,[1,t,1])})}function tP(e){let t=[ya(e.shape)];return V(e,t)}function nP(e){if(e.rank<=1)throw new G(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],ya(e.shape,1)];return V(e,t)}function ii(e,t,n){return H(()=>{switch(e.rank){case 1:return Lh(e,t,n);case 2:return FA(e,[t,0],[n,e.shape[1]]);case 3:return Bh(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Uc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return _e(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return _e(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 G(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function e1(e,t,n){return H(()=>{switch(e.rank){case 1:return Lh(e,t,n);case 2:return FA(e,[0,t],[e.shape[0],n]);case 3:return Bh(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Uc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function of(e,t,n,s){return H(()=>{switch(e.rank){case 1:return Lh(e,t,n);case 2:switch(s){case 1:return ii(e,t,n);case 2:return e1(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return ii(e,t,n);case 2:return Bh(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return e1(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return ii(e,t,n);case 2:return Uc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Uc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return e1(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}default:throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function t1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),gt(e,t)}function z3(e,t){switch(e.rank){case 1:return Sb([e,t]);case 2:return Yl([e,t],0);case 3:return Cb([e,t],0);case 4:return Tb([e,t],0);default:throw new G(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function n1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new G(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return bs(e,t)}function lf(e,t=0,n=1,s,r){return Gb(e,t,n,s,r)}function pr(e,t,n,s){if(e.rank<2||t.rank<2)throw new ze(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let r=e.shape.slice(-1)[0],a=t.shape.slice(-2)[0];if(r!==a)throw new ze(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let r=!1,a=!1;return ga.matMul({a:e,b:t,transposeA:r,transposeB:a,bias:s?s1(e.rank,s,Gs()):null,activation:n})}else{let r=e.shape.slice(),a=r.pop();e=V(e,[-1,a]);let o=t.shape.slice(),i=o.pop(),l=o.pop(),u=[...o,i],c=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=V(Ze(t,c),[l,-1]);let d=[...r,...u],p=!1,h=!1;return V(ga.matMul({a:e,b:t,transposeA:p,transposeB:h,bias:s?s1(e.rank,s,Gs()):null,activation:n}),d)}}function L3(e,t,n){return H(()=>(Array.isArray(t)?t=Gt(t,"int32"):t=pe(t,"int32"),Zo(e,t,n)))}function Jc(e){return z(e,e)}function s1(e,t,n){let s=t.shape;if(t.rank!==1&&t.rank!==e)throw new G(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return s.length===1?V(t,[1,s[0],1,1,1]):V(t,[1,s[3],s[0],s[1],s[2]]);if(n==="channelsLast")return s.length===1?V(t,[1,1,1,1,s[0]]):V(t,[1].concat(s))}else if(e===4){if(n==="channelsFirst")return s.length===1?V(t,[1,s[0],1,1]):V(t,[1,s[2],s[0],s[1]]);if(n==="channelsLast")return s.length===1?V(t,[1,1,1,s[0]]):V(t,[1].concat(s))}else if(e===3){if(n==="channelsFirst")return s.length===1?V(t,[1,s[0],1]):V(t,[1,s[1],s[0]]);if(n==="channelsLast")return s.length===1?V(t,[1,1,s[0]]):V(t,[1].concat(s))}else if(e<3)return t;throw new G(`Unsupported input rank by biasAdd: ${t.rank}`)}function Xs(e,t,n){return H(()=>(n==null&&(n=Gs()),Bt(n),ie(e,s1(e.rank,t,n))))}function sP(e,t=1){if(t!==1)throw new ze(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Ql(e)}function rP(e){return H(()=>he(e,ie(Ut(e),1)))}function B3(e,t,n,s){return H(()=>Jb(e,t,n,s))}function aP(e){return H(()=>{let t=ie(.5,z(.2,e));return Un(t,0,1)})}function Qc(e,t,n=!1){return n?e():t()}var oP=["fanIn","fanOut","fanAvg"],iP=["normal","uniform","truncatedNormal"];function lP(e){ai(oP,"FanMode",e)}function uP(e){ai(iP,"Distribution",e)}var Os=class extends ue.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},r1=class extends Os{apply(e,t){return Mt(e,t)}};r1.className="Zeros";ue.registerClass(r1);var uf=class extends Os{apply(e,t){return as(e,t)}};uf.className="Ones";ue.registerClass(uf);var a1=class extends Os{constructor(e){super();if(typeof e!="object")throw new G(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new G(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return H(()=>z(Te(this.value),as(e,t)))}getConfig(){return{value:this.value}}};a1.className="Constant";ue.registerClass(a1);var o1=class extends Os{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 su(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};o1.className="RandomUniform";ue.registerClass(o1);var i1=class extends Os{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`randomNormal does not support dType ${t}.`);return lf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};i1.className="RandomNormal";ue.registerClass(i1);var l1=class extends Os{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`truncatedNormal does not support dType ${t}.`);return Uh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};l1.className="TruncatedNormal";ue.registerClass(l1);var u1=class extends Os{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return H(()=>{if(e.length!==2||e[0]!==e[1])throw new G("Identity matrix initializer can only be used for 2D square matrices.");return z(this.gain,xA(e[0]))})}getConfig(){return{gain:this.gain}}};u1.className="Identity";ue.registerClass(u1);function cP(e,t="channelsLast"){let n,s;if(Bt(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=ya(e,2);n=e[1]*r,s=e[0]*r}else if(t==="channelsLast"){let r=ya(e,0,e.length-2);n=e[e.length-2]*r,s=e[e.length-1]*r}}else{let r=ya(e);n=Math.sqrt(r),s=Math.sqrt(r)}return[n,s]}var jn=class extends Os{constructor(e){super();if(e.scale<0)throw new G(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,lP(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,uP(this.distribution),this.seed=e.seed}apply(e,t){let n=cP(e),s=n[0],r=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,s):this.mode==="fanOut"?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`${this.getClassName()} does not support dType ${t}.`);return Uh(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return su(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};jn.className="VarianceScaling";ue.registerClass(jn);var cf=class extends jn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return jn.className}};cf.className="GlorotUniform";ue.registerClass(cf);var df=class extends jn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return jn.className}};df.className="GlorotNormal";ue.registerClass(df);var pf=class extends jn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return jn.className}};pf.className="HeNormal";ue.registerClass(pf);var hf=class extends jn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return jn.className}};hf.className="HeUniform";ue.registerClass(hf);var ff=class extends jn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return jn.className}};ff.className="LeCunNormal";ue.registerClass(ff);var mf=class extends jn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return jn.className}};mf.className="LeCunNormal";ue.registerClass(mf);var c1=class extends Os{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return H(()=>{if(e.length<2)throw new ze("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,s=lf(n,0,1,"float32"),r=d3.gramSchmidt(s);return e[0]>e[1]&&(r=Ze(r)),z(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};c1.className="Orthogonal";ue.registerClass(c1);var W3={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 V3(e,t={}){return Xc(e,ue.SerializationMap.getMap().classNameMap,t,"initializer")}function Ft(e){return jA(e)}function Tt(e){if(typeof e=="string"){let t=e in W3?W3[e]:e;if(t==="GlorotNormal")return new df;if(t==="GlorotUniform")return new cf;if(t==="HeNormal")return new pf;if(t==="HeUniform")return new hf;if(t==="LeCunNormal")return new ff;if(t==="LeCunUniform")return new mf;{let n={};return n.className=t,n.config={},V3(n)}}else return e instanceof Os?e:V3(e)}function dP(){return new r1}function pP(){return new uf}function hP(e){return new a1(e)}function fP(e){return new o1(e)}function mP(e){return new i1(e)}function gP(e){return new l1(e)}function AP(e){return new u1(e)}function yP(e){return new jn(e)}function xP(e){return new cf(e)}function bP(e){return new df(e)}function vP(e){return new pf(e)}function wP(e){return new hf(e)}function kP(e){return new ff(e)}function IP(e){return new mf(e)}function SP(e){return new c1(e)}var U3={};Le(U3,{Layer:()=>Qe,RNN:()=>mr,RNNCell:()=>ld,activation:()=>iz,add:()=>gz,alphaDropout:()=>Qz,average:()=>Az,averagePooling1d:()=>Ny,averagePooling2d:()=>Ey,averagePooling3d:()=>Ry,avgPool1d:()=>Cz,avgPool2d:()=>Nz,avgPool3d:()=>Rz,avgPooling1d:()=>Tz,avgPooling2d:()=>Ez,avgPooling3d:()=>Dz,batchNormalization:()=>kz,bidirectional:()=>Gz,concatenate:()=>yz,conv1d:()=>JM,conv2d:()=>QM,conv2dTranspose:()=>ez,conv3d:()=>tz,conv3dTranspose:()=>nz,convLstm2d:()=>Wz,convLstm2dCell:()=>Vz,cropping2D:()=>rz,dense:()=>lz,depthwiseConv2d:()=>oz,dot:()=>wz,dropout:()=>uz,elu:()=>jM,embedding:()=>mz,flatten:()=>dz,gaussianDropout:()=>Jz,gaussianNoise:()=>Yz,globalAveragePooling1d:()=>_z,globalAveragePooling2d:()=>Fz,globalMaxPool1d:()=>qz,globalMaxPool2d:()=>Xz,globalMaxPooling1d:()=>Jv,globalMaxPooling2d:()=>Qv,gru:()=>Oz,gruCell:()=>Pz,input:()=>wv,inputLayer:()=>GM,layerNormalization:()=>Iz,leakyReLU:()=>XM,lstm:()=>Mz,lstmCell:()=>zz,masking:()=>eL,maxPool1d:()=>Kz,maxPool2d:()=>Zz,maxPooling1d:()=>ew,maxPooling2d:()=>tw,maxPooling3d:()=>$z,maximum:()=>xz,minimum:()=>bz,multiply:()=>vz,permute:()=>fz,prelu:()=>KM,reLU:()=>qM,repeatVector:()=>pz,reshape:()=>hz,rnn:()=>Uz,separableConv2d:()=>sz,simpleRNN:()=>Lz,simpleRNNCell:()=>Bz,softmax:()=>ZM,spatialDropout1d:()=>cz,stackedRNNCells:()=>Hz,thresholdedReLU:()=>YM,timeDistributed:()=>jz,upSampling2d:()=>az,zeroPadding2d:()=>Sz});var CP=0;function H3(){return CP++}var gf={};function Af(e=""){return e in gf||(gf[e]=0),gf[e]+=1,e+gf[e].toString()}function d1(e){return Array.isArray(e)&&Array.isArray(e[0])}function yf(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function We(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new G(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function dt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new G(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function xf(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 G3="Variable",j3=class{constructor(e,t="float32",n=G3,s=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=H3(),n=n==null?G3:n,this.originalName=O3(n),this.name=P3(this.originalName),this.trainable_=s,this.constraint=r,this.val=qb(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),TP(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 TP(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function p1(e){return e.map(t=>t.read())}function h1(e){e.forEach(t=>{t[0].write(t[1])})}var jt=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||{}}},Ks=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=H3(),a!=null&&(this.originalName=O3(a),this.name=P3(this.originalName)),this.rank=t.length}},NP=0,bf=class{constructor(e,t){this.callArgs=t,this.id=NP++,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}}},EP=0,Qe=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=EP++,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=Pr(n)+"_"+Af(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 js(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new G(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Gn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Gn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Or(`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 Or(`Layer ${this.name} is not connected, no input to return.`);return Gn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Or(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Or(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Gn(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=vt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=vt(this.inputSpec);if(e.length!==t.length)throw new G(`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 G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`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 G(`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 G(`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),u=r.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} 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 G(`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=vt(e),s=!0;for(let a of n)if(!(a instanceof Ks)){s=!1;break}let r=!0;for(let a of n)if(a instanceof Ks){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return oi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of vt(e))a.push(o.shape);this.build(Gn(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=vt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=Gn(i),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=RP(e),o=this.computeOutputShape(a),i,l=DP(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new Ks(l,u,this,vt(e),t,this.name,c)):i=new Ks(l,o,this,vt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new ze("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 Or(`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 Or(`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 js(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return xf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return p1(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${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=p1(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!w.arraysEqual(a.shape,i.shape))throw new G(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}h1(n)})}addWeight(e,t,n,s,r,a,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new G(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Tt("zeros"));let i=s.apply(t,n),l=new j3(i,n,e,a,o);return i.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=vt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=vt(e);t=vt(t),n=vt(n),s=vt(s),r=yf(r),a=yf(a);let l=[],u=[],c=[];for(let d of i)l.push(d.sourceLayer),u.push(d.nodeIndex),c.push(d.tensorIndex);new bf({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 RP(e){e=vt(e);let t=[];for(let n of e)t.push(n.shape);return Gn(t)}function DP(e){return"float32"}function q3(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],u=q3(o,i,l);for(let c of u)r.indexOf(c)===-1&&r.push(c)}return r}}}var cu=class extends Qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Af("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 G("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 G("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new G("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 Ks(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new bf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new G(`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}}};cu.className="InputLayer";ue.registerClass(cu);function X3(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 G("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 cu({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function ba(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];Z(s)}}function K3(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var Z3;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Z3||(Z3={}));var _P=125,du=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){}},Y3=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)}},FP=class extends du{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=H(()=>ie(this.totals[s],z(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:H(()=>{let s=z(he(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),un(t[n])}))}},J3=class extends du{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]}},Q3=class extends du{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=_P),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");w.isNumber(this.yieldEvery)&&(this.maybeWait=BO(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let s=[];this.yield!=null&&(await ba(n),s.push(this.yield(e,t,n))),s.push(sf()),await Promise.all(s)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await ba(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await ba(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(sf()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await ba(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await ba(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(sf()):w.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await ba(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await ba(e),await this.trainEnd(e))}};function ev(e,t){return e==null&&(e={}),e instanceof du?[e]:Array.isArray(e)&&e[0]instanceof du?e:vt(e).map(s=>new Q3(s,t))}var Ps=class{constructor(){}static registerCallbackConstructor(e,t){w.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),Ps.checkForDuplicate(t),Ps.constructors[e]==null&&(Ps.constructors[e]=[]),Ps.constructors[e].push(t)}static checkForDuplicate(e){for(let t in Ps.constructors)Ps.constructors[+t].forEach(s=>{if(s===e)throw new G("Duplicate callback constructor.")})}static clear(){Ps.constructors={}}static createCallbacks(e){let t=[];for(let n in Ps.constructors){let s=+n;e>=s&&t.push(...Ps.constructors[s])}return t.map(n=>new n)}};Ps.constructors={};function tv(e,t,n,s,r,a,o,i,l){let u=new J3,c=[new FP,...Ps.createCallbacks(t)];e!=null&&c.push(...e),c.push(u);let d=new Y3(c);return d.setParams({epochs:n,initialEpoch:s,samples:r,steps:a,batchSize:o,verbose:t,doValidation:i,metrics:l}),{callbackList:d,history:u}}function Zs(e,t={},n=!1){return Xc(e,ue.SerializationMap.getMap().classNameMap,t,"layer",n)}function vf(e,t){return H(()=>{e.dtype!=="float32"&&(e=pe(e,"float32"));let n=ke(Jc(e),t,!0),s=eu(n.shape,Qt()),r=gn(ur(n,s));return he(e,r)})}function li(e,t){return H(()=>_t(Jc(ye(t,e)),-1))}function wf(e,t){return H(()=>_t(Ut(ye(t,e)),-1))}function pu(e,t){return H(()=>{let n=ye(e,t),s=Un(Ut(e),Qt(),Number.MAX_VALUE),r=Ut(he(n,s));return z(100,_t(r,-1))})}function $P(e,t){return H(()=>{let n=Un(t,Qt(),Number.MAX_VALUE),s=ss(ie(1,n)),r=Un(e,Qt(),Number.MAX_VALUE),a=ss(ie(1,r));return _t(Jc(ye(s,a)),-1)})}function OP(e,t){return H(()=>{let n=ur(0,ye(1,z(e,t)));return _t(Jc(n),-1)})}function PP(e,t){return H(()=>{let n=ur(0,ye(1,z(e,t)));return _t(n,-1)})}function MP(e,t){return H(()=>{let n=ke(z(e,t),-1),s=rs(z(ye(1,e),t),-1);return ur(0,ie(1,ye(s,n)))})}function zP(e,t){return H(()=>{let n=Math.log(2),s=ye(t,e),r=ye(ie(s,Yo(z(-2,s))),n);return _t(r,-1)})}function ed(e,t,n=!1){return H(()=>{if(n)t=ei(t);else{let s=ke(t,t.shape.length-1,!0);t=he(t,s)}return t=Un(t,Qt(),1-Qt()),Ct(ke(z(pe(e,"float32"),ss(t)),t.shape.length-1))})}function kf(e,t,n=!1){return H(()=>{let s=pe(tu(tP(e)),"int32");t=Un(t,Qt(),1-Qt());let r=t.shape,a=V(jl(s,r[r.length-1]),r);return ed(a,t,n)})}function LP(e,t){if(!w.arraysEqual(e.shape,t.shape))throw new G(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return H(()=>{let n=Us(t),s=Ct(Ut(t));return ie(ye(n,z(t,e)),Pc(ns(s)))})}function If(e,t){return H(()=>{let n;return n=Un(t,Qt(),1-Qt()),n=ss(he(n,ye(1,n))),_t(LP(e,n),-1)})}function BP(e,t){return H(()=>{let n=Un(e,Qt(),1),s=Un(t,Qt(),1);return ke(z(e,ss(he(n,s))),-1)})}function WP(e,t){return H(()=>{let n=ss(ie(Qt(),t));return _t(ye(t,z(e,n)),-1)})}function f1(e,t){return H(()=>{let n=vf(e,-1),s=vf(t,-1),r=z(n,s);return Ct(ke(r,-1))})}var Sf={meanSquaredError:li,meanAbsoluteError:wf,meanAbsolutePercentageError:pu,meanSquaredLogarithmicError:$P,squaredHinge:OP,hinge:PP,categoricalHinge:MP,logcosh:zP,categoricalCrossentropy:ed,sparseCategoricalCrossentropy:kf,binaryCrossentropy:If,kullbackLeiblerDivergence:BP,poisson:WP,cosineProximity:f1};function m1(e){if(typeof e=="string"){if(e in Sf)return Sf[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 G(t)}else return e}function g1(e,t){return H(()=>{let n=z(.5,os(t)),s=af(Hn(t,n),e.dtype);return _t(ts(e,s),-1)})}function A1(e,t){return H(()=>af(ts(Vs(e,-1),Vs(t,-1)),"float32"))}function nv(e,t){return H(()=>pe(ke(Fs(ts(e,1),ts(t,1))),"float32"))}function VP(e,t){return H(()=>pe(ke(Fs(ts(e,1),ts(t,0))),"float32"))}function UP(e,t){return H(()=>pe(ke(Fs(ts(e,0),ts(t,1))),"float32"))}function sv(e,t){return H(()=>{let n=nv(e,t),s=UP(e,t),r=ie(n,s);return pe(wn(Hn(r,0),he(n,r),0),"float32")})}function HP(e,t){return H(()=>{let n=nv(e,t),s=VP(e,t),r=ie(n,s);return pe(wn(Hn(r,0),he(n,r),0),"float32")})}function rv(e,t){return If(e,t)}function av(e,t){return e.rank===t.rank&&(e=st(e,[e.rank-1])),t=Vs(t,-1),t.dtype!==e.dtype&&(t=pe(t,e.dtype)),pe(ts(e,t),"float32")}var GP=li,jP=li,qP=wf,XP=wf,KP=pu,ZP=pu,y1=ed,YP=f1,ov=kf,Cf={binaryAccuracy:g1,categoricalAccuracy:A1,precision:sv,categoricalCrossentropy:y1,sparseCategoricalCrossentropy:ov,mse:GP,MSE:jP,mae:qP,MAE:XP,mape:KP,MAPE:ZP,cosine:YP};function JP(e){if(typeof e=="string"&&e in Cf)return Cf[e];if(typeof e!="string"&&e!=null)return e;throw new G(`Unknown metric ${e}`)}function Tf(e){if(dr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Sf))if(Sf[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Cf))if(Cf[n]===e){t=n;break}return t!==void 0?t:e.name}}function QP(e){let t={Adagrad:()=>ni.adagrad(.01),Adadelta:()=>ni.adadelta(1,.95,Qt()),Adam:()=>ni.adam(.001,.9,.999,Qt()),Adamax:()=>ni.adamax(.002,.9,.999,Qt(),0),RMSProp:()=>ni.rmsprop(.001,.9,0,Qt()),SGD:()=>ni.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 G(`Unknown Optimizer ${e}`)}var iv=1*1024*1024;function lv(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!x1(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let s=JSON.stringify(e);s.length>iv&&console.warn(`User-defined metadata of model "${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 <= ${iv}.`)}}function x1(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"||!x1(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!x1(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function eM(e,t,n,s=console.log){let r=nM(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(c=>Math.floor(t*c)));let o;if(!r){a.push("Receives inputs"),o=[];for(let c in e.nodesByDepth)o.push(...e.nodesByDepth[c])}s("_".repeat(t)),Nf(a,n,s),s("=".repeat(t));let i=e.layers;for(let c=0;c<i.length;++c)r?sM(i[c],n,s):rM(i[c],n,o,s),s((c===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=tM(e),u=xf(e.nonTrainableWeights);s(`Total params: ${l+u}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${u}`),s("_".repeat(t))}function tM(e){let t;return e.collectedTrainableWeights!=null?t=xf(e.collectedTrainableWeights):t=xf(e.trainableWeights),t}function nM(e){let t=!0,n=[],s=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}s.push(...r)}if(t)for(let r of e.layers){let a=!1;for(let o of r.inboundNodes)if(s.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function Nf(e,t,n=console.log){let s="";for(let r=0;r<e.length;++r)r>0&&(s=s.slice(0,s.length-1)+" "),s+=e[r],s=s.slice(0,t[r]),s+=" ".repeat(t[r]-s.length);n(s)}function sM(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(i){s="multiple"}let r=e.name,a=e.getClassName(),o=[`${r} (${a})`,s,e.countParams().toString()];Nf(o,t,n)}function rM(e,t,n,s){let r;try{r=JSON.stringify(e.outputShape)}catch(c){r="multiple"}let a=[];for(let c of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(c)===-1))for(let d=0;d<c.inboundLayers.length;++d){let p=c.inboundLayers[d].name,h=c.nodeIndices[d],f=c.tensorIndices[d];a.push(`${p}[${h}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],u=[`${o} (${i})`,r,e.countParams().toString(),l];Nf(u,t,s);for(let c=1;c<a.length;++c)Nf(["","","",a[c]],t,s)}function uv(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function td(e,t){if(e===null)return null;if(typeof e=="string")return ri(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];uv(t,r,a)?n.push(a):n.push(td(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=ri(s);n[a]=td(r,a)}}return n}}function b1(e,t){if(e==null)return null;if(typeof e=="string")return Pr(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];uv(t,r,a)?n.push(a):n.push(b1(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s],a=Pr(s);(s==="name"||s==="className")&&typeof r=="string"?n[a]=r:n[a]=b1(r,s)}return n}}var v1="3.9.0";function aM(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return pe(t,e.dtype)}catch(n){throw new G(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var ui=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof ui)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=aM(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new G(`Duplicate key: name=${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 Ks){if(this.id2Value[e.id]==null)throw new G(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new G(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Ks){if(this.id2Value[e.id]==null)throw new G(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new G(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Z(this.id2Mask)}},w1={},cv={};function nd(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=[],u=t.names();for(let f of i)u.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);s!=null&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);let c=i.join(",")+"|"+t.names().join(","),d,p;if(w1[c]==null){let f=oM(o,t);d=f.sorted,p=f.recipientCounts,w1[c]=d,cv[c]=p}d=w1[c],p={},r||Object.assign(p,cv[c]);let h=new ui(t);for(let f=0;f<d.length;++f){if(s!=null){let D=gh().numTensors;D>s.maxNumTensors&&(s.maxNumTensors=D),D<s.minNumTensors&&(s.minNumTensors=D)}let m=d[f],g=m.sourceLayer;if(g instanceof cu)continue;let A=[],y=[],x=[],b=!1;for(let D of m.inputs){let O=h.getValue(D),E=h.getMask(D);A.push(O),y.push(E),E!=null&&(b=!0),r||(p[D.name]--,p[D.name]===0&&!t.hasKey(D)&&i.indexOf(D.name)===-1&&!O.isDisposed&&D.sourceLayer.stateful!==!0&&x.push(O))}b&&(n=n||{},n.mask=y[0]);let v=vt(g.apply(A,n)),k=null;g.supportsMasking&&(k=g.computeMask(A,y));let S=lM(m),C=Array.isArray(S)?S:[S];for(let D=0;D<C.length;++D){h.hasKey(C[D])||h.add(C[D],v[D],Array.isArray(k)?k[0]:k);let O=i.indexOf(C[D].name);O!==-1&&(l[O]=v[D])}r||Z(x)}return h.disposeMasks(),a?l:l[0]}function oM(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=dv(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:o,recipientMap:i}=dv(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(u=>s[l].add(u))}}return{sorted:n,recipientCounts:iM(s)}}function iM(e){let t={};for(let n in e)t[n]=e[n].size;return t}function dv(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 u of i.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(i.name),!n.has(u.name)&&a.push(u)}}return{sorted:s,recipientMap:r}}function lM(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 hr=class extends Qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=Af(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],Aa(this.inputs).length!==this.inputs.length)throw new G(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);Aa(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 y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;dr(x===0,"input layer has >1 nodes"),dr(b===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let y=this.inputLayers[A];if(!(y instanceof cu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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,y,x,b,v,k)=>{(b==null||v==null||k==null)&&(b=A.sourceLayer,v=A.nodeIndex,k=A.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new js(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(y.indexOf(S)!==-1)return;this.containerNodes.add(hr.nodeKey(b,v)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let C=S.inboundLayers.length;for(let D=0;D<C;D++){let O=S.inputTensors[D],E=S.inboundLayers[D],R=S.nodeIndices[D],T=S.tensorIndices[D];i(O,y,x,E,R,T)}for(y.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],u=[];for(let A of this.outputs)i(A,l,u);let c=o.slice().reverse();for(let A of c){n[A.id]=A,A.id in t||(t[A.id]=0);let y=t[A.id],x=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];y=Math.max(y,x),s[A.outboundLayer.id]=y,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=y;for(let b=0;b<A.inboundLayers.length;b++){let v=A.inboundLayers[b],k=A.nodeIndices[b],S=v.inboundNodes[k],C=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(y+1,C),n[S.id]=S}}let d={};for(let A in t){let y=t[A];y in d||(d[y]=[]),d[y].push(n[A])}let p={};for(let A in s){let y=s[A];y in p||(p[y]=[]),p[y].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(rf);this.layers=[];for(let A of h){let y=p[A];y.sort((x,b)=>{let v=a[x.id],k=a[b.id];return v<k?-1:v>k?1:0});for(let x of y)x instanceof hr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(rf);let f=this.inputs.slice(),m=[];for(let A of h)for(let y of d[A]){let x=y.outboundLayer;if(x!=null){for(let b of y.inputTensors)if(f.indexOf(b)===-1)throw new js(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of y.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let y=g.filter(x=>x===A).length;if(y!==1)throw new js(`The name "${A}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new bf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 G("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 G(`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 G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}h1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${v1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=b1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=vt(e);let n=new ui;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return nd(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=vt(e);let n;return t==null?n=si(null,e.length):n=vt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=yf(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(rf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],y=`${m.name}_${g}_${A}`,x=n[y];c.push(x)}let d=u.computeOutputShape(Gn(c)),p=yf(d),h=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];dr(i in n),r.push(n[i])}return Gn(r)}runInternalGraph(e,t){t==null&&(t=si(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(rf);for(let i of s){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,d=u.inputTensors,p=u.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,y;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),A=vt(c.call(x,f)),y=vt(c.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),A=vt(c.call(m,f)),y=vt(c.computeMask(m,g));if(c.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=A[x],k=y[x];n[b.id]=[v,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){dr(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),r.push(l),a.push(u)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof hr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=hr.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 G(`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 G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=hr.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 c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],p=hr.nodeKey(a,c),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],y=d.tensorIndices[m],x=hr.nodeKey(g,A),b=t[x];b==null&&(b=0),f.push([g.name,b,y,h])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=hr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,u,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=hr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,u,c])}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=[],y;for(let x of g){let b=x[0],v=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=v){o(m,g);return}let C=S.inboundNodes[v];A.push(C.outputTensors[k])}A.length>0&&m.apply(Gn(A),y)}function l(m){let g=m.name,A=Zs(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${x}`);o(A,x)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!LO(a);)for(let m of c){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let y of A)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],y=m[2];dr(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[y])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],y=m[2];dr(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[y])}return new e({inputs:d,outputs:p,name:u})}get stateful(){if(this._stateful)throw new G("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(){H(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function uM(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: ${JSON.stringify(e)}`)}function pv(e,t){return uM(e,t,"classWeight")}async function hv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=H(()=>{if(e.shape.length===1)return Ws(e);if(e.shape.length===2){if(e.shape[1]>1)return Vs(e,1);if(e.shape[1]===1)return V(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());Z(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])}),Gt(o,"float32")}else return null}function cM(e,t){return z(e,t)}var dM=32;function fv(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.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=mv("input",e.inputNames,n),o=mv("output",e.outputNames,s),i=a[0].shape[0];w.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)})`),w.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++)w.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++)w.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 mv(e,t,n){if(n instanceof Ge)return[n];if(Array.isArray(n))return w.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 G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function pM(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function hM(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.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}`),w.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}`),w.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(gv(n.validationData))w.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=pM(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=ev(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=tv(c,d,n.epochs,null,null,fM(t,n),null,r,u);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,y=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:b,ys:v}=fv(e,x.value),k={};k.batch=y,k.size=b[0].shape[0],await p.onBatchBegin(y,k);let S=[];if(n.classWeight!=null){let O=pv(n.classWeight,e.outputNames);for(let E=0;E<O.length;++E)S.push(await hv(v[E],null,O[E]))}let C=b.concat(v).concat(S),D=i(C);Z(C);for(let O=0;O<l.length;++O){let E=l[O],R=D[O];k[E]=R,un(R)}await p.onBatchEnd(y,k),K3(k),y++,A++}if(s?A>=n.batchesPerEpoch:x.done){if(r){let b;gv(n.validationData)?b=vt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=vt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?dM:n.validationBatchSize,verbose:0}));for(let v=0;v<e.metricsNames.length;++v)g[`val_${e.metricsNames[v]}`]=b[v]}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 fM(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function gv(e){return typeof e.iterator=="function"}function mM(e){return typeof e.next=="function"}async function gM(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new ze("Verbose mode is not implemented yet.");w.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=mM(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let u=await o.next();if(a=H(()=>{if(u.value){let{xs:c,ys:d}=fv(e,u.value),p=c.concat(d),h=H(()=>r(p));if(Z(p),l===0)for(let m=0;m<h.length;++m)a.push(Te(0));let f=p[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],A=a[m];a[m]=H(()=>ie(a[m],z(f,g))),l>0&&Z(A)}Z(h),i+=f,++l}return a}),u.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 u=0;u<a.length;++u){let c=a[u];a[u]=he(a[u],i),Z(c)}return Gn(a)}function k1(e){w.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function sd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>ii(s,t,n-t)):ii(e,t,n-t)}function I1(e,t){return H(()=>e==null?null:Array.isArray(e)?e.map(n=>I1(n,t)):L3(e,t.dtype==="int32"?t:pe(t,"int32")))}function S1(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 AM(e,t,n,s,r,a,o,i,l,u,c,d,p,h,f){r==null&&(r=32),a==null&&(a=1),c==null&&(c=!0),p==null&&(p=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,h==null))throw new G("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=qs(0,g)),o==null&&(o=1);let{callbackList:y,history:x}=tv(i,o,a,p,g,h,r,m,d);y.setModel(e),e.history=x,await y.onTrainBegin(),e.stopTraining_=!1;for(let b=p;b<a;++b){await y.onEpochBegin(b);let v={};if(h!=null)throw new ze("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new ze("batch shuffling is not implemneted yet");c&&w.shuffle(A);let k=Gt(A),S=S1(g,r);for(let C=0;C<S.length;++C){let D={};if(await y.onBatchBegin(C,D),H(()=>{let O=S[C][0],E=S[C][1],R=ii(k,O,E-O);D.batch=C,D.size=E-O;let T=I1(n,R),P=t(T);for(let U=0;U<s.length;++U){let j=s[U],q=P[U];D[j]=q,un(q)}if(C===S.length-1&&m){let U=e.testLoop(l,u,r);for(let j=0;j<s.length;++j){let q=s[j],X=U[j];un(X),v["val_"+q]=X}}}),await y.onBatchEnd(C,D),K3(D),e.stopTraining_)break}k.dispose()}if(await y.onEpochEnd(b,v),e.stopTraining_)break}return await y.onTrainEnd(),await e.history.syncData(),e.history}async function yM(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,u,c;try{let d=s.batchSize==null?32:s.batchSize;k1(d);let p=!1,h=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,p,d);r=h[0],a=h[1],c=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 ze("validationData including sample weights is not supported yet."):new G(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);let S=!0,C=await e.standardizeUserData(o,i,null,null,S,d);l=C[0],u=C[1],m=l.concat(u)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){f=!0;let S=Math.floor(r[0].shape[0]*(1-s.validationSplit)),C=r[0].shape[0];l=sd(r,S,C),r=sd(r,0,S),u=sd(a,S,C),a=sd(a,0,S),m=l.concat(u)}else s.validationSteps!=null&&(f=!0);let g=r.concat(a).concat(c);e.checkTrainableWeightsConsistency();let A=e.makeTrainFunction(),y=e.getDedupedMetricsNames(),x,b;f?(e.makeTestFunction(),x=e.testFunction,b=y.slice().concat(y.map(S=>"val_"+S))):(x=null,m=[],b=y.slice());let v=ev(s.callbacks,s.yieldEvery);return await AM(e,A,g,y,d,s.epochs,s.verbose,v,x,m,s.shuffle,b,s.initialEpoch,null,null)}finally{e.isTraining=!1,ci(r,t),ci(a,n),ci(l,o),ci(u,i),c!=null&&Z(c)}}function Av(e){let t=[];e instanceof Ge&&(e=[e]);for(let n=0;n<e.length;++n){let s=e[n];if(s.rank===1)t.push(Yc(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 ci(e,t){if(e==null)return;let n=[];if(t instanceof Ge)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 Ge)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 xM(e){return e instanceof Ge}function C1(e){return Array.isArray(e)}function yv(e){return!xM(e)&&!C1(e)}function xv(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(C1(e)&&e.length>0)o=!0;else if(yv(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new G(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(yv(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new G(`No data provided for "${o}". Need data for each key in: ${t}`);a.push(e[o])}}else if(C1(e)){if(e=e,e.length!==t.length)throw new G(`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 G(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=Av(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 G(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let u=i.shape[l],c=n[o][l];if(c!=null&&c>=0&&u!==c)throw new G(`${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 bM(e,t,n){let s=Aa(e.map(a=>a.shape[0]));s.sort();let r=Aa(t.map(a=>a.shape[0]));if(r.sort(),s.length>1)throw new G(`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 G(`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&&!w.arraysEqual(s,r))throw new G(`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 vM(e,t,n){let s=[li,If,ed];for(let r=0;r<e.length;++r){let a=e[r],o=t[r],i=n[r];if(o!=null){if(o===ed&&a.shape[a.shape.length-1]===1)throw new G(`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),u=i.slice(1);for(let c=0;c<l.length;++c){let d=l[c],p=u[c];if(p!=null&&d!==p)throw new G(`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 bv(e,t,n,s=!0,r=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new G(`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 G(`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 G(`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 u=i.shape[l],c=n[o][l];if(c!=null&&c!==u)throw new G(`Error when checking ${r}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}function wM(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 kM="layers-model",Mr=class extends hr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new G("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).");eM(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=QP(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof $r))throw new G("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new G(`Unknown entry in loss dictionary: "${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(m1(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new G(`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=>m1(o))}else{let a=m1(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=[],oi("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=wM(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])};oi("metric",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=s[a];(l=>{let u="",c,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]===If?["accuracy","acc"].indexOf(h)!==-1?d=g1:["crossentropy","ce"].indexOf(h)!==-1&&(d=rv):this.lossFunctions[a]===kf?["accuracy","acc"].indexOf(h)!==-1?d=av:["crossentropy","ce"].indexOf(h)!==-1&&(d=ov):["accuracy","acc"].indexOf(h)!==-1?d=A1:["crossentropy","ce"].indexOf(h)!==-1&&(d=y1);let g;["accuracy","acc"].indexOf(h)!==-1?g="acc":["crossentropy","ce"].indexOf(h)!==-1&&(g="ce"),p=d,c=u+g}else p=JP(h),c=u+Tf(h);let f;oi(c,()=>{f=p}),r(a,c,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;k1(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 Gn(l)}finally{ci(a[0],e),ci(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),gM(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new G(`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 G(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new G("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new ui;if(e instanceof Ge&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new G(`The number of inputs provided (${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 G(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=nd(r,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=si(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 G(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return H(()=>{let s=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");let r=S1(s,t),a=this.outputs.map(o=>[]);for(let o=0;o<r.length;++o)H(()=>{let l=r[o][0],u=r[o][1],c=sd(e,l,u),d=[];if(Array.isArray(c))for(let h=0;h<c.length;++h)d.push({key:this.inputs[h],value:c[h]});else d.push({key:this.inputs[0],value:c});let p=new ui(d);return nd(this.outputs,p)}).forEach((l,u)=>a[u].push(l));return Gn(a.map(o=>gt(o,0)))})}predict(e,t={}){let n=Av(e);bv(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return k1(s),this.predictLoop(n,s)}finally{ci(n,e)}}predictOnBatch(e){bv(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 js("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]===kf?r.push(o.slice(0,o.length-1).concat([1])):r.push(o)}if(e=xv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=xv(t,this.feedOutputNames,r,!1,"target"),bM(e,t,null),vM(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!=0)throw new G(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let u=pv(s,this.outputNames);l=[];for(let c=0;c<u.length;++c)l.push(await hv(i[c],null,u[c]))}return[o,i,l]}testLoop(e,t,n,s=0,r){return H(()=>{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(r!=null)throw new ze("steps mode in testLoop() is not implemented yet");{let i=S1(a,n),l=Gt(qs(0,a));for(let u=0;u<i.length;++u){let c=i[u][0],d=i[u][1],p=ii(l,c,d-c),h=I1(t,p),f=e(h);if(u===0)for(let m=0;m<f.length;++m)o.push(Te(0));for(let m=0;m<f.length;++m){let g=f[m];o[m]=ie(o[m],z(d-c,g))}}for(let u=0;u<o.length;++u)o[u]=he(o[u],a)}return o})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;T3(e,s)>1&&(r+=`_${T3(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 c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let d=new ui(c),p=nd(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=cM(g,r[f]));let A=_t(g);t.push(A),f===0?h=g:h=ie(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=_t(g(s[A],p[A]))}un(m),a.push(m)}return h=_t(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},i=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>H(()=>{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 ui(a),i=nd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=_t(u(r[l],i[l]));l===0?n=c:n=ie(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],d=_t(u(r[c],i[c]));t.push(d)}return t})}async fit(e,t,n={}){return yM(this,e,t,n)}async fitDataset(e,t){return hM(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 u=await l.data();i.push(u[0])}return Z(o),Gn(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=gh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-gh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Pr(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=>Pr(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]=Pr(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[Pr(Tf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Pr(Tf(e)));{let e={};for(let t in this.metrics)e[t]=Pr(Tf(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=td(e.optimizer_config),n=Zs(t),s;if(typeof e.loss=="string")s=ri(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>ri(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=ri(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>ri(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=ri(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=Wn.getSaveHandlers(e);if(l.length===0)throw new G(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new G(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Wn.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:kM,generatedBy:`TensorFlow.js tfjs-layers v${v1}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:u,specs:c}=await Wn.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...c),n.data=Wn.concatenateArrayBuffers([n.data,u])}if(this.userDefinedMetadata!=null){let l=!0;lv(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){lv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Mr.className="Model";ue.registerClass(Mr);var vv=class extends Mr{};vv.className="Functional";ue.registerClass(vv);async function IM(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=td(n),r=Zs(s,t);if(e.weightsManifest!=null){let a=await Wn.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),Z(a)}return r}async function SM(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Wn.getLoadHandlers(e,t);if(n.length===0)n.push(Wn.browserHTTPRequest(e,t));else if(n.length>1)throw new G(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return CM(e,void 0,t)}async function CM(e,t,n){if(n==null&&(n={}),e.load==null)throw new G("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=Zs(td(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 G("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=TM(s.weightData,s.weightSpecs);i.loadWeights(u,a),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),Z(u),Z(c.map(d=>d.tensor))}return i}function TM(e,t){let n=Wn.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 hu=class extends Mr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Af("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 G(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof hu||e instanceof Mr,n;if(t){if(n=e,n.outputs.length!==1)throw new G("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 G("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 G("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=X3({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 G(`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 G("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=q3(this.outputs[0])}this.inboundNodes=[],new bf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:si(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(dt(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 Mr({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 js("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 js("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 js("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 js("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 G("Legacy serialization format not supported yet.");r=t}else w.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 hu))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Zs(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("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 G("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}}};hu.className="Sequential";ue.registerClass(hu);function NM(e){return new Mr(e)}function EM(e){return new hu(e)}function RM(e,t){return t==null&&(t={}),SM(e,t)}function wv(e){return X3(e)}function DM(e,t){Ps.registerCallbackConstructor(e,t)}var qn=class extends ue.Serializable{getConfig(){return{}}},kv=class extends qn{apply(e,t=1){return sP(e,t)}};kv.className="elu";ue.registerClass(kv);var Iv=class extends qn{apply(e){return Ph(e)}};Iv.className="selu";ue.registerClass(Iv);var Sv=class extends qn{apply(e){return Us(e)}};Sv.className="relu";ue.registerClass(Sv);var Cv=class extends qn{apply(e){return H(()=>nu(6,Us(e)))}};Cv.className="relu6";ue.registerClass(Cv);var Tv=class extends qn{apply(e){return e}};Tv.className="linear";ue.registerClass(Tv);var Nv=class extends qn{apply(e){return Vn(e)}};Nv.className="sigmoid";ue.registerClass(Nv);var Ev=class extends qn{apply(e){return aP(e)}};Ev.className="hardSigmoid";ue.registerClass(Ev);var Rv=class extends qn{apply(e){return Yo(e)}};Rv.className="softplus";ue.registerClass(Rv);var Dv=class extends qn{apply(e){return rP(e)}};Dv.className="softsign";ue.registerClass(Dv);var _v=class extends qn{apply(e){return Xo(e)}};_v.className="tanh";ue.registerClass(_v);var T1=class extends qn{apply(e,t=-1){return ei(e,t)}};T1.className="softmax";ue.registerClass(T1);var Fv=class extends qn{apply(e,t=-1){return Eh(e,t)}};Fv.className="logSoftmax";ue.registerClass(Fv);var $v=class extends qn{apply(e,t=1){return H(()=>z(Vn(z(e,t)),e))}};$v.className="swish";ue.registerClass($v);var Ov=class extends qn{apply(e){return H(()=>z(e,Xo(Yo(e))))}};Ov.className="mish";ue.registerClass(Ov);function va(e){return e.getClassName()}function N1(e,t={}){return Xc(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function wa(e){if(e==null){let t={};return t.className="linear",t.config={},N1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},N1(t)}else return e instanceof qn?e:N1(e)}function E1(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 Pv=class extends ue.Serializable{},rd=class extends Pv{constructor(e){super();E1(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 H(()=>{let t=Mt([1]);return this.hasL1&&(t=ie(t,ke(z(this.l1,Ut(e))))),this.hasL2&&(t=ie(t,ke(z(this.l2,Jc(e))))),V(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};rd.className="L1L2";ue.registerClass(rd);function _M(e){return E1(e),new rd({l1:e!=null?e.l1:null,l2:0})}function FM(e){return E1(e),new rd({l2:e!=null?e.l2:null,l1:0})}var Mv={l1l2:"L1L2"};function At(e){return jA(e)}function zv(e,t={}){return Xc(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Mv?Mv[e]:e,config:{}};return zv(n)}else return e instanceof Pv?e:zv(e)}var R1=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=We(e);let n=Us(e);return this.maxValue!=null&&(n=Un(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};R1.className="ReLU";ue.registerClass(R1);var D1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=We(e);return Oc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};D1.className="LeakyReLU";ue.registerClass(D1);var _1=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=tn(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 G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=dt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=We(e),Wc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ft(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:en(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};_1.className="PReLU";ue.registerClass(_1);var F1=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=We(e);return Ql(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};F1.className="ELU";ue.registerClass(F1);var $1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=We(e);return z(n,pe(Hn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};$1.className="ThresholdedReLU";ue.registerClass($1);var O1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new T1().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=We(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O1.className="Softmax";ue.registerClass(O1);function fu(e,t,n){if(typeof e=="number")return si(e,t);if(e.length!==t)throw new G(`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(!QO(r))throw new G(`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 Ys(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 fr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+xa([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function P1(e,t){return H(()=>(Bt(t),t==="channelsFirst"?Ze(e,[0,2,3,1]):e))}function Lv(e,t){return H(()=>(Bt(t),t==="channelsFirst"?Ze(e,[0,2,3,4,1]):e))}function $M(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=Gs()),Bt(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ze(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=wh(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Xs(i,n)),i})}function Bv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=Gs()),Bt(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P1(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ga.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function OM(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=Gs()),Bt(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Lv(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=hA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Xs(i,n)),a==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var M1=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",M1.verifyArgs(t),this.rank=e,cn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=fu(t.kernelSize,e,"kernelSize"),this.strides=fu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,vs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Bt(this.dataFormat),this.activation=wa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=tn(t.biasConstraint),this.biasRegularizer=Nt(t.biasRegularizer),this.activityRegularizer=Nt(t.activityRegularizer),this.dilationRate=fu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`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 G(`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 G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(dr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,3))throw new G(`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:va(this.activation),useBias:this.useBias,biasInitializer:Ft(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:en(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ad=class extends M1{constructor(e,t){super(e,t);this.kernel=null,ad.verifyArgs(t),this.filters=t.filters,cn(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=tn(t.kernelConstraint),this.kernelRegularizer=Nt(t.kernelRegularizer)}build(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=We(e);let n,s=this.bias==null?null:this.bias.read(),r=E3(this.activation.getClassName());if(r!=null&&this.rank===2)n=Bv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$M(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Bv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=OM(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=dt(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=Ys(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:Ft(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:en(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 G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},od=class extends ad{constructor(e){super(2,e);od.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};od.className="Conv2D";ue.registerClass(od);var id=class extends ad{constructor(e){super(3,e);id.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 G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};id.className="Conv3D";ue.registerClass(id);var z1=class extends od{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=fr(i,d,u,this.padding),f=fr(l,p,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let g=kh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ze(g,[0,3,1,2])),this.bias!=null&&(g=Xs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=dt(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]=fr(t[s],i,a,this.padding),t[r]=fr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z1.className="Conv2DTranspose";ue.registerClass(z1);var L1=class extends id{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=s[a],c=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=fr(l,f,d,this.padding),y=fr(u,m,p,this.padding),x=fr(c,g,h,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let v=Eb(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ze(v,[0,4,1,2,3])),this.bias!==null&&(v=Xs(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=dt(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],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=fr(t[s],u,o,this.padding),t[r]=fr(t[r],c,i,this.padding),t[a]=fr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L1.className="Conv3DTranspose";ue.registerClass(L1);var Wv=class extends ad{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 G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("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 G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=tn(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=tn(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${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 G(`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 jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=We(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=DA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Xs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ft(this.depthwiseInitializer),e.pointwiseInitializer=Ft(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=en(this.depthwiseConstraint),e.pointwiseConstraint=en(this.pointwiseConstraint),e}};Wv.className="SeparableConv";var B1=class extends Wv{constructor(e){super(2,e)}};B1.className="SeparableConv2D";ue.registerClass(B1);var Ef=class extends ad{constructor(e){super(1,e);Ef.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"&&!XA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Ef.className="Conv1D";ue.registerClass(Ef);var W1=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=We(e),this.dataFormat==="channelsLast"){let n=of(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return of(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=of(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return of(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}};W1.className="Cropping2D";ue.registerClass(W1);var V1=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ZO(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 H(()=>{let n=We(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a]);return Ze(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};V1.className="UpSampling2D";ue.registerClass(V1);function PM(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=Gs()),Bt(r);let o=P1(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Jl(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}var U1=class extends M1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=tn(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=dt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 H(()=>{e=We(e);let n=PM(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Xs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(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=Ys(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ys(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=Ft(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=en(this.depthwiseRegularizer),e}};U1.className="DepthwiseConv2D";ue.registerClass(U1);function Vv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("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 Uv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qs(2,l));if(t=Ze(t,u),a!=null)throw new ze("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=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Lt(r,-1)),r=Ze(r,u)),s&&(t=is(t,0),r!=null&&(r=is(r,0)));let c=[],d,p=n,h=t.shape[0],f=Nn(t),m;r!=null&&(m=Nn(r));for(let A=0;A<h;++A){let y=f[A],x=H(()=>e(y,p));if(r==null)d=x[0],p=x[1];else{let b=H(()=>{let v=m[A],k=ye(os(v),v),S=ie(z(x[0],v),z(p[0],k)),C=p.map((D,O)=>ie(z(x[1][O],v),z(D,k)));return{output:S,newStates:C}});d=b.output,p=b.newStates}i&&c.push(d)}let g;return i&&(g=An(c,1)),[d,g,p]})}var mr=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new _f({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("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 jt({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 qs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){d1(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 H(()=>{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 ze("Constants support is not implemented in RNN yet.");d1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new jt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("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(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`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 jt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Or("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("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=>Mt([n,s])):this.states_=[Mt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Mt([n,s])):this.states_[0]=Mt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`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()):Z(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(!w.arraysEqual(r.shape,o))throw new G(`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=>un(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Vv(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 jt({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 Ks){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=We(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 G(`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=Uv((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),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?c:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=Mt(e.shape);return t=ke(t,[1,2]),t=Yc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?n1(t,[1,n]):t):this.cell.stateSize>1?[n1(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()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Zs(s,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ue.registerClass(mr);var ld=class extends Qe{},Rf=class extends ld{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,cn(this.units,"units"),this.activation=wa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=uu([1,xa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=uu([1,xa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 H(()=>{if(e=e,e.length!==2)throw new G(`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=ka({ones:()=>os(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>os(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=pr(z(e,a),this.kernel.read()):r=pr(e,this.kernel.read()),this.bias!=null&&(r=Xs(r,this.bias.read())),o!=null&&(n=z(n,o));let i=ie(r,pr(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:va(this.activation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Rf.className="SimpleRNNCell";ue.registerClass(Rf);var H1=class extends mr{constructor(e){e.cell=new Rf(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};H1.className="SimpleRNN";ue.registerClass(H1);var Df=class extends ld{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 G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cn(this.units,"units"),this.activation=wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=uu([1,xa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=uu([1,xa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 H(()=>{if(e=e,e.length!==2)throw new G(`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=ka({ones:()=>os(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>os(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=pr(e,this.kernel.read());this.useBias&&(u=Xs(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,a[0]));let c=this.recurrentKernel.read(),[d,p]=Ht(c,[2*this.units,this.units],c.rank-1),h=pr(s,d),[f,m,g]=Ht(u,3,u.rank-1),[A,y]=Ht(h,2,h.rank-1);o=this.recurrentActivation.apply(ie(f,A)),i=this.recurrentActivation.apply(ie(m,y));let x=pr(z(i,s),p);l=this.activation.apply(ie(g,x));let b=ie(z(o,s),z(ie(1,Ct(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:va(this.activation),recurrentActivation:va(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Df.className="GRUCell";ue.registerClass(Df);var G1=class extends mr{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 Df(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};G1.className="GRU";ue.registerClass(G1);var ud=class extends ld{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,cn(this.units,"units"),this.activation=wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=uu([1,xa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=uu([1,xa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=dt(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 Os{apply(i,l){let u=r.apply([a]),c=new uf().apply([a]),d=r.apply([a*2]);return z3(z3(u,c),d)}},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 H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`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=ka({ones:()=>os(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>os(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=z(e,a[0]));let d=pr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,o[0])),d=ie(d,pr(s,this.recurrentKernel.read())),this.useBias&&(d=Xs(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),u=ie(z(l,r),z(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=z(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:va(this.activation),recurrentActivation:va(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};ud.className="LSTMCell";ue.registerClass(ud);var j1=class extends mr{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 ud(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};j1.className="LSTM";ue.registerClass(j1);var _f=class extends ld{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 H(()=>{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){d1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{oi(`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 Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Zs(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 p1(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]])}h1(t)}};_f.className="StackedRNNCells";ue.registerClass(_f);function ka(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>B3(t(),n),o=()=>Qc(a,t,s);return!r||r<=1?un(o().clone()):Array(r).fill(void 0).map(o).map(l=>un(l.clone()))}var MM=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},Hv=class extends mr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new jt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("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 H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Mt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Or("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 G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Mt(r)):this.states_=[Mt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Mt(r)):this.states_[0]=Mt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`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()):Z(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!w.arraysEqual(i.shape,l))throw new G(`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=>un(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],u=e[i?4:3],c=Ys(l,s[0],r,a[0],o[0]),d=Ys(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};Hv.className="ConvRNN2D";var Ff=class extends ud{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,cn(this.filters,"filters"),this.kernelSize=fu(n,2,"kernelSize"),this.kernelSize.forEach(i=>cn(i,"kernelSize")),this.strides=fu(s||1,2,"strides"),this.strides.forEach(i=>cn(i,"strides")),this.padding=r||"valid",vs(this.padding),this.dataFormat=a||"channelsLast",Bt(this.dataFormat),this.dilationRate=fu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>cn(i,"dilationRate"))}build(e){var t;e=dt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`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,u=this.filters;i=new(t=class extends Os{apply(d,p){let h=l.apply([u]),f=as([u]),m=l.apply([u*2]);return t1([h,f,m])}},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 H(()=>{if(e.length!==3)throw new G(`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=ka({ones:()=>os(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(te,ne,se)=>!ne||!ne[se]?te:z(ne[se],te),u=l(s,i,0),c=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=ka({ones:()=>os(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,b,v,k]=Ht(this.kernel.read(),o,y),[S,C,D,O]=this.useBias?Ht(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,D,this.padding),p=this.inputConv(p,k,O,this.padding);let[E,R,T,P]=Ht(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,E),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let U=this.recurrentActivation.apply(ie(u,f)),j=this.recurrentActivation.apply(ie(c,m)),q=ie(z(j,a),z(U,this.activation.apply(ie(d,g)))),X=z(this.recurrentActivation.apply(ie(p,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=MM(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Rr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Xs(r,n,this.dataFormat):r}recurrentConv(e,t){return Rr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ff.className="ConvLSTM2DCell";ue.registerClass(Ff);var q1=class extends Hv{constructor(e){let t=new Ff(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};q1.className="ConvLSTM2D";ue.registerClass(q1);var $f=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Qc(()=>B3(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$f.className="Dropout";ue.registerClass($f);var X1=class extends $f{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X1.className="SpatialDropout1D";ue.registerClass(X1);var K1=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cn(this.units,"units"),this.activation=wa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=tn(e.kernelConstraint),this.biasConstraint=tn(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(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=dt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e),s=E3(this.activation.getClassName()),r;return s!=null?r=pr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=pr(n,this.kernel.read()),this.bias!=null&&(r=Xs(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:va(this.activation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),biasConstraint:en(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};K1.className="Dense";ue.registerClass(K1);var Z1=class extends Qe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(e);for(let t of e.slice(1))if(t==null)throw new G(`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],ya(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(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=Ze(n,s)}return nP(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Z1.className="Flatten";ue.registerClass(Z1);var Y1=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=wa(e.activation)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return this.activation.apply(n)})}getConfig(){let e={activation:va(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Y1.className="Activation";ue.registerClass(Y1);var J1=class extends Qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return H(()=>(e=We(e),eP(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};J1.className="RepeatVector";ue.registerClass(J1);var Q1=class extends Qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",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 G("Can only specifiy one unknown dimension.");else r*=l}let o=ya(e);if(a!==null){if(r===0||o%r!=0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(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 H(()=>{this.invokeCallHook(e,t);let n=We(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return V(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Q1.className="Reshape";ue.registerClass(Q1);var ey=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=qs(1,e.dims.length+1);if(!w.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 jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ze(We(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ey.className="Permute";ue.registerClass(ey);var ty=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=We(e),s=-1;return Rc(Qo(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e),s=-1,r=!0,a=Rc(Qo(n,this.maskValue),s,r);return z(n,pe(a,n.dtype))})}};ty.className="Masking";ue.registerClass(ty);var ny=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(vt(e.inputLength))}this.inputDim=e.inputDim,cn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cn(this.outputDim,"outputDim"),this.embeddingsInitializer=Tt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Nt(e.embeddingsRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.embeddingsConstraint=tn(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 H(()=>this.maskZero?(e=We(e),Qo(e,Ye(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new G(`"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 G(`"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 H(()=>{this.invokeCallHook(e,t);let n=We(e);n.dtype!=="int32"&&(n=af(n,"int32"));let s=L3(this.embeddings.read(),V(n,[n.size]));return V(s,dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ft(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:en(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};ny.className="Embedding";ue.registerClass(ny);var di=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let 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 G("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=[dt(e)]),e=e,e.length<2)throw new G(`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=Aa(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Aa(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return H(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=xa(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Yc(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],d=u.slice(1).concat([c]),p=V(i,[c].concat(ya(u.slice(1))));p=Ze(p,[1,0]),p=V(p,d),n.push(p),r=!0}else if(l>1){let u=qs(1,l).concat([0]);n.push(Ze(i,u)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=V(Ze(V(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(qs(0,o-1));a=Ze(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Aa(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return H(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Lt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Fs(n,t[s]);return n})}},sy=class extends di{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};sy.className="Add";ue.registerClass(sy);var ry=class extends di{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=z(t,e[n]);return t})}};ry.className="Multiply";ue.registerClass(ry);var ay=class extends di{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return z(1/e.length,t)})}};ay.className="Average";ue.registerClass(ay);var oy=class extends di{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ur(t,e[n]);return t})}};oy.className="Maximum";ue.registerClass(oy);var iy=class extends di{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=nu(t,e[n]);return t})}};iy.className="Minimum";ue.registerClass(iy);var ly=class extends di{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 G("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(w.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return H(()=>t1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new G("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 G("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new G("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new G(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return H(()=>{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(pe(os(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Lt(t[a],-1)):s.push(t[a]);let r=gt(s,this.axis);return bh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Concatenate";ue.registerClass(ly);function cd(e,t){for(;e<0;)e+=t;return e}function zM(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);t=V(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);e=V(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(z(e,t),a[0]):i=ke(z(Ze(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=Ue(e,t,l,u)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=st(i,u)}return i.shape.length===1&&(i=Lt(i,1)),i})}var uy=class extends di{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`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)=>cd(r,e[a].shape.length)):s=[cd(this.axes,t.shape.length),cd(this.axes,n.shape.length)],this.normalize&&(t=vf(t,s[0]),n=vf(n,s[1])),zM(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[cd(this.axes,e.length),cd(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let 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}};uy.className="Dot";ue.registerClass(uy);var cy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return Qc(()=>ie(lf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};cy.className="GaussianNoise";ue.registerClass(cy);var dy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return this.rate>0&&this.rate<1?Qc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return z(n,lf(n.shape,1,r))},()=>n,t.training||!1):n})}};dy.className="GaussianDropout";ue.registerClass(dy);var py=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||We(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Qc(()=>{let r=We(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=fa(su(n),this.rate);l=af(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ie(z(r,l),z(ie(l,-1),i));return ie(z(d,u),c)},()=>We(e),t.training||!1)}return e})}};py.className="AlphaDropout";ue.registerClass(py);function dd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=vb(e,t,n,s,r,a);else if(e.rank===3)o=wb(e,t,n,s,r,a);else if(e.rank===4)o=kb(e,t,n,s,r,a);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function LM(e,t,n,s,r=.001){return H(()=>{let a=Dh(e,s),o=a.mean,i=a.variance;return[dd(e,o,i,n,t,r),o,i]})}function BM(e,t,n,s,r=.001){return H(()=>{let a=Dh(e,s),o=a.mean,i=a.variance,l=[];for(let f of qs(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=V(o,l),c=V(i,l),d=t==null?null:V(t,l),p=n==null?null:V(n,l);return[dd(e,u,c,p,d,r),o,i]})}function WM(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),qs(0,e.rank-1))?LM(e,t,n,s,r):BM(e,t,n,s,r)}var hy=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Tt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Tt(e.movingVarianceInitializer||"ones"),this.betaConstraint=tn(e.betaConstraint),this.gammaConstraint=tn(e.gammaConstraint),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer)}build(e){e=dt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new jt({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 H(()=>{let n=t.training==null?!1:t.training,s=We(e),r=s.shape,a=r.length,o=qs(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=si(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,qs(0,a).slice(0,a-1)),d=()=>{if(c){let A=V(this.movingMean.read(),l),y=V(this.movingVariance.read(),l),x=this.center?V(this.beta.read(),l):null,b=this.scale?V(this.gamma.read(),l):null;return dd(s,A,y,x,b,this.epsilon)}else return dd(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]=WM(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=z(ye(v,y),b);A.write(ye(v,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:Ft(this.betaInitializer),gammaInitializer:Ft(this.gammaInitializer),movingMeanInitializer:Ft(this.movingMeanInitializer),movingVarianceInitializer:Ft(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:en(this.betaConstraint),gammaConstraint:en(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hy.className="BatchNormalization";ue.registerClass(hy);var fy=class extends Qe{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=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(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!==Aa(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=We(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=Dh(n,this.axis,a),l=si(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?V(f,l):f,c=u(this.gamma.read()),d=u(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=bs(o,p),i=bs(i,p),c=bs(c,h),d=bs(d,h),dd(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ft(this.betaInitializer),gammaInitializer:Ft(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};fy.className="LayerNormalization";ue.registerClass(fy);function VM(e,t,n){return H(()=>{if(e.rank!==4)throw new G(`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 G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Gs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Dr(e,s)})}var my=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Gs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=dt(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 H(()=>VM(We(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};my.className="ZeroPadding2D";ue.registerClass(my);function Of(e,t,n,s,r,a){return H(()=>{Bt(r),F3(a),vs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=Gs()),a==null&&(a="max"),e=P1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=zc(e,t,n,i):o=_c(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}function Gv(e,t,n,s,r,a){return H(()=>{Bt(r),F3(a),vs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Gs()),a==null&&(a="max"),e=Lv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=SA(e,t,n,i):o=uA(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var jv=class extends Qe{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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cn(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 G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vs(this.padding),this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){e=dt(e);let t=Ys(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=Yc(We(e),2);let n=this.poolingFunction(We(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return st(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},gy=class extends jv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Of(e,t,n,s,r,"max")}};gy.className="MaxPooling1D";ue.registerClass(gy);var Ay=class extends jv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Of(e,t,n,s,r,"avg")}};Ay.className="AveragePooling1D";ue.registerClass(Ay);var qv=class extends Qe{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 G(`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];cn(this.poolSize,"poolSize"),cn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vs(this.padding),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ys(t,this.poolSize[0],this.padding,this.strides[0]),n=Ys(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},yy=class extends qv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Of(e,t,n,s,r,"max")}};yy.className="MaxPooling2D";ue.registerClass(yy);var xy=class extends qv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Of(e,t,n,s,r,"avg")}};xy.className="AveragePooling2D";ue.registerClass(xy);var Xv=class extends Qe{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 G(`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];cn(this.poolSize,"poolSize"),cn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vs(this.padding),this.inputSpec=[new jt({ndim:5})]}computeOutputShape(e){e=dt(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=Ys(t,this.poolSize[0],this.padding,this.strides[0]),n=Ys(n,this.poolSize[1],this.padding,this.strides[1]),s=Ys(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},by=class extends Xv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Gv(e,t,n,s,r,"max")}};by.className="MaxPooling3D";ue.registerClass(by);var vy=class extends Xv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Gv(e,t,n,s,r,"avg")}};vy.className="AveragePooling3D";ue.registerClass(vy);var Kv=class extends Qe{constructor(e){super(e);this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},wy=class extends Kv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=We(e);return _t(n,1)})}};wy.className="GlobalAveragePooling1D";ue.registerClass(wy);var ky=class extends Kv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=We(e);return rs(n,1)})}};ky.className="GlobalMaxPooling1D";ue.registerClass(ky);var Zv=class extends Qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Iy=class extends Zv{call(e,t){return H(()=>{let n=We(e);return this.dataFormat==="channelsLast"?_t(n,[1,2]):_t(n,[2,3])})}};Iy.className="GlobalAveragePooling2D";ue.registerClass(Iy);var Sy=class extends Zv{call(e,t){return H(()=>{let n=We(e);return this.dataFormat==="channelsLast"?rs(n,[1,2]):rs(n,[2,3])})}};Sy.className="GlobalMaxPooling2D";ue.registerClass(Sy);var Yv=class extends Qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=Zs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Cy=class extends Yv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(e),e.length<3)throw new G(`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=dt(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 H(()=>(e=We(e),Uv((a,o)=>[We(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Cy.className="TimeDistributed";ue.registerClass(Cy);function UM(e){ai(KO,"BidirectionalMergeMode",e)}var HM="concat",Ty=class extends Yv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Zs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Zs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?HM:e.mergeMode,UM(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,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()):Gn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Vv(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 G("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 u=n.map(c=>new jt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(s!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Ks;for(let l of a)if(l instanceof Ks!==i)throw new G("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),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{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=is(r,1));let o;return this.mergeMode==="concat"?o=t1([s,r]):this.mergeMode==="sum"?o=ie(s,r):this.mergeMode==="ave"?o=z(.5,ie(s,r)):this.mergeMode==="mul"?o=z(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){oi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),oi(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=Zs(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};Ty.className="Bidirectional";ue.registerClass(Ty);function GM(e){return new cu(e)}function jM(e){return new F1(e)}function qM(e){return new R1(e)}function XM(e){return new D1(e)}function KM(e){return new _1(e)}function ZM(e){return new O1(e)}function YM(e){return new $1(e)}function JM(e){return new Ef(e)}function QM(e){return new od(e)}function ez(e){return new z1(e)}function tz(e){return new id(e)}function nz(e){return new L1(e)}function sz(e){return new B1(e)}function rz(e){return new W1(e)}function az(e){return new V1(e)}function oz(e){return new U1(e)}function iz(e){return new Y1(e)}function lz(e){return new K1(e)}function uz(e){return new $f(e)}function cz(e){return new X1(e)}function dz(e){return new Z1(e)}function pz(e){return new J1(e)}function hz(e){return new Q1(e)}function fz(e){return new ey(e)}function mz(e){return new ny(e)}function gz(e){return new sy(e)}function Az(e){return new ay(e)}function yz(e){return new ly(e)}function xz(e){return new oy(e)}function bz(e){return new iy(e)}function vz(e){return new ry(e)}function wz(e){return new uy(e)}function kz(e){return new hy(e)}function Iz(e){return new fy(e)}function Sz(e){return new my(e)}function Ny(e){return new Ay(e)}function Cz(e){return Ny(e)}function Tz(e){return Ny(e)}function Ey(e){return new xy(e)}function Nz(e){return Ey(e)}function Ez(e){return Ey(e)}function Ry(e){return new vy(e)}function Rz(e){return Ry(e)}function Dz(e){return Ry(e)}function _z(e){return new wy(e)}function Fz(e){return new Iy(e)}function Jv(e){return new ky(e)}function Qv(e){return new Sy(e)}function ew(e){return new gy(e)}function tw(e){return new yy(e)}function $z(e){return new by(e)}function Oz(e){return new G1(e)}function Pz(e){return new Df(e)}function Mz(e){return new j1(e)}function zz(e){return new ud(e)}function Lz(e){return new H1(e)}function Bz(e){return new Rf(e)}function Wz(e){return new q1(e)}function Vz(e){return new Ff(e)}function Uz(e){return new mr(e)}function Hz(e){return new _f(e)}function Gz(e){return new Ty(e)}function jz(e){return new Cy(e)}var qz=Jv,Xz=Qv,Kz=ew,Zz=tw;function Yz(e){return new cy(e)}function Jz(e){return new dy(e)}function Qz(e){return new py(e)}function eL(e){return new ty(e)}var nw={};Le(nw,{MAPE:()=>dL,MSE:()=>fL,binaryAccuracy:()=>tL,binaryCrossentropy:()=>nL,categoricalAccuracy:()=>rL,categoricalCrossentropy:()=>aL,cosineProximity:()=>lL,mape:()=>pL,meanAbsoluteError:()=>uL,meanAbsolutePercentageError:()=>cL,meanSquaredError:()=>hL,mse:()=>mL,precision:()=>oL,recall:()=>iL,sparseCategoricalAccuracy:()=>sL});function tL(e,t){return g1(e,t)}function nL(e,t){return rv(e,t)}function sL(e,t){return av(e,t)}function rL(e,t){return A1(e,t)}function aL(e,t){return y1(e,t)}function oL(e,t){return sv(e,t)}function iL(e,t){return HP(e,t)}function lL(e,t){return f1(e,t)}function uL(e,t){return wf(e,t)}function cL(e,t){return pu(e,t)}function dL(e,t){return pu(e,t)}function pL(e,t){return pu(e,t)}function hL(e,t){return li(e,t)}function fL(e,t){return li(e,t)}function mL(e,t){return li(e,t)}var sw={};Le(sw,{modelFromJSON:()=>IM});var rw={};Le(rw,{l1:()=>AL,l1l2:()=>gL,l2:()=>yL});function gL(e){return new rd(e)}function AL(e){return _M(e)}function yL(e){return FM(e)}var aw=class extends du{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof Mr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function Pf(e,t){return e<t}function ow(e,t){return e>t}var iw=class extends aw{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new ze("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=Pf:this.mode==="max"?this.monitorFunc=ow:this.monitor.indexOf("acc")!==-1?this.monitorFunc=ow:this.monitorFunc=Pf,this.monitorFunc===Pf&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Pf?1/0:-1/0}async onEpochEnd(e,t){await ba(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 xL(e){return new iw(e)}var bL={earlyStopping:xL},Js;(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"})(Js||(Js={}));var lw;(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={}))})(lw||(lw={}));var Dy={};function vL(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Dy[e]=n}function uw(e){return Dy[e]}function wL(e){delete Dy[e]}function I(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 Rn(t.inputNames[a.inputIndexStart],n,s,r);if(a.type==="tensors")return t.inputNames.slice(i,l).map(p=>Rn(p,n,s,r));let u=Rn(t.inputNames.slice(i)[0],n,s,r),c=u.dataSync();return a.type==="number"?c[0]:w.toNestedArray(u.shape,c)}let o=t.attrParams[e];return o&&o.value}function Rn(e,t,n,s){let[r,a]=ls(e);if(s!=null){let i=s.getHashTableHandleByName(r);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[Mf(r,i)]);return o!==void 0?t[Mf(r,o)][a]:void 0}function kL(e,t,n){return t[Mf(e,n.currentContextId)]}function zr(e,t){let[n,s,r]=ls(e);return[Mf(n,t&&t.currentContextId),s,r]}function Mf(e,t){return t?`${e}-${t}`:e}function ls(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 zf(e,t,n){let s=I("pad",e,t,n);if(s==="explicit"){s=I("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 Lr(e){return e.kept?e:Ws(e)}var cw={};Le(cw,{json:()=>IL});var IL=[{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}]}],dw={};Le(dw,{json:()=>SL});var SL=[{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}]}],pw={};Le(pw,{json:()=>CL});var CL=[{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"}]}],hw={};Le(hw,{json:()=>TL});var TL=[{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"}]}],fw={};Le(fw,{json:()=>NL});var NL=[{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"}]}],mw={};Le(mw,{json:()=>EL});var EL=[{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}]}],gw={};Le(gw,{json:()=>RL});var RL=[{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"}]}],Aw={};Le(Aw,{json:()=>DL});var DL=[{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"}]}],yw={};Le(yw,{json:()=>_L});var _L=[{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"}]}],xw={};Le(xw,{json:()=>FL});var FL=[{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"}]}],bw={};Le(bw,{json:()=>$L});var $L=[{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}]}],vw={};Le(vw,{json:()=>OL});var OL=[{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"}]}],ww={};Le(ww,{json:()=>PL});var PL=[{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}]}],kw={};Le(kw,{json:()=>ML});var ML=[{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"}]}],Iw={};Le(Iw,{json:()=>zL});var zL=[{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}]}],Sw={};Le(Sw,{json:()=>LL});var LL=[{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"}]}],Cw={};Le(Cw,{json:()=>BL});var BL=[{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}]}],Tw={};Le(Tw,{json:()=>WL});var WL=[{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"}]}],Nw={};Le(Nw,{json:()=>VL});var VL=[{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:[]}],Ew=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[cw,dw,pw,hw,fw,mw,gw,Aw,yw,xw,bw,vw,ww,kw,Iw,Sw,Cw,Tw,Nw],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=[],u={},c={};t!=null&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));let d=Object.keys(o);d.forEach(f=>{let m=o[f];m.inputNames.forEach((g,A)=>{let[y,,x]=zr(g),b=o[y];if(b.outputs!=null){let v=b.outputs.indexOf(x);if(v!==-1){let k=`${y}:${v}`;m.inputNames[A]=k}}m.inputs.push(b),b.children.push(m)})}),Object.keys(c).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(c).forEach(f=>{let[m]=zr(f),g=o[m];g!=null&&(g.signatureKey=c[f],l.push(g))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=zr(f),g=o[m];g&&(g.signatureKey=u[f],i.push(g))}):i=s;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let h={nodes:o,inputs:i,outputs:l,weights:r,placeholders:s,signature:t,functions:p};return a.length>0&&(h.initNodes=a),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=uw(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,r)=>(s[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,r)=>{let a=r.type,o;switch(r.type){case"string":o=_y(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=_y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":o=By(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=By(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":o=$y(e.attr,r.tfName,r.defaultValue||0),o===void 0&&!!r.tfDeprecatedName&&(o=$y(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":o=Ly(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Ly(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":o=Fy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Fy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":o=Vy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Vy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":o=zy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=zy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":o=Wy(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Wy(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":o=Py(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Py(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":o=My(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=My(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":o=Dw(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Dw(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return s[r.name]={value:o,type:a},s},{})),n}mapFunction(e){let t=e.nodeDef,n=[],s=[],r={};t!=null&&(r=t.reduce((c,d)=>(c[d.name]=this.mapNode(d),d.op==="Const"&&s.push(c[d.name]),c),{}));let a=[],o=[];e.signature.inputArg.forEach(c=>{let[d]=zr(c.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Oy(c.type),type:"dtype"}},children:[]};p.signatureKey=c.name,a.push(p),r[d]=p}),Object.keys(r).forEach(c=>{let d=r[c];d.inputNames.forEach((p,h)=>{let[f,,m]=zr(p),g=r[f];if(g.outputs!=null){let A=g.outputs.indexOf(m);if(A!==-1){let y=`${f}:${A}`;d.inputNames[h]=y}}d.inputs.push(g),g.children.push(d)})});let l=e.ret;e.signature.outputArg.forEach(c=>{let[d,p]=zr(l[c.name]),h=r[d];h!=null&&(h.defaultOutput=p,o.push(h))});let u=this.mapArgsToSignature(e);return{nodes:r,inputs:a,outputs:o,weights:s,placeholders:n,signature:u}}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 UL(e){let t=Y().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function Rw(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):UL(e);return t?n:n.toLowerCase()}function _y(e,t,n,s=!1){let r=e[t];return r!=null?Rw(r.s,s):n}function Fy(e,t,n){let s=e[t];return s?s.b:n}function $y(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 Oy(e){switch(typeof e=="string"&&(e=Js[e]),e){case Js.DT_FLOAT:return"float32";case Js.DT_INT32:case Js.DT_INT64:case Js.DT_INT8:case Js.DT_UINT8:return"int32";case Js.DT_BOOL:return"bool";case Js.DT_DOUBLE:return"float32";case Js.DT_STRING:return"string";default:return null}}function Dw(e,t,n){let s=e[t];return s&&s.func?s.func.name:n}function Py(e,t,n){let s=e[t];return s&&s.type?Oy(s.type):n}function My(e,t,n){let s=e[t];return s&&s.list&&s.list.type?s.list.type.map(r=>Oy(r)):n}function _w(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function zy(e,t,n){let s=e[t];return s&&s.shape?_w(s.shape):n}function Ly(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 By(e,t,n,s=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(a=>Rw(a,s)):n}function Wy(e,t,n){let s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(r=>_w(r)):n}function Vy(e,t,n){let s=e[t];return s&&s.list&&s.list.b?s.list.b:n}var HL=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 Rn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Rn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return $y(this.node.rawAttrs,e,t);if(n.s!=null)return _y(this.node.rawAttrs,e,t);if(n.b!=null)return Fy(this.node.rawAttrs,e,t);if(n.shape!=null)return zy(this.node.rawAttrs,e,t);if(n.type!=null)return Py(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return Ly(this.node.rawAttrs,e,t);if(n.list.s!=null)return By(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Wy(this.node.rawAttrs,e,t);if(n.list.b!=null)return Vy(this.node.rawAttrs,e,t);if(n.list.type!=null)return My(this.node.rawAttrs,e,t)}return t}},GL=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ie(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[xh(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[TA(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[z(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[he(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[gA(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[yh(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ye(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[nu(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[ur(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[_r(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Vh(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jL=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Ut(I("x",e,t,n))];case"Acos":return[Qg(I("x",e,t,n))];case"Acosh":return[eA(I("x",e,t,n))];case"Asin":return[nA(I("x",e,t,n))];case"Asinh":return[sA(I("x",e,t,n))];case"Atan":return[rA(I("x",e,t,n))];case"Atan2":return[aA(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[oA(I("x",e,t,n))];case"Ceil":return[dA(I("x",e,t,n))];case"Complex":return[ua(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[$c(I("x",e,t,n))];case"Cosh":return[Ih(I("x",e,t,n))];case"Elu":return[Ql(I("x",e,t,n))];case"Erf":return[AA(I("x",e,t,n))];case"Exp":return[ns(I("x",e,t,n))];case"Expm1":return[yA(I("x",e,t,n))];case"Floor":return[tu(I("x",e,t,n))];case"Log":return[ss(I("x",e,t,n))];case"Log1p":return[Pc(I("x",e,t,n))];case"Imag":return[Ch(I("x",e,t,n))];case"Neg":return[Ct(I("x",e,t,n))];case"Reciprocal":return[RA(I("x",e,t,n))];case"Real":return[Vc(I("x",e,t,n))];case"Relu":return[Us(I("x",e,t,n))];case"Round":return[$h(I("x",e,t,n))];case"Selu":return[Ph(I("x",e,t,n))];case"Sigmoid":return[Vn(I("x",e,t,n))];case"Sin":return[Mh(I("x",e,t,n))];case"Sign":return[_A(I("x",e,t,n))];case"Sinh":return[zh(I("x",e,t,n))];case"Softplus":return[Yo(I("x",e,t,n))];case"Sqrt":return[gn(I("x",e,t,n))];case"Square":return[ft(I("x",e,t,n))];case"Tanh":return[Xo(I("x",e,t,n))];case"Tan":return[OA(I("x",e,t,n))];case"ClipByValue":return[Un(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Fh(I("x",e,t,n))];case"Rsqrt":return[Oh(Rn(e.inputNames[0],t,n))];case"Prod":return[_h(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[Oc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Wc(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[bA(Rn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ms(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){w.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];w.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function Fw(e){return!(typeof e=="number"||e.some(t=>t<0))}function pd(e,t,n){let s=Uy(e,n),r=!Fw(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=Uy(a.shape,s)}),!Fw(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Uy(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 qL=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=Te(0),un(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),Ms(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,un(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 ln([],[0].concat(this.elementShape));let n=this.readMany(e);return Ms(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),An(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return ln([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return Ms(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),gt(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,Nn(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=[];H(()=>{t=V(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],u=[0,l,0],c=[1,e[i],r];a[i]=V(_e(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},hd=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}`);Ms(t,r.shape,"TensorList shape mismatch: "),un(r)}),this.idTensor=Te(0),this.maxNumElements=s,un(this.idTensor)}get id(){return this.idTensor.id}copy(){return new hd([...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.`);Ms(e,this.elementShape,"TensorList shape mismatch: ");let s=pd(this.elementShape,this.tensors,e);return H(()=>{let r=this.tensors.map(a=>V(a,s));return An(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=pd(this.elementShape,this.tensors,e),s=this.tensors.pop();return Ms(s.shape,e,"TensorList shape mismatch: "),V(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(Ms(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");un(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.`);Ms(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=pd(this.elementShape,this.tensors,t);return V(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.`);Ms(this.elementShape,t.shape,"TensorList shape mismatch: "),un(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}`);Ms(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=pd(this.elementShape,this.tensors,n);return e.length===0?ln([],[0].concat(s)):H(()=>{let r=e.map(a=>V(this.tensors[a],s));return An(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Ms(this.elementShape,t,"TensorList shape mismatch: ");let n=pd(this.elementShape,this.tensors,t);return this.size()===0?ln([],[0].concat(n)):H(()=>{let s=this.tensors.map(r=>V(r,n));return gt(s,0)})}};function XL(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);Ms(r,t,"TensorList shape mismatch: ");let a=Nn(e);return new hd(a,t,s)}function KL(e,t,n){return new hd([],e,t,n)}function ZL(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 hd([],n,e.dtype,s),o=Nn(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function YL(e,t,n){let s=0,r=t.map(c=>(s+=c,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=Uy(a,n),i=s===0?0:e.size/s,l=H(()=>{let c=[];e=V(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];c[d]=V(_e(e,h,f),o)}return e.dispose(),c}),u=new hd([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var JL=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),o=I("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=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await n.functionMap[s].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);c.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let p=await n.functionMap[r].executeFunctionAsync(u,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 u}case"LoopCond":{let s=I("pred",e,t,n);return[Lr(s)]}case"Switch":{let s=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Lr(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>Rn(r,t,n)!==void 0);if(s){let r=Rn(s,t,n);return[Lr(r)]}return}case"Enter":{let s=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(s),[Lr(r)]}case"Exit":{let s=I("tensor",e,t,n);return n.exitFrame(),[Lr(s)]}case"NextIteration":{let s=I("tensor",e,t,n);return n.nextIteration(),[Lr(s)]}case"TensorArrayV3":{let s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),o=I("dynamicSize",e,t,n),i=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),c=new qL(u,r,s,a,l,o,i);return n.addTensorArray(c),[c.idTensor,Te(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.write(r,a),[o.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.scatter(r,a),[o.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),a=I("lengths",e,t,n),o=n.getTensorArray(s.id);return o.split(a,r),[o.idTensor]}case"TensorArraySizeV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Te(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorList(s.id);return o.setItem(r,a),[o.idTensor]}case"TensorListGetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let s=I("indices",e,t,n),r=I("tensor",e,t,n),a=I("elementShape",e,t,n),o=I("numElements",e,t,n),i=ZL(r,s,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=KL(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=XL(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("lengths",e,t,n),o=YL(s,a,r);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function $w(e,t,n){let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",o=!a,i=r==="prelu",l=s==="fusedbatchnorm",u=I("numArgs",e,t,n);if(a){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",e,t,n),d=zf(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),h=I("dilations",e,t,n),[f,m]=I("args",e,t,n);o&&(m=f,f=void 0);let g=I("leakyreluAlpha",e,t,n);return{stride:c,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var QL=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=I("stride",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilation",e,t,n);return[wh(I("x",e,t,n),I("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let s=I("strides",e,t,n),r=zf(e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilations",e,t,n);return[Rr(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=$w(e,t,n);return[ga.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=$w(e,t,n);return[ga.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let s=I("outputShape",e,t,n),r=I("strides",e,t,n),a=zf(e,t,n);return[kh(I("x",e,t,n),I("filter",e,t,n),s,[r[1],r[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let s=I("strides",e,t,n),r=zf(e,t,n),a=I("dilations",e,t,n),o=I("dataFormat",e,t,n).toUpperCase();return[Jl(I("input",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,o,[a[1],a[2]])]}case"Conv3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilations",e,t,n);return[hA(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2],s[3]],r,a,[o[1],o[2],o[3]])]}case"AvgPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[_c(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[zc(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPoolWithArgmax":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n),o=I("includeBatchInIndex",e,t,n),{result:i,indexes:l}=Vb(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,o);return[i,l]}case"AvgPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[uA(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[SA(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dilations",e,t,n),o=s[1],i=s[2],l=a[1],u=a[2];return[mA(I("x",e,t,n),I("filter",e,t,n),[o,i],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},eB=(e,t,n)=>{switch(e.op){case"Fill":{let s=I("shape",e,t,n),r=I("dtype",e,t,n),a=I("value",e,t,n);return[eu(s,a,r)]}case"LinSpace":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("num",e,t,n);return[Ob(s,r,a)]}case"Multinomial":{let s=I("logits",e,t,n),r=I("numSamples",e,t,n),a=I("seed",e,t,n);return[Ub(s,r,a)]}case"OneHot":{let s=I("indices",e,t,n),r=I("depth",e,t,n),a=I("onValue",e,t,n),o=I("offValue",e,t,n);return[jl(s,r,a,o)]}case"Ones":return[as(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[os(I("x",e,t,n))];case"RandomUniform":return[su(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("step",e,t,n);return[ru(s,r,a,I("dtype",e,t,n))]}case"TruncatedNormal":{let s=I("shape",e,t,n),r=I("mean",e,t,n),a=I("stdDev",e,t,n),o=I("seed",e,t,n);return[Uh(s,r,a,I("dtype",e,t,n),o)]}case"Zeros":return[Mt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ye(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Hy(e,t,n){let s=I("boxes",e,t,n),r=I("scores",e,t,n),a=I("maxOutputSize",e,t,n),o=I("iouThreshold",e,t,n),i=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}}var tB=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=Hy(e,t,n),u=await De.nonMaxSuppressionWithScoreAsync(s,r,a,o,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=Hy(e,t,n),l=I("padToMaxOutputSize",e,t,n),u=await De.nonMaxSuppressionPaddedAsync(s,r,a,o,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=Hy(e,t,n);return[await De.nonMaxSuppressionAsync(s,r,a,o,i)]}case"Where":{let s=pe(I("condition",e,t,n),"bool"),r=[await zA(s)];return s.dispose(),r}case"ListDiff":return jb(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},nB=(e,t,n)=>{switch(e.op){case"TopKV2":{let s=I("x",e,t,n),r=I("k",e,t,n),a=I("sorted",e,t,n),o=PA(s,r,a);return[o.values,o.indices]}case"Unique":{let s=I("x",e,t,n),r=Hh(s);return[r.values,r.indices]}case"UniqueV2":{let s=I("x",e,t,n),r=I("axis",e,t,n),a=Hh(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sB=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let s=I("default",e,t,n);return[Rn(e.name,t,n)||s];case"Placeholder":return[Rn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=I("x",e,t,n);return[Lr(u)]}case"IdentityN":return I("x",e,t,n).map(u=>Lr(u));case"Snapshot":let r=I("x",e,t,n);return[Lr(r)];case"Shape":return[Gt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(u=>Gt(u.shape));case"Size":return[Te(I("x",e,t,n).size,"int32")];case"Rank":return[Te(I("x",e,t,n).rank,"int32")];case"NoOp":return[Te(1)];case"Print":let a=I("x",e,t,n),o=I("data",e,t,n),i=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<o.length;u++)console.log(Array.prototype.slice.call(o[u].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rB=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Te(0),this.tensorMap=new Map,un(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 Te(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(),H(()=>{let s=Nn(t),r=n.length,a=s.length;w.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];un(l),this.tensorMap.set(i,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return H(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],o=this.findWithDefault(a,t);s.push(o)}return An(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}`)}},aB=async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),a=I("valueDType",e,t,n),o=new rB(r,a);return s.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=I("tableHandle",e,t,n,s),a=I("keys",e,t,n),o=I("values",e,t,n);return[await s.getHashTableById(r.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let r=I("tableHandle",e,t,n,s),a=I("keys",e,t,n),o=I("defaultValue",e,t,n);return[await s.getHashTableById(r.id).find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=I("tableHandle",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},oB=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let s=I("images",e,t,n),r=I("size",e,t,n),a=I("alignCorners",e,t,n),o=I("halfPixelCenters",e,t,n);return[De.resizeBilinear(s,[r[0],r[1]],a,o)]}case"ResizeNearestNeighbor":{let s=I("images",e,t,n),r=I("size",e,t,n),a=I("alignCorners",e,t,n),o=I("halfPixelCenters",e,t,n);return[De.resizeNearestNeighbor(s,[r[0],r[1]],a,o)]}case"CropAndResize":{let s=I("image",e,t,n),r=I("boxes",e,t,n),a=I("boxInd",e,t,n),o=I("cropSize",e,t,n),i=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[De.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},iB=(e,t,n)=>{switch(e.op){case"Equal":return[ts(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[Qo(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Hn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[fa(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Th(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[ma(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Fs(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Mc(I("a",e,t,n))];case"LogicalOr":return[Rh(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[wn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lB=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ue(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[_b(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Ze(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",o=r==="prelu",i=I("numArgs",e,t,n),l=I("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[u,c]=I("args",e,t,n);return[ga.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:u,activation:r,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},uB=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ko(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[Ko(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[vA(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[ei(I("x",e,t,n))];case"LogSoftmax":return[Eh(I("x",e,t,n))];case"SparseToDense":return[LA(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},cB=(e,t,n)=>{switch(e.op){case"Max":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[rs(I("x",e,t,n),o,i)]}case"Mean":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[_t(I("x",e,t,n),o,i)]}case"Min":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Lc(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[ke(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[bh(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Rc(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[Vs(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[tA(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[_h(I("x",e,t,n),o,i)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Sh(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[cA(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[Rb(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dB=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[gt(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Zo(s,pe(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[Zo(a,pe(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=I("x",e,t,n);return[is(a,r)]}case"ReverseV2":{let s=I("axis",e,t,n),r=I("x",e,t,n);return[is(r,s)]}case"Slice":{let s=I("begin",e,t,n),r=I("size",e,t,n);return[_e(I("x",e,t,n),s,r)]}case"StridedSlice":{let s=I("begin",e,t,n),r=I("end",e,t,n),a=I("strides",e,t,n),o=I("beginMask",e,t,n),i=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),u=I("newAxisMask",e,t,n),c=I("shrinkAxisMask",e,t,n),d=I("x",e,t,n);return[$A(d,s,r,a,o,i,l,u,c)]}case"Pack":return H(()=>{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=st(r[0]).shape,i=r.map(l=>{let u=w.arraysEqual(l.shape,a);if(!u&&!w.arraysEqual(st(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:V(l,a)});return[An(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Nn(r,s)}case"Tile":{let s=I("reps",e,t,n);return[bs(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Ht(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[Zb(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Yb(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[LA(s,a,r,a.dtype===o.dtype?o:pe(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},pB=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=jc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=jc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[jc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[jc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hB=(e,t,n)=>{switch(e.op){case"FFT":return[Hc(I("x",e,t,n))];case"IFFT":return[au(I("x",e,t,n))];case"RFFT":return[Gc(I("x",e,t,n))];case"IRFFT":return[Wh(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fB=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Zh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Zh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Zh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mB=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Lt(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[st(I("x",e,t,n),s)]}case"Reshape":return[V(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[CA(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Dr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Bc(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Fc(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[fA(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Zl(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[Ib(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ow(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>GL(a,o,i));case"basic_math":return H(()=>jL(a,o,i));case"control":return JL(a,o,i);case"convolution":return H(()=>QL(a,o,i));case"creation":return H(()=>eB(a,o,i));case"dynamic":return tB(a,o,i);case"evaluation":return H(()=>nB(a,o,i));case"image":return H(()=>oB(a,o,i));case"graph":return H(()=>sB(a,o,i));case"logical":return H(()=>iB(a,o,i));case"matrices":return H(()=>lB(a,o,i));case"normalization":return H(()=>uB(a,o,i));case"reduction":return H(()=>cB(a,o,i));case"slice_join":return H(()=>dB(a,o,i));case"sparse":return H(()=>pB(a,o,i));case"spectral":return H(()=>hB(a,o,i));case"string":return H(()=>fB(a,o,i));case"transformation":return H(()=>mB(a,o,i));case"hash_table":return aB(a,o,i,s);case"custom":let l=uw(a.op);if(l&&l.customExecutor)return l.customExecutor(new HL(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 w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Pw=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 Mw(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(p=>ls(p)[0]),c=[];s!=null&&(c=s.map(p=>ls(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((zw(p)||bB(p)||vB(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&&u.indexOf(p.name)===-1&&c.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 gB(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>ls(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return u}var AB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],yB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],xB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function zw(e){return AB.indexOf(e.op)>=0}function bB(e){return yB.indexOf(e.op)>=0}function vB(e){return xB.indexOf(e.op)>=0}var Gy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Gy(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=Mw(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 gB(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(c=>this.graph.nodes[ls(c)[0]]),r=t.map(c=>ls(c)[0]),a=r.map(c=>this.graph.nodes[c]);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={},u={};return H(()=>{let c=new Pw(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ls(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=Ow(m,d,c,this._resourceManager);if(w.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,c,p,r,h)}}return this.parent==null&&c.dispose(p),t.map(f=>Rn(f,d,c))})}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=kL(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}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));let a=new Pw(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Rn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!c.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(c),i}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(y=>this.graph.nodes[ls(y)[0]]),o=n.map(y=>ls(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=Mw(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,b]=ls(y),v=[];v[b]=e[y],h[x]=v});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(y)}c==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(y=>!zw(y)&&!Rn(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`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: [${u}]. ${y}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([d]=zr(c.node.name,n)),s[c.node.name]==null){let p=Ow(c.node,s,n,this._resourceManager);d||([d]=zr(c.node.name,n));let h=n.currentContext;w.isPromise(p)?u.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=zr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Rn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Rn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=ls(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);w.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ls(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ls(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},wB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},kB="?tfjs-format=file",IB="model.json",Lw=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new wB}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Wn.browserHTTPRequest(e,this.loadOptions);else{let t=Wn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Wn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=Wn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Gy(Ew.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Ew.Instance.transformGraph(e.modelInitializer);this.initializer=new Gy(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Wn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ge)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ot(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${IB}${kB}`);let n=new Lw(e,t);return await n.load(),n}var SB="3.9.0",Bw={};Le(Bw,{CSVDataset:()=>Jw,Dataset:()=>gu,FileDataSource:()=>a7,TextLineDataset:()=>Kw,URLDataSource:()=>o7,array:()=>XB,csv:()=>aW,func:()=>oW,generator:()=>iW,microphone:()=>uW,version_data:()=>cW,webcam:()=>lW,zip:()=>KB});var CB=za(n5()),TB=za(n5());function NB(e,t){return Lf(e,t)}function Lf(e,t,n=new Map,s=new Set){if(e==null)return null;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(mu(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=Lf(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function EB(e,t=Vw){return Ww(e,t)}function Ww(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(mu(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=Ww(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 Vw(e){return e===null?null:mu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Uw(e,t){let n=new Map;Lf(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(w.isPromise(a)){let o=await a;n.set(r,o)}}return Lf(e,t,n)}function mu(e){let t=!1;if(Y().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=s5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ge)&&!(e instanceof Promise)&&!t)}function RB(e){return e==null||DB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ge||w.isTypedArray(e)}function DB(e){return e===null||typeof e!="object"&&typeof e!="function"}function _B(e){return NB(e,FB)}function FB(e){return e instanceof Ge?{value:e.clone(),recurse:!1}:mu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Hw=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}},jy=class extends Hw{constructor(){super(jy.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}};jy.INITIAL_CAPACITY=32;function Gw(e){return new PB(e)}function qy(e){return new MB(e)}function $B(e,t){return new qw(e,t)}function OB(e,t=Ia.FAIL){return new jB(e,t)}var dn=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 HB(this,e)}filter(e){return new VB(this,e)}map(e){return new UB(this,e)}mapAsync(e){return new jw(this,e)}serialMapAsync(e){return new jw(this,e).serial()}flatmap(e){return new GB(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 WB(this,e,t)}columnMajorBatch(e,t=!0,n=Vw){return this.rowMajorBatch(e,t).map(r=>EB(r,n))}concatenate(e,t){return new qw(Gw([this,e]),t)}take(e){return e<0||e==null?this:new BB(this,e)}skip(e){return e<0||e==null?this:new LB(this,e)}prefetch(e){return new Xw(this,e)}shuffle(e,t){return new qB(this,e,t)}serial(){return new zB(this)}},PB=class extends dn{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:_B(e),done:!1}}},MB=class extends dn{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: ${e.message}`,e}}},zB=class extends dn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},LB=class extends dn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Z(e.value)}return this.upstream.next()}},BB=class extends dn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},WB=class extends dn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},VB=class extends dn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Z(e.value)}}},UB=class extends dn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ls.getTensorsInContainer(e.value),n=this.transform(e.value),s=Ls.getTensorsInContainer(n);for(let r of t)Ls.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},HB=class extends dn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},jw=class extends dn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ls.getTensorsInContainer(e.value),n=await this.transform(e.value),s=Ls.getTensorsInContainer(n);for(let r of t)Ls.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Xy=class extends dn{constructor(){super();this.outputQueue=new jy,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},GB=class extends Xy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ls.getTensorsInContainer(e.value),n=this.transform(e.value),s=Ls.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ls.isTensorInList(r,s)||r.dispose();return!0}},qw=class extends dn{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}},Ia;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Ia||(Ia={}));var jB=class extends dn{constructor(e,t=Ia.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof dn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await Uw(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ia.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ia.SHORTEST:return{value:null,done:!0};case Ia.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Xw=class extends dn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Hw(t)}summary(){return`${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()}},qB=class extends Xw{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=TB.alea(n||w.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}}},gu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.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),us(async()=>(await n.iterator()).columnMajorBatch(e,t,ZB),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,us(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,us(async()=>(await t.iterator()).filter(s=>H(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return us(async()=>(await t.iterator()).map(n=>H(()=>e(n))),this.size)}mapAsync(e){let t=this;return us(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 us(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,us(async()=>{let s=qy(async()=>({value:await t.iterator(),done:!1}));return $B(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,us(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=CB.alea(t||w.now().toString());return us(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,us(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()}};gu.MAX_BUFFER_SIZE=1e4;function us(e,t=null){return new class extends gu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function XB(e){return us(async()=>Gw(e),e.length)}function KB(e){if(!mu(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 us(async()=>{let n=await Uw(e,s=>{if(s instanceof gu)return{value:s.iterator(),recurse:!1};if(mu(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return OB(n,Ia.SHORTEST)},t)}function ZB(e){if(e===null)return null;let t=e[0];return RB(t)?{value:YB(e),recurse:!1}:{value:null,recurse:!0}}function YB(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ge?An(e):ln(e)}var Kw=class extends gu{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))}},Bf='"',fd=Symbol("out"),Zw=Symbol("field"),Wf=Symbol("quote"),Ky=Symbol("quoteafterquote"),Yw=Symbol("quoteinquote"),Jw=class extends gu{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 Kw(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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&&w.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(w.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 u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}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=fd;for(let o=0;o<r;o++)switch(a){case fd:switch(e.charAt(o)){case Bf:s=o+1,a=Wf;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=fd;break;default:a=Zw,s=o;break}break;case Zw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=fd,s=o+1;break;default:}break;case Wf:switch(e.charAt(o)){case Bf:a=Ky;break;default:}break;case Ky:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=fd,s=o+1;break;case Bf:a=Wf;break;default:a=Yw;break}break;case Yw:switch(e.charAt(o)){case Bf:a=Wf;break;default:}break;default:}if(a===Ky?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}},Qw=class extends dn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Qw(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(w.sizeFromShape(t));return n.set(e,n.length-e.length),ln(n,t)}},e7=class extends dn{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=Gt([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=Hs([a,r,i,o],[1,4])}else this.cropBox=Hs([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new e7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=_s.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=Lt(pe(e,"float32"),0),n;n=De.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return V(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},t7=class{},n7=class extends dn{split(e){return new JB(this,e)}},JB=class extends n7{constructor(e,t){super();this.upstream=e,this.impl=new QB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},QB=class extends Xy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},eW=class extends dn{decodeUTF8(){return new tW(this)}},tW=class extends n7{constructor(e){super();this.upstream=e,this.impl=new nW(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},nW=class extends Xy{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=s5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},s7=class extends eW{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function sW(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=rW(e));let r=await w.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new s7(a,t)}else throw new Error(r.statusText)}var rW=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function r7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var a7=class extends t7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(r7(this.input)&&Y().get("IS_NODE")){let e=Mi("fs");this.input=e.readFileSync(this.input.substr(7))}return new s7(this.input,this.options)}},o7=class extends t7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return r7(this.url)?new a7(this.url,this.fileOptions).iterator():sW(this.url,this.fileOptions)}};function aW(e,t={}){return new Jw(new o7(e),t)}function oW(e){let t=qy(e);return us(async()=>t)}function iW(e){return us(async()=>{let t=await e();return qy(()=>t.next())})}async function lW(e,t){return e7.create(e,t)}async function uW(e){return Qw.create(e)}var cW="3.9.0";function Ce(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var dW=cr.whereImpl,Zy=class extends tc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ap(this,es())}nextDataId(){return Zy.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&_.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&&w.isString(n[0])){let r=n.map(a=>w.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 _.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=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return je(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return es().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=w.now();return e(),{kernelMs:w.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){Ce([e],"where");let t=this.readSync(e.dataId);return dW(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Zy.nextDataId=0;var i7={};Le(i7,{addImpl:()=>u7,bincountImpl:()=>Jy,bincountReduceImpl:()=>c7,ceilImpl:()=>d7,concatImpl:()=>Qy,equalImpl:()=>p7,expImpl:()=>f7,expm1Impl:()=>g7,floorImpl:()=>A7,gatherNdImpl:()=>y7,gatherV2Impl:()=>x7,greaterEqualImpl:()=>v7,greaterImpl:()=>b7,lessEqualImpl:()=>k7,lessImpl:()=>w7,linSpaceImpl:()=>I7,logImpl:()=>S7,maxImpl:()=>C7,maximumImpl:()=>T7,minimumImpl:()=>N7,multiplyImpl:()=>e2,negImpl:()=>E7,notEqualImpl:()=>R7,prodImpl:()=>D7,rangeImpl:()=>n2,rsqrtImpl:()=>_7,sigmoidImpl:()=>QW,simpleAbsImpl:()=>l7,sliceImpl:()=>Hf,sparseFillEmptyRowsImpl:()=>$7,sparseReshapeImpl:()=>O7,sparseSegmentReductionImpl:()=>s2,sqrtImpl:()=>nV,squaredDifferenceImpl:()=>P7,stridedSliceImpl:()=>M7,stringNGramsImpl:()=>z7,stringSplitImpl:()=>L7,stringToHashBucketFastImpl:()=>B7,subImpl:()=>W7,tileImpl:()=>V7,topKImpl:()=>H7,transposeImpl:()=>t2,uniqueImpl:()=>G7});function l7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var pW=e=>{let{x:t}=e.inputs,n=e.backend;Ce(t,"abs");let s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=l7(r),n.makeOutput(s,t.shape,"float32")},hW={kernelName:Bi,backendName:"cpu",kernelFunc:pW};function qt(e){return(t,n,s,r,a)=>{let o=_.assertAndGetBroadcastShape(t,n),i=o.length,l=w.computeStrides(o),u=w.sizeFromShape(o),c=w.getTypedArrayFromDType(a,u),d=t.length,p=n.length,h=w.computeStrides(t),f=w.computeStrides(n),m=_.getBroadcastDims(t,o),g=_.getBroadcastDims(n,o);if(m.length+g.length===0)for(let A=0;A<c.length;++A)c[A]=e(s[A%s.length],r[A%r.length]);else for(let A=0;A<c.length;++A){let y=w.indexToLoc(A,i,l),x=y.slice(-d);m.forEach(S=>x[S]=0);let b=w.locToIndex(x,d,h),v=y.slice(-p);g.forEach(S=>v[S]=0);let k=w.locToIndex(v,p,f);c[A]=e(s[b],r[k])}return[c,o]}}function cs(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 fW={kernelName:Sp,backendName:"cpu",kernelFunc:cs};function Vf(e,t,n="float32"){if(n==="complex64"){let r=Vf(e,t,"float32"),a=Vf(e,t,"float32");return cs({inputs:{real:r,imag:a},backend:e})}let s=w.makeZerosTypedArray(w.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function gr(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 mW={kernelName:oo,backendName:"cpu",kernelFunc:gr};function pi(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 gW={kernelName:jp,backendName:"cpu",kernelFunc:pi};function Sa(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return gr({inputs:{x:r},backend:n});let o=Vf(n,r.shape,r.dtype),i=Sa({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=cs({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=pi({inputs:{input:r},backend:n}),i=Sa({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=gr({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=w.toTypedArray([0],r.dtype),[l,u]=qt((c,d)=>c!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var AW={kernelName:Ga,backendName:"cpu",kernelFunc:Sa};function pn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ce([o,i],e);let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=o.dtype==="string"?_.fromUint8ToStringArray(u):u,p=o.dtype==="string"?_.fromUint8ToStringArray(c):c,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 u=Sa({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),d=c.complexTensorInfos.real,p=c.complexTensorInfos.imag,h=l.data.get(d.dataId).values,f=l.data.get(p.dataId).values,m=Sa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),A=g.complexTensorInfos.real,y=g.complexTensorInfos.imag,x=l.data.get(A.dataId).values,b=l.data.get(y.dataId).values,[v,k,S]=n(o.shape,i.shape,h,f,x,b),C=l.makeTensorInfo(S,"float32",v),D=l.makeTensorInfo(S,"float32",k),O=cs({inputs:{real:C,imag:D},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(C),l.disposeIntermediateTensorInfo(D),O}else{let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=s||o.dtype,[p,h]=t(o.shape,i.shape,u,c,d);return l.makeTensorInfo(h,d,p)}}}function Yy(e){return(t,n,s,r,a,o)=>{let i=_.assertAndGetBroadcastShape(t,n),l=w.sizeFromShape(i),u=i.length,c=w.computeStrides(i),d=w.getTypedArrayFromDType("float32",l),p=w.getTypedArrayFromDType("float32",l),h=_.getBroadcastDims(t,i),f=_.getBroadcastDims(n,i),m=_.mergeRealAndImagArrays(s,r),g=_.mergeRealAndImagArrays(a,o),A=t.length,y=w.computeStrides(t),x=n.length,b=w.computeStrides(n);if(h.length+f.length===0)for(let v=0;v<d.length;v++){let k=v%m.length,S=v%g.length,C=e(m[k*2],m[k*2+1],g[S*2],g[S*2+1]);d[v]=C.real,p[v]=C.imag}else for(let v=0;v<d.length;v++){let k=w.indexToLoc(v,u,c),S=k.slice(-A);h.forEach(R=>S[R]=0);let C=w.locToIndex(S,A,y),D=k.slice(-x);f.forEach(R=>D[R]=0);let O=w.locToIndex(D,x,b),E=e(m[C*2],m[C*2+1],g[O*2],g[O*2+1]);d[v]=E.real,p[v]=E.imag}return[d,p,i]}}var u7=qt((e,t)=>e+t),yW=Yy((e,t,n,s)=>({real:e+n,imag:t+s})),md=pn(sa,u7,yW),xW={kernelName:sa,backendName:"cpu",kernelFunc:md};function Jy(e,t,n,s,r){let a=w.sizeFromShape(s),o=w.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 c7(e,t,n,s=!1){let r=e.shape[0],a=e.shape[1],o=je([r,n],t.dtype);for(let i=0;i<r;i++)for(let l=0;l<a;l++){let u=e.get(i,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(s?o.set(1,i,u):t.size>0?o.set(o.get(i,u)+t.get(i,l),i,u):o.set(o.get(i,u)+1,i,u))}return o}function Ca(e){return(t,n,s)=>{let r=w.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)r[a]=e(t[a],s);return r}}function pt(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Ce(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,u=w.sizeFromShape(o.shape),c=n||o.dtype,d=w.getArrayFromDType(c,u);for(let p=0;p<u;++p)d[p]=t(l[p],r);return i.makeTensorInfo(o.shape,c,d)}}function Au(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Ce(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,u=n||o.dtype,c=t(l,u,r);return i.makeTensorInfo(o.shape,u,c)}}var d7=Ca(e=>Math.ceil(e)),bW=Au(ja,d7),vW={kernelName:ja,backendName:"cpu",kernelFunc:bW};function Qy(e,t,n,s){let r=w.getArrayFromDType(n,w.sizeFromShape(t));if(s&&n!=="string"){let a=0;e.forEach(o=>{let i=w.sizeFromShape(o.shape);r.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?_.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let u=0;u<o.shape[0];++u){let c=u*t[1]+a;for(let d=0;d<o.shape[1];++d)r[c+d]=i[l++]}a+=o.shape[1]})}return r}var p7=qt((e,t)=>e===t?1:0),h7=pn(tl,p7,null,"bool"),wW={kernelName:tl,backendName:"cpu",kernelFunc:h7},f7=Ca(e=>Math.exp(e)),m7=Au(to,f7),kW={kernelName:to,backendName:"cpu",kernelFunc:m7},g7=Ca(e=>Math.expm1(e)),IW=Au(sl,g7),SW={kernelName:sl,backendName:"cpu",kernelFunc:IW},A7=Ca(e=>Math.floor(e)),CW=Au(no,A7),TW={kernelName:no,backendName:"cpu",kernelFunc:CW};function y7(e,t,n,s,r,a,o,i,l){let u=je([s,a],n);for(let c=0;c<s;c++){let d=[],p=0;for(let h=0;h<r;h++){let f=e[c*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 ${i}`);for(let h=0;h<a;h++)u.values[c*a+h]=t.get(...t.indexToLoc(p*a+h))}return u}function x7(e,t,n){let s=je(n,e.dtype);for(let r=0;r<s.size;++r){let o=s.indexToLoc(r).slice(),i=o[0],l=o[2],u=t.locToIndex([i,l]);o[2]=t.values[u];let c=e.locToIndex(o);s.values[r]=e.values[c]}return s}var b7=qt((e,t)=>e>t?1:0),NW=pn(il,b7,null,"bool"),EW={kernelName:il,backendName:"cpu",kernelFunc:NW},v7=qt((e,t)=>e>=t?1:0),RW=pn(ao,v7,null,"bool"),DW={kernelName:ao,backendName:"cpu",kernelFunc:RW},w7=qt((e,t)=>e<t?1:0),_W=pn(dl,w7,null,"bool"),FW={kernelName:dl,backendName:"cpu",kernelFunc:_W},k7=qt((e,t)=>e<=t?1:0),$W=pn(pl,k7,null,"bool"),OW={kernelName:pl,backendName:"cpu",kernelFunc:$W};function I7(e,t,n){let s=(t-e)/(n-1),r=w.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var S7=Ca(e=>Math.log(e)),PW=Au(lo,S7),MW={kernelName:lo,backendName:"cpu",kernelFunc:PW};function C7(e,t,n,s){let r=w.getTypedArrayFromDType(s,w.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 u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}r[a]=i}return r}var T7=qt((e,t)=>Math.max(e,t)),zW=pn(co,T7),LW={kernelName:co,backendName:"cpu",kernelFunc:zW},N7=qt((e,t)=>Math.min(e,t)),BW=pn(mo,N7),WW={kernelName:mo,backendName:"cpu",kernelFunc:BW},e2=qt((e,t)=>e*t),VW=Yy((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Uf=pn(Ao,e2,VW),UW={kernelName:Ao,backendName:"cpu",kernelFunc:Uf};function E7(e,t,n){let s=w.createScalarValue(-1,n);return e2([],t,s,e,n)}function HW(e){let{inputs:t,backend:n}=e,{x:s}=t;Ce(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=E7(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var GW={kernelName:gl,backendName:"cpu",kernelFunc:HW},R7=qt((e,t)=>e!==t?1:0),jW=pn(Al,R7,null,"bool"),qW={kernelName:Al,backendName:"cpu",kernelFunc:jW};function t2(e,t,n,s,r){let a=t.length,o=w.sizeFromShape(t),i=w.computeStrides(t),l=w.computeStrides(r),u=w.getTypedArrayFromDType(n,w.sizeFromShape(r));for(let c=0;c<o;++c){let d=w.indexToLoc(c,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[s[f]];let h=w.locToIndex(p,a,l);u[h]=e[c]}return u}function ws(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;Ce(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,u=t2(l,r.shape,r.dtype,a,i);return{dataId:s.write(u,i,r.dtype),shape:i,dtype:r.dtype}}var XW={kernelName:Mo,backendName:"cpu",kernelFunc:ws};function D7(e,t,n,s){let[r,a]=_.computeOutAndReduceShapes(e,s),o=Ds(t,"int32"),i=w.makeZerosTypedArray(w.sizeFromShape(r),o),l=w.sizeFromShape(a);for(let u=0;u<i.length;++u){let c=u*l,d=1;for(let p=0;p<l;++p)d*=n[c+p];i[u]=d}return{outVals:i,outShape:r,outDtype:o}}function KW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ce(r,"prod");let i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=_.getAxesPermutation(l,i),c=l,d=r,p=[];u!=null&&(d=ws({inputs:{x:r},backend:n,attrs:{perm:u}}),p.push(d),c=_.getInnerMostAxes(c.length,i));let h=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:g}=D7(d.shape,d.dtype,h,c),A=m;return o&&(A=_.expandShapeToKeepDim(m,l)),p.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(A,g,f)}var ZW={kernelName:kl,backendName:"cpu",kernelFunc:KW};function n2(e,t,n,s){let r=e===t,a=e<t&&n<0,o=t<e&&n>1;if(r||a||o)return w.makeZerosTypedArray(0,s);let i=Math.abs(Math.ceil((t-e)/n)),l=w.makeZerosTypedArray(i,s);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var _7=Ca(e=>1/Math.sqrt(e)),YW=Au(To,_7),JW={kernelName:To,backendName:"cpu",kernelFunc:YW},QW=Ca(e=>1/(1+Math.exp(-e))),F7=pt(Eo,e=>1/(1+Math.exp(-e))),eV={kernelName:Eo,backendName:"cpu",kernelFunc:F7};function Hf(e,t,n,s,r){let a=Tn.isSliceContinous(s,t,n),o=w.sizeFromShape(n),i=w.computeStrides(s);if(a){let d=Tn.computeFlatOffset(t,i);return r==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=r==="string"?_.fromUint8ToStringArray(e):e,u=je(s,r,l),c=je(n,r);for(let d=0;d<c.size;++d){let p=c.indexToLoc(d),h=p.map((f,m)=>f+t[m]);c.set(u.get(...h),...p)}return r==="string"?_.fromStringArrayToUint8(c.values):c.values}function hi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Ce(r,"slice");let[i,l]=Tn.parseSliceParams(r,a,o);Tn.assertParamsValid(r,i,l);let u=n.data.get(r.dataId).values,c=Hf(u,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}var tV={kernelName:El,backendName:"cpu",kernelFunc:hi};function $7(e,t,n,s,r,a,o){let i=t[0],l=a[0],u=new Array(l),c=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] = ${i}`);let g=w.getArrayFromDType(n,0),A=w.getArrayFromDType(r,0);return[g,[0,d],A,u,c]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let A=e[g*d];if(A<0)throw new Error(`indices(${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;u[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 y=0;y<i;++y)c[y]=y;return[g,[i,d],A,u,c]}else{let g=f[l-1],A=w.getArrayFromDType(n,g*d),y=w.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],k=x[v],S=(v===0?0:f[v-1])+k;x[v]++;for(let C=0;C<d;++C)A[S*d+C]=e[b*d+C];y[S]=s[b],c[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let k=b===0?0:f[b-1];A[k*d+0]=b;for(let S=1;S<d;++S)A[k*d+S]=0;y[k]=o}return[A,[g,d],y,u,c]}}function O7(e,t,n,s,r){let a=w.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let A=r[g];if(A===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${g}`);c=g,l.push(1)}else{if(A<0)throw new Error(`size ${g} must be non-negative, not ${A}`);u*=A,l.push(A)}}if(c!==-1){if(u<=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/u);if(u*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
|
|
dense values, but the requested shape requires a multiple of ${u}. inputShape=${s} outputShape= ${l}`);l[c]=g}let d=w.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=w.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let A=0;for(let y=0;y<p;++y)A+=e[g*p+y]*h[y];for(let y=0;y<i;++y)m[g*i+y]=Math.trunc(A/f[y]),A%=f[y]}return[m,[o,i],l]}function s2(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]],u=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((x,b)=>x*b,1),f=w.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,y=r[m];for(;;){let x=0;if(g<i){if(x=r[g],y===x){++g;continue}if(y>=x)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>A&&f.fill(o,A*u,y*u);for(let b=m;b<g;++b){let v=s[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<u;k++)f[y*u+k]+=e[v*u+k]}if(a)for(let b=0;b<u;b++)f[y*u+b]/=g-m;if(m=g,++g,A=y+1,y=x,g>i)break}return A<d&&f.fill(o,A*u,d*u),[f,p]}var nV=Ca(e=>Math.sqrt(e)),sV=pt(Ro,e=>Math.sqrt(e)),rV={kernelName:Ro,backendName:"cpu",kernelFunc:sV},P7=qt((e,t)=>{let n=e-t;return n*n}),aV=pn(Fo,P7),oV={kernelName:Fo,backendName:"cpu",kernelFunc:aV};function M7(e,t,n,s){let r=je(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 iV=class{constructor(e,t,n,s,r,a){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(n),this.rightPad=w.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),u=Math.max(0,i-(r-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let A=0;A<c;++A)p+=e[d+A].length;p+=u*this.rightPad.length,p+=(l+u+c-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=A=>A.forEach(y=>f[m++]=y);for(let A=0;A<l;++A)g(this.leftPad),g(this.separator);for(let A=0;A<c-1;++A)g(e[d+A]),g(this.separator);if(c>0){g(e[d+c-1]);for(let A=0;A<u;++A)g(this.separator),g(this.rightPad)}else{for(let A=0;A<u-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 u=t[l]>=i;if(u=u&&t[l]<=n,!u)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 ${i}`)}let r=s-1,a=w.getArrayFromDType("int32",s);if(n===0||s===0){let i=new Array(n);for(let l=0;l<=r;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=r;++i){let l=t[i]-t[i-1],u=0;this.nGramWidths.forEach(c=>{u+=this.getNumNGrams(l,c)}),this.preserveShort&&l>0&&u===0&&(u=1),a[i]=a[i-1]+u}let o=new Array(a[r]);for(let i=0;i<r;++i){let l=t[i],u=a[i];if(this.nGramWidths.forEach(c=>{let d=t[i+1]-t[i],p=this.getNumNGrams(d,c);this.createNGrams(e,l,o,u,p,c),u+=p}),this.preserveShort&&u===a[i]){let c=t[i+1]-t[i];if(c===0)continue;let d=c+2*this.padWidth,p=1;this.createNGrams(e,l,o,u,p,d)}}return[o,a]}};function z7(e,t,n,s,r,a,o,i){return new iV(n,s,r,a,o,i).compute(e,t)}function lV(e,t,n,s){if(!e.length)return;if(t.length===0){for(let a=0;a<e.length;++a)s.push(e.subarray(a,a+1));return}if(t.length===1){let a=t[0],o=e.indexOf(a);for(;o!==-1;){let i=e.subarray(0,o);(!n||i.length!==0)&&s.push(i),e=e.subarray(o+1),o=e.indexOf(a)}(!n||e.length!==0)&&s.push(e);return}let r=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}}function L7(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let p=0;p<s;++p){let h=r.length;lV(e[p],t,n,r);let f=r.length-h;i[p]=f,a+=f,o=Math.max(o,f)}let l=w.getArrayFromDType("int32",a*2),u=new Array(a),c=[s,o],d=0;for(let p=0;p<s;++p)for(let h=0;h<i[p];++h)l[d*2]=p,l[d*2+1]=h,u[d]=r[d],++d;return[l,u,c]}function B7(e,t){let n=w.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=w.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var W7=qt((e,t)=>e-t),uV=Yy((e,t,n,s)=>({real:e-n,imag:t-s})),r2=pn($o,W7,uV),cV={kernelName:$o,backendName:"cpu",kernelFunc:r2};function V7(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=je(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 gd=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function U7(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,u=Math.log(i),c=.5*Math.exp(2*u/3),d=.5*Math.sqrt(u*c*(i-c)/i)*Math.sign(l-i/2),p=Math.max(n,Math.floor(t-l*c/i+d)),h=Math.min(s,Math.floor(t+(i-l)*c/i+d));U7(e,t,p,h)}let r=e[t],a=n,o=s;for(w.swap(e,n,t),gd(e[s],r)>0&&w.swap(e,n,s);a<o;){for(w.swap(e,a,o),a++,o--;gd(e[a],r)<0;)a=a+1;for(;gd(e[o],r)>0;)o=o-1}gd(e[n],r)===0?w.swap(e,n,o):(o=o+1,w.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function H7(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=w.getTypedArrayFromDType(n,o*s),u=w.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((y,x)=>f[x]={value:y,index:x}),s<f.length&&(U7(f,s),f=f.slice(0,s)),r&&f.sort(gd);let m=d*s,g=l.subarray(m,m+s),A=u.subarray(m,m+s);for(let y=0;y<s;y++)g[y]=f[y].value,A[y]=f[y].index}let c=t.slice();return c[c.length-1]=s,[je(c,n,l),je(c,"int32",u)]}function G7(e,t,n,s){let r=w.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 Yt(a,s,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let A=0;A<a[0];A++)for(let y=0;y<a[2];y++)g.push(l.get(A,f,y));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,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new Yt(d,s);u.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}}Xl("cpu",()=>new Zy,1);var j7=pt(eo,e=>e>=0?e:Math.exp(e)-1),dV={kernelName:eo,backendName:"cpu",kernelFunc:j7};function q7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ce([r],"leakyRelu");let o=w.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=w.getTypedArrayFromDType("float32",o);for(let u=0;u<i.length;u++)l[u]=i[u]<0?a*i[u]:i[u];return n.makeTensorInfo(r.shape,"float32",l)}var pV={kernelName:io,backendName:"cpu",kernelFunc:q7},hV=qt((e,t)=>e<0?t*e:e);function X7(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Ce([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=hV(s.shape,r.shape,a,o,s.dtype);return n.makeTensorInfo(l,s.dtype,i)}var fV={kernelName:vo,backendName:"cpu",kernelFunc:X7},K7=pt(wo,e=>Math.max(0,e)),mV={kernelName:wo,backendName:"cpu",kernelFunc:K7},Z7=pt(Io,e=>Math.min(Math.max(0,e),6)),gV={kernelName:Io,backendName:"cpu",kernelFunc:Z7};function a2(e,t,n,s,r){if(n==="linear")return gr({inputs:{x:t},backend:e});if(n==="relu")return K7({inputs:{x:t},backend:e});if(n==="elu")return j7({inputs:{x:t},backend:e});if(n==="relu6")return Z7({inputs:{x:t},backend:e});if(n==="prelu")return X7({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return q7({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return F7({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function wt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=w.sizeFromShape(r.shape),i=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(i);w.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,d=u.complexTensorInfos.imag;c.shape=i,d.shape=i}return{dataId:r.dataId,shape:i,dtype:r.dtype}}var AV={kernelName:Sl,backendName:"cpu",kernelFunc:wt};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;Ce([r,a],"matMul");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),A=w.sizeFromShape(m),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,p]:[g,p,c],k=i?[A,h,d]:[A,d,h],S=wt({inputs:{x:r},backend:n,attrs:{shape:v}}),C=wt({inputs:{x:a},backend:n,attrs:{shape:k}}),D=o?S.shape[1]:S.shape[2],O=o?S.shape[2]:S.shape[1],E=i?C.shape[1]:C.shape[2],R=Math.max(g,A),T=n.data.get(S.dataId).values,P=n.data.get(C.dataId).values,U=w.computeStrides(S.shape),j=w.computeStrides(C.shape),[q,X,te]=o?[U[0],1,U[1]]:[U[0],U[1],1],[ne,se,ae]=i?[1,j[1],j[0]]:[j[1],1,j[0]],Q=O*E,ce=je([R,O,E],S.dtype),de=ce.values,fe=n.blockSize;for(let be=0;be<R;be++)for(let Ee=0;Ee<O;Ee+=fe)for(let Re=0;Re<E;Re+=fe)for(let Pe=0;Pe<D;Pe+=fe){let Be=Math.min(Ee+fe,O),Me=Math.min(Re+fe,E),mt=Math.min(Pe+fe,D);for(let it=Ee;it<Be;it++)for(let lt=Re;lt<Me;lt++){let rt=0;for(let ht=Pe;ht<mt;ht++){let Xe=Math.min(be,g-1)*q,zn=Math.min(be,A-1)*ae,Rt=T[Xe+it*X+ht*te],Yn=P[ht*ne+lt*se+zn];rt+=Rt*Yn}de[be*Q+(it*E+lt)]+=rt}}return n.disposeIntermediateTensorInfo(S),n.disposeIntermediateTensorInfo(C),n.makeTensorInfo(b,ce.dtype,ce.values)}var yV={kernelName:Ha,backendName:"cpu",kernelFunc:Y7};function xV(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,p,h,f,m=[];p=Y7({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:u},backend:n}),o&&(h=md({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),c&&(f=a2(n,p,c,i,d),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var bV={kernelName:zo,backendName:"cpu",kernelFunc:xV},vV=pt(Wi,e=>Math.acos(e)),wV={kernelName:Wi,backendName:"cpu",kernelFunc:vV},kV=pt(Vi,e=>Math.acosh(e)),IV={kernelName:Vi,backendName:"cpu",kernelFunc:kV};function SV(e){let{inputs:t,backend:n}=e,s=t;Ce(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=je(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let u=0;u<o.length;u++)o[u]+=l[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var CV={kernelName:Wa,backendName:"cpu",kernelFunc:SV};function TV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ce(r,"all");let i=w.parseAxisParam(a,r.shape),l=i,u=_.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ws({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("all",l,c.shape.length);let[d,p]=_.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;A<f.length;++A){let y=A*h,x=m[y];for(let b=0;b<h;++b){let v=m[y+b];x=x&&v}f[A]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let A=_.expandShapeToKeepDim(d,i),y=wt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),y}return g}var NV={kernelName:Ui,backendName:"cpu",kernelFunc:TV};function EV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ce(r,"any");let i=w.parseAxisParam(a,r.shape),l=i,u=_.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ws({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("any",l,c.shape.length);let[d,p]=_.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;A<f.length;++A){let y=A*h,x=m[y];for(let b=0;b<h;++b){let v=m[y+b];x=x||v}f[A]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let A=_.expandShapeToKeepDim(d,i),y=wt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),y}return g}var RV={kernelName:Hi,backendName:"cpu",kernelFunc:EV};function DV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ce(r,"argMax");let o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ws({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[c,d]=_.computeOutAndReduceShapes(l.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,y=m[A],x=0;for(let b=0;b<f;++b){let v=m[A+b];v>y&&(y=v,x=b)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var _V={kernelName:Va,backendName:"cpu",kernelFunc:DV};function FV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ce(r,"argMin");let o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ws({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=_.computeOutAndReduceShapes(l.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,y=m[A],x=0;for(let b=0;b<f;++b){let v=m[A+b];v<y&&(y=v,x=b)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var $V={kernelName:rc,backendName:"cpu",kernelFunc:FV},OV=pt(Gi,e=>Math.asin(e)),PV={kernelName:Gi,backendName:"cpu",kernelFunc:OV},MV=pt(ji,e=>Math.asinh(e)),zV={kernelName:ji,backendName:"cpu",kernelFunc:MV},LV=pt(qi,e=>Math.atan(e)),BV={kernelName:qi,backendName:"cpu",kernelFunc:LV},WV=qt((e,t)=>Math.atan2(e,t)),VV=pn(Ki,WV),UV={kernelName:Ki,backendName:"cpu",kernelFunc:VV},HV=pt(Xi,e=>Math.atanh(e)),GV={kernelName:Xi,backendName:"cpu",kernelFunc:HV};function o2(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=je(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let v=b*A,k=b*s[0];for(let S=0;S<r.inChannels;++S)for(let C=0;C<r.outHeight;++C){let D=C*o-p,O=Math.max(0,D),E=Math.min(r.inHeight,c+D),R=v+C*y;for(let T=0;T<r.outWidth;++T){let P=T*i-h,U=Math.max(0,P),j=Math.min(r.inWidth,d+P),q=f,X=0,te=0;for(let se=O;se<E;se+=l){let ae=k+se*s[1];for(let Q=U;Q<j;Q+=u){let ce=ae+Q*s[2],de=e[ce+S];a==="max"&&de>q?q=de:a==="avg"&&(X+=de,te++)}if(isNaN(q))break}let ne=R+T*x+S;g[ne]=a==="avg"?X/te:q}}}return m}function J7(e,t,n,s,r=!1,a=!1){let o=je(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=je(t,n,e);for(let g=0;g<s.batchSize;++g)for(let A=0;A<s.inChannels;++A)for(let y=0;y<s.outHeight;++y){let x=y*i-h,b=x;for(;b<0;)b+=u;let v=Math.min(s.inHeight,d+x);for(let k=0;k<s.outWidth;++k){let S=k*l-f,C=S;for(;C<0;)C+=c;let D=Math.min(s.inWidth,p+S),O=Number.NEGATIVE_INFINITY,E=-1;for(let R=b;R<v;R+=u){let T=R-x;for(let P=C;P<D;P+=c){let U=P-S,j=m.get(g,R,P,A);j>O&&(O=j,r?E=a?((g*s.inHeight+R)*s.inWidth+P)*s.inChannels+A:(R*s.inWidth+P)*s.inChannels+A:E=T*p+U)}}o.set(E,g,y,k,A)}}return o}function Q7(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,c=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,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=je(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let D=0;D<r.batchSize;++D){let O=D*v,E=D*s[0];for(let R=0;R<r.inChannels;++R)for(let T=0;T<r.outDepth;++T){let P=T*o-m,U=P;for(;U<0;)U+=u;let j=Math.min(r.inDepth,p+P),q=O+T*k;for(let X=0;X<r.outHeight;++X){let te=X*i-g,ne=te;for(;ne<0;)ne+=c;let se=Math.min(r.inHeight,h+te),ae=q+X*S;for(let Q=0;Q<r.outWidth;++Q){let ce=Q*l-A,de=ce;for(;de<0;)de+=d;let fe=Math.min(r.inWidth,f+ce),be=ae+Q*C,Ee=y,Re=0,Pe=0;for(let Me=U;Me<j;Me+=u){let mt=E+Me*s[1];for(let it=ne;it<se;it+=c){let lt=mt+it*s[2];for(let rt=de;rt<fe;rt+=d){let ht=lt+rt*s[3],Xe=e[ht+R];if(a==="max"&&Xe>Ee?Ee=Xe:a==="avg"&&(Re+=Xe,Pe++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let Be=be+R;b[Be]=a==="avg"?Re/Pe:Ee}}}}return x}function jV(e,t){let n=je(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=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 y=A*s-p,x=y;for(;x<0;)x+=o;let b=Math.min(t.inDepth,u+y);for(let v=0;v<t.outHeight;++v){let k=v*r-h,S=k;for(;S<0;)S+=i;let C=Math.min(t.inHeight,c+k);for(let D=0;D<t.outWidth;++D){let O=D*a-f,E=O;for(;E<0;)E+=l;let R=Math.min(t.inWidth,d+O),T=Number.NEGATIVE_INFINITY,P=-1;for(let U=x;U<b;U+=o){let j=U-y;for(let q=S;q<C;q+=i){let X=q-k;for(let te=E;te<R;te+=l){let ne=te-O,se=e.get(m,U,q,te,g);se>=T&&(T=se,P=j*c*d+X*c+ne)}}}n.set(P,m,A,v,D,g)}}}return n}function qV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ce(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=gr({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),f=o2(p,r.shape,r.dtype,h,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var XV={kernelName:Ua,backendName:"cpu",kernelFunc:qV};function KV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;Ce(r,"avgPool3d");let c=_.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,p=Q7(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var ZV={kernelName:ac,backendName:"cpu",kernelFunc:KV};function YV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;Ce([r,a],"avgPool3DGrad");let c=_.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,D=v-1-c.padInfo.top,O=je(a.shape,"float32"),E=1/(f*m*g),R=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let P=0;P<c.inChannels;++P)for(let U=0;U<c.inDepth;++U)for(let j=0;j<c.inHeight;++j)for(let q=0;q<c.inWidth;++q){let X=U-S,te=j-D,ne=q-C,se=0;for(let ae=0;ae<b;ae+=A){let Q=(X+ae)/d;if(!(Q<0||Q>=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce<v;ce+=y){let de=(te+ce)/p;if(!(de<0||de>=c.outHeight||Math.floor(de)!==de))for(let fe=0;fe<k;fe+=x){let be=(ne+fe)/h;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;se+=R.get(T,Q,de,be,P)}}}O.set(se*E,T,U,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var JV={kernelName:kp,backendName:"cpu",kernelFunc:YV};function QV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ce([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=je(o.shape,"float32"),k=1/(h*f),S=n.data.get(r.dataId).values,C=je(r.shape,"float32",S);for(let D=0;D<c.batchSize;++D)for(let O=0;O<c.inChannels;++O)for(let E=0;E<c.inHeight;++E)for(let R=0;R<c.inWidth;++R){let T=E-b,P=R-x,U=0;for(let j=0;j<A;j+=m){let q=(T+j)/d;if(!(q<0||q>=c.outHeight||Math.floor(q)!==q))for(let X=0;X<y;X+=g){let te=(P+X)/p;if(te<0||te>=c.outWidth||Math.floor(te)!==te)continue;U+=C.get(D,q,te,O)}}v.set(U*k,D,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var eU={kernelName:wp,backendName:"cpu",kernelFunc:QV};function tU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ce([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=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(c.length),g=f.length,A=h.length,y=p.length,x=d.length,b=0,v=0,k=0,S=0;for(let C=0;C<c.length;++C)m[C]=f[b++]+(c[C]-d[v++])*h[k++]/Math.sqrt(p[S++]+u),b>=g&&(b=0),v>=x&&(v=0),k>=A&&(k=0),S>=y&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var nU={kernelName:ro,backendName:"cpu",kernelFunc:tU};function sU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ce([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=wt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=ws({inputs:{x:h},backend:n,attrs:{perm:u}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=hi({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var rU={kernelName:Zi,backendName:"cpu",kernelFunc:sU};function aU(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,u=Jy(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var oU={kernelName:Ip,backendName:"cpu",kernelFunc:aU};function iU(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=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var lU={kernelName:bg,backendName:"cpu",kernelFunc:iU},uU=pt(ra,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),cU={kernelName:ra,backendName:"cpu",kernelFunc:uU},dU=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.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 u=0;u<i.length;u++){let c=i[u],d=l[u];s[u]=Math.hypot(c,d)}return n.makeOutput(s,t.shape,"float32")},pU={kernelName:oc,backendName:"cpu",kernelFunc:dU};function yu(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 hU={kernelName:Lp,backendName:"cpu",kernelFunc:yu};function xu(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=_.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return gr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(_.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>pi({inputs:{input:b},backend:n})),g=i.map(b=>yu({inputs:{input:b},backend:n})),A=xu({inputs:m,backend:n,attrs:{axis:a}}),y=xu({inputs:g,backend:n,attrs:{axis:a}}),x=cs({inputs:{real:A,imag:y},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return wt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=_.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,p=Qy(c,o,t[0].dtype,d),h=_.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var fU={kernelName:Yi,backendName:"cpu",kernelFunc:xu};function e6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;Ce([r,a],"conv2d");let d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,y=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new Yt(p.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],D=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],U=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X<p.batchSize;++X){let te=X*S,ne=X*E;for(let se=0;se<p.outHeight;++se){let ae=ne+se*R,Q=se*p.strideHeight-y;for(let ce=0;ce<h;++ce){let de=Q+ce*m;if(de<0||de>=p.inHeight)continue;let fe=ce*k[0],be=te+de*C;for(let Ee=0;Ee<p.outWidth;++Ee){let Re=ae+Ee*T,Pe=Ee*p.strideWidth-A;for(let Be=0;Be<f;++Be){let Me=Pe+Be*g;if(Me<0||Me>=p.inWidth)continue;let mt=fe+Be*k[1],it=be+Me*D,lt=mt;for(let rt=0;rt<p.inChannels;++rt){let ht=U[it+rt*O];for(let Xe=0;Xe<p.outChannels;++Xe)q[Re+Xe*P]+=ht*j[lt+Xe];lt+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,q)}var mU={kernelName:qa,backendName:"cpu",kernelFunc:e6};function gU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s;Ce([r,a],"conv2dBackpropFilter");let d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,A=p.dataFormat==="channelsLast",y=new Yt(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=new Yt(r.shape,r.dtype,v),C=new Yt(a.shape,a.dtype,k);for(let D=0;D<m;++D){let O=Math.max(0,Math.ceil((b-D)/h)),E=Math.min(p.outHeight,(p.inHeight+b-D)/h);for(let R=0;R<g;++R){let T=Math.max(0,Math.ceil((x-R)/f)),P=Math.min(p.outWidth,(p.inWidth+x-R)/f);for(let U=0;U<p.inChannels;++U)for(let j=0;j<p.outChannels;++j){let q=0;for(let X=0;X<p.batchSize;++X)for(let te=O;te<E;++te){let ne=D+te*h-b;for(let se=T;se<P;++se){let ae=R+se*f-x;A?q+=S.get(X,ne,ae,U)*C.get(X,te,se,j):q+=S.get(X,U,ne,ae)*C.get(X,j,te,se)}}y.set(q,D,R,U,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var AU={kernelName:Cp,backendName:"cpu",kernelFunc:gU};function yU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s;Ce([r,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(r.shape),h=_.convertConv2DDataFormat(u),f=_.computeConv2DInfo(o,a.shape,i,1,l,c,!1,h),m=new Yt(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[x,b,v]=d,{batchSize:k,filterHeight:S,filterWidth:C,inChannels:D,inHeight:O,inWidth:E,outChannels:R,outHeight:T,outWidth:P,strideHeight:U,strideWidth:j}=f;h=f.dataFormat;let q=S-1-f.padInfo.top,X=C-1-f.padInfo.left,te=h==="channelsLast",ne=m.strides[0],se=te?m.strides[1]:m.strides[2],ae=te?m.strides[2]:1,Q=te?1:m.strides[1],ce=p[0],de=te?p[1]:p[2],fe=te?p[2]:1,be=te?1:p[1];for(let Ee=0;Ee<k;++Ee)for(let Re=0;Re<D;++Re)for(let Pe=0;Pe<O;++Pe){let Be=Pe-q,Me=Math.max(0,Math.ceil(Be/U)),mt=Math.min(T,(S+Be)/U);for(let it=0;it<E;++it){let lt=it-X,rt=Math.max(0,Math.ceil(lt/j)),ht=Math.min(P,(C+lt)/j),Xe=0;for(let Rt=Me;Rt<mt;++Rt){let Yn=Rt*U-Be;for(let hn=rt;hn<ht;++hn){let Ns=hn*j-lt,kn=ce*Ee+de*Rt+fe*hn,ms=x*(S-1-Yn)+b*(C-1-Ns)+v*Re;for(let gs=0;gs<R;++gs){let fn=A[kn+be*gs],As=y[ms+gs];Xe+=fn*As}}}let zn=ne*Ee+se*Pe+ae*it+Q*Re;g[zn]=Xe}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var xU={kernelName:Xa,backendName:"cpu",kernelFunc:yU};function bU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Ce([r,a],"conv3d");let u=_.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=u,A=g.front,y=g.left,x=g.top,b=new Yt(u.outShape,r.dtype),v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=b.values,C=w.computeStrides(r.shape),D=w.computeStrides(a.shape);for(let O=0;O<u.batchSize;++O){let E=O*C[0],R=O*b.strides[0];for(let T=0;T<u.outDepth;++T){let P=R+T*b.strides[1],U=T*u.strideDepth-A;for(let j=0;j<c;++j){let q=U+j*h;if(q<0||q>=u.inDepth)continue;let X=j*D[0],te=E+q*C[1];for(let ne=0;ne<u.outHeight;++ne){let se=P+ne*b.strides[2],ae=ne*u.strideHeight-x;for(let Q=0;Q<d;++Q){let ce=ae+Q*f;if(ce<0||ce>=u.inHeight)continue;let de=X+Q*D[1],fe=te+ce*C[2];for(let be=0;be<u.outWidth;++be){let Ee=se+be*u.outChannels,Re=be*u.strideWidth-y;for(let Pe=0;Pe<p;++Pe){let Be=Re+Pe*m;if(Be<0||Be>=u.inWidth)continue;let Me=de+Pe*D[2],mt=fe+Be*u.inChannels,it=Me;for(let lt=0;lt<u.inChannels;++lt){let rt=v[mt+lt];for(let ht=0;ht<u.outChannels;++ht)S[Ee+ht]+=rt*k[it+ht];it+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var vU={kernelName:ic,backendName:"cpu",kernelFunc:bU};function wU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ce([r,a],"conv3dBackpropFilterV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=_.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,y=new Yt(d.filterShape,"float32"),x=y.values,[b,v,k,S]=y.strides,C=n.data.get(a.dataId).values,[D,O,E,R]=c,T=n.data.get(r.dataId).values,[P,U,j,q]=u,X=d.padInfo.front,te=d.padInfo.left,ne=d.padInfo.top;for(let se=0;se<m;++se){let ae=Math.max(0,Math.ceil((X-se)/p)),Q=Math.min(d.outDepth,(d.inDepth+X-se)/p),ce=se*b;for(let de=0;de<g;++de){let fe=Math.max(0,Math.ceil((ne-de)/h)),be=Math.min(d.outHeight,(d.inHeight+ne-de)/h),Ee=de*v+ce;for(let Re=0;Re<A;++Re){let Pe=Math.max(0,Math.ceil((te-Re)/f)),Be=Math.min(d.outWidth,(d.inWidth+te-Re)/f),Me=Re*k+Ee;for(let mt=0;mt<d.inChannels;++mt){let it=mt*S+Me;for(let lt=0;lt<d.outChannels;++lt){let rt=0;for(let ht=0;ht<d.batchSize;++ht){let Xe=ht*P,zn=ht*D;for(let Rt=ae;Rt<Q;++Rt){let hn=(se+Rt*p-X)*U+Xe,Ns=Rt*O+zn;for(let kn=fe;kn<be;++kn){let gs=(de+kn*h-ne)*j+hn,fn=kn*E+Ns;for(let As=Pe;As<Be;++As){let Jn=(Re+As*f-te)*q+gs,er=As*R+fn;rt+=T[Jn+mt]*C[er+lt]}}}}x[it+lt]=rt}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var kU={kernelName:Tp,backendName:"cpu",kernelFunc:wU};function IU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ce([r],"conv3dBackpropInputV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=_.computeConv3DInfo(l,a.shape,i,1,o),p=new Yt(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,y=n.data.get(r.dataId).values,[x,b,v,k]=u,S=n.data.get(a.dataId).values,[C,D,O,E]=c,{batchSize:R,filterDepth:T,filterHeight:P,filterWidth:U,inChannels:j,inDepth:q,inHeight:X,inWidth:te,outChannels:ne,outDepth:se,outHeight:ae,outWidth:Q,strideDepth:ce,strideHeight:de,strideWidth:fe}=d,be=T-1-d.padInfo.front,Ee=P-1-d.padInfo.top,Re=U-1-d.padInfo.left;for(let Pe=0;Pe<R;++Pe)for(let Be=0;Be<j;++Be)for(let Me=0;Me<q;++Me){let mt=Me-be,it=Math.max(0,Math.ceil(mt/ce)),lt=Math.min(se,(T+mt)/ce);for(let rt=0;rt<X;++rt){let ht=rt-Ee,Xe=Math.max(0,Math.ceil(ht/de)),zn=Math.min(ae,(P+ht)/de);for(let Rt=0;Rt<te;++Rt){let Yn=Rt-Re,hn=Math.max(0,Math.ceil(Yn/fe)),Ns=Math.min(Q,(U+Yn)/fe),kn=0;for(let ms=it;ms<lt;++ms){let gs=ms*ce-mt;for(let fn=Xe;fn<zn;++fn){let As=fn*de-ht;for(let ys=hn;ys<Ns;++ys){let Jn=ys*fe-Yn,er=x*Pe+b*ms+v*fn+k*ys,vr=C*(T-1-gs)+D*(P-1-As)+O*(U-1-Jn)+E*Be;for(let Xr=0;Xr<ne;++Xr){let Ci=y[er+Xr],tr=S[vr+Xr];kn+=Ci*tr}}}}h[f*Pe+m*Me+g*rt+A*Rt+Be]=kn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var SU={kernelName:Np,backendName:"cpu",kernelFunc:IU},CU=pt(Ka,e=>Math.cos(e)),TU={kernelName:Ka,backendName:"cpu",kernelFunc:CU},NU=pt(Za,e=>Math.cosh(e)),EU={kernelName:Za,backendName:"cpu",kernelFunc:NU};function RU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=je([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S<f;S++){let C=S*4,D=y[C],O=y[C+1],E=y[C+2],R=y[C+3],T=x[S];if(T>=c)continue;let P=m>1?(E-D)*(d-1)/(m-1):0,U=g>1?(R-O)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let q=m>1?D*(d-1)+j*P:.5*(D+E)*(d-1);if(q<0||q>d-1){for(let X=0;X<g;X++)for(let te=0;te<h;te++){let ne=te+X*k[2]+j*k[1]+S*k[0];A.values[ne]=u}continue}if(l==="bilinear"){let X=Math.floor(q),te=Math.ceil(q),ne=q-X;for(let se=0;se<g;se++){let ae=g>1?O*(p-1)+se*U:.5*(O+R)*(p-1);if(ae<0||ae>p-1){for(let fe=0;fe<h;fe++){let be=fe+se*k[2]+j*k[1]+S*k[0];A.values[be]=u}continue}let Q=Math.floor(ae),ce=Math.ceil(ae),de=ae-Q;for(let fe=0;fe<h;fe++){let be=fe+Q*v[2]+X*v[1]+T*v[0],Ee=b[be];be=fe+ce*v[2]+X*v[1]+T*v[0];let Re=b[be];be=fe+Q*v[2]+te*v[1]+T*v[0];let Pe=b[be];be=fe+ce*v[2]+te*v[1]+T*v[0];let Be=b[be],Me=Ee+(Re-Ee)*de,mt=Pe+(Be-Pe)*de;be=fe+se*k[2]+j*k[1]+S*k[0],A.values[be]=Me+(mt-Me)*ne}}}else for(let X=0;X<g;++X){let te=g>1?O*(p-1)+X*U:.5*(O+R)*(p-1);if(te<0||te>p-1){for(let ae=0;ae<h;ae++){let Q=ae+X*k[2]+j*k[1]+S*k[0];A.values[Q]=u}continue}let ne=Math.round(te),se=Math.round(q);for(let ae=0;ae<h;ae++){let Q=ae+ne*v[2]+se*v[1]+T*v[0],ce=ae+X*k[2]+j*k[1]+S*k[0];A.values[ce]=b[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var DU={kernelName:Ji,backendName:"cpu",kernelFunc:RU};function _U(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ce(r,"cumsum");let l=_.getAxesPermutation([a],r.shape.length),u=r;l!=null&&(u=ws({inputs:{x:r},backend:n,attrs:{perm:l}}));let c=_.getInnerMostAxes(1,r.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let d=Ds(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(A,y)=>A+f-y-1:(A,y)=>A+y;for(let A=0;A<h.length;A+=f)for(let y=0;y<f;y++){let x=m(A,y);if(y===0)p[x]=o?0:h[x];else{let b=m(A,y-1);p[x]=o?h[b]+p[b]:h[x]+p[b]}}let g=n.makeTensorInfo(u.shape,d,p);if(l!=null){let A=_.getUndoAxesPermutation(l),y=ws({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),y}return g}var FU={kernelName:Ya,backendName:"cpu",kernelFunc:_U};function $U(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=Jy(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=c7(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var OU={kernelName:Ep,backendName:"cpu",kernelFunc:$U};function PU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,p=u*a,h=c/(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 y=0;y<d;++y){let x=Math.floor(y/a),b=y%a;for(let v=0;v<p;++v){let k=Math.floor(v/a),S=v%a,C=(b*a+S)*h;for(let D=0;D<h;++D){let E=D+C+c*(k+u*(x+l*A));m[g++]=f[E]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var MU={kernelName:Qi,backendName:"cpu",kernelFunc:PU};function t6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s;Ce([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),d=w.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=_.computeConv2DInfo(r.shape,a.shape,o,p,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:y}=h,x=y.left,b=y.top,v=h.outChannels/h.inChannels,k=new Yt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,D=k.values;for(let O=0;O<h.batchSize;++O){let E=O*c[0],R=O*k.strides[0];for(let T=0;T<h.outHeight;++T){let P=R+T*k.strides[1],U=T*h.strideHeight-b;for(let j=0;j<f;++j){let q=U+j*g;if(q<0||q>=h.inHeight)continue;let X=j*d[0],te=E+q*c[1];for(let ne=0;ne<h.outWidth;++ne){let se=P+ne*k.strides[2],ae=ne*h.strideWidth-x;for(let Q=0;Q<m;++Q){let ce=ae+Q*A;if(ce<0||ce>=h.inWidth)continue;let de=X+Q*d[1],fe=te+ce*h.inChannels,be=se,Ee=de;for(let Re=0;Re<h.inChannels;++Re){let Pe=S[fe+Re];for(let Be=0;Be<v;++Be)D[be+Be]+=Pe*C[Ee+Be];be+=v,Ee+=v}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var zU={kernelName:Ja,backendName:"cpu",kernelFunc:t6};function LU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s;Ce([r,a],"depthwiseConv2dNativeBackpropFilter");let d=_.computeConv2DInfo(r.shape,c,o,i,l,u,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Yt(d.filterShape,"float32"),A=d.padInfo.left,y=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,v=new Yt(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,S=new Yt(a.shape,a.dtype,k);for(let C=0;C<f;++C){let D=Math.max(0,Math.ceil((y-C)/p)),O=Math.min(d.outHeight,(d.inHeight+y-C)/p);for(let E=0;E<m;++E){let R=Math.max(0,Math.ceil((A-E)/h)),T=Math.min(d.outWidth,(d.inWidth+A-E)/h);for(let P=0;P<d.outChannels;++P){let U=Math.trunc(P/x),j=P%x,q=0;for(let X=0;X<d.batchSize;++X)for(let te=D;te<O;++te){let ne=C+te*p-y;for(let se=R;se<T;++se){let ae=E+se*h-A;q+=v.get(X,ne,ae,U)*S.get(X,te,se,P)}}g.set(q,C,E,U,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var BU={kernelName:Rp,backendName:"cpu",kernelFunc:LU};function WU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;Ce([r,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(r.shape),p=w.computeStrides(a.shape),h=_.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new Yt(h.inShape,"float32"),m=f.values,[g,A,y]=f.strides,x=n.data.get(r.dataId).values,[b,v,k]=d,S=n.data.get(a.dataId).values,[C,D,O]=p,{batchSize:E,filterHeight:R,filterWidth:T,inChannels:P,inHeight:U,inWidth:j,outChannels:q,outHeight:X,outWidth:te,strideHeight:ne,strideWidth:se}=h,ae=R-1-h.padInfo.top,Q=T-1-h.padInfo.left,ce=q/P;for(let de=0;de<E;++de)for(let fe=0;fe<P;++fe)for(let be=0;be<U;++be){let Ee=be-ae,Re=Math.max(0,Math.ceil(Ee/ne)),Pe=Math.min(X,(R+Ee)/ne);for(let Be=0;Be<j;++Be){let Me=Be-Q,mt=Math.max(0,Math.ceil(Me/se)),it=Math.min(te,(T+Me)/se),lt=0;for(let rt=Re;rt<Pe;++rt){let ht=rt*ne-Ee;for(let Xe=mt;Xe<it;++Xe){let zn=Xe*se-Me,Rt=b*de+v*rt+k*Xe,Yn=C*(R-1-ht)+D*(T-1-zn)+O*fe;for(let hn=0;hn<ce;++hn){let Ns=fe*ce+hn,kn=x[Rt+Ns],ms=S[Yn+hn];lt+=kn*ms}}}m[g*de+A*be+y*Be+fe]=lt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var VU={kernelName:Dp,backendName:"cpu",kernelFunc:WU};function UU(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=je([r,r],s.dtype),i=o.values;for(let u=0;u<a.length;u++)i[u*r+u]=a[u];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var HU={kernelName:_p,backendName:"cpu",kernelFunc:UU},GU={kernelName:lc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=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:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:D,outShape:O}=_.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),E=w.sizeFromShape(O),R=O.length,T=w.getArrayFromDType(s.dtype,E);for(let U=0;U<h;++U)for(let j=0;j<A;++j){let q=j*b-x.top;for(let X=0;X<y;++X){let te=X*v-x.left;for(let ne=0;ne<g;++ne){let se=Number.MIN_SAFE_INTEGER;for(let Q=0;Q<k;++Q){let ce=q+Q*C;if(ce>=0&&ce<f)for(let de=0;de<S;++de){let fe=te+de*D;if(fe>=0&&fe<m){let be=w.locToIndex([U,ce,fe,ne],c,w.computeStrides(s.shape)),Ee=w.locToIndex([Q,de,ne],p,w.computeStrides(r.shape)),Re=u[be]+d[Ee];Re>se&&(se=Re)}}}let ae=w.locToIndex([U,j,X,ne],R,w.computeStrides(O));T[ae]=se}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},jU={kernelName:$p,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:D}=_.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===D.length,()=>`Error in ${$p}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let O=w.toNestedArray(D,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<p;++T)for(let P=0;P<g;++P){let U=P*x-y.top;for(let j=0;j<A;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let te=Number.MIN_SAFE_INTEGER,ne=0,se=0;for(let ae=0;ae<v;++ae){let Q=U+ae*S;if(Q>=0&&Q<h)for(let ce=0;ce<k;++ce){let de=q+ce*C;if(de>=0&&de<f){let fe=c[T][Q][de][X]+d[ae][ce][X];fe>te&&(te=fe,ne=ae,se=ce)}}}E[ne][se][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},qU={kernelName:Fp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:D}=_.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===D.length,()=>`Error in ${Fp}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let O=w.toNestedArray(D,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T<p;++T)for(let P=0;P<g;++P){let U=P*x-y.top;for(let j=0;j<A;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let te=Number.MIN_SAFE_INTEGER,ne=U<0?0:U,se=q<0?0:q;for(let ae=0;ae<v;++ae){let Q=U+ae*S;if(Q>=0&&Q<h)for(let ce=0;ce<k;++ce){let de=q+ce*C;if(de>=0&&de<f){let fe=c[T][Q][de][X]+d[ae][ce][X];fe>te&&(te=fe,ne=Q,se=de)}}}E[T][ne][se][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Ad(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ce(r,"sum");let i;r.dtype==="bool"?i=Sa({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=gr({inputs:{x:r},backend:n});let l=i.shape.length,u=w.parseAxisParam(a,i.shape),c=_.getAxesPermutation(u,l),d=u,p=i;c!=null&&(p=ws({inputs:{x:i},backend:n,attrs:{perm:c}}),d=_.getInnerMostAxes(d.length,l)),_.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=_.computeOutAndReduceShapes(p.shape,d),m=_.upcastType(p.dtype,"int32"),g=Vf(n,h,m),A=w.sizeFromShape(f),y=n.data.get(g.dataId).values,x=n.data.get(p.dataId).values;for(let b=0;b<y.length;++b){let v=b*A,k=0;for(let S=0;S<A;++S)k+=x[v+S];y[b]=k}if(o){let b=_.expandShapeToKeepDim(g.shape,u),v=g;g=wt({inputs:{x:g},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(v)}return n.disposeIntermediateTensorInfo(i),c!=null&&n.disposeIntermediateTensorInfo(p),g}var XU={kernelName:Do,backendName:"cpu",kernelFunc:Ad};function KU(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=_.decodeEinsumEquation(r,a.length);_.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=_.getEinsumComputePath(i,l),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:A,expandDims:y}=_.getEinsumPermutation(h,l[g]),x;_.isIdentityPermutation(A)?x=a[g]:(x=ws({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);w.arraysEqual(x.shape,b)||(x=wt({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Uf({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=Ad({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var ZU={kernelName:Op,backendName:"cpu",kernelFunc:KU};function YU(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Ce([s,r],"eluGrad");let a=new Float32Array(w.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 u=o[l];u>=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",a)}var JU={kernelName:Pp,backendName:"cpu",kernelFunc:YU},QU=_.ERF_P,eH=_.ERF_A1,tH=_.ERF_A2,nH=_.ERF_A3,sH=_.ERF_A4,rH=_.ERF_A5,aH=pt(el,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+QU*n);return t*(1-((((rH*s+sH)*s+nH)*s+tH)*s+eH)*s*Math.exp(-n*n))}),oH={kernelName:el,backendName:"cpu",kernelFunc:aH};function Gf(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&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),wt({inputs:{x:r},backend:n,attrs:{shape:i}})}var iH={kernelName:nl,backendName:"cpu",kernelFunc:Gf},lH=qt((e,t)=>e/t),i2=pn(Qa,lH),l2={kernelName:Qa,backendName:"cpu",kernelFunc:i2};function n6(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,u=[r,a],c=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",c),p=w.getTypedArrayFromDType("float32",c);for(let g=0;g<r;g++){let A=hi({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=hi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,a]}}),x=cs({inputs:{real:A,imag:y},backend:n}),{real:b,imag:v}=uH(x,t,n),k=_.mergeRealAndImagArrays(b,v);for(let S=0;S<a;S++){let C=_.getComplexWithIndex(k,S);d[g*a+S]=C.real,p[g*a+S]=C.imag}n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(u,"float32",d),f=n.makeTensorInfo(u,"float32",p),m=cs({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function uH(e,t,n){let s=w.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(cH(s)){let i=u2(a,o,s,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",i.real),c=n.makeTensorInfo(l,"float32",i.imag),d=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),p=gr({inputs:{x:d},backend:n}),h=l2.kernelFunc({inputs:{a:u,b:d},backend:n}),f=l2.kernelFunc({inputs:{a:c,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=_.mergeRealAndImagArrays(a,o),l=dH(i,s,t);return _.splitRealAndImagArrays(l)}}function cH(e){return(e&e-1)==0}function u2(e,t,n,s,r){if(n===1)return{real:e,imag:t};let a=_.mergeRealAndImagArrays(e,t),o=n/2,i=_.complexWithEvenIndex(a),l=i.real,u=i.imag,c=[l.length],d=r.makeTensorInfo(c,"float32",l),p=r.makeTensorInfo(c,"float32",u),h=cs({inputs:{real:d,imag:p},backend:r}),f=_.complexWithOddIndex(a),m=f.real,g=f.imag,A=[m.length],y=r.makeTensorInfo(A,"float32",m),x=r.makeTensorInfo(A,"float32",g),b=cs({inputs:{real:y,imag:x},backend:r}),v=u2(l,u,o,s,r),k=v.real,S=v.imag,C=[k.length],D=r.makeTensorInfo(C,"float32",k),O=r.makeTensorInfo(C,"float32",S),E=cs({inputs:{real:D,imag:O},backend:r}),R=u2(m,g,o,s,r),T=R.real,P=R.imag,U=[T.length],j=r.makeTensorInfo(U,"float32",T),q=r.makeTensorInfo(U,"float32",P),X=cs({inputs:{real:j,imag:q},backend:r}),te=_.exponents(n,s),ne=[te.real.length],se=r.makeTensorInfo(ne,"float32",te.real),ae=r.makeTensorInfo(ne,"float32",te.imag),Q=cs({inputs:{real:se,imag:ae},backend:r}),ce=Uf({inputs:{a:Q,b:X},backend:r}),de=md({inputs:{a:E,b:ce},backend:r}),fe=r2({inputs:{a:E,b:ce},backend:r}),be=pi({inputs:{input:de},backend:r}),Ee=pi({inputs:{input:fe},backend:r}),Re=yu({inputs:{input:de},backend:r}),Pe=yu({inputs:{input:fe},backend:r}),Be=xu({inputs:[be,Ee],backend:r,attrs:{axis:0}}),Me=xu({inputs:[Re,Pe],backend:r,attrs:{axis:0}}),mt=r.data.get(Be.dataId).values,it=r.data.get(Me.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(D),r.disposeIntermediateTensorInfo(O),r.disposeIntermediateTensorInfo(E),r.disposeIntermediateTensorInfo(j),r.disposeIntermediateTensorInfo(q),r.disposeIntermediateTensorInfo(X),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(ae),r.disposeIntermediateTensorInfo(Q),r.disposeIntermediateTensorInfo(ce),r.disposeIntermediateTensorInfo(de),r.disposeIntermediateTensorInfo(fe),r.disposeIntermediateTensorInfo(be),r.disposeIntermediateTensorInfo(Re),r.disposeIntermediateTensorInfo(Ee),r.disposeIntermediateTensorInfo(Pe),r.disposeIntermediateTensorInfo(Be),r.disposeIntermediateTensorInfo(Me),{real:mt,imag:it}}function dH(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=_.exponent(r*i,t,n),u=_.getComplexWithIndex(e,i);a+=u.real*l.real-u.imag*l.imag,o+=u.real*l.imag+u.imag*l.real}n&&(a/=t,o/=t),_.assignToTypedArray(s,a,o,r)}return s}function pH(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=wt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=n6(i,!1,n),u=wt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var hH={kernelName:Mp,backendName:"cpu",kernelFunc:pH};function c2(e){let{backend:t,attrs:n}=e,{shape:s,value:r,dtype:a}=n,o=a||w.inferDtype(r),i=w.getArrayFromDType(o,w.sizeFromShape(s));return mH(i,r,o),t.makeTensorInfo(s,o,i)}var fH={kernelName:uc,backendName:"cpu",kernelFunc:c2};function mH(e,t,n){e.fill(t)}var gH={kernelName:rl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,r=n,a=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[o,i,l,u]=s.shape,c=r.data.get(s.dataId).values;for(let p=0;p<o;p++){let h=p*l*i*u;for(let f=0;f<i;f++){let m=f*(l*u);for(let g=0;g<l;g++){let A=g*u;for(let y=0;y<u;y++){let x=Math.round(l-g-1),b=h+m+A+y,v=c[b];if(x>=0&&x<l){let k=x*u,S=h+m+k+y;v=c[S]}a[b]=v}}}}return{dataId:r.write(a,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},AH=qt((e,t)=>Math.floor(e/t)),yH=pn(so,AH,null,"int32"),xH={kernelName:so,backendName:"cpu",kernelFunc:yH};function bH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=e6({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=md({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=a2(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var vH={kernelName:Lo,backendName:"cpu",kernelFunc:bH};function wH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=t6({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=md({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=a2(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var kH={kernelName:Bo,backendName:"cpu",kernelFunc:wH};function IH(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=w.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,u,c,d]=_.prepareAndValidate(s,r);if(u===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),f=y7(p,h,s.dtype,u,i,c,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var SH={kernelName:ol,backendName:"cpu",kernelFunc:IH};function CH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Ce([r,a],"gatherV2");let l=i;i==null&&(l=0);let u=w.sizeFromShape(a.shape),c=w.parseAxisParam(o,r.shape)[0],d=_.segment_util.collectGatherOpShapeInfo(r,a,c,l),p=wt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=wt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],m=n.bufferSync(h),g=n.bufferSync(p),A=x7(g,m,f);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(d.outputShape,A.dtype,A.values)}var TH={kernelName:al,backendName:"cpu",kernelFunc:CH};function NH(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=wt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=n6(i,!0,n),u=wt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var EH={kernelName:zp,backendName:"cpu",kernelFunc:NH},RH=pt(ll,e=>Number.isFinite(e)?1:0,"bool"),DH={kernelName:ll,backendName:"cpu",kernelFunc:RH},_H=pt(ul,e=>Math.abs(e)===1/0?1:0,"bool"),FH={kernelName:ul,backendName:"cpu",kernelFunc:_H},$H=pt(cl,e=>Number.isNaN(e)?1:0,"bool"),OH={kernelName:cl,backendName:"cpu",kernelFunc:$H};function PH(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=I7(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var MH={kernelName:Bp,backendName:"cpu",kernelFunc:PH},zH=pt(hl,e=>Math.log1p(e)),LH={kernelName:hl,backendName:"cpu",kernelFunc:zH},BH=qt((e,t)=>e&&t),WH=pn(fl,BH,null,"bool"),VH={kernelName:fl,backendName:"cpu",kernelFunc:WH},UH=pt(cc,e=>e?0:1,"bool"),HH={kernelName:cc,backendName:"cpu",kernelFunc:UH},GH=qt((e,t)=>e||t),jH=pn(dc,GH,null,"bool"),qH={kernelName:dc,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Ce(r,"LRN");let u=r.shape[3],c=u-1,d=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),h=new Float32Array(p);function f(m){let g=m%u,A=m-g+Math.max(0,g-a),y=m-g+Math.min(g+a,c),x=0;for(;A<=y;A++){let b=d[A];x+=b*b}return x}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 KH={kernelName:pc,backendName:"cpu",kernelFunc:XH};function ZH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s;Ce(o,"LRNGrad");let d=w.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 y=0;y<A;y++){let x=y%p,b=y-x+Math.max(0,x-i),v=y-x+Math.min(p,x+i+1),k=0;for(let S=b;S<v;S++)k+=Math.pow(f[S],2);k=u*k+l;for(let S=b;S<v;S++){let C=-2*u*c*f[S]*m[y]/k;y===S&&(C+=Math.pow(k,-c)),C*=h[y],g[S]+=C}}return n.makeTensorInfo(o.shape,r.dtype,g)}var YH={kernelName:Wp,backendName:"cpu",kernelFunc:ZH};function s6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=n,l=r.shape,u=l.length,c=w.parseAxisParam(a,l),d=c,p=_.getAxesPermutation(d,u),h=i.data.get(r.dataId).values;if(p!=null){let b=new Array(u);for(let v=0;v<b.length;v++)b[v]=l[p[v]];h=t2(h,l,r.dtype,p,b),d=_.getInnerMostAxes(d.length,u),l=b}Ce(r,"max"),_.assertAxesAreInnerMostDims("max",d,u);let[f,m]=_.computeOutAndReduceShapes(l,d),g=w.sizeFromShape(m),A=C7(h,g,f,r.dtype),y=i.write(A,f,r.dtype),x=f;return o&&(x=_.expandShapeToKeepDim(f,c)),{dataId:y,shape:x,dtype:r.dtype}}var JH={kernelName:uo,backendName:"cpu",kernelFunc:s6};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ce(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=gr({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),f=o2(p,r.shape,r.dtype,h,c,"max");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var eG={kernelName:po,backendName:"cpu",kernelFunc:QH};function tG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;Ce(r,"maxPool3d");let c=_.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,p=Q7(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"max");return n.makeTensorInfo(p.shape,"float32",p.values)}var nG={kernelName:hc,backendName:"cpu",kernelFunc:tG};function sG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;Ce([r,a],"maxPool3DGrad");let c=_.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),p=jV(d,c),h=c.strideDepth,f=c.strideHeight,m=c.strideWidth,g=c.dilationDepth,A=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,k=x-1-c.padInfo.front,S=v-1-c.padInfo.left,C=b-1-c.padInfo.top,D=je(a.shape,"float32"),O=n.bufferSync(r);for(let E=0;E<c.batchSize;++E)for(let R=0;R<c.inChannels;++R)for(let T=0;T<c.inDepth;++T)for(let P=0;P<c.inHeight;++P)for(let U=0;U<c.inWidth;++U){let j=T-k,q=P-C,X=U-S,te=0;for(let ne=0;ne<x;ne+=g){let se=(j+ne)/h;if(!(se<0||se>=c.outDepth||Math.floor(se)!==se))for(let ae=0;ae<b;ae+=A){let Q=(q+ae)/f;if(!(Q<0||Q>=c.outHeight||Math.floor(Q)!==Q))for(let ce=0;ce<v;ce+=y){let de=(X+ce)/m;if(de<0||de>=c.outWidth||Math.floor(de)!==de)continue;let fe=x*b*v-1-p.get(E,se,Q,de,R),be=ne*b*v+ae*v+ce,Ee=fe===be?1:0;if(Ee===0)continue;te+=O.get(E,se,Q,de,R)*Ee}}}D.set(te,E,T,P,U,R)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var rG={kernelName:Up,backendName:"cpu",kernelFunc:sG};function aG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Ce([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=n.data.get(i.dataId).values,f=je(p.outShape,i.dtype,J7(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,A=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,v=b-1-p.padInfo.left,k=x-1-p.padInfo.top,S=je(i.shape,"float32"),C=n.data.get(r.dataId).values,D=je(r.shape,"float32",C);for(let O=0;O<p.batchSize;++O)for(let E=0;E<p.inChannels;++E)for(let R=0;R<p.inHeight;++R)for(let T=0;T<p.inWidth;++T){let P=R-k,U=T-v,j=0;for(let q=0;q<x;q+=A){let X=(P+q)/m;if(!(X<0||X>=p.outHeight||Math.floor(X)!==X))for(let te=0;te<b;te+=y){let ne=(U+te)/g;if(ne<0||ne>=p.outWidth||Math.floor(ne)!==ne)continue;let se=x*b-1-f.get(O,X,ne,E),ae=q*b+te,Q=se===ae?1:0;if(Q===0)continue;j+=D.get(O,X,ne,E)*Q}}S.set(j,O,R,T,E)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var oG={kernelName:Vp,backendName:"cpu",kernelFunc:aG};function iG(e,t,n,s,r){let a=w.computeStrides(t),o=o2(e,t,n,a,r,"max"),i=J7(e,t,n,r,!0,s);return[o.values,i.values]}var lG={kernelName:Hp,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Ce(s,"MaxPoolWithArgmax");let u=l.data.get(s.dataId).values,c=_.computePool2DInfo(s.shape,r,a,[1,1],o),[d,p]=iG(u,s.shape,s.dtype,i,c),h=l.write(d,c.outShape,s.dtype),f=l.write(p,c.outShape,s.dtype);return[{dataId:h,shape:c.outShape,dtype:s.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function uG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=w.parseAxisParam(a,r.shape),u=_.computeOutAndReduceShapes(r.shape,i)[1],c=w.sizeFromShape(u),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([c]));d.push(p);let h=Sa({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(h);let f=i2({inputs:{a:h,b:p},backend:n});d.push(f);let m=Ad({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),m}var cG={kernelName:ho,backendName:"cpu",kernelFunc:uG};function dG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ce(r,"min");let i=w.parseAxisParam(a,r.shape),l=i,u=_.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ws({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",l,c.shape.length);let[d,p]=_.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;A<f.length;++A){let y=A*h,x=m[y];for(let b=0;b<h;++b){let v=m[y+b];(Number.isNaN(v)||v<x)&&(x=v)}f[A]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let A=_.expandShapeToKeepDim(d,i),y=wt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),y}return g}var pG={kernelName:fo,backendName:"cpu",kernelFunc:dG};function hG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,mode:o}=s;Ce(r,"mirrorPad");let i=a.map((x,b)=>x[0]+r.shape[b]+x[1]),l=a.map(x=>x[0]),u=a.map((x,b)=>x[0]+r.shape[b]),c=o==="reflect"?0:1,d=n.data.get(r.dataId).values,p=r.shape.length,h=w.computeStrides(r.shape),f=w.sizeFromShape(i),m=i.length,g=w.computeStrides(i),A=w.getTypedArrayFromDType(r.dtype,f);for(let x=0;x<f;x++){let b=w.indexToLoc(x,m,g);for(let k=0;k<m;k++)b[k]<l[k]?b[k]=l[k]*2-b[k]-c:b[k]>=u[k]&&(b[k]=(u[k]-1)*2-b[k]+c);b=b.map((k,S)=>k-l[S]);let v=w.locToIndex(b,p,h);A[x]=d[v]}return{dataId:n.write(A,i,r.dtype),shape:i,dtype:r.dtype}}var fG={kernelName:go,backendName:"cpu",kernelFunc:hG},mG=qt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),gG=pn(ml,mG),AG={kernelName:ml,backendName:"cpu",kernelFunc:gG},yG=za(t5());function r6(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=w.parseAxisParam([i],r.shape),u=s6({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=_.expandShapeToKeepDim(u.shape,l),d=wt({inputs:{x:u},backend:n,attrs:{shape:c}}),p=r2({inputs:{a:r,b:d},backend:n}),h=m7({inputs:{x:p},backend:n}),f=Ad({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=i2({inputs:{a:h,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var xG={kernelName:_o,backendName:"cpu",kernelFunc:r6};function bG(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;Ce(r,"multinomial");let l=i?r:r6({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],d=n.data.get(l.dataId).values,p=[u,a],h=w.makeZerosTypedArray(w.sizeFromShape(p),"int32");for(let f=0;f<u;++f){let m=f*c,g=new Float32Array(c-1);g[0]=d[m];for(let x=1;x<g.length;++x)g[x]=g[x-1]+d[m+x];let A=yG.alea(o.toString()),y=f*a;for(let x=0;x<a;++x){let b=A();h[y+x]=g.length;for(let v=0;v<g.length;v++)if(b<g[v]){h[y+x]=v;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",h)}var vG={kernelName:Gp,backendName:"cpu",kernelFunc:bG},wG=cr.nonMaxSuppressionV3Impl;function kG(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;Ce(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:d}=wG(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var IG={kernelName:yl,backendName:"cpu",kernelFunc:kG},SG=cr.nonMaxSuppressionV4Impl;function CG(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s;Ce(r,"NonMaxSuppressionPadded");let c=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:p,validOutputs:h}=SG(c,d,o,i,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var TG={kernelName:xl,backendName:"cpu",kernelFunc:CG},NG=cr.nonMaxSuppressionV5Impl;function EG(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s;Ce(r,"NonMaxSuppressionWithScore");let c=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,p=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=NG(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var RG={kernelName:bl,backendName:"cpu",kernelFunc:EG};function DG(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s;Ce(r,"oneHot");let l=w.sizeFromShape(r.shape),u=new Float32Array(l*a);u.fill(i);let c=n.data.get(r.dataId).values;for(let d=0;d<l;++d)c[d]>=0&&c[d]<a&&(u[d*a+c[d]]=o);return n.makeTensorInfo([...r.shape,a],"int32",u)}var _G={kernelName:yo,backendName:"cpu",kernelFunc:DG};function jf(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=pi({inputs:{input:s},backend:n}),a=jf({inputs:{x:r},backend:n}),o=yu({inputs:{input:s},backend:n}),i=jf({inputs:{x:o},backend:n}),l=cs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return c2({backend:n,attrs:{shape:s.shape,value:0,dtype:s.dtype}})}var FG={kernelName:Ll,backendName:"cpu",kernelFunc:jf};function a6(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=pi({inputs:{input:s},backend:n}),a=a6({inputs:{x:r},backend:n}),o=yu({inputs:{input:s},backend:n}),i=jf({inputs:{x:o},backend:n}),l=cs({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return c2({backend:n,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}var $G={kernelName:vl,backendName:"cpu",kernelFunc:a6};function o6(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Gf({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=Gf({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=xu({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var OG={kernelName:wl,backendName:"cpu",kernelFunc:o6};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;Ce(r,"pad");let i=a.map((y,x)=>y[0]+r.shape[x]+y[1]),l=a.map(y=>y[0]),u=n.data.get(r.dataId).values,c=w.sizeFromShape(r.shape),d=r.shape.length,p=w.computeStrides(r.shape),h=w.sizeFromShape(i),f=i.length,m=w.computeStrides(i),g=w.getTypedArrayFromDType(r.dtype,h);o!==0&&g.fill(o);for(let y=0;y<c;y++){let b=w.indexToLoc(y,d,p).map((k,S)=>k+l[S]),v=w.locToIndex(b,f,m);g[v]=u[y]}return{dataId:n.write(g,i,r.dtype),shape:i,dtype:r.dtype}}var i6={kernelName:xo,backendName:"cpu",kernelFunc:PG},MG=qt((e,t)=>Math.pow(e,t)),zG=pn(bo,MG),LG={kernelName:bo,backendName:"cpu",kernelFunc:zG};function BG(e){let{backend:t,attrs:n}=e,{start:s,stop:r,dtype:a,step:o}=n,i=n2(s,r,o,a);return t.makeTensorInfo([i.length],a,i)}var WG={kernelName:fc,backendName:"cpu",kernelFunc:BG},VG=pt(Il,e=>1/e),UG={kernelName:Il,backendName:"cpu",kernelFunc:VG};function HG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ce(r,"resizeBilinear");let l=w.computeStrides(r.shape),[u,c]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(w.sizeFromShape([d,u,c,f])),A=[a&&u>1?p-1:p,a&&c>1?h-1:h],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=0,b=A[0]/y[0],v=A[1]/y[1];for(let k=0;k<d;k++)for(let S=0;S<u;S++){let C;o?C=b*(S+.5)-.5:C=b*S;let D=Math.max(0,Math.floor(C)),O=C-D,E=Math.min(p-1,Math.ceil(C)),R=k*l[0]+D*l[1],T=k*l[0]+E*l[1];for(let P=0;P<c;P++){let U;o?U=v*(P+.5)-.5:U=v*P;let j=Math.max(0,Math.floor(U)),q=U-j,X=Math.min(h-1,Math.ceil(U)),te=R+j*l[2],ne=T+j*l[2],se=R+X*l[2],ae=T+X*l[2];for(let Q=0;Q<f;Q++){let ce=m[te+Q],de=m[ne+Q],fe=m[se+Q],be=m[ae+Q],Ee=ce+(fe-ce)*q,Re=de+(be-de)*q,Pe=Ee+(Re-Ee)*O;g[x++]=Pe}}}return n.makeTensorInfo([d,u,c,f],"float32",g)}var GG={kernelName:ko,backendName:"cpu",kernelFunc:HG};function jG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ce([a,r],"resizeBilinearGrad");let i=w.computeStrides(r.shape),[l,u,c,d]=r.shape,[,p,h]=a.shape,f=new Float32Array(l*u*c*d),m=[o&&p>1?u-1:u,o&&h>1?c-1:c],g=[o&&p>1?p-1:p,o&&h>1?h-1:h],A=m[0]/g[0],y=m[1]/g[1],x=n.data.get(a.dataId).values,b=0;for(let v=0;v<l;v++){let k=v*i[0];for(let S=0;S<p;S++){let C=S*A,D=Math.floor(C),O=Math.min(Math.ceil(C),u-1),E=k+D*i[1],R=k+O*i[1],T=C-D,P=1-T;for(let U=0;U<h;U++){let j=U*y,q=Math.floor(j),X=Math.min(Math.ceil(j),c-1),te=j-q,ne=1-te,se=E+q*i[2],ae=E+X*i[2],Q=R+q*i[2],ce=R+X*i[2],de=P*ne,fe=P*te,be=T*ne,Ee=T*te;for(let Re=0;Re<d;Re++){let Pe=x[b++];f[se+Re]+=Pe*de,f[ae+Re]+=Pe*fe,f[Q+Re]+=Pe*be,f[ce+Re]+=Pe*Ee}}}}return n.makeTensorInfo([l,c,u,d],"float32",f)}var qG={kernelName:Xp,backendName:"cpu",kernelFunc:jG};function XG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ce(r,"resizeNearestNeighbor");let l=w.computeStrides(r.shape),[u,c]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(d*u*c*f),A=[a&&u>1?p-1:p,a&&c>1?h-1:h],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=A[0]/y[0],b=A[1]/y[1],v=0;for(let k=0;k<d;k++){let S=k*l[0];for(let C=0;C<u;C++){let D=o?x*(C+.5):x*C,O=Math.min(p-1,a?Math.round(D):Math.floor(D));o&&(O=Math.max(0,O));let E=S+O*l[1];for(let R=0;R<c;R++){let T=o?b*(R+.5):b*R,P=Math.min(h-1,a?Math.round(T):Math.floor(T));o&&(P=Math.max(0,P));let U=E+P*l[2];for(let j=0;j<f;j++){let q=m[U+j];g[v++]=q}}}}return n.makeTensorInfo([d,u,c,f],r.dtype,g)}var KG={kernelName:mc,backendName:"cpu",kernelFunc:XG};function ZG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ce([a,r],"resizeNearestNeighborGrad");let i=w.computeStrides(r.shape),l=w.computeStrides(a.shape),[u,c,d,p]=r.shape,[,h,f]=a.shape,m=new Float32Array(u*c*d*p),g=n.data.get(a.dataId).values,A=[o&&h>1?c-1:c,o&&f>1?d-1:d],y=[o&&h>1?h-1:h,o&&f>1?f-1:f],x=A[0]/y[0],b=A[1]/y[1],v=1/x,k=1/b,S=Math.ceil(v)*2+2,C=Math.ceil(k)*2+2;for(let D=0;D<u;D++){let O=D*i[0];for(let E=0;E<c;E++){let R=O+E*i[1],T=Math.floor(E*v),P=Math.floor(T-S/2);for(let U=0;U<d;U++){let j=R+U*i[2],q=Math.floor(U*k),X=Math.floor(q-C/2);for(let te=0;te<p;te++){let ne=0;for(let se=0;se<S;se++){let ae=se+P;if(ae<0||ae>=h)continue;let Q=O+ae*l[1],ce=ae*x,de=Math.min(c-1,o?Math.round(ce):Math.floor(ce));if(E===de)for(let fe=0;fe<C;fe++){let be=fe+X;if(be<0||be>=f)continue;let Ee=Q+be*l[2],Re=be*b,Pe=Math.min(d-1,o?Math.round(Re):Math.floor(Re));U===Pe&&(ne+=g[Ee+te])}}m[j+te]=ne}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var YG={kernelName:qp,backendName:"cpu",kernelFunc:ZG};function JG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;Ce(r,"reverse");let o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return gr({inputs:{x:r},backend:n});let l=new Yt(r.shape,r.dtype),u=n.bufferSync(r);for(let c=0;c<l.size;c++){let d=l.indexToLoc(c),p=d.slice();i.forEach(h=>p[h]=r.shape[h]-1-p[h]),l.set(u.get(...p),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var QG={kernelName:So,backendName:"cpu",kernelFunc:JG},ej={kernelName:Bl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[u,c,d,p]=s.shape,[h,f]=_.getImageCenter(o,c,d),m=255,g=Math.sin(r),A=Math.cos(r),y=i.data.get(s.dataId).values;for(let b=0;b<u;b++){let v=b*d*c*p;for(let k=0;k<c;k++){let S=k*(d*p);for(let C=0;C<d;C++){let D=C*p;for(let O=0;O<p;O++){let E=[u,k,C,O],R=E[2],T=E[1],P=(R-h)*A-(T-f)*g,U=(R-h)*g+(T-f)*A;P=Math.round(P+h),U=Math.round(U+f);let j=a;if(typeof a!="number"&&(O===3?j=m:j=a[O]),P>=0&&P<d&&U>=0&&U<c){let X=U*(d*p),te=P*p,ne=v+X+te+O;j=y[ne]}let q=v+S+D+O;l[q]=j}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},tj=pt(Co,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}),nj={kernelName:Co,backendName:"cpu",kernelFunc:tj};function l6(e,t,n,s,r,a,o,i,l,u){let c=[s/r,r],d=e.values,p=t.values;if(s===0)return je(n,t.dtype);let h=je(c,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 y=d[f*o+A];m.push(y),g+=y*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++)u?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 sj(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=_.calculateShapes(a,r,o),p=!0,h=n.bufferSync(r),f=n.bufferSync(a),m=l6(h,f,o,d,u,l,i,c,0,p);return n.makeTensorInfo(o,m.dtype,m.values)}var rj={kernelName:Cl,backendName:"cpu",kernelFunc:sj};function aj(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ce([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=Ds(r.dtype,a.dtype),d=w.makeZerosTypedArray(w.sizeFromShape(r.shape),c),p=0,h=o===0||o>1||r.shape.length===1?1:w.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++]=u[f];return n.makeTensorInfo(r.shape,c,d)}var oj={kernelName:Tl,backendName:"cpu",kernelFunc:aj},ij=_.SELU_SCALEALPHA,lj=_.SELU_SCALE,uj=pt(Nl,e=>e>=0?lj*e:ij*(Math.exp(e)-1)),cj={kernelName:Nl,backendName:"cpu",kernelFunc:uj},dj=pt(Dl,e=>e<0?-1:e>0?1:0),pj={kernelName:Dl,backendName:"cpu",kernelFunc:dj},hj=pt(No,e=>Math.sin(e)),fj={kernelName:No,backendName:"cpu",kernelFunc:hj},mj=pt(Rl,e=>Math.sinh(e)),gj={kernelName:Rl,backendName:"cpu",kernelFunc:mj},Aj=11920928955078125e-23,u6=Math.log(Aj)+2,yj=pt(_l,e=>{let t=e>-u6,n=e<u6,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),xj={kernelName:_l,backendName:"cpu",kernelFunc:yj};function bj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ce([r],"spaceToBatchND");let i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=i6.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=_.getReshaped(u.shape,a,i,!1),d=_.getPermuted(c.length,a.length,!1),p=_.getReshapedPermuted(u.shape,a,i,!1),m=wt({inputs:{x:u},backend:n,attrs:{shape:c}}),y=ws({inputs:{x:m},backend:n,attrs:{perm:d}}),v=wt({inputs:{x:y},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),v}var vj={kernelName:Fl,backendName:"cpu",kernelFunc:bj};function wj(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,u=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values[0],[d,p,h,f,m]=$7(i,s.shape,s.dtype,l,r.dtype,u,c);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 kj={kernelName:Kp,backendName:"cpu",kernelFunc:wj};function Ij(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),[u,c,d]=O7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Sj={kernelName:Zp,backendName:"cpu",kernelFunc:Ij};function Cj(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,[u,c]=s2(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Tj={kernelName:Yp,backendName:"cpu",kernelFunc:Cj};function Nj(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
|
|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=s2(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Ej={kernelName:Jp,backendName:"cpu",kernelFunc:Nj};function Rj(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:p}=_.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=l6(f,m,i,p,c,u,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var Dj={kernelName:Qp,backendName:"cpu",kernelFunc:Rj};function _j(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let p=[...c];p[i]=d;let h=hi({inputs:{x:r},backend:n,attrs:{begin:u,size:p}});return u[i]+=d,h})}var Fj={kernelName:$l,backendName:"cpu",kernelFunc:_j},$j={kernelName:gc,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Ce(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},Oj=pt(oa,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),Pj={kernelName:oa,backendName:"cpu",kernelFunc:Oj};function Mj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s;Ce(r,"stridedSlice");let{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=Tn.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=wt({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=hi({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=wt({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else{let k=n.bufferSync(x),S=M7(y,k,m,f);b=n.makeTensorInfo(S.shape,S.dtype,S.values)}let v=wt({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var zj={kernelName:Ol,backendName:"cpu",kernelFunc:Mj};function Lj(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.data.get(c.dataId).values,h=n.data.get(d.dataId).values,[f,m]=z7(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Bj={kernelName:eh,backendName:"cpu",kernelFunc:Lj};function Wj(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],[u,c,d]=L7(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Vj={kernelName:th,backendName:"cpu",kernelFunc:Wj};function Uj(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=B7(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Hj={kernelName:nh,backendName:"cpu",kernelFunc:Uj},Gj=pt(Oo,e=>Math.tan(e)),jj={kernelName:Oo,backendName:"cpu",kernelFunc:Gj},qj=pt(Po,e=>Math.tanh(e)),Xj={kernelName:Po,backendName:"cpu",kernelFunc:qj};function Kj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;Ce(r,"tile");let o=V7(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var Zj={kernelName:aa,backendName:"cpu",kernelFunc:Kj};function Yj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;Ce(r,"topk");let i=n.data.get(r.dataId).values,[l,u]=H7(i,r.shape,r.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var Jj={kernelName:Pl,backendName:"cpu",kernelFunc:Yj};function Qj(e){let{inputs:t,attrs:n,backend:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=n,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=w.computeStrides(r.shape),y=A[0],x=A[1],b=A[2],v=w.getTypedArrayFromDType(r.dtype,w.sizeFromShape(g));v.fill(l);let k=s.data.get(r.dataId).values,S=s.data.get(a.dataId).values;for(let D=0;D<c;++D){let O=a.shape[0]===1?S:S.subarray(D*8,D*8+8);for(let E=0;E<f;++E)for(let R=0;R<m;++R)for(let T=0;T<h;++T){let P,U=O[6]*R+O[7]*E+1;if(U===0)continue;let j=(O[0]*R+O[1]*E+O[2])/U,q=(O[3]*R+O[4]*E+O[5])/U,X=c6(j,p,i),te=c6(q,d,i);switch(o){case"nearest":P=aq(k,d,p,y,x,b,D,te,X,T,l);break;case"bilinear":P=oq(k,d,p,y,x,b,D,te,X,T,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let ne=D*y+E*x+R*b+T;v[ne]=P}return s.makeTensorInfo(g,r.dtype,v)}return{dataId:s.write(v,g,r.dtype),shape:r.shape,dtype:r.dtype}}var eq={kernelName:Ml,backendName:"cpu",kernelFunc:Qj};function c6(e,t,n){switch(n){case"reflect":return tq(e,t);case"wrap":return nq(e,t);case"nearest":return rq(e,t);case"constant":default:return sq(e,t)}}function tq(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 w.clamp(0,n,t-1)}function nq(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 w.clamp(0,n,t-1)}function sq(e,t){return e}function rq(e,t){return w.clamp(0,e,t-1)}function yd(e,t,n,s,r,a,o,i,l,u,c){let d=o*s+i*r+l*a+u;return 0<=i&&i<t&&0<=l&&l<n?e[d]:c}function aq(e,t,n,s,r,a,o,i,l,u,c){let d=Math.round(i),p=Math.round(l);return yd(e,t,n,s,r,a,o,d,p,u,c)}function oq(e,t,n,s,r,a,o,i,l,u,c){let d=Math.floor(i),p=Math.floor(l),h=d+1,f=p+1,m=(f-l)*yd(e,t,n,s,r,a,o,d,p,u,c)+(l-p)*yd(e,t,n,s,r,a,o,d,f,u,c),g=(f-l)*yd(e,t,n,s,r,a,o,h,p,u,c)+(l-p)*yd(e,t,n,s,r,a,o,h,f,u,c);return(h-i)*m+(i-d)*g}function iq(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Ce(a,"unique");let o=s.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:u}=G7(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var lq={kernelName:sh,backendName:"cpu",kernelFunc:iq};function uq(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),u=0;for(let h=0;h<o;h++)h!==a&&(l[u++]=r.shape[h]);let c=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++){c[a]=h;let f=hi({inputs:{x:r},backend:n,attrs:{begin:c,size:d}});p[h]=wt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return p}var cq={kernelName:zl,backendName:"cpu",kernelFunc:uq};function dq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s;Ce(r,"unsortedSegmentSum");let i=r.shape.length,l=a.shape.length,u=[],c=[],d=i-l,p=a;for(let f=0;f<d;++f){let m=Gf({inputs:{input:p},backend:n,attrs:{dim:f+1}});p=m,c.push(m)}for(let f=0;f<o;++f){let m=w.createScalarValue(f,"int32"),g=n.makeTensorInfo([],"int32",m),A=h7({inputs:{a:g,b:p},backend:n}),y=Sa({inputs:{x:A},backend:n,attrs:{dtype:"float32"}}),x=Uf({inputs:{a:y,b:r},backend:n}),b=Ad({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});u.push(b),c.push(g),c.push(A),c.push(y),c.push(x),c.push(b)}let h=o6({inputs:u,backend:n,attrs:{axis:0}});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var pq={kernelName:Ac,backendName:"cpu",kernelFunc:dq},hq=[bV,hW,wV,IV,xW,CV,NV,RV,_V,$V,PV,zV,BV,UV,GV,XV,ZV,JV,eU,yV,nU,rU,oU,lU,AW,vW,cU,fW,pU,fU,AU,xU,mU,kU,SU,vU,TU,EU,DU,FU,OU,MU,zU,BU,VU,HU,GU,qU,jU,l2,ZU,dV,JU,wW,oH,kW,iH,SW,hH,fH,gH,TW,xH,vH,kH,SH,TH,EW,DW,mW,EH,hU,DH,FH,OH,pV,FW,OW,MH,MW,LH,VH,HH,qH,KH,YH,LW,eG,nG,rG,oG,lG,JH,cG,pG,WW,fG,AG,vG,UW,GW,IG,TG,RG,qW,_G,$G,OG,i6,LG,fV,ZW,WG,gW,UG,mV,gV,AV,GG,qG,KG,YG,QG,ej,nj,JW,rj,oj,cj,eV,pj,fj,gj,tV,xG,xj,vj,kj,Sj,Tj,Ej,Dj,Fj,rV,$j,oV,Pj,zj,Bj,Vj,Hj,cV,XU,jj,Xj,Zj,Jj,XW,eq,lq,cq,pq,FG];for(let e of hq)ia(e);var d6={};Le(d6,{assertNotComplex:()=>vu,bindCanvasToFramebuffer:()=>Sq,bindColorTextureToFramebuffer:()=>Zf,bindTextureToProgramUniformSampler:()=>C6,bindTextureUnit:()=>k6,bindVertexBufferToProgramAttribute:()=>h2,callAndCheck:()=>Ie,canBeRepresented:()=>p6,createFragmentShader:()=>m6,createFramebuffer:()=>w6,createProgram:()=>g6,createStaticIndexBuffer:()=>x6,createStaticVertexBuffer:()=>y6,createTexture:()=>b6,createVertexShader:()=>f6,getBatchDim:()=>mi,getExtensionOrThrow:()=>vd,getFramebufferErrorMessage:()=>T6,getMaxTexturesInShader:()=>D6,getNumChannels:()=>kq,getProgramUniformLocation:()=>S6,getProgramUniformLocationOrThrow:()=>I6,getRowsCols:()=>gi,getShapeAs3D:()=>Yf,getTextureShapeFromLogicalShape:()=>E6,getWebGLDisjointQueryTimerVersion:()=>_6,getWebGLErrorMessage:()=>h6,getWebGLMaxTextureSize:()=>R6,hasExtension:()=>Is,isCapableOfRenderingToFloatTexture:()=>F6,isDownloadFloatTextureEnabled:()=>$6,isReshapeFree:()=>kd,isWebGLFenceEnabled:()=>O6,isWebGLVersionEnabled:()=>m2,linkProgram:()=>A6,resetMaxTextureSize:()=>Cq,resetMaxTexturesInShader:()=>Tq,unbindColorTextureFromFramebuffer:()=>f2,unbindTextureUnit:()=>Iq,validateFramebuffer:()=>wd,validateProgram:()=>Kf,validateTextureSize:()=>v6});var fi={},d2={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function qf(e,t){fi[e]=t}function Ar(e){if(!(e in fi)){let n=mq(e);if(n!==null)fi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=fi[e];return t.isContextLost()?(delete fi[e],Ar(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),fi[e])}function fq(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 mq(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=fq(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete fi[e]},!1),e===1?t.getContext("webgl",d2)||t.getContext("experimental-webgl",d2):t.getContext("webgl2",d2)}var xd;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(xd||(xd={}));var ks;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(ks||(ks={}));var yn;(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"})(yn||(yn={}));function bd(e,t){return[t,e]}function gq(e,t){return e*t}function Xf(e){let t=w.sizeFromShape(e),n=Math.ceil(t/4);return w.sizeToSquarishShape(n)}function bu(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function Aq(e,t){let[n,s]=bu(e,t);return n*s*4}function p2(e,t){let n=e,s,r,a,o,i,l,u,c,d,p;return Y().getNumber("WEBGL_VERSION")===2?(s=n.R32F,r=n.R16F,a=n.RGBA16F,o=n.RGBA32F,i=n.RED,u=4,c=1,d=n.HALF_FLOAT,p=n.FLOAT):(s=e.RGBA,r=e.RGBA,a=e.RGBA,o=n.RGBA,i=e.RGBA,u=4,c=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:u,defaultNumChannels:c,textureTypeHalfFloat:d,textureTypeFloat:p}}function Ie(e,t){let n=t();return Y().getBool("DEBUG")&&yq(e),n}function yq(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+h6(e,t))}var xq=596e-10,bq=65504;function p6(e){return!!(Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||xq<Math.abs(e)&&Math.abs(e)<bq)}function h6(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 vd(e,t){return Br(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function f6(e,t){let n=Br(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Ie(e,()=>e.shaderSource(n,t)),Ie(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 m6(e,t){let n=Br(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Ie(e,()=>e.shaderSource(n,t)),Ie(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw wq(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var vq=/ERROR: [0-9]+:([0-9]+):/g;function wq(e,t){let n=vq.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)=>w.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),u=o.slice(s-1,s),c=o.slice(s);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${w.rightPad(u[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
|
|
`))}function g6(e){return Br(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function A6(e,t){if(Ie(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Kf(e,t){if(Ie(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function y6(e,t){let n=Br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ie(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function x6(e,t){let n=Br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Ie(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function kq(){return Y().getNumber("WEBGL_VERSION")===2?1:4}function b6(e){return Br(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function v6(e,t){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let s=`[${e}x${t}]`;throw new Error("Requested texture size "+s+" is invalid.")}if(e>n||t>n){let s=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+s+" greater than WebGL maximum on this browser / GPU "+r+".")}}function w6(e){return Br(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function h2(e,t,n,s,r,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s)),Ie(e,()=>e.vertexAttribPointer(i,r,e.FLOAT,!1,a,o)),Ie(e,()=>e.enableVertexAttribArray(i)),!0)}function k6(e,t,n){N6(e,n),Ie(e,()=>e.activeTexture(e.TEXTURE0+n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function Iq(e,t){N6(e,t),Ie(e,()=>e.activeTexture(e.TEXTURE0+t)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function I6(e,t,n){return Br(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function S6(e,t,n){return e.getUniformLocation(t,n)}function C6(e,t,n,s){Ie(e,()=>k6(e,t,s)),Ie(e,()=>e.uniform1i(n,s))}function Sq(e){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),Ie(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function Zf(e,t,n){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Ie(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function f2(e,t){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Ie(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function wd(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+T6(e,t))}function T6(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 ${t}`}}function Br(e,t,n){let s=Ie(e,()=>t());if(s==null)throw new Error(n);return s}function N6(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function mi(e,t=2){return w.sizeFromShape(e.slice(0,e.length-t))}function gi(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 Yf(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[mi(e),...gi(e)]),t}function E6(e,t=!1){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?w.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=w.squeezeShape(e).newShape);let s=w.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=mi(e),a=2,o=2;return e.length&&([a,o]=gi(e)),s=r*(a/2)*(o/2),w.sizeToSquarishShape(s).map(i=>i*2)}return w.sizeToSquarishShape(s)}function Jf(e){return e%2==0}function kd(e,t){if(e=e.slice(-2),t=t.slice(-2),w.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||Jf(n)&&Jf(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Jf(e[0])&&Jf(t[0])}var Qf,em;function R6(e){if(Qf==null){let t=Ar(e);Qf=t.getParameter(t.MAX_TEXTURE_SIZE)}return Qf}function Cq(){Qf=null}function Tq(){em=null}function D6(e){if(em==null){let t=Ar(e);em=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,em)}function _6(e){if(e===0)return 0;let t,n=Ar(e);return Is(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Is(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Is(e,t){return e.getExtension(t)!=null}function m2(e){try{if(Ar(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function F6(e){if(e===0)return!1;let t=Ar(e);if(e===1){if(!Is(t,"OES_texture_float"))return!1}else if(!Is(t,"EXT_color_buffer_float"))return!1;return g2(t)}function $6(e){if(e===0)return!1;let t=Ar(e);if(e===1){if(!Is(t,"OES_texture_float")||!Is(t,"WEBGL_color_buffer_float"))return!1}else{if(Is(t,"EXT_color_buffer_float"))return g2(t);let s="EXT_color_buffer_half_float";if(Is(t,s)){let r=t.getExtension(s);return Nq(t,r)}return!1}return g2(t)}function g2(e){let t=p2(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function Nq(e,t){let n=p2(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function O6(e){return e!==2?!1:Ar(e).fenceSync!=null}function vu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=Y();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>m2(2)?2:m2(1)?1:0);Fe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Fe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Fe.get("WEBGL_VERSION")===2);Fe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Fe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Fe.registerFlag("WEBGL_PACK",()=>Fe.getBool("HAS_WEBGL"));Fe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_CLIP",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_REDUCE",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_CONV_IM2COL",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>R6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>D6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:_6(e)});Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Fe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Tc.isMobile());Fe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>F6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Fe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Fe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Fe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>$6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>O6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Fe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Fe.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}.`)});Fe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Tc.isMobile()&&Fe.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Fe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Fe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Fe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Fe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Dn(){let e,t,n,s,r,a,o,i,l,u;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",u=`
|
|
#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));
|
|
}
|
|
`,u=`
|
|
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:u}}function Ai(e,t,n="index"){let s=w.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 tm(e,t,n="index"){let s=w.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}; ${i};`}).join("")}function Eq(e,t){let n=e.length,s=e.map(a=>`${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 Rq(e,t,n="index"){let s=e.map((a,o)=>o),r=Eq(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 A2(e){let t=w.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function y2(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var P6=`
|
|
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:M6}=_;function Dq(e,t,n){let s=[];if(e.forEach(h=>{let f=w.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}=x2(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?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>_q(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Dn(),l=Oq(i),u,c,d=zq(i);return t.isPacked?(u=Fq(t.logicalShape,o,n.enableShapeUniforms),c=Mq(i)):(u=$q(t.logicalShape,o,n.enableShapeUniforms),c=Pq(i)),n.packedInputs&&(d+=Vq),[d,l,c,r,u,a,n.userCode].join(`
|
|
`)}function wu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return eX(e,t);case 1:return nX(e,t);case 2:return rX(e,t);case 3:return oX(e,t);case 4:return lX(e,t);case 5:return uX(e);case 6:return cX(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function z6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Qq(e);case 1:return tX(e,t);case 2:return sX(e,t);case 3:return aX(e,t);default:return iX(e,t)}}function _q(e,t,n=!1,s){let r="";n?r+=z6(e,s):r+=wu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=dX(e,t):r+=pX(e,t)),r}function Fq(e,t,n){switch(e.length){case 0:return L6();case 1:return Uq(e,t,n);case 2:return Yq(e,t,n);case 3:return Gq(e,t,n);default:return qq(e,t,n)}}function $q(e,t,n){switch(e.length){case 0:return L6();case 1:return Hq(e,t,n);case 2:return Jq(e,t,n);case 3:return jq(e,t,n);case 4:return Xq(e,t,n);case 5:return Kq(e,t);case 6:return Zq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Oq(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function Pq(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function Mq(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function zq(e){return`${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);
|
|
}
|
|
|
|
${Lq}
|
|
${Bq}
|
|
${Wq}
|
|
`}var Lq=`
|
|
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);
|
|
}
|
|
`,Bq=`
|
|
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);
|
|
}
|
|
`,Wq=`
|
|
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);
|
|
}
|
|
`,Vq=`
|
|
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 L6(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function Uq(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 Hq(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function Gq(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 jq(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;
|
|
${tm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=Ai(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function qq(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
|
|
int b${u} = index / ${o};
|
|
index -= b${u} * ${o};
|
|
`+i,l=`b${u}, `+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 Xq(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;
|
|
${tm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=Ai(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function Kq(e,t){let n=Ai(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function Zq(e,t){let n=Ai(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function Yq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Jq(e,t,n){return w.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function yi(e){return`offset${e}`}function Qq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Dn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function eX(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=yi(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 tX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Dn();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 nX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${ku(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=yi(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 sX(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=Dn();if(a!=null&&w.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 u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function rX(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&&w.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}=w.squeezeShape(n),l=o;if(l.length<n.length){let p=Iu(e,l),h=["row","col"];return`
|
|
${wu(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${Su(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${ku(e)}
|
|
}
|
|
`;let u=a[0],c=a[1],d=yi(s);return 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(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) / ${u}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`: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((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) / ${c}.0, 0.5);
|
|
return sampleTexture(${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 + ${d};
|
|
vec2 uv = uvFromFlat(${u}, ${c}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function aX(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=Iu(e,p),m=["b","row","col"];return`
|
|
${z6(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${Su(m,h)});
|
|
}
|
|
`}let i=Dn();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],u=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${u}, ${d}, ${c}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function oX(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}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=Iu(e,u),g=["row","col","depth"];return`
|
|
${wu(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${Su(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)));
|
|
${ku(e)}
|
|
}
|
|
`;let c=e.shapeInfo.texShape,d=c[0],p=c[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=yi(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 iX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Dn();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)],u=l[0],c=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 / ${c};
|
|
int texC = index - texR * ${c};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function lX(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:u}=w.squeezeShape(n);if(l.length<n.length){let y=Iu(e,l),x=["row","col","depth","depth2"];return`
|
|
${wu(y,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${Su(x,u)});
|
|
}
|
|
`}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)));
|
|
${ku(e)}
|
|
}
|
|
`;let c=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&&c==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&&c==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=yi(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 uX(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:u}=w.squeezeShape(t);if(l.length<t.length){let m=Iu(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${wu(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${Su(g,u)});
|
|
}
|
|
`}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;
|
|
${ku(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&c==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&&c==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=yi(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 cX(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=Iu(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${wu(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${Su(A,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${ku(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===c&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${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=yi(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 * ${c} + col * ${u} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function ku(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function dX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=M6(e.shapeInfo.logicalShape,t.logicalShape),l=yt(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,A=w.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 y=a-2,x=a-1;i.indexOf(y)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function pX(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&&w.arraysEqual(o,a))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let u=yt(l),c=M6(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&c.length>=1?p="coords = 0;":p=c.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}() {
|
|
${u} coords = getOutputCoords();
|
|
${p}
|
|
return get${s}(${f});
|
|
}
|
|
`}function yt(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 ${e} is not yet supported`)}function x2(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function Iu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Su(e,t){return t.map(n=>e[n]).join(", ")}function hX(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=Dq(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,A;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),A=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function B6(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(!w.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(!w.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function fX(e,t,n,s,r){t.program.enableShapeUniforms||(B6(t.inShapeInfos,n),B6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=x2(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(w.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,u)}});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=w.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,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function mX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:d}=x2(e.packedInputs,o.shape,l),p="",h="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),A=w.sizeFromShape(o.shape)===1,y=_.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${u?d:""}_${c.length}_${A}_${y}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function Ss(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var gX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Dn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?tm(["r","c","d"],e):Ai(["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;
|
|
}
|
|
`}},AX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Dn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?tm(["r","c","d"],e):Ai(["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;
|
|
}
|
|
`}},yX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ks.DOWNLOAD;let t=Dn();this.outputShape=e,this.userCode=`
|
|
${P6}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},xX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ks.DOWNLOAD;let t=Dn();this.outputShape=e,this.userCode=`
|
|
${P6}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},bX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Dn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?y2():A2(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.);
|
|
}
|
|
`}},vX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Dn();this.outputShape=e,this.enableShapeUniforms=Ss(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?y2():A2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},W6={};Le(W6,{bindVertexProgramAttributeStreams:()=>Z6,createBufferFromOutputTexture:()=>Q6,createFloat16MatrixTexture:()=>j6,createFloat16PackedMatrixTexture:()=>K6,createFloat32MatrixTexture:()=>G6,createIndexBuffer:()=>H6,createPackedMatrixTexture:()=>X6,createUnsignedBytesMatrixTexture:()=>q6,createVertexBuffer:()=>U6,createVertexShader:()=>V6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>t4,downloadFloat32MatrixFromBuffer:()=>e4,downloadMatrixFromPackedOutputTexture:()=>s4,downloadPackedMatrixFromBuffer:()=>n4,getInternalFormatForFloat16MatrixTexture:()=>v2,getInternalFormatForFloat16PackedMatrixTexture:()=>I2,getInternalFormatForFloat32MatrixTexture:()=>b2,getInternalFormatForPackedMatrixTexture:()=>k2,getInternalFormatForUnsignedBytesMatrixTexture:()=>w2,uploadDenseMatrixToTexture:()=>Y6,uploadPixelDataToTexture:()=>J6});function V6(e){let t=Dn(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return f6(e,n)}function U6(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 y6(e,t)}function H6(e){let t=new Uint16Array([0,1,2,2,1,3]);return x6(e,t)}function Id(e,t,n,s,r,a){v6(t,n);let o=b6(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function b2(e){return e.internalFormatFloat}function G6(e,t,n,s){let[r,a]=bd(t,n);return Id(e,r,a,b2(s),s.textureFormatFloat,e.FLOAT)}function v2(e){return e.internalFormatHalfFloat}function j6(e,t,n,s){let[r,a]=bd(t,n);return Id(e,r,a,v2(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function w2(e){return e.downloadTextureFormat}function q6(e,t,n,s){let[r,a]=bd(t,n);return Id(e,r,a,w2(s),e.RGBA,e.UNSIGNED_BYTE)}function k2(e){return e.internalFormatPackedFloat}function X6(e,t,n,s){let[r,a]=bu(t,n);return Id(e,r,a,k2(s),e.RGBA,e.FLOAT)}function I2(e){return e.internalFormatPackedHalfFloat}function K6(e,t,n,s){let[r,a]=bu(t,n);return Id(e,r,a,I2(s),e.RGBA,s.textureTypeHalfFloat)}function Z6(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),h2(e,t,"clipSpacePos",n,3,a,s)&&h2(e,t,"uv",n,2,a,r)}function Y6(e,t,n,s,r,a){Ie(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),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function J6(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Q6(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function e4(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 t4(e,t,n,s){let[r,a]=bd(t,n),o=4,i=new Uint8Array(gq(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function n4(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(Aq(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function s4(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var nm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,qf(t,e)):this.gl=Ar(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=vd(this.gl,r),Is(this.gl,a))this.textureHalfFloatExtension=vd(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Is(this.gl,s))this.colorBufferHalfFloatExtension=vd(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Is(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Is(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=U6(this.gl),this.indexBuffer=H6(this.gl),this.framebuffer=w6(this.gl),this.textureConfig=p2(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),G6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),j6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),q6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),J6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),Y6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),K6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),X6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(f2(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>t4(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return n4(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return e4(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=Q6(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>s4(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=m6(t,e);this.vertexShader==null&&(this.vertexShader=V6(t));let s=g6(t);return Ie(t,()=>t.attachShader(s,this.vertexShader)),Ie(t,()=>t.attachShader(s,n)),A6(t,s),this.debug&&Kf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=Z6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Kf(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?I6(this.gl,e,t):S6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),C6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=bu(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&&Kf(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vd(this.gl,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=wX(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)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Zf(this.gl,e,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Zf(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):f2(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;Zf(s,e,this.framebuffer),this.debug&&wd(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(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 wX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:kX,bincountImpl:r4,bincountReduceImpl:IX,ceilImpl:SX,concatImpl:CX,equalImpl:TX,expImpl:NX,expm1Impl:EX,floorImpl:RX,gatherNdImpl:DX,gatherV2Impl:_X,greaterImpl:FX,greaterEqualImpl:$X,lessImpl:OX,lessEqualImpl:PX,linSpaceImpl:MX,logImpl:zX,maxImpl:LX,maximumImpl:BX,minimumImpl:WX,multiplyImpl:VX,negImpl:UX,notEqualImpl:HX,prodImpl:GX,rangeImpl:jX,rsqrtImpl:qX,sigmoidImpl:XX,simpleAbsImpl:a4,sliceImpl:KX,sparseFillEmptyRowsImpl:ZX,sparseReshapeImpl:YX,sparseSegmentReductionImpl:o4,sqrtImpl:JX,stridedSliceImpl:QX,stringNGramsImpl:eK,stringSplitImpl:tK,stringToHashBucketFastImpl:nK,subImpl:sK,tileImpl:rK,topKImpl:aK,transposeImpl:S2,uniqueImpl:oK}=i7;function i4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function _n(e,t){return t===1?[e]:i4(e,t)}function iK(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 lK=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=_n("rc",t),s=yt(t),r=cK(t,e,n),a=dK(t,e[e.length-1],e[e.length-2],n),o=pK(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function uK(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 cK(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 dK(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 pK(e,t){let n=e.length,s=uK(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 l4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ss(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=`
|
|
${hK(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?y2():A2(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 hK(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?Rq(["r","c","d"],"inputShape"):Ai(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var fK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=c4(t,n),r=d4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=u4(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===yn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===yn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===yn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===yn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===yn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=c4(n,s),a=d4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=u4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 mK(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 u4(e,t,n,s,r){let a=gK(t,s),o;if(r){let[l,u]=bu(e[0],e[1]);o=l*u}else{let[l,u]=bd(e[0],e[1]);o=l*u}let i=mK(n,a);return o*i}function gK(e,t){switch(e){case yn.PACKED_2X2_FLOAT32:return k2(t);case yn.PACKED_2X2_FLOAT16:return I2(t);case yn.UNPACKED_FLOAT32:return b2(t);case yn.UNPACKED_FLOAT16:return v2(t);case yn.PACKED_4X1_UNSIGNED_BYTE:return w2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function AK(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?yn.PACKED_2X2_FLOAT32:yn.UNPACKED_FLOAT32:e?yn.PACKED_2X2_FLOAT16:yn.UNPACKED_FLOAT16}function c4(e,t){if(e===ks.UPLOAD)return yn.PACKED_2X2_FLOAT32;if(e===ks.RENDER||e==null)return AK(t);if(e===ks.DOWNLOAD||e===ks.PIXELS)return yn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function d4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ta=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},Qs="if (isnan(x)) return x;",yK="return x;",p4="return abs(x);",xK="return (x >= 0.0) ? x : (exp(x) - 1.0);",bK=Qs+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,vK=Qs+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,sm="return x;",wK="return 1.0 / (1.0 + exp(-1.0 * x));",kK="return x;",IK=`
|
|
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;
|
|
`,SK=`
|
|
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;
|
|
`,CK=`
|
|
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;
|
|
`,TK="return 1.0 / (1.0 + exp(-1.0 * x));",Cu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},NK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=_n("rc",t),s=yt(t),r=iK(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},EK=cr.whereImpl,RK=1e-7,DK=1e-4,rm={};function _K(e){return e in rm||(rm[e]={}),rm[e]}var FK=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),$K=600;function OK(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*$K/1024/1024}var Tu=class extends tc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ar(Y().getNumber("WEBGL_VERSION"));this.binaryCache=_K(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new nm(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 fK(this.gpgpu),this.numMBBeforeWarning=OK(),this.texData=new Ap(this,es())}nextDataId(){return Tu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:ks.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:ks.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 Cu(o,sm):d=new Ta(o,sm);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,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);c=_.mergeRealAndImagArrays(d,p)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}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 Cu(s,sm):h=new Ta(s,sm);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Xf(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=_.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),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)&&es().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=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return je(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!p6(n))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Xf(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Yf(t):t,i=a?new xX(o):new yX(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=FK){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return EK(e.shape,t)}packedUnaryOp(e,t,n){let s=new Cu(e.shape,t),r=this.compileAndRun(s,[e],n);return es().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=a4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,p4,e.dtype);let t=new Ta(e.shape,p4),n=this.compileAndRun(t,[e]);return es().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.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 es().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new NK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new lK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[mi(e.shape),...gi(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[mi(t),...gi(t)],a=new l4(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=Yf(s),o,i=Xf(a);n?o=new AX(a):o=new gX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.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===xd.DENSE){let m=Xf(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.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&&w.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!kd(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=mX(e,l,u),d=this.getAndSaveBinary(c,()=>hX(this.gpgpu,e,l,u)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),fX(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=H(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Te(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?RK:DK}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,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=E6(n,i),t.texShape=c),r!=null){let d=Yf(n),p,h=c[1],f=c[0],m=r instanceof Uint8Array;i?([h,f]=bu(c[0],c[1]),p=new vX(d,m)):p=new bX(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=ks.PIXELS:this.texData.get(g.dataId).usage=ks.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],y=!0,x=this.runWebGLProgram(p,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(c,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=PK(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]*w.bytesPerElement(t)}};Tu.nextDataId=0;function PK(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 MK="3.9.0";function h4(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Tc.isBrowser()&&Xl("webgl",()=>new Tu,2);var zK={forceHalfFloat:h4},f4=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Nu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},am=`
|
|
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;
|
|
`,Sd=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Ss(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${yt(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) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=_n("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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 ds(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 LK={kernelName:oo,backendName:"webgl",kernelFunc:ds};function Na(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=ds({inputs:{x:s},backend:n}),l=ds({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var BK={kernelName:Sp,backendName:"webgl",kernelFunc:Na},m4="return (a < 0.) ? b * a : a;",g4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function WK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(g4,r.shape,o.shape):new Nu(m4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var VK={kernelName:io,backendName:"webgl",kernelFunc:WK},A4="return (a < 0.) ? b * a : a;",y4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function UK(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(y4,s.shape,r.shape):new Nu(A4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var HK={kernelName:vo,backendName:"webgl",kernelFunc:UK},x4="if (isnan(x)) return x;",GK=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,jK=`
|
|
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 tt({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 u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Cu(o.shape,t):c=new Ta(o.shape,e),i.runWebGLProgram(c,[o],l)}}function xn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new Nu(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,S],Ds(b.dtype,v.dtype))}),y=Na({inputs:{real:g,imag:A},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(A),y}let d=a||Ds(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?_.fromUint8ToStringArray(f):f,A=l.dtype==="string"?_.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,g,A,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Sd(t,l.shape,u.shape,n):h=new Nu(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],d)}}function om(e,t=!1){if(e==="linear")return t?kK:yK;if(e==="relu")return t?SK:bK;if(e==="elu")return t?IK:xK;if(e==="relu6")return t?CK:vK;if(e==="prelu")return t?y4:A4;if(e==="leakyrelu")return t?g4:m4;if(e==="sigmoid")return t?TK:wK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var b4=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=Ss(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/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 y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${c}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${c}; i++) {
|
|
int batchA = ${y};
|
|
int batchB = ${x};
|
|
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);
|
|
}
|
|
`}},v4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},w4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},k4="return a * b;";function C2(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=_.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new w4(v4.REAL,s.shape,r.shape),c=new w4(v4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=Na({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=VX(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),p=n.texData.get(d.dataId);return p.values=u,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Sd(k4,s.shape,r.shape):o=new Nu(k4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var qK={kernelName:Ao,backendName:"webgl",kernelFunc:C2};function XK(e,t,n){let s=[mi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[mi(t),...gi(t)],o=new l4(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!kd(r.shape,l)&&!(c.texture!==null&&kd(c.shape,l))?XK(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var KK={kernelName:Sl,backendName:"webgl",kernelFunc:ve},I4=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 c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
|
|
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) {
|
|
${u}
|
|
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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},ZK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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 u=Math.floor(n/4)*4,c=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 < ${u}; 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 + ${u};
|
|
if (${c===1}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===2}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===3}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function YK(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=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function xi(e,t,n,s){let r=YK(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,d;n==="mean"?c=o===0?new I4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new I4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new ZK({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=s.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var JK=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=yt(this.rank),r=QK(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function QK(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 eZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=yt(this.rank),r=i4("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function im(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eZ(e.shape,t):new JK(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function tZ(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,l=_.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=im(e,l,s),i=_.getInnerMostAxes(i.length,a)),_.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=_.computeOutAndReduceShapes(c.shape,i),h=d;n&&(h=_.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,A=ve({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=ch(e.dtype),x=xi(A,y,"sum",s),b=ve({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),u&&s.disposeIntermediateTensorInfo(c),b}function lm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return tZ(r,a,o,n)}var nZ={kernelName:Do,backendName:"webgl",kernelFunc:lm};function Fn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=S2(d,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=p}else u=im(r,a,o);return u}var sZ={kernelName:Mo,backendName:"webgl",kernelFunc:Fn},S4=1e3;function um({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],p=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=w.sizeFromShape(m),y=w.sizeFromShape(g),x=A===y||A===1||y===1;w.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,h]:[A,h,d],S=s?[y,f,p]:[y,p,f],C=ve({inputs:{x:e},backend:r,attrs:{shape:k}}),D=ve({inputs:{x:t},backend:r,attrs:{shape:S}}),O=[C,D],E=Math.max(A,y),R=n?C.shape[1]:C.shape[2],T=a!=null,P=o!=null,U=l==="leakyrelu",j=l!=null?om(l,!0):null,q=T||P||U||j!=null,X;if((h===1||f===1)&&R>S4&&q===!1){let ne=C,se=D;n&&(ne=Fn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),O.push(ne)),s&&(se=Fn({inputs:{x:D},backend:r,attrs:{perm:[0,2,1]}}),O.push(se));let ae=f!==1,Q=f===1,ce=ne;ae&&(ce=ve({inputs:{x:ne},backend:r,attrs:{shape:[E,R,1]}}),O.push(ce));let de=f===1?2:1,fe=se;Q&&(fe=ve({inputs:{x:se},backend:r,attrs:{shape:[E,1,R]}}),O.push(fe));let be=C2({inputs:{a:ce,b:fe},backend:r});X=lm({inputs:{x:be},backend:r,attrs:{axis:de,keepDims:!0}}),O.push(be)}else{let ne=Ds(e.dtype,t.dtype),se=new b4(k,S,[E,h,f],n,s,T,j,P,U),ae=[C,D];if(a!=null&&ae.push(a),P&&ae.push(o),U){let Q=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ae.push(Q),O.push(Q)}X=r.runWebGLProgram(se,ae,ne)}let te=ve({inputs:{x:X},backend:r,attrs:{shape:v}});O.push(X);for(let ne of O)r.disposeIntermediateTensorInfo(ne);return te}function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return um({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var aZ={kernelName:zo,backendName:"webgl",kernelFunc:rZ},C4="return abs(x);";function oZ(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=a4(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Cu(s.shape,C4):r=new Ta(s.shape,C4),n.runWebGLProgram(r,[s],s.dtype)}var iZ={kernelName:Bi,backendName:"webgl",kernelFunc:oZ},lZ=Qs+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,uZ=tt({opSnippet:lZ}),cZ={kernelName:Wi,backendName:"webgl",kernelFunc:uZ},dZ=Qs+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,pZ=tt({opSnippet:dZ}),hZ={kernelName:Vi,backendName:"webgl",kernelFunc:pZ},T4="return a + b;",fZ=xn({opSnippet:T4,packedOpSnippet:T4,supportsComplex:!0,cpuKernelImpl:kX}),mZ={kernelName:sa,backendName:"webgl",kernelFunc:fZ},gZ=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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},AZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 cm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ds({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=cm({inputs:s.slice(0,l),backend:n}),c=cm({inputs:s.slice(l),backend:n});return cm({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Ds(l,u)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new AZ(s[0].shape,a):new gZ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var yZ={kernelName:Wa,backendName:"webgl",kernelFunc:cm};function xZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=Fn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("all",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"all",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var bZ={kernelName:Ui,backendName:"webgl",kernelFunc:xZ};function vZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=Fn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("any",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"any",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var wZ={kernelName:Hi,backendName:"webgl",kernelFunc:vZ},kZ=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 ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},IZ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${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=yt(i),u=_n("coords",i),c,d;if(a===1){d=i+1;let S=yt(d);c=`
|
|
${S} sourceLocR = ${S}(${u.join()}, 0);
|
|
++${u[i-1]};
|
|
${S} sourceLocG = ${S}(${u.join()}, 0);
|
|
++${u[i-2]};
|
|
${S} sourceLocA = ${S}(${u.join()}, 0);
|
|
--${u[i-1]};
|
|
${S} sourceLocB = ${S}(${u.join()}, 0);
|
|
--${u[i-2]};`}else d=i,c=`
|
|
${l} sourceLocR = coords;
|
|
++${u[i-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[i-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[i-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=_n("sourceLocR",d-1).concat("inIdx.r"),g=_n("sourceLocG",d-1).concat("inIdx.g"),A=_n("sourceLocB",d-1).concat("inIdx.b"),y=_n("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()})));`,v=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${y.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 = ${u[i-1]} < ${o[i-1]-1};
|
|
bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
|
|
${c}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${v};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${v};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function N4(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=_.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new kZ(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=N4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function E4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=_.computeOptimalWindowSize(a),i=new IZ(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=E4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function R4(e,t,n,s){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=_.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(c),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=N4(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return E4(e,t,s)}function SZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Fn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=R4(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var CZ={kernelName:Va,backendName:"webgl",kernelFunc:SZ};function TZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Fn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=R4(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var NZ={kernelName:rc,backendName:"webgl",kernelFunc:TZ},EZ=Qs+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,RZ=tt({opSnippet:EZ}),DZ={kernelName:Gi,backendName:"webgl",kernelFunc:RZ},_Z=Qs+"return log(x + sqrt(x * x + 1.0));",FZ=tt({opSnippet:_Z}),$Z={kernelName:ji,backendName:"webgl",kernelFunc:FZ},OZ=Qs+`
|
|
return atan(x);
|
|
`,PZ=tt({opSnippet:OZ}),MZ={kernelName:qi,backendName:"webgl",kernelFunc:PZ},zZ=GK+`
|
|
return atan(a, b);
|
|
`,LZ=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+jK+`
|
|
return result;
|
|
`,BZ=xn({opSnippet:zZ,packedOpSnippet:LZ}),WZ={kernelName:Ki,backendName:"webgl",kernelFunc:BZ},VZ=Qs+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,UZ=tt({opSnippet:VZ}),HZ={kernelName:Xi,backendName:"webgl",kernelFunc:UZ},Cd=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,u=e.dilationWidth,c=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) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${u}) {
|
|
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 ${S} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,k=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${y}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 < ${c};
|
|
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 * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${v===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},T2=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,u=e.dilationDepth,c=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 y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let D=">=";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 += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
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 ${D} 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 * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,C=`
|
|
if (${y}) {
|
|
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 = ${x};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
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)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${S===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===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
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${v});
|
|
}
|
|
}
|
|
`}};function GZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;vu(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return ds({inputs:{x:r},backend:n});let d=new Cd(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var jZ={kernelName:Ua,backendName:"webgl",kernelFunc:GZ};function qZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,l,u),p=new T2(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var XZ={kernelName:ac,backendName:"webgl",kernelFunc:qZ},KZ=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,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${c});
|
|
const float avgMultiplier = float(${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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},ZZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 < ${c};
|
|
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 += ${u}) {
|
|
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 YZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new ZZ(p);return n.runWebGLProgram(h,[r],o.dtype)}var JZ={kernelName:kp,backendName:"webgl",kernelFunc:YZ};function QZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;vu([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=new KZ(c);return n.runWebGLProgram(d,[r],o.dtype)}var eY={kernelName:wp,backendName:"webgl",kernelFunc:QZ};function tY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return um({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var nY={kernelName:Ha,backendName:"webgl",kernelFunc:tY},sY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(_.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)));
|
|
}
|
|
`}},rY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(_.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);
|
|
}
|
|
`}},aY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.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 u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new rY(s.shape,r.shape,a.shape,c,d,l):new sY(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(p,u,u[0].dtype)},oY={kernelName:ro,backendName:"webgl",kernelFunc:aY},iY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=lY(this.rank),s,r=e.map((a,o)=>`sourceLoc.${N2[o]} = start[${o}] + coords.${N2[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},N2=["x","y","z","w","u","v"];function lY(e){if(e===1)return"sourceLoc";if(e<=6)return N2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var uY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=_n("coords",this.rank),s=_n("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function cY(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=Tn.computeFlatOffset(t,w.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 Eu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Tn.parseSliceParams(r,a,o);if(Tn.assertParamsValid(r,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=KX(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:u}=n.texData.get(r.dataId),c=Tn.isSliceContinous(r.shape,i,l);if(u||!c){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uY(l):new iY(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),cY(r,i,l,n)}var dY={kernelName:El,backendName:"webgl",kernelFunc:Eu},pY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Fn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:c}}),A=Eu({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},hY={kernelName:Zi,backendName:"webgl",kernelFunc:pY};function fY(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),u=r4(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var mY={kernelName:Ip,backendName:"webgl",kernelFunc:fY},gY="return float(a != b);",D4=xn({opSnippet:gY,cpuKernelImpl:HX,dtype:"bool"}),AY={kernelName:Al,backendName:"webgl",kernelFunc:D4};function Td(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ds({inputs:{x:r.complexTensorInfos.real},backend:n})}var yY={kernelName:jp,backendName:"webgl",kernelFunc:Td},xY="return float(int(x));";function bY(e,t){let n=new Ta(e.shape,xY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function E2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ds({inputs:{x:r},backend:n});let o=Mt(r.shape),i=E2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Na({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Td({inputs:{input:r},backend:n}),i=E2({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=ds({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return bY(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=D4({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 vY={kernelName:Ga,backendName:"webgl",kernelFunc:E2},_4="return ceil(x);",wY=tt({opSnippet:_4,packedOpSnippet:_4,cpuKernelImpl:SX}),kY={kernelName:ja,backendName:"webgl",kernelFunc:wY},IY=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));
|
|
}
|
|
`}},SY=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 CY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new SY(r.shape):i=new IY(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var TY={kernelName:ra,backendName:"webgl",kernelFunc:CY},NY=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 F4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function EY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new NY(s.shape),o=[F4(s,r.complexTensorInfos.real),F4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var RY={kernelName:oc,backendName:"webgl",kernelFunc:EY},DY=class{constructor(e){this.outputShape=[],this.outputShape=_.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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},_Y=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=yt(s),a=_n("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${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],u=o.slice(-2),c=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${c}), vec2(${u.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${dm(o,l,m)}),
|
|
vec2(${dm(u,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${dm(o,l,h)}),
|
|
vec2(${dm(u,l,h)}));`,this.userCode=`
|
|
float getValue(${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 dm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function pm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ds({inputs:{x:r.complexTensorInfos.imag},backend:n})}var FY={kernelName:Lp,backendName:"webgl",kernelFunc:pm};function Ru(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Td({inputs:{input:m},backend:n})),d=e.map(m=>pm({inputs:{input:m},backend:n})),p=Ru(c,t,n),h=Ru(d,t,n),f=Na({inputs:{real:p,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(A=>{let y=w.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),d=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=_.computeOutShape(c.map(A=>A.shape),1),h=c[0].shape[0]===1,f=CX(d,p,s,h),m=_.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=Ru(e.slice(0,c),t,n),p=Ru(e.slice(c),t,n),h=Ru([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new _Y(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=$Y(e,t,n),i=new DY(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function $Y(e,t,n){let s=_.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function $4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=_.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return ds({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return _.assertParamsConsistent(l,a),Ru(i,a,n)}var OY={kernelName:Yi,backendName:"webgl",kernelFunc:$4},O4=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,u=e.dilationHeight,c=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,y=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${y}];
|
|
|
|
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 * ${u};
|
|
|
|
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 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${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;
|
|
${v}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},PY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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 < ${c}; 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 * ${u};
|
|
|
|
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);
|
|
}
|
|
`}},MY=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=Ss(this.outputShape.length);let{dataFormat:n}=t,s=Dn(),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 u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
|
|
blockIndex = rc.y + ${c};
|
|
pos = rc.x + ${u};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${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[${u*2+c}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${u*2+c}] = 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 P4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=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)&&c>S4)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(kd(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let C=um({a:v,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),D=s.texData.get(C.dataId);w.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,D.shape=n.outShape,g=ds({inputs:{x:C},backend:s}),g.shape=n.outShape,A.push(C)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=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]}}),S=um({a:v,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(v),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function M4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=p*d,A=[m,g],y=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let S=new MY(A,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],D=s.runWebGLProgram(S,[v],"float32",C),O=ve({inputs:{x:D},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(D),b.push(O);let E=r!=null,R=a!=null,T=i==="leakyrelu",P=i?om(i,!0):null,U=new b4(O.shape,k.shape,[1,g,n.outChannels],y,x,E,P,R,T),j=[O,k];if(r&&j.push(r),R&&j.push(a),T){let ne=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(ne),b.push(ne)}let q=s.runWebGLProgram(U,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],te=ve({inputs:{x:q},backend:s,attrs:{shape:X}});b.push(q);for(let ne of b)s.disposeIntermediateTensorInfo(ne);return te}function zY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!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=P4({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=M4({x:r,filter:a,convInfo:p,backend:n});else{let m=new O4(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 LY={kernelName:qa,backendName:"webgl",kernelFunc:zY},BY=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);
|
|
}
|
|
`}},WY=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,u=a?2:3,c=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${c}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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);
|
|
}
|
|
`}},VY=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},UY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${i}, ${l}, ${u});
|
|
|
|
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 HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),h=new BY(p);return n.runWebGLProgram(h,[r,a],"float32")}var GY={kernelName:Cp,backendName:"webgl",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(u),p=_.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),h=new WY(p);return n.runWebGLProgram(h,[r,a],"float32")}var qY={kernelName:Xa,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new PY(u);return n.runWebGLProgram(c,[r,a],"float32")}var KY={kernelName:ic,backendName:"webgl",kernelFunc:XY};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=_.computeConv3DInfo(r.shape,l,o,1,i),c=new VY(u);return n.runWebGLProgram(c,[r,a],"float32")}var YY={kernelName:Tp,backendName:"webgl",kernelFunc:ZY};function JY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=_.computeConv3DInfo(l,a.shape,i,1,o),c=new UY(u);return n.runWebGLProgram(c,[r,a],"float32")}var QY={kernelName:Np,backendName:"webgl",kernelFunc:JY},eJ=x4+`
|
|
return cos(x);
|
|
`,tJ=tt({opSnippet:eJ}),nJ={kernelName:Ka,backendName:"webgl",kernelFunc:tJ},sJ=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,rJ=tt({opSnippet:sJ}),aJ={kernelName:Za,backendName:"webgl",kernelFunc:rJ},oJ=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,b]=d>1?[`${(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(${y});
|
|
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 = ${x};
|
|
|
|
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(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},iJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new oJ(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},lJ={kernelName:Ji,backendName:"webgl",kernelFunc:iJ},z4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${L4(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() {
|
|
${yt(s)} coords = getOutputCoords();
|
|
int end = ${B4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${B4(s,"coords")} = idx;
|
|
val += getX(${L4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function L4(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 ${e} is not yet supported`)}function B4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function uJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,u=_.getAxesPermutation([a],l),c=r;u!=null&&(c=Fn({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=ds({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new z4(c.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new z4(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=_.getUndoAxesPermutation(u),m=Fn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var cJ={kernelName:Ya,backendName:"webgl",kernelFunc:uJ};function dJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=r4(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=IX(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var pJ={kernelName:Ep,backendName:"webgl",kernelFunc:dJ},hJ=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 fJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new hJ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var mJ={kernelName:Qi,backendName:"webgl",kernelFunc:fJ},W4=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=Ss(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";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}
|
|
}
|
|
`,u="result = activation(result);");let c=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;
|
|
${c}
|
|
${u}
|
|
setOutput(result);
|
|
}
|
|
`}},V4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ss(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,d=c,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)p+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;for(let g=0;g<u;g++){for(let A=0;A<c;A++)p+=`
|
|
xTexelC${A*2} = vec4(0.0);
|
|
xTexelC${A*2}Ready = 0;
|
|
xTexelC${A*2+1} = vec4(0.0);
|
|
xTexelC${A*2+1}Ready = 0;
|
|
xC${A} = vec4(0.0);`;p+=`
|
|
xR = xRCorner + ${g} * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let A=0;A<(d+1)/2;A++){let y=A*2;if(p+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,i===1){if(y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?p+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.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${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<c)){let x=o%2==0?w.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<c&&(p+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<c&&(p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<c&&(p+=`
|
|
wTexel = getW(${g}, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<c&&(p+=`
|
|
wTexel = getW(${g}, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}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 gJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=_.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new V4(d):p=new W4(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 AJ={kernelName:Ja,backendName:"webgl",kernelFunc:gJ},yJ=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},xJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function bJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=_.computeConv2DInfo(r.shape,c,o,i,l,u,!0),p=new yJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var vJ={kernelName:Rp,backendName:"webgl",kernelFunc:bJ};function wJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=_.computeConv2DInfo(c,a.shape,o,i,l,u,!0),p=new xJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var kJ={kernelName:Dp,backendName:"webgl",kernelFunc:wJ},IJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function SJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new IJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var CJ={kernelName:_p,backendName:"webgl",kernelFunc:SJ},TJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${a});
|
|
const ivec2 pads = ivec2(${c}, ${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 * ${u};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function NJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new TJ(u);c=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}var EJ={kernelName:lc,backendName:"webgl",kernelFunc:NJ};function RJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=_.decodeEinsumEquation(r,a.length);_.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=_.getEinsumComputePath(i,l),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:A,expandDims:y}=_.getEinsumPermutation(h,l[g]),x;_.isIdentityPermutation(A)?x=a[g]:(x=Fn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=C2({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=lm({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var DJ={kernelName:Op,backendName:"webgl",kernelFunc:RJ},_J="return (x >= 0.0) ? x : (exp(x) - 1.0);",FJ=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,$J=tt({opSnippet:_J,packedOpSnippet:FJ}),OJ={kernelName:eo,backendName:"webgl",kernelFunc:$J},PJ="return (b >= 1.0) ? a : a * (b + 1.0);",MJ=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,zJ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(MJ,s.shape,r.shape):new Nu(PJ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},LJ={kernelName:Pp,backendName:"webgl",kernelFunc:zJ},BJ=`
|
|
return vec4(equal(a, b));
|
|
`,WJ="return float(a == b);",VJ=xn({opSnippet:WJ,packedOpSnippet:BJ,dtype:"bool",cpuKernelImpl:TX}),UJ={kernelName:tl,backendName:"webgl",kernelFunc:VJ},HJ=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${_.ERF_P};
|
|
float a1 = ${_.ERF_A1};
|
|
float a2 = ${_.ERF_A2};
|
|
float a3 = ${_.ERF_A3};
|
|
float a4 = ${_.ERF_A4};
|
|
float a5 = ${_.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,GJ=tt({opSnippet:HJ}),jJ={kernelName:el,backendName:"webgl",kernelFunc:GJ},U4="return exp(x);",H4=tt({opSnippet:U4,packedOpSnippet:U4,cpuKernelImpl:NX}),qJ={kernelName:to,backendName:"webgl",kernelFunc:H4};function R2(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var XJ={kernelName:nl,backendName:"webgl",kernelFunc:R2},G4="return exp(x) - 1.0;",KJ=tt({opSnippet:G4,packedOpSnippet:G4,cpuKernelImpl:EX}),ZJ={kernelName:sl,backendName:"webgl",kernelFunc:KJ},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=w.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,u=new j4("real",l,t),c=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(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=Na({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 YJ(e){let{inputs:t,backend:n}=e,{input:s}=t;return q4(s,!1,n)}var JJ={kernelName:Mp,backendName:"webgl",kernelFunc:YJ},QJ=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 Nd(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new QJ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var eQ={kernelName:uc,backendName:"webgl",kernelFunc:Nd},tQ=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},nQ={kernelName:rl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new tQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},X4="return floor(x);",sQ=tt({opSnippet:X4,packedOpSnippet:X4,cpuKernelImpl:RX}),rQ={kernelName:no,backendName:"webgl",kernelFunc:sQ},aQ=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,oQ=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,iQ=xn({opSnippet:aQ,packedOpSnippet:oQ,dtype:"int32"}),lQ={kernelName:so,backendName:"webgl",kernelFunc:iQ},uQ=class{constructor(e){this.variableNames=["A"];let t=Dn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},cQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Dn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},dQ={kernelName:rh,backendName:"webgl",kernelFunc:pQ},Du;function pQ(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,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(Du==null&&(Du=document.createElement("canvas").getContext("2d")),Du.canvas.width=l,Du.canvas.height=u,Du.drawImage(r,0,0,l,u),r=Du.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=ks.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new cQ(d):new uQ(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function hQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=_.convertConv2DDataFormat(c),g=_.computeConv2DInfo(r.shape,a.shape,l,d,u,p,!1,m),A,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"))A=P4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=M4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=h==="leakyrelu",S=h?om(h,!1):null,C=new O4(g,b,S,v,k),D=[r,a];if(o&&D.push(o),i&&D.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));D.push(O),y.push(O)}A=n.runWebGLProgram(C,D,"float32")}let x=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var fQ={kernelName:Lo,backendName:"webgl",kernelFunc:hQ};function mQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=_.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),A=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?om(p,A):null,x=[r,a],b=o!=null,v=i!=null,k=p==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(O),f.push(O)}let S;A?S=new V4(g,b,y,v,k):S=new W4(g,b,y,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=n.runWebGLProgram(S,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),D}var gQ={kernelName:Bo,backendName:"webgl",kernelFunc:mQ},AQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=yt(t.length),r=yt(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 yQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[l,u,c,d]=_.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(s),x=DX(A,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new AQ(o,d,[u,c]),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 xQ={kernelName:ol,backendName:"webgl",kernelFunc:yQ},bQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),s=vQ(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function vQ(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(`${n[r]}`);return s.join()}function K4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=_.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=w.sizeFromShape(a.shape),d=[],p=ve({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ve({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});d.push(p),d.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(p),b=_X(x,y,f);return d.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new bQ(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let A=ve({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var wQ={kernelName:al,backendName:"webgl",kernelFunc:K4},kQ="return float(a > b);",IQ=`
|
|
return vec4(greaterThan(a, b));
|
|
`,SQ=xn({opSnippet:kQ,packedOpSnippet:IQ,cpuKernelImpl:FX,dtype:"bool"}),CQ={kernelName:il,backendName:"webgl",kernelFunc:SQ},TQ="return float(a >= b);",NQ=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,EQ=xn({opSnippet:TQ,packedOpSnippet:NQ,dtype:"bool",cpuKernelImpl:$X}),RQ={kernelName:ao,backendName:"webgl",kernelFunc:EQ};function DQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return q4(s,!0,n)}var _Q={kernelName:zp,backendName:"webgl",kernelFunc:DQ},FQ="return float(!isnan(x) && !isinf(x));",$Q=tt({opSnippet:FQ,dtype:"bool"}),OQ={kernelName:ll,backendName:"webgl",kernelFunc:$Q},PQ="return float(isinf(x));",MQ=tt({opSnippet:PQ,dtype:"bool"}),zQ={kernelName:ul,backendName:"webgl",kernelFunc:MQ},LQ="return float(isnan(x));",BQ=tt({opSnippet:LQ,dtype:"bool"}),WQ={kernelName:cl,backendName:"webgl",kernelFunc:BQ},VQ="return float(a < b);",UQ=`
|
|
return vec4(lessThan(a, b));
|
|
`,HQ=xn({opSnippet:VQ,packedOpSnippet:UQ,cpuKernelImpl:OX,dtype:"bool"}),GQ={kernelName:dl,backendName:"webgl",kernelFunc:HQ},jQ="return float(a <= b);",qQ=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,XQ=xn({opSnippet:jQ,packedOpSnippet:qQ,cpuKernelImpl:PX,dtype:"bool"}),KQ={kernelName:pl,backendName:"webgl",kernelFunc:XQ};function ZQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=MX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var YQ={kernelName:Bp,backendName:"webgl",kernelFunc:ZQ},JQ=`if (x < 0.0) return NAN;
|
|
return log(x);`,QQ=`
|
|
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;
|
|
`,eee=tt({opSnippet:JQ,packedOpSnippet:QQ,cpuKernelImpl:zX}),tee={kernelName:lo,backendName:"webgl",kernelFunc:eee},nee="return log(1.0 + x);",see=tt({opSnippet:nee}),ree={kernelName:hl,backendName:"webgl",kernelFunc:see},aee="return float(a >= 1.0 && b >= 1.0);",oee=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,iee=xn({opSnippet:aee,packedOpSnippet:oee,dtype:"bool"}),lee={kernelName:fl,backendName:"webgl",kernelFunc:iee},uee="return float(!(x >= 1.0));",cee=tt({opSnippet:uee}),dee={kernelName:cc,backendName:"webgl",kernelFunc:cee},pee="return float(a >= 1.0 || b >= 1.0);",hee=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,fee=xn({opSnippet:pee,packedOpSnippet:hee,dtype:"bool"}),mee={kernelName:dc,backendName:"webgl",kernelFunc:fee},gee=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 * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Aee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
|
|
}
|
|
`}},yee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Aee(r.shape,a,o,i,l):new gee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},xee={kernelName:pc,backendName:"webgl",kernelFunc:yee},bee=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);
|
|
}
|
|
`}},vee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new bee(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},wee={kernelName:Wp,backendName:"webgl",kernelFunc:vee};function kee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=xi(i,e.dtype,"max",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function Z4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[c[S]];let v=S2(x,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=v}else h=im(r,c,n);u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("max",u,i);let[f,m]=_.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=_.expandShapeToKeepDim(f,l));let A;if(p){let x=n.texData.get(h.dataId).values,b=LX(x,w.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(A.dataId);v.values=b}else A=kee(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Iee={kernelName:uo,backendName:"webgl",kernelFunc:Z4},See=f4+`
|
|
return max(a, b);
|
|
`,Cee=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+am+`
|
|
return result;
|
|
`,Tee=xn({opSnippet:See,packedOpSnippet:Cee,cpuKernelImpl:BX}),Nee={kernelName:co,backendName:"webgl",kernelFunc:Tee};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;vu(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return ds({inputs:{x:r},backend:n});let d=new Cd(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Ree={kernelName:po,backendName:"webgl",kernelFunc:Eee};function Dee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,u,l),p=new T2(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var _ee={kernelName:hc,backendName:"webgl",kernelFunc:Dee},Fee=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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},$ee=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,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${c}, ${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 < ${u};
|
|
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} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Oee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new T2(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new $ee(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Pee={kernelName:Up,backendName:"webgl",kernelFunc:Oee};function Mee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;vu([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=!0,f=new Cd(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Fee(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var zee={kernelName:Vp,backendName:"webgl",kernelFunc:Mee};function Lee(e,t,n,s){let r=new Cd(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Cd(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Bee={kernelName:Hp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];w.assert(_.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=_.computePool2DInfo(s.shape,r,a,u,o),[d,p]=Lee(s,i,c,l);return[d,p]}};function Wee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=xi(i,"float32","mean",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var Vee={kernelName:ho,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=w.parseAxisParam(a,s.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let C=0;C<v.length;C++)v[C]=s.shape[c[C]];let k=S2(b,s.shape,s.dtype,c,v);f=o.makeTensorInfo(v,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=im(s,c,o);h.push(f),u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=_.computeOutAndReduceShapes(f.shape,u),A=m;r&&(A=_.expandShapeToKeepDim(m,l));let y=Wee(f,g,A,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return y}};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=Fn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"min",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var Hee={kernelName:fo,backendName:"webgl",kernelFunc:Uee},Gee=f4+`
|
|
return min(a, b);
|
|
`,jee=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+am+`
|
|
return result;
|
|
`,qee=xn({opSnippet:Gee,packedOpSnippet:jee,cpuKernelImpl:WX}),Xee={kernelName:mo,backendName:"webgl",kernelFunc:qee},Kee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=yt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).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}));
|
|
}
|
|
`}},Zee=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=yt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=_n("rc",s),l=_n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${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()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`}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()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},Yee=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zee(s.shape,r,a):new Kee(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Jee={kernelName:go,backendName:"webgl",kernelFunc:Yee},Qee=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,ete=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+am+`
|
|
return result;
|
|
`,tte=xn({opSnippet:Qee,packedOpSnippet:ete}),nte={kernelName:ml,backendName:"webgl",kernelFunc:tte},ste=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}));
|
|
}
|
|
`}},rte=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,ate=`
|
|
// 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;
|
|
`,Y4=xn({opSnippet:rte,packedOpSnippet:ate,checkOutOfBounds:!0}),ote={kernelName:Qa,backendName:"webgl",kernelFunc:Y4},J4="return a - b;",Q4=xn({opSnippet:J4,packedOpSnippet:J4,supportsComplex:!0,cpuKernelImpl:sK}),ite={kernelName:$o,backendName:"webgl",kernelFunc:Q4};function ek(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=Z4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=_.expandShapeToKeepDim(i.shape,o),u=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),c=Q4({inputs:{a:r,b:u},backend:n}),d=H4({inputs:{x:c},backend:n}),p=lm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=Y4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var lte={kernelName:_o,backendName:"webgl",kernelFunc:ek};function ute(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:ek({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new ste(u,c,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var cte={kernelName:Gp,backendName:"webgl",kernelFunc:ute},tk="return -x;";function dte(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=UX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Cu(s.shape,tk):r=new Ta(s.shape,tk),n.runWebGLProgram(r,[s],s.dtype)}var pte={kernelName:gl,backendName:"webgl",kernelFunc:dte},hte=cr.nonMaxSuppressionV3Impl;function fte(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,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=hte(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var mte={kernelName:yl,backendName:"webgl",kernelFunc:fte},gte=cr.nonMaxSuppressionV4Impl;function Ate(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:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=gte(c,d,o,i,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var yte={kernelName:xl,backendName:"webgl",kernelFunc:Ate},xte=cr.nonMaxSuppressionV5Impl;function bte(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:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=xte(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var vte={kernelName:bl,backendName:"webgl",kernelFunc:bte},wte=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)));
|
|
}
|
|
`}},kte=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new wte(l,a,o,i),c=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Ite={kernelName:yo,backendName:"webgl",kernelFunc:kte};function hm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Td({inputs:{input:s},backend:n}),a=hm({inputs:{x:r},backend:n}),o=pm({inputs:{input:s},backend:n}),i=hm({inputs:{x:o},backend:n}),l=Na({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Nd({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Ste={kernelName:Ll,backendName:"webgl",kernelFunc:hm};function nk(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=Td({inputs:{input:s},backend:n}),a=nk({inputs:{x:r},backend:n}),o=pm({inputs:{input:s},backend:n}),i=hm({inputs:{x:o},backend:n}),l=Na({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Nd({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Cte={kernelName:vl,backendName:"webgl",kernelFunc:nk};function Tte(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return R2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=R2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=$4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Nte={kernelName:wl,backendName:"webgl",kernelFunc:Tte},Ete=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=yt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).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}));
|
|
}
|
|
}
|
|
`}},Rte=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=yt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=_n("rc",s),l=_n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${u}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${u}) {`],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()}), ${c});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},sk=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return Nd({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Rte(r.shape,a,o):new Ete(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Dte={kernelName:xo,backendName:"webgl",kernelFunc:sk},_te=`
|
|
if(a < 0.0 && floor(b) < b){
|
|
return NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
return (round(mod(b, 2.0)) != 1) ?
|
|
pow(abs(a), b) : sign(a) * pow(abs(a), b);
|
|
`,Fte=`
|
|
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
|
|
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
|
|
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
|
|
vec4 result = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
bvec4 isExpZero = equal(b, vec4(0.0));
|
|
result.r = isExpZero.r ? 1.0 : result.r;
|
|
result.g = isExpZero.g ? 1.0 : result.g;
|
|
result.b = isExpZero.b ? 1.0 : result.b;
|
|
result.a = isExpZero.a ? 1.0 : result.a;
|
|
|
|
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
|
|
`+am+`
|
|
return result;
|
|
`,$te=xn({opSnippet:_te,packedOpSnippet:Fte}),Ote={kernelName:bo,backendName:"webgl",kernelFunc:$te};function Pte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=_.getAxesPermutation(c,i),p=r;d!=null&&(p=Fn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=_.getInnerMostAxes(c.length,i),l.push(p)),_.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=GX(p.shape,p.dtype,f,c);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=_.computeOutAndReduceShapes(p.shape,c),g=w.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=ch(r.dtype),x=xi(A,y,"prod",n);h=ve({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(h);let f=_.expandShapeToKeepDim(h.shape,u);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Mte={kernelName:kl,backendName:"webgl",kernelFunc:Pte},rk=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=jX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zte={kernelName:fc,backendName:"webgl",kernelFunc:rk},Lte="return 1.0 / x;",Bte=tt({opSnippet:Lte}),Wte={kernelName:Il,backendName:"webgl",kernelFunc:Bte},Vte=Qs+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Ute=`
|
|
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;
|
|
`,Hte=tt({opSnippet:Vte,packedOpSnippet:Ute}),Gte={kernelName:wo,backendName:"webgl",kernelFunc:Hte},jte=Qs+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,qte=`
|
|
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;
|
|
`,Xte=tt({opSnippet:jte,packedOpSnippet:qte}),Kte={kernelName:Io,backendName:"webgl",kernelFunc:Xte},Zte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${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);
|
|
}
|
|
`}},Yte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${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 Jte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Yte(r.shape,l,u,a,o):new Zte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var Qte={kernelName:ko,backendName:"webgl",kernelFunc:Jte},ene=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],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,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(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${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 tne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ene(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var nne={kernelName:Xp,backendName:"webgl",kernelFunc:tne},sne=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${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);
|
|
}
|
|
`}},rne=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function ane(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rne(r.shape,l,u,a,o):new sne(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var one={kernelName:mc,backendName:"webgl",kernelFunc:ane},ine=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function lne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ine(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var une={kernelName:qp,backendName:"webgl",kernelFunc:lne},cne=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},dne=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-${n} tensor is not yet supported`);this.outputShape=e;let s=_n("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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 = ${u(s.slice())};
|
|
if(${r}) {
|
|
result.a = ${c(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function pne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return ds({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dne(r.shape,i):new cne(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var hne={kernelName:So,backendName:"webgl",kernelFunc:pne},fne=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},mne={kernelName:Bl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new fne(s.shape,a),[u,c]=_.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},gne=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Ane=tt({opSnippet:gne}),yne={kernelName:Co,backendName:"webgl",kernelFunc:Ane},xne="return inversesqrt(x);",bne=tt({opSnippet:xne,cpuKernelImpl:qX}),vne={kernelName:To,backendName:"webgl",kernelFunc:bne},ak=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(r.length),l=yt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,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 < ${t}; j++) {
|
|
int index = round(${c});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${p};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function wne(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=_.calculateShapes(a,r,o),p=[d/u,u];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,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new ak(l,i,h.shape.length,f.shape.length,c,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),y=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),y}var kne={kernelName:Cl,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Sne(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Ine(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ds(r.dtype,a.dtype))}var Cne={kernelName:Tl,backendName:"webgl",kernelFunc:Sne},Tne=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${_.SELU_SCALEALPHA};
|
|
float scale = ${_.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Nne=tt({opSnippet:Tne}),Ene={kernelName:Nl,backendName:"webgl",kernelFunc:Nne},ok="return 1.0 / (1.0 + exp(-1.0 * x));",Rne=tt({opSnippet:ok,packedOpSnippet:ok,cpuKernelImpl:XX}),Dne={kernelName:Eo,backendName:"webgl",kernelFunc:Rne},_ne=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Fne=tt({opSnippet:_ne}),$ne={kernelName:Dl,backendName:"webgl",kernelFunc:Fne},One=x4+`
|
|
return sin(x);
|
|
`,Pne=tt({opSnippet:One}),Mne={kernelName:No,backendName:"webgl",kernelFunc:Pne},zne=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Lne=tt({opSnippet:zne}),Bne={kernelName:Rl,backendName:"webgl",kernelFunc:Lne},Wne=`
|
|
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;
|
|
`,Vne=tt({opSnippet:Wne}),Une={kernelName:_l,backendName:"webgl",kernelFunc:Vne},Hne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let u=[],c=sk({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=_.getReshaped(c.shape,a,i,!1),p=_.getPermuted(d.length,a.length,!1),h=_.getReshapedPermuted(c.shape,a,i,!1),f=ve({inputs:{x:c},backend:n,attrs:{shape:d}}),m=Fn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},Gne={kernelName:Fl,backendName:"webgl",kernelFunc:Hne};function jne(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),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,p,h,f,m]=ZX(i,s.shape,s.dtype,l,r.dtype,u,c);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 qne={kernelName:Kp,backendName:"webgl",kernelFunc:jne};function Xne(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)),[u,c,d]=YX(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Kne={kernelName:Zp,backendName:"webgl",kernelFunc:Xne};function Zne(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),[u,c]=o4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Yne={kernelName:Yp,backendName:"webgl",kernelFunc:Zne};function Jne(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
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=o4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Qne={kernelName:Jp,backendName:"webgl",kernelFunc:Jne};function ese(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=_.calculateShapes(a,r,i),p=!1,h=new ak(u,l,r.shape.length,a.shape.length,c,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var tse={kernelName:Qp,backendName:"webgl",kernelFunc:ese};function nse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=Eu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var sse={kernelName:$l,backendName:"webgl",kernelFunc:nse},ik="return sqrt(x);",rse=tt({opSnippet:ik,packedOpSnippet:ik,cpuKernelImpl:JX}),ase={kernelName:Ro,backendName:"webgl",kernelFunc:rse},ose="return x * x;",ise=tt({opSnippet:ose}),lse={kernelName:gc,backendName:"webgl",kernelFunc:ise},lk="return (a - b) * (a - b);",use=xn({opSnippet:lk,packedOpSnippet:lk}),cse={kernelName:Fo,backendName:"webgl",kernelFunc:use};function dse({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=Qs+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new Ta(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var pse={kernelName:oa,backendName:"webgl",kernelFunc:dse},hse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=yt(n.length),a=yt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}};function fse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=Tn.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=ve({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=Eu({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ve({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,D=je(x.shape,x.dtype,C),O=QX(y,D,m,f);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new hse(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ve({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var mse={kernelName:Ol,backendName:"webgl",kernelFunc:fse};function gse(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=eK(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Ase={kernelName:eh,backendName:"webgl",kernelFunc:gse};function yse(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],[u,c,d]=tK(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var xse={kernelName:th,backendName:"webgl",kernelFunc:yse};function bse(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=nK(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var vse={kernelName:nh,backendName:"webgl",kernelFunc:bse},wse="return tan(x);",kse=tt({opSnippet:wse}),Ise={kernelName:Oo,backendName:"webgl",kernelFunc:kse},Sse=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Cse=tt({opSnippet:Sse}),Tse={kernelName:Po,backendName:"webgl",kernelFunc:Cse},Nse=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=yt(this.rank),r=Ese(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function Ese(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 uk(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),u=r.dtype==="string"?l.map(p=>w.decodeString(p)):l,c=je(r.shape,r.dtype,u),d=rK(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Nse(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Rse={kernelName:aa,backendName:"webgl",kernelFunc:uk},Dse=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));
|
|
}
|
|
}
|
|
`}},_se=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 bi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function ck(e){let t=1;for(;t<e;)t*=2;return t}function Fse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let O=n.readSync(r.dataId),[E,R]=aK(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,Nd({attrs:{shape:u,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=w.sizeFromShape(u)/c,g=ve({inputs:{x:h},attrs:{shape:[m,c]},backend:n});p&&bi(n,h);let A=ck(a),y=ck(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new Dse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),bi(n,q)};for(let O=1;O<A;O*=2){let E=O*2;for(let R=O;R>=1;R/=2)v(E,R,[m,y])}for(let O=y;O>A;O/=2){let E=b(),R=new _se([m,O/2]),P=[[c],[x===null?1:0],[A]],U=x;x=n.runWebGLProgram(R,E,"int32",P),bi(n,U);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=Eu({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),bi(n,k);let S=K4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});bi(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=ve({inputs:{x},attrs:{shape:C},backend:n}),bi(n,k);let D=S;return S=ve({inputs:{x:S},attrs:{shape:C},backend:n}),bi(n,D),[S,x]}var $se={kernelName:Pl,backendName:"webgl",kernelFunc:Fse},Ose=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${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 Pse(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=new Ose(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var Mse={kernelName:Ml,backendName:"webgl",kernelFunc:Pse};function zse(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;vu(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:u}=oK(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Lse={kernelName:sh,backendName:"webgl",kernelFunc:zse};function Bse(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],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=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=Eu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Wse={kernelName:zl,backendName:"webgl",kernelFunc:Bse},Vse=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",u=Math.floor(n/4)*4,c=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 < ${u}; 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 + ${u};
|
|
if (${c===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
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===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
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===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 Use(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=_.getAxesPermutation([u],i),d=r;c!=null&&(d=Fn({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,u,o),h=w.sizeFromShape([d.shape[u]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=ch(r.dtype),g=(b,v,k,S,C)=>{let D=b.shape[0],O=b.shape[1],E=_.segment_util.segOpComputeOptimalWindowSize(O,C),R={windowSize:E,inSize:O,batchSize:D,numSegments:C},T=new Vse(R,v),P=n.compileAndRun(T,[b,k],S);if(l.push(P),P.shape[1]===C)return P;let U=rk({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=uk({inputs:{x:U},backend:n,attrs:{reps:[O/E]}});return l.push(U),l.push(j),g(P,v,j,S,C)},A=g(f,"unsortedSegmentSum",a,m,o),y=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),x=y;if(c!=null){l.push(y);let b=_.getUndoAxesPermutation(c);x=Fn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Hse={kernelName:Ac,backendName:"webgl",kernelFunc:Use},Gse=[xee,wee,aZ,iZ,cZ,hZ,mZ,yZ,bZ,wZ,CZ,NZ,DZ,$Z,WZ,MZ,HZ,XZ,jZ,JZ,eY,nY,oY,hY,mY,vY,kY,TY,RY,BK,OY,GY,qY,LY,YY,QY,KY,nJ,aJ,lJ,cJ,pJ,mJ,vJ,kJ,AJ,CJ,EJ,DJ,OJ,LJ,UJ,jJ,qJ,XJ,ZJ,JJ,eQ,nQ,rQ,lQ,dQ,fQ,gQ,xQ,wQ,CQ,RQ,LK,_Q,FY,OQ,zQ,WQ,VK,GQ,KQ,YQ,ree,tee,lee,dee,mee,Iee,_ee,Ree,Pee,zee,Bee,Nee,Vee,Hee,Xee,Jee,nte,cte,qK,pte,mte,yte,vte,AY,Ite,Cte,Nte,Dte,Ote,HK,Mte,zte,yY,ote,Wte,Kte,Gte,KK,Qte,nne,one,une,hne,mne,yne,vne,kne,Cne,Ene,Dne,$ne,Mne,Bne,dY,lte,Une,Gne,qne,Kne,Yne,Qne,tse,sse,ase,lse,cse,pse,mse,Ase,xse,vse,ite,nZ,Ise,Tse,Rse,$se,Mse,sZ,Lse,Wse,Hse,Ste];for(let e of Gse)ia(e);var Xn;(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"})(Xn||(Xn={}));var Ed;(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"})(Ed||(Ed={}));var dk;function jse(e){dk=e.wasm.cwrap(zo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qse(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:u,activation:c,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Ed[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return dk(p,k,r.shape.length,h,S,a.shape.length,l,u,g,f,m,d||0,v),b}var Xse={kernelName:zo,backendName:"wasm",setupFunc:jse,kernelFunc:qse};function bn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var Kse=bn(Bi);function $n(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:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,p=i.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,f=_.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,u.shape.length,p,A,c.shape.length,Xn[u.dtype],y);if(t&&u.dtype==="float32")return x(),m;let b=_.getBroadcastDims(u.shape,f),v=_.getBroadcastDims(c.shape,f),k=b.every((C,D)=>C===D),S=v.every((C,D)=>C===D);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Zse=!0,Yse=$n(sa,Zse),pk;function Jse(e){pk=e.wasm.cwrap(Wa,null,["array","number","number","number"])}function Qse(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.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 pk(a,r.length,Xn[s.dtype],o),s}var ere={kernelName:Wa,backendName:"wasm",setupFunc:Jse,kernelFunc:Qse};function fm(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 tre={kernelName:oo,backendName:"wasm",kernelFunc:fm},hk;function nre(e){hk=e.wasm.cwrap(Mo,null,["number","array","number","number","number","array","number"])}function _u(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=rre(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=sre(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=fm({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return hk(c,h,l.shape.length,Xn[l.dtype],d,p,a.length),u}function sre(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function rre(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 are={kernelName:Mo,backendName:"wasm",kernelFunc:_u,setupFunc:nre};function Ea(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=_.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=_.getInnerMostAxes(o.length,r),l=_u({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var fk;function ore(e){fk=e.wasm.cwrap(Ui,null,["number, number, number"])}function ire(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;_.assertAxesAreInnerMostDims("all",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;fk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var lre={kernelName:Ui,backendName:"wasm",setupFunc:ore,kernelFunc:ire},mk;function ure(e){mk=e.wasm.cwrap(Hi,null,["number, number, number"])}function cre(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;_.assertAxesAreInnerMostDims("any",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;mk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var dre={kernelName:Hi,backendName:"wasm",setupFunc:ure,kernelFunc:cre},gk;function pre(e){gk=e.wasm.cwrap(Va,null,["number","number","number","number","number"])}function hre(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:u,axes:c,inputWasTransposed:d}=Ea(a,r,t);if(d){let A=t.dataIdMap.get(u.dataId).id;A!==o&&(l=u,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=l.shape[c[0]];return gk(i,Xn[l.dtype],m,g,f),d&&t.disposeData(u.dataId),h}var fre={kernelName:Va,backendName:"wasm",kernelFunc:hre,setupFunc:pre},Ak;function mre(e){Ak=e.wasm.cwrap(Ua,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gre(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:u}=n,c=_.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,A=c.strideHeight,y=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),v=s.dataIdMap.get(b.dataId).id;return Ak(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,v),b}var Are={kernelName:Ua,backendName:"wasm",setupFunc:mre,kernelFunc:gre};function Kn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var yre={kernelName:Sl,backendName:"wasm",kernelFunc:Kn},yk;function xre(e){yk=e.wasm.cwrap(Ha,null,["number","array","number","number","array","number","number","number","number"])}function bre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),A=w.sizeFromShape(m),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,p]:[g,p,c],k=i?[A,h,d]:[A,d,h],S=Kn({inputs:{x:r},backend:n,attrs:{shape:v}}),C=Kn({inputs:{x:a},backend:n,attrs:{shape:k}}),D=n.dataIdMap.get(S.dataId).id,O=n.dataIdMap.get(C.dataId).id,E=o?S.shape[2]:S.shape[1],R=i?C.shape[1]:C.shape[2],T=Math.max(g,A),P=n.makeOutput([T,E,R],S.dtype),U=n.dataIdMap.get(P.dataId).id,j=new Uint8Array(new Int32Array(S.shape).buffer),q=new Uint8Array(new Int32Array(C.shape).buffer);return yk(D,j,S.shape.length,O,q,C.shape.length,o,i,U),n.disposeData(S.dataId),n.disposeData(C.dataId),P.shape=b,P}var vre={kernelName:Ha,backendName:"wasm",setupFunc:xre,kernelFunc:bre};function Rd(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Tn.parseSliceParams(t,n,s),i=Tn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let f=Tn.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Hf(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let p=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)wre(l,c[0],p,a,o);else if(h===3)kre(l,c[0],c[1],p,a,o);else if(h===4)Ire(l,c[0],c[1],c[2],p,a,o);else{let f=Hf(l,a,o,t.shape,t.dtype);p.set(f)}return u}function wre(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function kre(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],d=l+a[1];for(let p=i;p<c;p++)for(let h=l;h<d;h++){let f=p*t+h*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Ire(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],d=l+o[0],p=u+o[1],h=c+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=u;g<p;g++)for(let A=c;A<h;A++){let y=m*t+g*n+A*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Sre={kernelName:El,backendName:"wasm",kernelFunc:Rd};function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,y)=>A*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=Kn({inputs:{x:r},backend:n,attrs:{shape:l}}),f=_u({inputs:{x:h},backend:n,attrs:{perm:u}}),m=Kn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=Rd({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 Tre={kernelName:Zi,backendName:"wasm",kernelFunc:Cre};function mm(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 Nre={kernelName:Ga,backendName:"wasm",kernelFunc:mm},Ere=bn(ja),xk;function Rre(e){xk=e.wasm.cwrap(ra,null,["number","number","number","number"])}function Dre(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),u=n.dataIdMap.get(l.dataId).id;return xk(i,a,o,u),l}var _re={kernelName:ra,backendName:"wasm",setupFunc:Rre,kernelFunc:Dre};function bk(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return fm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(_.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return Kn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=Qy(f,r,t[0].dtype,m),A=_.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return u+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*u;for(let m=0;m<d.length;m++){let g=c[m],A=h*g,y=d[m].subarray(A,A+g);p.set(y,f),f+=g}}return o}var Fre={kernelName:Yi,backendName:"wasm",kernelFunc:bk},vk;function $re(e){vk=e.wasm.cwrap(qa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ore(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d,dataFormat:p}=n,h=_.convertConv2DDataFormat(p),f=_.computeConv2DInfo(r.shape,a.shape,l,u,c,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,y=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,C=f.strideWidth,D=f.inChannels,O=f.outChannels,E=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(R.dataId).id;return vk(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,A,y,x,b,E,v,k,S,C,D,O,T),R}var Pre={kernelName:qa,backendName:"wasm",setupFunc:$re,kernelFunc:Ore},wk;function Mre(e){wk=e.wasm.cwrap(Xa,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 zre(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=s,d=1,p=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(c,a.shape,o,d,i,u,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:A,inHeight:y,inWidth:x,outChannels:b,outHeight:v,outWidth:k,strideHeight:S,strideWidth:C}=h,D=m-1-h.padInfo.top,O=g-1-h.padInfo.left,E=h.dataFormat==="channelsLast",R=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[P,U,j]=w.computeStrides(a.shape),q=R[0],X=E?R[1]:R[2],te=E?R[2]:1,ne=E?1:R[1],se=T[0],ae=E?T[1]:T[2],Q=E?T[2]:1,ce=E?1:T[1],de=t.makeOutput(h.inShape,"float32"),fe=t.dataIdMap.get(de.dataId).id,be=t.dataIdMap.get(r.dataId).id,Ee=t.dataIdMap.get(a.dataId).id;return wk(be,Ee,f,m,g,y,x,A,v,k,b,S,C,D,O,P,U,j,q,X,te,ne,se,ae,Q,ce,fe),de}var Lre={kernelName:Xa,backendName:"wasm",setupFunc:Mre,kernelFunc:zre},Bre=bn(Ka),Wre=bn(Za),D2;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(D2||(D2={}));var kk;function Vre(e){kk=e.wasm.cwrap(Ji,null,["number","number","number","number","array","number","number","number","number","number"])}function Ure(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[d,p]=o,h=[c,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=mm({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,y=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(h,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(i.shape).buffer);return kk(g,A,y,c,v,d,p,D2[r],a,b),m!=null&&t.disposeData(m.dataId),x}var Hre={kernelName:Ji,backendName:"wasm",setupFunc:Vre,kernelFunc:Ure},Ik;function Gre(e){Ik=e.wasm.cwrap(Ya,null,["number","number","number","number","number","number"])}function jre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=_.getAxesPermutation([a],l),c=r;u!==null&&(c=_u({inputs:{x:r},attrs:{perm:u},backend:n}));let d=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(c.shape,c.dtype),h=c.shape[d],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(p.dataId).id;Ik(f,o?1:0,i?1:0,h,m,Xn[r.dtype]);let g=p;if(u!==null){let A=_.getUndoAxesPermutation(u);g=_u({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(c.dataId),n.disposeData(p.dataId)}return g}var qre={kernelName:Ya,backendName:"wasm",setupFunc:Gre,kernelFunc:jre},Sk;function Xre(e){Sk=e.wasm.cwrap(Qi,null,["number","number","number","array","number","array","array","number","number"])}function Kre(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return Sk(A,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,f.length,v),m}var Zre={kernelName:Qi,backendName:"wasm",setupFunc:Xre,kernelFunc:Kre},Ck;function Yre(e){Ck=e.wasm.cwrap(Ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d}=n,p=u==null?[1,1]:u,h=_.computeConv2DInfo(r.shape,a.shape,l,p,c,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,b=h.dilationHeight,v=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,C=h.inChannels,D=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=s.makeOutput(h.outShape,"float32"),R=s.dataIdMap.get(E.dataId).id;return Ck(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,y,x,O,b,v,k,S,C,D,R),E}var Qre={kernelName:Ja,backendName:"wasm",setupFunc:Yre,kernelFunc:Jre},eae=bn(eo),tae=!1,nae=$n(tl,tae,"bool"),sae=bn(to);function _2(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Kn({inputs:{x:r},backend:s,attrs:{shape:i}})}var rae={kernelName:nl,backendName:"wasm",kernelFunc:_2};function Tk(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var aae={kernelName:uc,backendName:"wasm",kernelFunc:Tk},Nk;function oae(e){Nk=e.wasm.cwrap(rl,null,["number","number","number","number","number","number"])}function iae(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,u,c]=s.shape;return Nk(a,i,l,u,c,o),r}var lae={kernelName:rl,backendName:"wasm",kernelFunc:iae,setupFunc:oae},uae=bn(no),cae=!1,dae=$n(so,cae),Ek;function pae(e){Ek=e.wasm.cwrap(ro,null,["number","number","number","number","number","number","number"])}function hae(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=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=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return Ek(c,d,p,h,f,r,g),m}var fae={kernelName:ro,backendName:"wasm",setupFunc:pae,kernelFunc:hae},Rk;function mae(e){Rk=e.wasm.cwrap(Lo,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 gae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=_.computeConv2DInfo(r.shape,a.shape,l,c,u,p),g=Ed[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,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,C=m.padInfo.right,D=m.padInfo.bottom,O=m.padInfo.left,E=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,P=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,te=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),se=s.dataIdMap.get(ne.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return Rk(A,q,X,te,y,v,k,b,S,C,D,O,j,E,R,T,P,U,x,g,ae,f||0,se),ne}var Aae={kernelName:Lo,backendName:"wasm",setupFunc:mae,kernelFunc:gae},Dk;function yae(e){Dk=e.wasm.cwrap(Bo,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 xae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=_.computeConv2DInfo(r.shape,a.shape,l,c,u,p,!0),g=Ed[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,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,C=m.padInfo.right,D=m.padInfo.bottom,O=m.padInfo.left,E=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,P=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,te=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),se=s.dataIdMap.get(ne.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return Dk(A,q,X,te,y,v,k,b,S,C,D,O,j,E,R,T,P,U,x,g,ae,f||0,se),ne}var bae={kernelName:Bo,backendName:"wasm",setupFunc:yae,kernelFunc:xae},_k;function vae(e){_k=e.wasm.cwrap(ol,null,["number","number","number","number","number","number","array","number"])}function wae(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=Hg.prepareAndValidate(s,r),u=t.makeOutput(a,s.dtype);if(o===0)return u;let c=r.shape,d=c[c.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(u.dataId).id;return _k(h,Xn[s.dtype],m,o,d,i,g,A),u}var kae={kernelName:ol,backendName:"wasm",setupFunc:vae,kernelFunc:wae},Fk;function Iae(e){Fk=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Sae(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=_.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=Kn({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),d=w.sizeFromShape(a.shape),p=Kn({inputs:{x:a},attrs:{shape:[u.batchSize,d/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize],f=t.makeOutput(h,r.dtype);if(w.sizeFromShape(r.shape)===0)return f;let m=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(p.dataId).id,b=t.dataIdMap.get(f.dataId).id,v=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),k=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return Fk(A,Xn[r.dtype],v,m,x,u.batchSize,k,b),t.disposeData(c.dataId),t.disposeData(p.dataId),f.shape=u.outputShape,f}var Cae={kernelName:al,backendName:"wasm",setupFunc:Iae,kernelFunc:Sae},Tae=!1,Nae=$n(il,Tae,"bool"),Eae=!1,Rae=$n(ao,Eae,"bool"),$k;function Dae(e){$k=e.wasm.cwrap(io,null,["number","number","number"])}function _ae(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;$k(r,n,o)}return a}var Fae={kernelName:io,backendName:"wasm",setupFunc:Dae,kernelFunc:_ae},$ae=!1,Oae=$n(dl,$ae,"bool"),Pae=!1,Mae=$n(pl,Pae,"bool"),zae=bn(lo),Lae=!1,Bae=$n(fl,Lae,"bool"),Ok;function Wae(e){Ok=e.wasm.cwrap(uo,null,["number, number, number"])}function Vae(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;_.assertAxesAreInnerMostDims("max",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Ok(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Uae={kernelName:uo,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},Hae=!1,Gae=$n(co,Hae),Pk;function jae(e){Pk=e.wasm.cwrap(po,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qae(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=_.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,A=c.dilationHeight,y=c.dilationWidth,x=c.strideHeight,b=c.strideWidth,v=c.inChannels,k=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=s.makeOutput(c.outShape,"float32"),C=s.dataIdMap.get(S.dataId).id;return Pk(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,b,v,k,C),S}var Xae={kernelName:po,backendName:"wasm",setupFunc:jae,kernelFunc:qae},Mk;function Kae(e){Mk=e.wasm.cwrap(ho,null,["number, number, number"])}function Zae(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,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t),f=d;if(h){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,f=_.getInnerMostAxes(f.length,u.shape.length))}_.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=_.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=mm({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(m,"float32");if(w.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;Mk(l,A,b)}if(h&&t.disposeData(c.dataId),a){let b=_.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Yae={kernelName:ho,backendName:"wasm",setupFunc:Kae,kernelFunc:Zae},zk;function Jae(e){zk=e.wasm.cwrap(fo,null,["number, number, number"])}function Qae(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,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;_.assertAxesAreInnerMostDims("min",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;zk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var eoe={kernelName:fo,backendName:"wasm",setupFunc:Jae,kernelFunc:Qae},toe=!1,noe=$n(mo,toe),F2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(F2||(F2={}));var Lk;function soe(e){Lk=e.wasm.cwrap(go,null,["number","array","number","number","array","array","number","number"])}function roe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(d).buffer);return Lk(o,u,t.shape.length,Xn[t.dtype],p,h,F2[r],l),i}var aoe={kernelName:go,backendName:"wasm",kernelFunc:roe,setupFunc:soe},ooe=!0,ioe=$n(Ao,ooe),loe=bn(gl);function $2(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 Bk;function uoe(e){Bk=e.wasm.cwrap(yl,"number",["number","number","number","number","number"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,d=Bk(u,c,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=$2(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var doe={kernelName:yl,backendName:"wasm",setupFunc:uoe,kernelFunc:coe},Wk;function poe(e){Wk=e.wasm.cwrap(xl,"number",["number","number","number","number","number","bool"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=Wk(c,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=$2(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[A,y]}var foe={kernelName:xl,backendName:"wasm",setupFunc:poe,kernelFunc:hoe},Vk;function moe(e){Vk=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function goe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=Vk(c,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=$2(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[A,y]}var Aoe={kernelName:bl,backendName:"wasm",setupFunc:moe,kernelFunc:goe},yoe=!1,xoe=$n(Al,yoe,"bool"),Uk;function boe(e){Uk=e.wasm.cwrap(yo,null,["number","number","number","number","number"])}function voe(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"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return Uk(d,a,o,i,u),l}var woe={kernelName:yo,backendName:"wasm",setupFunc:boe,kernelFunc:voe};function koe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Ioe={kernelName:vl,backendName:"wasm",kernelFunc:koe};function Soe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return _2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=_2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=bk({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var Coe={kernelName:wl,backendName:"wasm",kernelFunc:Soe},Hk;function Toe(e){Hk=e.wasm.cwrap(xo,null,["number","array","number","number","array","array","number","number"])}function Noe(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(w.sizeFromShape(t.shape)===0)return Tk({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,c=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 Hk(o,c,t.shape.length,Xn[t.dtype],h,f,r,u),i}var Gk={kernelName:xo,backendName:"wasm",kernelFunc:Noe,setupFunc:Toe},Eoe=!1,Roe=$n(bo,Eoe),jk;function Doe(e){jk=e.wasm.cwrap(vo,null,["number","number","number"])}function _oe(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=n.makeOutput(s.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return jk(a,o,l),i}var Foe={kernelName:vo,backendName:"wasm",setupFunc:Doe,kernelFunc:_oe},qk;function $oe(e){qk=e.wasm.cwrap(kl,null,["number","number","number","number"])}function Ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t),f=d;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=_.getInnerMostAxes(f.length,u.shape.length))}_.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=_.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;qk(l,A,Xn[y.dtype],x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Poe={kernelName:kl,backendName:"wasm",setupFunc:$oe,kernelFunc:Ooe},Moe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=n2(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},zoe={kernelName:fc,backendName:"wasm",kernelFunc:Moe},Loe=!0,Boe=$n(Qa,Loe),Woe=bn(wo),Voe=bn(Io),Xk;function Uoe(e){Xk=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number"])}function Hoe(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,[c,d,p,h]=r.shape,f=[c,l,u,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=mm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let A=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return Xk(A,c,d,p,h,l,u,a?1:0,o?1:0,x),g!=null&&t.disposeData(g.dataId),y}var Goe={kernelName:ko,backendName:"wasm",setupFunc:Uoe,kernelFunc:Hoe},Kk;function joe(e){Kk=e.wasm.cwrap(So,null,["number","array","number","array","number","number"])}function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return fm({inputs:{x:r},backend:n});let i=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);Kk(l,c,o.length,d,r.shape.length,u);let p=Kn({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),p}var Xoe={kernelName:So,backendName:"wasm",kernelFunc:qoe,setupFunc:joe},Zk;function Koe(e){Zk=e.wasm.cwrap(Bl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Zoe(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),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[d,p,h,f]=r.shape,[m,g]=_.getImageCenter(i,p,h),A=o===0,y=255,x=typeof o=="number"?[o,o,o,A?0:y]:[...o,y],b=new Uint8Array(new Int32Array(x).buffer);return Zk(u,d,p,h,f,a,m,g,b,x.length,c),l}var Yoe={kernelName:Bl,backendName:"wasm",kernelFunc:Zoe,setupFunc:Koe},Joe=bn(Co),Qoe=bn(To),Yk;function eie(e){Yk=e.wasm.cwrap(Cl,null,["number","number","number","number","number","number","array","number","number"])}function tie(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:o}=s,i=t.makeOutput(o,a.dtype);if(w.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:p}=Gg.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),y=t.dataIdMap.get(i.dataId).id;return Yk(f,g,Xn[a.dtype],l,u,c,A,p,y),i}var nie={kernelName:Cl,backendName:"wasm",setupFunc:eie,kernelFunc:tie},Jk;function sie(e){Jk=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function rie(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,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,d=s.shape.length,p=r.shape.length,h=d===0||d>1||p===1?1:w.sizeFromShape(r.shape.slice(1));return Jk(o,i,l,h,c),u}var aie={kernelName:Tl,backendName:"wasm",kernelFunc:rie,setupFunc:sie},Qk;function oie(e){Qk=e.wasm.cwrap(Eo,null,["number","number"])}function iie(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 w.sizeFromShape(r.shape)===0||Qk(s,a),r}var lie={kernelName:"Sigmoid",backendName:"wasm",setupFunc:oie,kernelFunc:iie},uie=bn(No),e8;function cie(e){e8=e.wasm.cwrap(_o,null,["number","number","number","number"])}function die(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=w.sizeFromShape(n.shape)/i;return w.sizeFromShape(a.shape)===0||e8(r,o,i,l),a}var pie={kernelName:_o,backendName:"wasm",setupFunc:cie,kernelFunc:die};function hie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=Gk.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=_.getReshaped(u.shape,a,i,!1),d=_.getPermuted(c.length,a.length,!1),p=_.getReshapedPermuted(u.shape,a,i,!1),m=Kn({inputs:{x:u},backend:n,attrs:{shape:c}}),y=_u({inputs:{x:m},backend:n,attrs:{perm:d}}),v=Kn({inputs:{x:y},backend:n,attrs:{shape:p}});return n.disposeData(u.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),v}var fie={kernelName:Fl,backendName:"wasm",kernelFunc:hie};function mie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let p=[...c];p[i]=d;let h=Rd({inputs:{x:r},attrs:{begin:u,size:p},backend:s});return u[i]+=d,h})}var gie={kernelName:$l,backendName:"wasm",kernelFunc:mie},Aie=bn(Ro),yie=bn(gc),xie=!0,bie=$n(Fo,xie),t8;function vie(e){t8=e.wasm.cwrap(oa,null,["number","number","number"])}function wie(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 t8(o,r,l),i}var kie={kernelName:oa,backendName:"wasm",setupFunc:vie,kernelFunc:wie},n8;function Iie(e){n8=e.wasm.cwrap(Ol,null,["number","array","number","array","array","array","array","array","number","number"])}function Sie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,h=_.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=_.slice_util.maskToAxes(d),g=r.shape.slice();m.forEach(E=>{a[E]=0,o[E]=1,g.splice(E,0,1)});let A=Kn({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:y,end:x,strides:b}=_.slice_util.getNormalizedAxes(A.shape,h,f,a,o,i,l,u,c);a=y,o=x,i=b;let v=_.slice_util.maskToAxes(p);v.forEach(E=>{o[E]=a[E]+1,i[E]=1});let k=_.slice_util.computeOutShape(a,o,i),S=k.filter((E,R)=>v.indexOf(R)===-1);if(i.every(E=>E===1)){let E=Rd({inputs:{x:A},attrs:{begin:a,size:k},backend:t});t.disposeData(A.dataId);let R=Kn({inputs:{x:E},attrs:{shape:S},backend:t});return t.disposeData(E.dataId),R}let D=t.makeOutput(S,"float32");if(!S.some(E=>E===0)){let E=t.dataIdMap.get(A.dataId).id,R=new Uint8Array(new Int32Array(w.computeStrides(A.shape)).buffer),T=new Uint8Array(new Int32Array(a).buffer),P=new Uint8Array(new Int32Array(o).buffer),U=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(S).buffer),q=new Uint8Array(new Int32Array(w.computeStrides(S)).buffer),X=t.dataIdMap.get(D.dataId).id;n8(E,R,A.shape.length,T,P,U,j,q,S.length,X)}t.disposeData(A.dataId);let O=Kn({inputs:{x:D},attrs:{shape:S},backend:t});return t.disposeData(D.dataId),O}var Cie={kernelName:Ol,backendName:"wasm",setupFunc:Iie,kernelFunc:Sie},Tie=!0,Nie=$n($o,Tie),s8;function Eie(e){s8=e.wasm.cwrap(Do,null,["number, number, number"])}function Rie(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,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ea(o,r,t),f=d;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=_.getInnerMostAxes(f.length,u.shape.length))}_.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=_.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;s8(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Die={kernelName:Do,backendName:"wasm",setupFunc:Eie,kernelFunc:Rie},_ie=bn(Oo),Fie=bn(Po),r8;function $ie(e){r8=e.wasm.cwrap(aa,null,["number","array","number","array","number","number"])}function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let p=0;p<i.length;p++)i[p]=r.shape[p]*o[p];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(i).buffer),c=n.makeOutput(i,r.dtype),d=n.dataIdMap.get(c.dataId).id;return r8(a,l,r.shape.length,u,i.length,Xn[c.dtype],d),c}var Pie={kernelName:aa,backendName:"wasm",setupFunc:$ie,kernelFunc:Oie},a8;function Mie(e){a8=e.wasm.cwrap(Pl,null,["number","array","number","number","number","bool","number","number"])}var zie=({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 u=t.makeOutput(l,s.dtype),c=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),p=t.dataIdMap.get(d.dataId).id;return a8(o,i,s.shape.length,Xn[s.dtype],r,a,c,p),[u,d]},Lie={kernelName:Pl,backendName:"wasm",setupFunc:Mie,kernelFunc:zie},o8;function Bie(e){o8=e.wasm.cwrap(Ml,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Wie(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),y=t.makeOutput(g,r.dtype),x=t.dataIdMap.get(y.dataId).id,v=t.dataIdMap.get(r.dataId).id,S=t.dataIdMap.get(a.dataId).id,C=o==="nearest"?1:2,D;switch(i){case"constant":D=1;break;case"reflect":D=2;break;case"wrap":D=3;break;case"nearest":D=4;break;default:D=1;break}return o8(v,S,a.shape[0]>1,c,f,m,h,p,d,A,r.shape.length-1,C,D,l,x),y}var Vie={kernelName:Ml,backendName:"wasm",setupFunc:Bie,kernelFunc:Wie};function Uie(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),u=0;for(let h=0;h<i;h++)h!==a&&(l[u++]=r.shape[h]);let c=new Array(o),d=new Array(i).fill(0),p=r.shape.slice();p[a]=1;for(let h=0;h<c.length;h++)d[a]=h,c[h]=Rd({inputs:{x:r},attrs:{begin:d,size:p},backend:n});return c.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Hie={kernelName:zl,backendName:"wasm",kernelFunc:Uie};function Gie(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var jie={kernelName:Ll,backendName:"wasm",kernelFunc:Gie},qie=[Kse,Yse,ere,lre,dre,fre,Are,vre,Tre,Nre,Ere,_re,Fre,Pre,Lre,Bre,Wre,Hre,qre,Zre,Qre,eae,nae,sae,rae,aae,lae,uae,dae,Xse,fae,Aae,bae,kae,Cae,Nae,Rae,tre,Fae,Oae,Mae,zae,Bae,Uae,Gae,Xae,Yae,eoe,noe,aoe,ioe,loe,doe,foe,Aoe,xoe,woe,Ioe,Coe,Gk,Roe,Foe,Poe,zoe,Boe,Woe,Voe,yre,Goe,Xoe,Yoe,Qoe,Joe,nie,aie,lie,uie,Sre,pie,fie,gie,Aie,yie,bie,kie,Cie,Nie,Die,_ie,Fie,Pie,Lie,Vie,are,Hie,jie];for(let e of qie)ia(e);var O2=Y();O2.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])));O2.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(O2.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 i8=za(KS()),Xie='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()}}}}',Kie=za(ZS()),l8=class extends tc{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Ap(this,es())}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=w.now();return e(),{kernelMs:w.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let u=t;this.dataIdMap.set(e,{id:a,stringBytes:u,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=w.sizeFromShape(n),i=o*w.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+w.sizeFromShape(s)*w.bytesPerElement(n));return Jie(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=w.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=w.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 ${t}`)}}};function Zie(e){return(t,n)=>(w.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function u8(e,t,n){if(gm!=null)return gm;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),_d!=null&&_d[s]!=null?_d[s]:n+s}async function Yie(){let[e,t]=await Promise.all([Y().getAsync("WASM_HAS_SIMD_SUPPORT"),Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=Xie,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?u8(e,t,Dd!=null?Dd:l):l+i},P2&&(r.instantiateWasm=Zie(u8(e,t,Dd!=null?Dd:"")));let a=!1;r.onAbort=()=>{if(a||Fd)return;Fd=!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&&gm==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+i8.default.toString()],{type:"text/javascript"}),o=(0,i8.default)(r)):o=(0,Kie.default)(r),o.then(i=>{a=!0,Fd=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),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 Jie(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 Qie=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],gm=null,Dd=null,_d={},Fd=!1,P2=!1;function ele(e,t=!1){if(Yg("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Fd)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");gm=e,P2=t}function c8(e,t=!1){if(Fd)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")Dd=e;else{_d=e;let n=Qie.filter(s=>_d[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.`)}P2=t}var tle="3.9.0",nle=2;Xl("wasm",async()=>{let{wasm:e}=await Yie();return new l8(e)},nle);var sle="3.9.0",rle="3.9.0",ale="3.9.0",ole="3.9.0",ile="3.9.0",lle="3.9.0",ule="3.9.0",cle="3.9.0",dle={tfjs:sle,"tfjs-core":rle,"tfjs-data":ale,"tfjs-layers":ole,"tfjs-converter":ile,"tfjs-backend-cpu":lle,"tfjs-backend-webgl":ule,"tfjs-backend-wasm":cle};function d8(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}}function $d(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Od(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Pd(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return De.cropAndResize(t,a,[0],n)}function Am(e,t=1.5){let n=Od(e),s=$d(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,landmarks:e.landmarks}}function ym(e){let t=Od(e),n=$d(e),r=Math.max(...n)/2,a=[Math.round(t[0]-r),Math.round(t[1]-r)],o=[Math.round(t[0]+r),Math.round(t[1]+r)];return{startPoint:a,endPoint:o,landmarks:e.landmarks}}function M2(e){let t=e.map(a=>a[0]),n=e.map(a=>a[1]),s=[Math.min(...t),Math.min(...n)],r=[Math.max(...t),Math.max(...n)];return{startPoint:s,endPoint:r,landmarks:e}}var p8=e=>({startPoint:_e(e,[0,0],[-1,2]),endPoint:_e(e,[0,2],[-1,2])});var xm=[[1,0,0],[0,1,0],[0,0,1]];function ple(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function h8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ple(n)}function f8(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Ra(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function hle(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function m8(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(Ra(e[r],hle(t,a)))}return n}function z2(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=f8(t[0],t[1]),o=m8(a,r),i=f8(-t[0],-t[1]);return m8(o,i)}function g8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ra(t[0],n),-Ra(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function A8(e,t){return[Ra(e,t[0]),Ra(e,t[1])]}function y8(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 u=r*(l+.5);for(let c=0;c<o;c++){let d=r*(c+.5);for(let p=0;p<i;p++)n.push([d,u])}}}return n}var x8=6;function fle(e,t,n){let s=_e(e,[0,1],[-1,2]),r=ie(s,t),a=_e(e,[0,3],[-1,2]),o=he(a,n),i=he(r,n),l=he(o,2),u=ye(i,l),c=ie(i,l),d=z(u,n),p=z(c,n);return Yl([d,p],1)}var b8=class{constructor(t,n){xe(this,"model");xe(this,"anchorsData");xe(this,"anchors");xe(this,"inputSize");xe(this,"config");this.model=t,this.anchorsData=y8(t.inputs[0].shape[1]),this.anchors=Hs(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t,n){var c,d,p,h;if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return{boxes:[]};let[s,r,a]=H(()=>{let f=De.resizeBilinear(t,[this.inputSize,this.inputSize]),m=ye(he(f,127.5),.5),g=this.model.execute(m),A;if(Array.isArray(g)){let v=g.sort((D,O)=>D.size-O.size),k=gt([v[0],v[2]],2),S=gt([v[1],v[3]],2),C=gt([S,k],1);A=st(C,0)}else A=st(g);let y=fle(A,this.anchors,[this.inputSize,this.inputSize]),x=_e(A,[0,0],[-1,1]),b=st(Vn(x));return[A,y,b]});this.config=Vt(this.config,n);let o=await De.nonMaxSuppressionAsync(r,a,((c=this.config.face.detector)==null?void 0:c.maxDetected)||0,((d=this.config.face.detector)==null?void 0:d.iouThreshold)||0,((p=this.config.face.detector)==null?void 0:p.minConfidence)||0),i=await o.array();Z(o);let l=[],u=await a.data();for(let f=0;f<i.length;f++){let m=u[i[f]];if(m>(((h=this.config.face.detector)==null?void 0:h.minConfidence)||0)){let g=_e(r,[i[f],0],[1,-1]),A=H(()=>V(st(_e(s,[i[f],x8-1],[1,-1])),[x8,-1]));l.push({box:p8(g),landmarks:A,anchor:this.anchorsData[i[f]],confidence:m}),Z(g)}}return Z(s),Z(r),Z(a),{boxes:l,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function v8(e){var s,r,a;let t=await ot(ct(e.modelBasePath,((s=e.face.detector)==null?void 0:s.modelPath)||""),{fromTFHub:(((r=e.face.detector)==null?void 0:r.modelPath)||"").includes("tfhub.dev")}),n=new b8(t,e);return!t||!t.modelUrl?re("load model failed:",((a=e.face.detector)==null?void 0:a.modelPath)||""):e.debug&&re("load model:",t.modelUrl),n}var yr={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]},L2=[{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]}],Md=[[.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]],wi=[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 mle=[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],gle=[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],Ale=[33,133,362,263,1,78,308],gue=mle.map(e=>Md[e]),Aue=gle.map(e=>Md[e]),yue=Ale.map(e=>Md[e]);function yle(e,t,n){let s=function(i,l,u){let c=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");i.replace(c,(d,p)=>(u[p]=0,d))},r=function(i,l){let u=e.createShader(l);if(e.shaderSource(u,i),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("filter: gl compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let a=r(t,e.VERTEX_SHADER),o=r(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,a),e.attachShader(this.id,o),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("filter: gl link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),s(t,"attribute",this.attribute);for(let i in this.attribute)this.attribute[i]=e.getAttribLocation(this.id,i);s(t,"uniform",this.uniform),s(n,"uniform",this.uniform);for(let i in this.uniform)this.uniform[i]=e.getUniformLocation(this.id,i)}function w8(e){e||(e={});let t=0,n=null,s=!1,r=-1,a=[null,null],o=[],i=-1,l=-1,u=null,c=null,d={},p=e.canvas||document.createElement("canvas"),h={},f={INTERMEDIATE:1},m=p.getContext("webgl");if(!m)throw new Error("filter: context failed");this.addFilter=function(v){let k=Array.prototype.slice.call(arguments,1),S=d[v];o.push({func:S,args:k})},this.reset=function(){o=[]};let g=function(v,k){if(!(v===i&&k===l)){if(p.width=v,i=v,p.height=k,l=k,!u){let S=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]);u=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,u),m.bufferData(m.ARRAY_BUFFER,S,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,i,l),a=[null,null]}},A=function(v,k){let S=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,S);let C=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,C);let D=m.createTexture();return m.bindTexture(m.TEXTURE_2D,D),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,v,k,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,D,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:S,texture:D}},y=function(v){return a[v]=a[v]||A(i,l),a[v]},x=function(v=null){var D,O;let k=null,S=null,C=!1;t===0?k=n:k=(D=y(r))==null?void 0:D.texture,t++,s&&!(v&f.INTERMEDIATE)?(S=null,C=t%2==0):(r=(r+1)%2,S=(O=y(r))==null?void 0:O.fbo),m.bindTexture(m.TEXTURE_2D,k),m.bindFramebuffer(m.FRAMEBUFFER,S),m.uniform1f(c.uniform.flipY,C?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(v){if(g(v.width,v.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,v),o.length===0)return x(),p;for(let k=0;k<o.length;k++){s=k===o.length-1;let S=o[k];S.func.apply(this,S.args||[])}return p};let b=function(v){if(h[v])return c=h[v],m.useProgram(c.id),c;let k={};k.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
|
|
`),k.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
|
|
`),c=new yle(m,k.VERTEX_IDENTITY,v);let S=Float32Array.BYTES_PER_ELEMENT,C=4*S;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,C,0*S),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,C,2*S),h[v]=c,c};d.colorMatrix=function(v){let k=new Float32Array(v);k[4]/=255,k[9]/=255,k[14]/=255,k[19]/=255;let S=k[18]===1&&k[3]===0&&k[8]===0&&k[13]===0&&k[15]===0&&k[16]===0&&k[17]===0&&k[19]===0?d.colorMatrix.SHADER.WITHOUT_ALPHA:d.colorMatrix.SHADER.WITH_ALPHA,C=b(S);m.uniform1fv(C.uniform.m,k),x()},d.colorMatrix.SHADER={},d.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
|
|
`),d.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
|
|
`),d.brightness=function(v){let k=(v||0)+1;d.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},d.saturation=function(v){let k=(v||0)*2/3+1,S=(k-1)*-.5;d.colorMatrix([k,S,S,0,0,S,k,S,0,0,S,S,k,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(v){let k=(v||0)+1,S=-128*(k-1);d.colorMatrix([k,0,0,0,S,0,k,0,0,S,0,0,k,0,S,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(v){v=(v||0)/180*Math.PI;let k=Math.cos(v),S=Math.sin(v),C=.213,D=.715,O=.072;d.colorMatrix([C+k*(1-C)+S*-C,D+k*-D+S*-D,O+k*-O+S*(1-O),0,0,C+k*-C+S*.143,D+k*(1-D)+S*.14,O+k*-O+S*-.283,0,0,C+k*-C+S*-(1-C),D+k*-D+S*D,O+k*(1-O)+S*O,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.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])},d.technicolor=function(){d.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])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(v){let k=new Float32Array(v),S=1/i,C=1/l,D=b(d.convolution.SHADER);m.uniform1fv(D.uniform.m,k),m.uniform2f(D.uniform.px,S,C),x()},d.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
|
|
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(v){let k=v||1;d.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},d.emboss=function(v){let k=v||1;d.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},d.blur=function(v){let k=v/7/i,S=v/7/l,C=b(d.blur.SHADER);m.uniform2f(C.uniform.px,0,S),x(f.INTERMEDIATE),m.uniform2f(C.uniform.px,k,0),x()},d.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
|
|
`),d.pixelate=function(v){let k=v/i,S=v/l,C=b(d.pixelate.SHADER);m.uniform2f(C.uniform.size,k,S),x()},d.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
|
|
`)}var bm=2048,Oe,Et,Xt;function ps(e,t){let n;return le.browser?le.offscreen?n=new OffscreenCanvas(e,t):(n=document.createElement("canvas"),n.width=e,n.height=t):typeof le.Canvas!="undefined"?n=new le.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t)),n}function Fu(e,t){let n;if(!e)return t.debug&&re("input is missing"),{tensor:null,canvas:null};if(!(e instanceof Ge)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof le.Canvas!="undefined"&&e instanceof le.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 Ge){if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Ws(e);else throw new Error(`input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`)}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&re("input stream is not ready"),{tensor:null,canvas:Oe};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&&re("cannot determine input dimensions"),{tensor:null,canvas:Oe};let a=s,o=r;if(a>bm&&(a=bm,o=Math.trunc(a*r/s)),o>bm&&(o=bm,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");(!Oe||(Oe==null?void 0:Oe.width)!==a||(Oe==null?void 0:Oe.height)!==o)&&(Oe=ps(a,o));let i=Oe.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,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),t.filter.enabled&&le.webgl.supported){if((!Xt||!Et||Oe.width!==Et.width||(Oe==null?void 0:Oe.height)!==(Et==null?void 0:Et.height))&&(Et=ps(Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),(Et==null?void 0:Et.width)!==(Oe==null?void 0:Oe.width)&&(Et.width=Oe==null?void 0:Oe.width),(Et==null?void 0:Et.height)!==(Oe==null?void 0:Oe.height)&&(Et.height=Oe==null?void 0:Oe.height),Xt=le.browser?new w8({canvas:Et}):null),!Xt)return{tensor:null,canvas:Oe};Xt.reset(),Xt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Xt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Xt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Xt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Xt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Xt.addFilter("hue",t.filter.hue),t.filter.negative&&Xt.addFilter("negative"),t.filter.sepia&&Xt.addFilter("sepia"),t.filter.vintage&&Xt.addFilter("brownie"),t.filter.sepia&&Xt.addFilter("sepia"),t.filter.kodachrome&&Xt.addFilter("kodachrome"),t.filter.technicolor&&Xt.addFilter("technicolor"),t.filter.polaroid&&Xt.addFilter("polaroid"),t.filter.pixelate!==0&&Xt.addFilter("pixelate",t.filter.pixelate),Xt.apply(Oe)}else Et=Oe,Xt&&(Xt=null);if(!n){let l;if(Et.data){let u=[Et.height,Et.width,3];l=hh(Et.data,u,"float32")}else if(typeof ImageData!="undefined"&&Et instanceof ImageData)l=_s?_s.fromPixels(Et):null;else if(t.backend==="webgl"||t.backend==="humangl"){let u=ps(a,o);u.width=a,u.height=o;let c=u.getContext("2d");c==null||c.drawImage(Et,0,0);try{l=_s&&le.browser?_s.fromPixels(u):null}catch(d){throw new Error("browser webgl error")}}else{let u=ps(a,o);if(!u)return{tensor:null,canvas:Oe};u.width=a,u.height=o;let c=u.getContext("2d");if(!c)return{tensor:null,canvas:Oe};c.drawImage(Et,0,0);let d=c.getImageData(0,0,a,o);_s&&le.browser?l=_s.fromPixels(d):l=H(()=>{let p=ln(Array.from(d.data),[a,o,4]),h=Ht(p,4,2),f=An([h[0],h[1],h[2]],2);return V(f,[p.shape[0],p.shape[1],3])})}if(l){let u=pe(l,"float32");n=Lt(u,0),Z(l),Z(u)}else throw n=Mt([1,a,o,3]),new Error("cannot create tensor from input")}}return{tensor:n,canvas:t.filter.return?Et:null}}var B2=0,k8=1;async function I8(e,t){if(e.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let s=De.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await s.data();Z(s);let a=0;for(let l=0;l<r.length/3;l++)a+=r[3*l+2];let o=100*(Math.max(a,B2)/Math.min(a,B2)-1);B2=a;let i=o<Math.max(e.cacheSensitivity,k8);return k8=o>10*e.cacheSensitivity?0:o,i}var le={browser:void 0,node:void 0,worker:void 0,platform:void 0,agent:void 0,initial:!0,backends:[],offscreen:void 0,tfjs:{version:void 0},wasm:{supported:void 0,backend:void 0,simd:void 0,multithread:void 0},webgl:{supported:void 0,backend:void 0,version:void 0,renderer:void 0},webgpu:{supported:void 0,backend:void 0,adapter:void 0},kernels:[],Canvas:void 0,Image:void 0,ImageData:void 0};async function xle(){var n;le.backends=Object.keys(es().registryFactory),le.wasm.supported=typeof WebAssembly!="undefined",le.wasm.backend=le.backends.includes("wasm"),le.wasm.supported&&le.wasm.backend&&or()==="wasm"&&(le.wasm.simd=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),le.wasm.multithread=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let e=ps(100,100),t=e?e.getContext("webgl2"):void 0;if(le.webgl.supported=typeof t!="undefined",le.webgl.backend=le.backends.includes("webgl"),le.webgl.supported&&le.webgl.backend&&(or()==="webgl"||or()==="humangl")){let s=Er().gpgpu!=="undefined"?await Er().getGPGPUContext().gl:null;s&&(le.webgl.version=s.getParameter(s.VERSION),le.webgl.renderer=s.getParameter(s.RENDERER))}le.webgpu.supported=le.browser&&typeof navigator.gpu!="undefined",le.webgpu.backend=le.backends.includes("webgpu"),le.webgpu.supported&&(le.webgpu.adapter=(n=await navigator.gpu.requestAdapter())==null?void 0:n.name),le.kernels=Tr(or()).map(s=>s.kernelName.toLowerCase())}async function vm(){if(le.browser=typeof navigator!="undefined",le.node=typeof process!="undefined",le.worker=le.browser?typeof WorkerGlobalScope!="undefined":void 0,le.tfjs.version=mh,le.offscreen=typeof le.offscreen=="undefined"?typeof OffscreenCanvas!="undefined":le.offscreen,typeof navigator!="undefined"){let e=navigator.userAgent.match(/\(([^()]+)\)/g);if(e&&e[0]){let t=e[0].match(/\(([^()]+)\)/g);le.platform=t&&t[0]?t[0].replace(/\(|\)/g,""):"",le.agent=navigator.userAgent.replace(e[0],""),le.platform[1]&&(le.agent=le.agent.replace(e[1],"")),le.agent=le.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(le.platform=`${process.platform} ${process.arch}`,le.agent=`NodeJS ${process.version}`);await xle()}async function S8(e){le=Vt(le,e)}var W2=yr.leftEyeLower0,V2=yr.rightEyeLower0,$u={leftBounds:[W2[0],W2[W2.length-1]],rightBounds:[V2[0],V2[V2.length-1]]},C8={count:468,mouth:13,symmetryLine:[13,yr.midwayBetweenEyes[0]]},ble={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Ou={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function wm(e,t,n,s){for(let r=0;r<L2.length;r++){let{key:a,indices:o}=L2[r],i=yr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var U2=class{constructor(t,n,s){xe(this,"storedBoxes");xe(this,"boundingBoxDetector");xe(this,"meshDetector");xe(this,"irisModel");xe(this,"boxSize");xe(this,"meshSize");xe(this,"irisSize");xe(this,"irisEnlarge");xe(this,"skipped");xe(this,"detectedFaces");var r,a;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=s,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2]),this.irisSize=(s==null?void 0:s.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,s,r){let a=$d({startPoint:n.startPoint,endPoint:n.endPoint}),o=t.map(d=>[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?z2(s,[0,0]):xm,l=s!==0?o.map(d=>[...A8(d,i),d[2]]):o,u=s!==0?g8(r):xm,c=[...Od({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+Ra(c,u[0])),Math.round(d[1]+Ra(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[$u.leftBounds[0]][2],s=t[$u.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let o=ym(Am(M2([t[s],t[r]]),this.irisEnlarge)),i=$d(o),l=De.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(a&&le.kernels.includes("flipleftright")){let u=De.flipLeftRight(l);Z(l),l=u}return{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,s,r=!1){let a=[];for(let o=0;o<Ou.numCoordinates;o++){let i=t[o*3],l=t[o*3+1],u=t[o*3+2];a.push([(r?1-i/this.irisSize:i/this.irisSize)*s[0]+n.startPoint[0],l/this.irisSize*s[1]+n.startPoint[1],u])}return{rawCoords:a,iris:a.slice(Ou.index)}}getAdjustedIrisCoords(t,n,s){let r=t[yr[`${s}EyeUpper0`][Ou.upperCenter]][2],a=t[yr[`${s}EyeLower0`][Ou.lowerCenter]][2],o=(r+a)/2;return n.map((i,l)=>{let u=o;return l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=C8.count?C8.symmetryLine:ble.symmetryLine,o=h8(n.landmarks[r],n.landmarks[a]),i=Od({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=De.rotateWithOffset(s,o,0,l),c=z2(-o,i),d=t.face.mesh.enabled?Pd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):Pd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),p=he(d,255);return Z(d),Z(u),[o,c,p]}async augmentIris(t,n,s){if(!this.irisModel)return s.debug&&re("face mesh detection requested, but model is not loaded"),t;let{box:r,boxSize:a,crop:o}=this.getEyeBox(t,n,$u.leftBounds[0],$u.leftBounds[1],!0),{box:i,boxSize:l,crop:u}=this.getEyeBox(t,n,$u.rightBounds[0],$u.rightBounds[1]),c=gt([o,u]);Z(o),Z(u);let d=this.irisModel.predict(c);Z(c);let p=await d.data();Z(d);let h=p.slice(0,Ou.numCoordinates*3),{rawCoords:f,iris:m}=this.getEyeCoords(h,r,a,!0),g=p.slice(Ou.numCoordinates*3),{rawCoords:A,iris:y}=this.getEyeCoords(g,i,l),x=this.getLeftToRightEyeDepthDifference(t);Math.abs(x)<30?(wm(t,f,"left",null),wm(t,A,"right",null)):x<1?wm(t,f,"left",["EyeUpper0","EyeLower0"]):wm(t,A,"right",["EyeUpper0","EyeLower0"]);let b=this.getAdjustedIrisCoords(t,m,"left"),v=this.getAdjustedIrisCoords(t,y,"right");return t.concat(b).concat(v)}async predict(t,n){let s=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t,n),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes){let l=await i.box.startPoint.data(),u=await i.box.endPoint.data(),c=await i.landmarks.array();this.storedBoxes.push({startPoint:l,endPoint:u,landmarks:c,confidence:i.confidence})}this.storedBoxes.length>0&&(s=!0)}if(s){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let l=d8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=Am(l),c=ym(u),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{Z(i.box.startPoint),Z(i.box.endPoint),Z(i.landmarks)});let a=[],o=[];for(let i of this.storedBoxes){let l,u=0,c;if(n.face.detector.rotation&&n.face.mesh.enabled&&le.kernels.includes("rotatewithoffset"))[u,c,l]=this.correctFaceRotation(n,i,t);else{c=xm;let d=t.clone(),p=n.face.mesh.enabled?Pd({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.meshSize,this.meshSize]):Pd({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.boxSize,this.boxSize]);l=he(p,255),Z(p),Z(d)}if(!n.face.mesh.enabled)a.push({mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l});else if(!this.meshDetector)n.debug&&re("face mesh detection requested, but model is not loaded");else{let[d,p,h]=this.meshDetector.execute(l);Z(d);let f=(await p.data())[0];Z(p);let m=V(h,[-1,3]),g=await m.array();if(Z(h),Z(m),f<n.face.detector.minConfidence)i.confidence=f,Z(l);else{n.face.iris.enabled&&(g=await this.augmentIris(g,l,n));let A=this.transformRawCoords(g,i,u,c);i={...Am(M2(A),1.5),confidence:i.confidence},n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&le.kernels.includes("rotatewithoffset")&&(Z(l),[u,c,l]=this.correctFaceRotation(n,i,t)),a.push({mesh:A,box:i,faceConfidence:f,boxConfidence:i.confidence,confidence:f,image:l}),i={...ym(i),confidence:i.confidence,faceConfidence:f}}}o.push(i)}return n.face.mesh.enabled&&(this.storedBoxes=o.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=a.length,a}};var kt=[null,null,null],H2;async function T8(e,t){let n=await H2.predict(e,t),s=[],r=0;for(let a of n||[]){if(!a||a.isDisposedInternal)continue;let o=a.mesh.map(c=>[c[0]/(e.shape[2]||0),c[1]/(e.shape[1]||0),c[2]/H2.meshSize]),i={};if(a.mesh&&a.mesh.length>0)for(let c of Object.keys(yr))i[c]=yr[c].map(d=>a.mesh[d]);let l=a.box?[Math.trunc(Math.max(0,a.box.startPoint[0])),Math.trunc(Math.max(0,a.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,a.box.endPoint[0])-Math.max(0,a.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,a.box.endPoint[1])-Math.max(0,a.box.startPoint[1]))]:[0,0,0,0],u=a.box?[a.box.startPoint[0]/(e.shape[2]||0),a.box.startPoint[1]/(e.shape[1]||0),(a.box.endPoint[0]-a.box.startPoint[0])/(e.shape[2]||0),(a.box.endPoint[1]-a.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];s.push({id:r++,score:Math.round(100*a.faceConfidence||100*a.boxConfidence||0)/100,boxScore:Math.round(100*a.boxConfidence)/100,faceScore:Math.round(100*a.faceConfidence)/100,box:l,boxRaw:u,mesh:a.mesh,meshRaw:o,annotations:i,tensor:a.image})}return s}async function km(e){var t;return le.initial&&(kt=[null,null,null]),!kt[0]&&e.face.enabled||!kt[1]&&e.face.mesh.enabled||!kt[2]&&e.face.iris.enabled||le.initial?(kt=await Promise.all([!kt[0]&&e.face.enabled?v8(e):null,!kt[1]&&e.face.mesh.enabled?ot(ct(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!kt[2]&&e.face.iris.enabled?ot(ct(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!kt[1]||!kt[1].modelUrl?re("load model failed:",e.face.mesh.modelPath):e.debug&&re("load model:",kt[1].modelUrl)),e.face.iris.enabled&&(!kt[2]||!kt[2].modelUrl?re("load model failed:",e.face.iris.modelPath):e.debug&&re("load model:",kt[2].modelUrl))):e.debug&&(kt[0]&&re("cached model:",kt[0].model.modelUrl),kt[1]&&re("cached model:",kt[1].modelUrl),kt[2]&&re("cached model:",kt[2].modelUrl)),H2=new U2(kt[0],kt[1],kt[2]),[((t=kt[0])==null?void 0:t.model)||null,kt[1],kt[2]]}var N8=wi,E8=Md;var On,Im=[],R8=0,G2=Number.MAX_SAFE_INTEGER;async function D8(e){var n,s;let t=ct(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return le.initial&&(On=null),On?e.debug&&re("cached model:",t):(On=await ot(t),On?e.debug&&re("load model:",t):re("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),On}function j2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let s=5*e.map((a,o)=>Math.abs(e[o]-t[o])**n).reduce((a,o)=>a+o,0)**(1/n);return Math.max(0,100-s)/100}function _8(e,t,n=0){let s={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return s;for(let r of t)if(r.embedding&&r.name){let a=j2(e,r.embedding);a>n&&a>s.similarity&&(s={...r,similarity:a})}return s}function q2(e){return H(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Ge))return null;let s=[[.05,.15,.85,.85]];if(!(On==null?void 0:On.inputs[0].shape))return null;let r=n.shape.length===3?De.cropAndResize(Lt(n,0),s,[0],[On.inputs[0].shape[2],On.inputs[0].shape[1]]):De.cropAndResize(n,s,[0],[On.inputs[0].shape[2],On.inputs[0].shape[1]]);return z(r,255)})}async function X2(e,t,n,s){var r,a,o;return On?G2<(((r=t.face.description)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&R8===s&&((a=Im[n])==null?void 0:a.age)&&((o=Im[n])==null?void 0:o.age)>0?(G2++,Im[n]):(G2=0,new Promise(async i=>{var d,p;let l=q2(e),u,c={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(((d=t.face.description)==null?void 0:d.enabled)&&(u=await(On==null?void 0:On.predict(l))),Z(l),u){let h=await u.find(b=>b.shape[1]===1).data(),f=Math.trunc(200*Math.abs(h[0]-.5))/100;f>(((p=t.face.description)==null?void 0:p.minConfidence)||0)&&(c.gender=h[0]<=.5?"female":"male",c.genderScore=Math.min(.99,f));let m=Vs(u.find(b=>b.shape[1]===100),1),g=(await m.data())[0];Z(m);let A=await u.find(b=>b.shape[1]===100).data();c.age=Math.round(A[g-1]>A[g+1]?10*g-100*A[g-1]:10*g+100*A[g+1])/10;let x=await u.find(b=>b.shape[1]===1024).data();c.descriptor=[...x],u.forEach(b=>Z(b))}Im[n]=c,R8=s,i(c)})):null}var vle=["angry","disgust","fear","happy","sad","surprise","neutral"],nn,Sm=[],F8=0,K2=Number.MAX_SAFE_INTEGER,Z2=[.2989,.587,.114];async function $8(e){var t;return le.initial&&(nn=null),nn?e.debug&&re("cached model:",nn.modelUrl):(nn=await ot(ct(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!nn||!nn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",nn.modelUrl)),nn}async function Y2(e,t,n,s){var r;return nn?K2<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&F8===s&&Sm[n]&&Sm[n].length>0?(K2++,Sm[n]):(K2=0,new Promise(async a=>{var g,A;let o=De.resizeBilinear(e,[(nn==null?void 0:nn.inputs[0].shape)?nn.inputs[0].shape[2]:0,(nn==null?void 0:nn.inputs[0].shape)?nn.inputs[0].shape[1]:0],!1),[i,l,u]=Ht(o,3,3);Z(o);let c=z(i,Z2[0]),d=z(l,Z2[1]),p=z(u,Z2[2]);Z(i),Z(l),Z(u);let h=xh([c,d,p]);Z(c),Z(d),Z(p);let f=H(()=>z(ye(h,.5),2));Z(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let y=await(nn==null?void 0:nn.predict(f)),x=await y.data();Z(y);for(let b=0;b<x.length;b++)x[b]>(((A=t.face.emotion)==null?void 0:A.minConfidence)||0)&&m.push({score:Math.min(.99,Math.trunc(100*x[b])/100),emotion:vle[b]});m.sort((b,v)=>v.score-b.score)}Z(f),Sm[n]=m,F8=s,a(m)})):null}var zd=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],O8=zd.length,Ld=zd.reduce((e,t,n)=>(e[t]=n,e),{}),wle=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],kle=wle.map(([e,t])=>[Ld[e],Ld[t]]),P8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function M8(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 z8(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/s,u.box[2]/r,u.box[3]/s],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.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((u,c)=>i(u,c))}var J2=class{constructor(t,n){xe(this,"priorityQueue");xe(this,"numberOfElements");xe(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 Q2(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+O8)}}function ex(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=Q2(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function tx(e,t,n){return e<t?t:e>n?n:e}function L8(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function nx(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Cm=1,Pu=16,Ile=50**2;function B8(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,y,x)=>({y:tx(Math.round(A.y/Pu),0,y-1),x:tx(Math.round(A.x/Pu),0,x-1)}),[u,c]=s.shape,d=l(t.position,u,c),p=i(d),f=nx(t.position,p);for(let A=0;A<o;A++){let y=l(f,u,c),x=Q2(y.y,y.x,n,r);f=nx({x:y.x*Pu,y:y.y*Pu},{x:x.x,y:x.y})}let m=l(f,u,c),g=s.get(m.y,m.x,n);return{position:f,part:zd[n],score:g}}function Sle(e,t,n,s,r){let a=P8.map(([p,h])=>[Ld[p],Ld[h]]),o=a.map(([,p])=>p),i=a.map(([p])=>p),l=t.shape[2],u=o.length,c=new Array(l),d=ex(e.part,Pu,n);c[e.part.id]={score:e.score,part:zd[e.part.id],position:d};for(let p=u-1;p>=0;--p){let h=o[p],f=i[p];c[h]&&!c[f]&&(c[f]=B8(p,c[h],f,t,n,r))}for(let p=0;p<u;++p){let h=i[p],f=o[p];c[h]&&!c[f]&&(c[f]=B8(p,c[h],f,t,n,s))}return c}function Cle(e,t,n,s,r){let[a,o]=r.shape,i=!0,l=Math.max(n-Cm,0),u=Math.min(n+Cm+1,a);for(let c=l;c<u;++c){let d=Math.max(s-Cm,0),p=Math.min(s+Cm+1,o);for(let h=d;h<p;++h)if(r.get(c,h,e)>t){i=!1;break}if(!i)break}return i}function Tle(e,t){let[n,s,r]=t.shape,a=new J2(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 u=t.get(o,i,l);u<e||Cle(l,u,o,i,t)&&a.enqueue({score:u,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function W8(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?L8(n,t,a.y,a.x)<=Ile:!1})}function Nle(e,t){return t.reduce((s,{position:r,score:a},o)=>(W8(e,r,o)||(s+=a),s),0)/t.length}function V8(e,t,n,s,r,a){let o=[],i=Tle(a,t);for(;o.length<r&&!i.empty();){let l=i.dequeue(),u=ex(l.part,Pu,e);if(W8(o,u,l.part.id))continue;let c=Sle(l,t,e,n,s);c=c.filter(h=>h.score>a);let d=Nle(o,c),p=M8(c);d>a&&o.push({keypoints:c,box:p,score:Math.round(100*d)/100})}return o}var hs,Ele=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function sx(e,t){let n=H(()=>{if(!hs.inputs[0].shape)return[];let o=De.resizeBilinear(e,[hs.inputs[0].shape[2],hs.inputs[0].shape[1]]),i=ye(he(pe(o,"float32"),127.5),1),u=hs.execute(i,Ele).map(c=>st(c,[0]));return u[1]=u[1].sigmoid(),u}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)Z(o);let r=await V8(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return hs.inputs[0].shape?z8(r,[e.shape[1],e.shape[2]],[hs.inputs[0].shape[2],hs.inputs[0].shape[1]]):[]}async function U8(e){return!hs||le.initial?(hs=await ot(ct(e.modelBasePath,e.body.modelPath||"")),!hs||!hs.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",hs.modelUrl)):e.debug&&re("cached model:",hs.modelUrl),hs}function Tm(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Bd(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function H8(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 De.cropAndResize(t,a,[0],n)}function G8(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 Nm(e,t=1.5){let n=Bd(e),s=Tm(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 Em(e){let t=Bd(e),n=Tm(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}}var j8=[{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 rx=class{constructor(t){xe(this,"model");xe(this,"anchors");xe(this,"anchorsTensor");xe(this,"inputSize");xe(this,"inputSizeTensor");xe(this,"doubleInputSizeTensor");this.model=t,this.anchors=j8.map(n=>[n.x,n.y]),this.anchorsTensor=Hs(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=Gt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Gt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return H(()=>{let n=_e(t,[0,0],[-1,2]),s=_e(t,[0,2],[-1,2]),r=ie(he(n,this.inputSizeTensor),this.anchorsTensor),a=he(s,this.doubleInputSizeTensor),o=z(ye(r,a),this.inputSizeTensor),i=z(ie(r,a),this.inputSizeTensor);return Yl([o,i],1)})}normalizeLandmarks(t,n){return H(()=>{let s=ie(he(V(t,[-1,7,2]),this.inputSizeTensor),this.anchors[n]);return z(s,this.inputSizeTensor)})}async getBoxes(t,n){let s={};s.batched=this.model.predict(t),s.predictions=st(s.batched),s.scores=H(()=>st(Vn(_e(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await De.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=_e(s.norm,[i,0],[1,-1]),u=H(()=>V(this.normalizeLandmarks(_e(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:u,confidence:r[i]})}for(let i of Object.keys(s))Z(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>ye(he(De.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Z(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),p=await l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(G8({startPoint:c,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function Rle(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function q8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Rle(n)}var X8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Da(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Dle(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function K8(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(Da(e[r],Dle(t,a)))}return n}function ax(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=X8(t[0],t[1]),o=K8(a,r),i=X8(-t[0],-t[1]);return K8(o,i)}function Z8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Da(t[0],n),-Da(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function ox(e,t){return[Da(e,t[0]),Da(e,t[1])]}var _le=5,Y8=1.65,J8=[0,5,9,13,17,1,2],Fle=0,$le=2,ix=class{constructor(t,n){xe(this,"handDetector");xe(this,"handPoseModel");xe(this,"inputSize");xe(this,"storedBoxes");xe(this,"skipped");xe(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=0,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=>ox([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Nm(Em(r),_le)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Nm(Em(n),Y8);s.palmLandmarks=[];for(let r=0;r<J8.length;r++)s.palmLandmarks.push(t[J8[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=Tm(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=ax(s,[0,0]),u=i.map(h=>[...ox(h,l),h[2]]),c=Z8(r),d=[...Bd(n),1],p=[Da(d,c[0]),Da(d,c[1])];return u.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;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&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 a=[];for(let o=0;o<this.storedBoxes.length;o++){let i=this.storedBoxes[o];if(!!i)if(n.hand.landmarks){let l=n.hand.rotation?q8(i.palmLandmarks[Fle],i.palmLandmarks[$le]):0,u=Bd(i),c=[u[0]/t.shape[2],u[1]/t.shape[1]],d=n.hand.rotation&&le.kernels.includes("rotatewithoffset")?De.rotateWithOffset(t,l,0,c):t.clone(),p=ax(-l,u),h=s?this.getBoxForPalmLandmarks(i.palmLandmarks,p):i,f=H8(h,d,[this.inputSize,this.inputSize]),m=he(f,255);Z(f),Z(d);let[g,A]=await this.handPoseModel.predict(m);Z(m);let y=(await g.data())[0];if(Z(g),y>=n.hand.minConfidence/4){let x=V(A,[-1,3]),b=await x.array();Z(A),Z(x);let v=this.transformRawCoords(b,h,l,p),k=this.getBoxForHandLandmarks(v);this.storedBoxes[o]={...k,confidence:y};let S={landmarks:v,confidence:y,boxConfidence:i.confidence,fingerConfidence:y,box:{topLeft:k.startPoint,bottomRight:k.endPoint}};a.push(S)}else this.storedBoxes[o]=null;Z(A)}else{let l=Nm(Em(i),Y8),u={confidence:i.confidence,boxConfidence:i.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};a.push(u)}}return this.storedBoxes=this.storedBoxes.filter(o=>o!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var qe={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=>qe.nameMapping[e],getPoints:e=>qe.pointsMapping[e]},Pn={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Pn.nameMapping[e]},He={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=>He.nameMapping[e]};var ki={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 Q8(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 eI(e,t){if(!e||!t)return[0,0];let n=Q8(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=Q8(e[1],e[2],t[1],t[2]);return[n,s]}function tI(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 Ole(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],u=e[2]-t[2],c=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+u*u),h=Math.sqrt(r*r+i*i+c*c),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>ki.NO_CURL_START_LIMIT?A=Pn.none:g>ki.HALF_CURL_START_LIMIT?A=Pn.half:A=Pn.full,A}function nI(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=He.horizontalLeft:r=He.horizontalRight:s===Math.abs(t)?t>0?r=He.horizontalLeft:r=He.horizontalRight:n>0?r=He.horizontalLeft:r=He.horizontalRight,r}function sI(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=He.verticalDown:r=He.verticalUp:s===Math.abs(t)?t<0?r=He.verticalDown:r=He.verticalUp:n<0?r=He.verticalDown:r=He.verticalUp,r}function Ple(e,t,n,s,r,a,o,i){let l,u=sI(e,t,n,s),c=nI(r,a,o,i);return u===He.verticalUp?c===He.horizontalLeft?l=He.diagonalUpLeft:l=He.diagonalUpRight:c===He.horizontalLeft?l=He.diagonalDownLeft:l=He.diagonalDownRight,l}function Mle(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],u=t[1]-n[1],c=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(u)),p=0,h=0,f=0,m=d/(c+1e-5);m>1.5?p+=ki.DISTANCE_VOTE_POWER:m>.66?h+=ki.DISTANCE_VOTE_POWER:f+=ki.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),y=Math.sqrt(o*o+u*u),x=Math.max(g,A,y),b=e[0],v=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===y&&(b=t[0],v=t[1]);let O=eI([b,v],[k,S]),E=tI(O,ki.TOTAL_ANGLE_VOTE_POWER);p+=E[0],h+=E[1],f+=E[2];for(let T of s){let P=tI(T,ki.SINGLE_ANGLE_VOTE_POWER);p+=P[0],h+=P[1],f+=P[2]}let R;return p===Math.max(p,h,f)?R=sI(l,i,u,d):f===Math.max(h,f)?R=nI(a,r,o,c):R=Ple(l,i,u,d,a,r,o,c),R}function lx(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of qe.all){let o=qe.getPoints(a),i=[],l=[];for(let u of o){let c=e[u[0]],d=e[u[1]],p=eI(c,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of qe.all){let o=a===qe.thumb?1:0,i=qe.getPoints(a),l=e[i[o][0]],u=e[i[o+1][1]],c=e[i[3][1]],d=Ole(l,u,c),p=Mle(l,u,c,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}var Wd=class{constructor(t){xe(this,"name");xe(this,"curls");xe(this,"directions");xe(this,"weights");xe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}addCurl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}addDirection(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}setWeight(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 _a=new Wd("thumbs up");_a.addCurl(qe.thumb,Pn.none,1);_a.addDirection(qe.thumb,He.verticalUp,1);_a.addDirection(qe.thumb,He.diagonalUpLeft,.25);_a.addDirection(qe.thumb,He.diagonalUpRight,.25);for(let e of[qe.index,qe.middle,qe.ring,qe.pinky])_a.addCurl(e,Pn.full,1),_a.addDirection(e,He.horizontalLeft,1),_a.addDirection(e,He.horizontalRight,1);var Kt=new Wd("victory");Kt.addCurl(qe.thumb,Pn.half,.5);Kt.addCurl(qe.thumb,Pn.none,.5);Kt.addDirection(qe.thumb,He.verticalUp,1);Kt.addDirection(qe.thumb,He.diagonalUpLeft,1);Kt.addCurl(qe.index,Pn.none,1);Kt.addDirection(qe.index,He.verticalUp,.75);Kt.addDirection(qe.index,He.diagonalUpLeft,1);Kt.addCurl(qe.middle,Pn.none,1);Kt.addDirection(qe.middle,He.verticalUp,1);Kt.addDirection(qe.middle,He.diagonalUpLeft,.75);Kt.addCurl(qe.ring,Pn.full,1);Kt.addDirection(qe.ring,He.verticalUp,.2);Kt.addDirection(qe.ring,He.diagonalUpLeft,1);Kt.addDirection(qe.ring,He.horizontalLeft,.2);Kt.addCurl(qe.pinky,Pn.full,1);Kt.addDirection(qe.pinky,He.verticalUp,.2);Kt.addDirection(qe.pinky,He.diagonalUpLeft,1);Kt.addDirection(qe.pinky,He.horizontalLeft,.2);Kt.setWeight(qe.index,2);Kt.setWeight(qe.middle,2);var rI=[_a,Kt];var zle=.7;function Rm(e){if(!e||e.length===0)return null;let t=lx(e),n={};for(let s of qe.all)n[qe.getName(s)]={curl:Pn.getName(t.curls[s]),direction:He.getName(t.directions[s])};return n}function aI(e){let t=[];if(!e||e.length===0)return t;let n=lx(e);for(let s of rI){let r=s.matchAgainst(n.curls,n.directions);r>=zle&&t.push({name:s.name,confidence:r})}return t}var oI={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]},Wr,Vr,iI;async function ux(e,t){let n=await iI.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let c of Object.keys(oI))a[c]=oI[c].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 c of o)c[0]<i[0]&&(i[0]=c[0]),c[1]<i[1]&&(i[1]=c[1]),c[0]>i[2]&&(i[2]=c[0]),c[1]>i[3]&&(i[3]=c[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 u=Rm(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:u})}return s}async function cx(e){var n,s,r,a,o,i;le.initial&&(Wr=null,Vr=null),!Wr||!Vr?([Wr,Vr]=await Promise.all([e.hand.enabled?ot(ct(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?ot(ct(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&&(!Wr||!Wr.modelUrl?re("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&re("load model:",Wr.modelUrl),!Vr||!Vr.modelUrl?re("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&re("load model:",Vr.modelUrl))):(e.debug&&re("cached model:",Wr.modelUrl),e.debug&&re("cached model:",Vr.modelUrl));let t=new rx(Wr);return iI=new ix(t,Vr),[Wr,Vr]}var $t={name:"humangl",priority:999,canvas:null,gl:null,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Lle(){let e=$t.gl;!e||($t.extensions=e.getSupportedExtensions())}async function lI(e){var t;if(e.config.backend==="humangl"&&($t.name in es().registry&&(!$t.gl||!$t.gl.getParameter($t.gl.VERSION))&&(re("error: humangl backend invalid context"),dx(e)),!Jg($t.name))){try{$t.canvas=await ps(100,100)}catch(s){re("error: cannot create canvas:",s);return}try{$t.gl=(t=$t.canvas)==null?void 0:t.getContext("webgl2",$t.webGLattr),$t.canvas&&($t.canvas.addEventListener("webglcontextlost",async s=>{throw re("error: humangl:",s.type),re("possible browser memory leak using webgl"),e.emit("error"),new Error("browser webgl error")}),$t.canvas.addEventListener("webglcontextrestored",s=>{re("error: humangl context restored:",s)}),$t.canvas.addEventListener("webglcontextcreationerror",s=>{re("error: humangl context create:",s)}))}catch(s){re("error: cannot get WebGL context:",s);return}try{qf(2,$t.gl)}catch(s){re("error: cannot set WebGL context:",s);return}let n=Er().getGPGPUContext?Er().getGPGPUContext().gl:null;if(n)re(`humangl webgl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`);else{re("error: no current gl context:",n,$t.gl);return}try{let s=new nm($t.gl);Xl($t.name,()=>new Tu(s),$t.priority)}catch(s){re("error: cannot register WebGL backend:",s);return}try{Tr("webgl").forEach(r=>{let a={...r,backendName:$t.name};ia(a)})}catch(s){re("error: cannot update WebGL backend registration:",s);return}try{sr.set("WEBGL_VERSION",2)}catch(s){re("error: cannot set WebGL backend flags:",s);return}Lle(),re("backend registered:",$t.name)}}async function Dm(e,t=!1){if(e.state="backend",t||le.initial||e.config.backend&&e.config.backend.length>0&&or()!==e.config.backend){let n=et();if(e.config.backend&&e.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&e.config.debug&&e.config.debug&&re("running inside web worker"),le.browser&&e.config.backend==="tensorflow"&&(e.config.debug&&re("override: backend set to tensorflow while running in browser"),e.config.backend="humangl"),le.node&&(e.config.backend==="webgl"||e.config.backend==="humangl")&&(e.config.debug&&re(`override: backend set to ${e.config.backend} while running in nodejs`),e.config.backend="tensorflow"),le.browser&&e.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")re("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&&re("enumerated webgpu adapter:",r)}e.config.backend==="humangl"&&await lI(e);let s=Object.keys(es().registryFactory);if(e.config.debug&&re("available backends:",s),s.includes(e.config.backend)||(re(`error: backend ${e.config.backend} not found in registry`),e.config.backend=le.node?"tensorflow":"humangl",e.config.debug&&re(`override: setting backend ${e.config.backend}`)),e.config.debug&&re("setting backend:",e.config.backend),e.config.backend==="wasm"){if(e.config.debug&&re("wasm path:",e.config.wasmPath),typeof(vi==null?void 0:vi.setWasmPaths)!="undefined")await c8(e.config.wasmPath);else throw new Error("wasm backend is not loaded");let r=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&re(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),e.config.debug&&!r&&re("warning: wasm simd support is not enabled")}try{await Ab(e.config.backend),await Ah()}catch(r){return re("error: cannot set backend:",e.config.backend,r),!1}}if(or()==="humangl"){sr.set("CHECK_COMPUTATION_FOR_ERRORS",!1),sr.set("WEBGL_CPU_FORWARD",!0),sr.set("WEBGL_PACK_DEPTHWISECONV",!1),sr.set("WEBGL_USE_SHAPES_UNIFORMS",!0),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(re("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),sr.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let s=await Er().getGPGPUContext().gl;e.config.debug&&re(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}gb(),await Ah(),e.performance.backend=Math.trunc(et()-n),e.config.backend=or(),vm(),e.env=le}return!0}function Mu(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&re("kernelFunc",n,t.backend)}};ia(s)}le.kernels=Tr(or()).map(n=>n.kernelName.toLowerCase())}var Vd=1.5,Wt=[null,null],Ble=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ur=[[0,0],[0,0]],Wle=["hand","fist","pinch","point","face","tip","pinchtip"],px=0,Hr,xr={handBoxes:[],fingerBoxes:[],tmpBoxes:[]},uI={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 cI(e){var t;if(le.initial&&(Wt[0]=null),Wt[0])e.debug&&re("cached model:",Wt[0].modelUrl);else{Mu(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Wt[0]=await ot(ct(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let n=Object.values(Wt[0].modelSignature.inputs);Ur[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Ur[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Wt[0]||!Wt[0].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Wt[0].modelUrl)}return Wt[0]}async function dI(e){var t;if(le.initial&&(Wt[1]=null),Wt[1])e.debug&&re("cached model:",Wt[1].modelUrl);else{Wt[1]=await ot(ct(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let n=Object.values(Wt[1].modelSignature.inputs);Ur[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Ur[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Wt[1]||!Wt[1].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Wt[1].modelUrl)}return Wt[1]}async function Vle(e,t){let n=[];if(!e||!Wt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,512),o=Math.round(a*r/8)*8;s.resize=De.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Wt[0].executeAsync(s.cast,Ble),s.boxes=st(s.rawBoxes,[0,2]),s.scores=st(s.rawScores,[0]);let i=Nn(s.scores,1),l=0;for(let u=0;u<i.length;u++){if(u!==0&&u!==1)continue;s.nms=await De.nonMaxSuppressionAsync(s.boxes,i[u],t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data();Z(s.nms);for(let d of Array.from(c)){let p=_e(s.boxes,d,1),h=[0,0,0,0];if(t.hand.landmarks){let x=await p.data(),b=[(x[0]+x[2])/2,(x[1]+x[3])/2],v=[+b[0]-x[0],+b[1]-x[1],-b[0]+x[2],-b[1]+x[3]];h=[b[0]-Vd*v[0],b[1]-Vd*v[1],b[0]+Vd*v[2],b[1]+Vd*v[3]]}else h=await p.data();let f=[h[1],h[0],h[3]-h[1],h[2]-h[0]],m=[Math.trunc(f[0]*Hr[0]),Math.trunc(f[1]*Hr[1]),Math.trunc(f[2]*Hr[0]),Math.trunc(f[3]*Hr[1])];Z(p);let g=_e(i[u],d,1),A=(await g.data())[0];Z(g);let y={id:l++,score:A,box:m,boxRaw:f,label:Wle[u],yxBox:h};n.push(y)}}return i.forEach(u=>Z(u)),Object.keys(s).forEach(u=>Z(s[u])),n.sort((u,c)=>c.score-u.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}function Ule(e,t){let n=[t.map(o=>o[0]),t.map(o=>o[1])],s=[Math.min(...n[0]),Math.max(...n[0]),Math.min(...n[1]),Math.max(...n[1])],r=[(s[0]+s[1])/2,(s[2]+s[3])/2],a=Math.max(r[0]-s[0],r[1]-s[2],-r[0]+s[1],-r[1]+s[3])*Vd;e.box=[Math.trunc(r[0]-a),Math.trunc(r[1]-a),Math.trunc(2*a),Math.trunc(2*a)],e.boxRaw=[e.box[0]/Hr[0],e.box[1]/Hr[1],e.box[2]/Hr[0],e.box[3]/Hr[1]],e.yxBox=[e.boxRaw[1],e.boxRaw[0],e.boxRaw[3]+e.boxRaw[1],e.boxRaw[2]+e.boxRaw[0]]}async function hx(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||!Wt[1])return s;if(n.hand.landmarks){let r={};if(!t.yxBox)return s;r.crop=De.cropAndResize(e,[t.yxBox],[0],[Ur[1][0],Ur[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=he(r.cast,255),[r.score,r.keypoints]=Wt[1].execute(r.div);let a=Math.round(100*(await r.score.data())[0]/100);if(a>(n.hand.minConfidence||0)){s.fingerScore=a,r.reshaped=V(r.keypoints,[-1,3]);let o=await r.reshaped.array();s.keypoints=o.map(i=>[t.box[2]*i[0]/Ur[1][0]+t.box[0],t.box[3]*i[1]/Ur[1][1]+t.box[1],(t.box[2]+t.box[3])/2/Ur[1][0]*i[2]]),Ule(t,s.keypoints),s.box=t.box,s.landmarks=Rm(s.keypoints);for(let i of Object.keys(uI))s.annotations[i]=uI[i].map(l=>s.landmarks&&s.keypoints[l]?s.keypoints[l]:null);xr.tmpBoxes.push(t)}Object.keys(r).forEach(o=>Z(r[o]))}return s}async function fx(e,t){Hr=[e.shape[2]||0,e.shape[1]||0];let n=[];if(xr.tmpBoxes=[],t.hand.landmarks||(xr.fingerBoxes=xr.handBoxes),px<(t.hand.skipFrames||0)&&t.skipFrame)px++,n=await Promise.all(xr.fingerBoxes.map(s=>hx(e,s,t)));else if(px=0,n=await Promise.all(xr.fingerBoxes.map(s=>hx(e,s,t))),n.length!==t.hand.maxDetected){xr.handBoxes=await Vle(e,t);let s=await Promise.all(xr.handBoxes.map(r=>hx(e,r,t)));n=n.concat(s)}return xr.fingerBoxes=[...xr.tmpBoxes],n}var pI=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],hI=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Mn;async function mx(e){return le.initial&&(Mn=null),Mn?e.debug&&re("cached model:",Mn.modelUrl):(Mn=await ot(ct(e.modelBasePath,e.body.modelPath||"")),Mn.width=parseInt(Mn.signature.inputs["input_1:0"].tensorShape.dim[2].size),Mn.height=parseInt(Mn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Mn||!Mn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",Mn.modelUrl)),Mn}async function gx(e,t){if(!Mn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},s=De.resizeBilinear(e,[Mn.width,Mn.height],!1),r=he(s,[255]);Z(s);let a=await Mn.predict(r),o=a.find(g=>g.size===195||g.size===155),i=await(o==null?void 0:o.data())||[];a.forEach(g=>Z(g)),Z(r);let l=[],u=(i==null?void 0:i.length)===195?pI:hI,c=5;for(let g=0;g<i.length/c;g++)l.push({id:g,part:u[g],position:[Math.trunc(n.width*i[c*g+0]/255),Math.trunc(n.height*i[c*g+1]/255),Math.trunc(i[c*g+2])+0],positionRaw:[i[c*g+0]/255,i[c*g+1]/255,i[c*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[c*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[c*g+4]))))/100});let d=l.map(g=>g.position[0]),p=l.map(g=>g.position[1]),h=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],f=[0,0,0,0],m=l.reduce((g,A)=>A.score>g?A.score:g,0);return[{id:0,score:m,box:h,boxRaw:f,keypoints:l}]}var sn,br=[],Ax=[0,0,0,0],yx=[0,0,0,0],_m=0,xx=Number.MAX_SAFE_INTEGER,Hle=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function fI(e){return le.initial&&(sn=null),sn?e.debug&&re("cached model:",sn.modelUrl):(sn=await ot(ct(e.modelBasePath,e.body.modelPath||"")),!sn||!sn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",sn.modelUrl)),sn}function Gle(e,t){let[n,s]=e.shape;return H(()=>{let r=(i,l)=>ye(i,z(he(i,Te(l,"int32")),Te(l,"int32"))),a=V(e,[s*n]),o=rs(a,0).dataSync()[0];if(o>t){let i=Vs(a,0),l=r(i,n).dataSync()[0],u=he(i,Te(n,"int32")).dataSync()[0];return[l,u,o]}return[0,0,o]})}async function bx(e,t){var n;return xx<(((n=t.body)==null?void 0:n.skipFrames)||0)&&t.skipFrame&&Object.keys(br).length>0?(xx++,[{id:0,score:_m,box:Ax,boxRaw:yx,keypoints:br}]):(xx=0,new Promise(async s=>{var c;let r=H(()=>{if(!(sn==null?void 0:sn.inputs[0].shape))return null;let d=De.resizeBilinear(e,[sn.inputs[0].shape[2],sn.inputs[0].shape[1]],!1);return z(d,2).sub(1)}),a;if(t.body.enabled&&(a=await(sn==null?void 0:sn.predict(r))),Z(r),a){br.length=0;let d=a.squeeze();Z(a);let p=d.unstack(2);Z(d);for(let h=0;h<p.length;h++){let[f,m,g]=Gle(p[h],t.body.minConfidence);_m>(((c=t.body)==null?void 0:c.minConfidence)||0)&&br.push({score:Math.round(100*g)/100,part:Hle[h],positionRaw:[f/sn.inputs[0].shape[2],m/sn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/sn.inputs[0].shape[2]),Math.round(e.shape[1]*m/sn.inputs[0].shape[1])]})}p.forEach(h=>Z(h))}_m=br.reduce((d,p)=>p.score>d?p.score:d,0);let o=br.map(d=>d.position[0]),i=br.map(d=>d.position[1]);Ax=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=br.map(d=>d.positionRaw[0]),u=br.map(d=>d.positionRaw[1]);yx=[Math.min(...l),Math.min(...u),Math.max(...l)-Math.min(...l),Math.max(...u)-Math.min(...u)],s([{id:0,score:_m,box:Ax,boxRaw:yx,keypoints:br}])}))}var Zn,Cs=[],vx=[0,0,0,0],Gr=[0,0,0,0],jr=0,wx=Number.MAX_SAFE_INTEGER,mI=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function gI(e){return le.initial&&(Zn=null),Zn?e.debug&&re("cached model:",Zn.modelUrl):(Mu(["size"],e),Zn=await ot(ct(e.modelBasePath,e.body.modelPath||"")),!Zn||!Zn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",Zn.modelUrl)),Zn}async function jle(e,t,n){Cs.length=0;let s=e[0][0];for(let u=0;u<s.length;u++)jr=s[u][2],jr>t.body.minConfidence&&Cs.push({score:Math.round(100*jr)/100,part:mI[u],positionRaw:[s[u][1],s[u][0]],position:[Math.round((n.shape[2]||0)*s[u][1]),Math.round((n.shape[1]||0)*s[u][0])]});jr=Cs.reduce((u,c)=>c.score>u?c.score:u,0);let r=Cs.map(u=>u.position[0]),a=Cs.map(u=>u.position[1]);vx=[Math.min(...r),Math.min(...a),Math.max(...r)-Math.min(...r),Math.max(...a)-Math.min(...a)];let o=Cs.map(u=>u.positionRaw[0]),i=Cs.map(u=>u.positionRaw[1]);Gr=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=[];return l.push({id:0,score:jr,box:vx,boxRaw:Gr,keypoints:Cs}),l}async function qle(e,t,n){let s=[];for(let r=0;r<e[0].length;r++){let a=e[0][r];if(jr=Math.round(100*a[51+4])/100,!(jr<t.body.minConfidence)){Cs.length=0;for(let o=0;o<17;o++){let i=Math.round(100*a[3*o+2])/100;i>t.body.minConfidence&&Cs.push({part:mI[o],score:i,positionRaw:[a[3*o+1],a[3*o+0]],position:[Math.trunc(a[3*o+1]*(n.shape[2]||0)),Math.trunc(a[3*o+0]*(n.shape[1]||0))]})}Gr=[a[51+1],a[51+0],a[51+3]-a[51+1],a[51+2]-a[51+0]],s.push({id:r,score:jr,boxRaw:Gr,box:[Math.trunc(Gr[0]*(n.shape[2]||0)),Math.trunc(Gr[1]*(n.shape[1]||0)),Math.trunc(Gr[2]*(n.shape[2]||0)),Math.trunc(Gr[3]*(n.shape[1]||0))],keypoints:[...Cs]})}}return s}async function kx(e,t){return wx<(t.body.skipFrames||0)&&t.skipFrame&&Object.keys(Cs).length>0?(wx++,[{id:0,score:jr,box:vx,boxRaw:Gr,keypoints:Cs}]):(wx=0,new Promise(async n=>{let s=H(()=>{if(!(Zn==null?void 0:Zn.inputs[0].shape))return null;let i=Zn.inputs[0].shape[2];i===-1&&(i=256);let l=De.resizeBilinear(e,[i,i],!1);return pe(l,"int32")}),r;t.body.enabled&&(r=await(Zn==null?void 0:Zn.predict(s))),Z(s),r||n([]);let a=await r.array(),o;r.shape[2]===17?o=await jle(a,t,e):r.shape[2]===56&&(o=await qle(a,t,e)),Z(r),n(o)}))}var zu=[{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 fs,Fm=[],Ix=Number.MAX_SAFE_INTEGER,$m=2.5;async function AI(e){if(!fs||le.initial){fs=await ot(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(fs.modelSignature.inputs);if(fs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!fs.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!fs||!fs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",fs.modelUrl)}else e.debug&&re("cached model:",fs.modelUrl);return fs}async function Xle(e,t,n,s){let r=0,a=[];for(let u of[1,2,4])H(async()=>{var g,A;let c=u*13,d=(g=e.find(y=>y.shape[1]===c**2&&y.shape[2]===zu.length))==null?void 0:g.squeeze(),p=(A=e.find(y=>y.shape[1]===c**2&&y.shape[2]<zu.length))==null?void 0:A.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let y=0;y<d.shape[0];y++)for(let x=0;x<d.shape[1];x++){let b=m[y][x];if(b>s.object.minConfidence&&x!==61){let v=(.5+Math.trunc(y%c))/c,k=(.5+Math.trunc(y/c))/c,S=f[y].map(U=>U*(c/u/t)),[C,D]=[v-$m/u*S[0],k-$m/u*S[1]],[O,E]=[v+$m/u*S[2]-C,k+$m/u*S[3]-D],R=[C,D,O,E];R=R.map(U=>Math.max(0,Math.min(U,1)));let T=[R[0]*n[0],R[1]*n[1],R[2]*n[0],R[3]*n[1]],P={id:r++,score:Math.round(100*b)/100,class:x+1,label:zu[x].label,box:T.map(U=>Math.trunc(U)),boxRaw:R};a.push(P)}}});e.forEach(u=>Z(u));let o=a.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),i=a.map(u=>u.score),l=[];if(o&&o.length>0){let u=await De.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await u.data(),Z(u)}return a=a.filter((u,c)=>l.includes(c)).sort((u,c)=>c.score-u.score),a}async function Sx(e,t){return Ix<(t.object.skipFrames||0)&&t.skipFrame&&Fm.length>0?(Ix++,Fm):(Ix=0,!le.kernels.includes("mod")||!le.kernels.includes("sparsetodense")?Fm:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[fs.inputSize,fs.inputSize],!1),a=he(r,255),o=a.transpose([0,3,1,2]);Z(a),Z(r);let i;t.object.enabled&&(i=await fs.predict(o)),Z(o);let l=await Xle(i,fs.inputSize,s,t);Fm=l,n(l)}))}var zs,Ii=0,Om=[],Cx=Number.MAX_SAFE_INTEGER;async function yI(e){if(le.initial&&(zs=null),zs)e.debug&&re("cached model:",zs.modelUrl);else{Mu(["floormod"],e),zs=await ot(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(zs.modelSignature.inputs);Ii=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!zs||!zs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",zs.modelUrl)}return zs}async function Kle(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=st(e);Z(e);let o=Ht(a,6,1);Z(a);let i=An([o[1],o[0],o[3],o[2]],1),l=st(i);Z(i);let u=st(o[4]),c=st(o[5]);o.forEach(f=>Z(f));let d=await De.nonMaxSuppressionAsync(l,u,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);Z(l),Z(u),Z(c);let p=await d.data();Z(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=zu[g].label,[y,x]=[r[0][f][0]/Ii,r[0][f][1]/Ii],b=[y,x,r[0][f][2]/Ii-y,r[0][f][3]/Ii-x],v=[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:v,boxRaw:b})}return s}async function Tx(e,t){return Cx<(t.object.skipFrames||0)&&t.skipFrame&&Om.length>0?(Cx++,Om):(Cx=0,!le.kernels.includes("mod")||!le.kernels.includes("sparsetodense")?Om:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[Ii,Ii]),a=t.object.enabled?zs==null?void 0:zs.execute(r,["tower_0/detections"]):null;Z(r);let o=await Kle(a,s,t);Om=o,n(o)}))}var Ts,Nx=!1;async function Ex(e){return!Ts||le.initial?(Ts=await ot(ct(e.modelBasePath,e.segmentation.modelPath||"")),!Ts||!Ts.modelUrl?re("load model failed:",e.segmentation.modelPath):e.debug&&re("load model:",Ts.modelUrl)):e.debug&&re("cached model:",Ts.modelUrl),Ts}async function xI(e,t,n){var m,g;if(Nx)return{data:[],canvas:null,alpha:null};Nx=!0,Ts||await Ex(n);let s=Fu(e,n),r=((m=s.canvas)==null?void 0:m.width)||0,a=((g=s.canvas)==null?void 0:g.height)||0;if(!s.tensor)return{data:[],canvas:null,alpha:null};let o={};o.resize=De.resizeBilinear(s.tensor,[Ts.inputs[0].shape?Ts.inputs[0].shape[1]:0,Ts.inputs[0].shape?Ts.inputs[0].shape[2]:0],!1),Z(s.tensor),o.norm=he(o.resize,255),o.res=Ts.predict(o.norm),o.squeeze=st(o.res,0),o.squeeze.shape[2]===2?(o.softmax=ei(o.squeeze),[o.bg,o.fg]=Nn(o.softmax,2),o.expand=Lt(o.fg,2),o.pad=Lt(o.expand,0),o.crop=De.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=st(o.crop,0)):o.data=De.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(le.node&&!le.Canvas&&typeof ImageData=="undefined")return n.debug&&re("canvas support missing"),Object.keys(o).forEach(A=>Z(o[A])),{data:i,canvas:null,alpha:null};let l=ps(r,a);await _s.toPixels(o.data,l);let u=l.getContext("2d");n.segmentation.blur&&n.segmentation.blur>0&&(u.filter=`blur(${n.segmentation.blur}px)`);let c=u.getImageData(0,0,r,a),d=ps(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]=c.data[4*A+0];p.putImageData(h,0,0);let f=null;if(t&&d){f=ps(r,a);let A=Fu(t,n);Z(A.tensor);let y=f.getContext("2d");y.drawImage(A.canvas,0,0,f.width,f.height),y.drawImage(d,0,0)}return Object.keys(o).forEach(A=>Z(o[A])),Nx=!1,{data:i,canvas:f||d,alpha:l}}var Fa;var Mde=Number.MAX_SAFE_INTEGER;async function bI(e){return le.initial&&(Fa=null),Fa?e.debug&&re("cached model:",Fa.modelUrl):(Fa=await ot(ct(e.modelBasePath,e.face.agegenderrace.modelPath)),!Fa||!Fa.modelUrl?re("load model failed:",e.face.agegenderrace.modelPath):e.debug&&re("load model:",Fa.modelUrl)),Fa}var Ud=class{constructor(){xe(this,"age",null);xe(this,"agegenderrace",null);xe(this,"blazepose",null);xe(this,"centernet",null);xe(this,"efficientpose",null);xe(this,"embedding",null);xe(this,"emotion",null);xe(this,"facedetect",null);xe(this,"faceiris",null);xe(this,"facemesh",null);xe(this,"faceres",null);xe(this,"gender",null);xe(this,"handpose",null);xe(this,"handskeleton",null);xe(this,"handtrack",null);xe(this,"movenet",null);xe(this,"nanodet",null);xe(this,"posenet",null);xe(this,"segmentation",null)}};function dx(e){for(let t of Object.keys(e.models))e.models[t]=null}async function vI(e){var t,n,s,r,a,o,i,l,u,c,d,p,h,f,m,g,A,y,x,b,v,k,S,C,D,O,E;le.initial&&dx(e),e.config.face.enabled&&(e.models.facedetect||([e.models.facedetect,e.models.facemesh,e.models.faceiris]=await km(e.config)),((t=e.config.face.mesh)==null?void 0:t.enabled)&&!e.models.facemesh&&([e.models.facedetect,e.models.facemesh,e.models.faceiris]=await km(e.config)),((n=e.config.face.iris)==null?void 0:n.enabled)&&!e.models.faceiris&&([e.models.facedetect,e.models.facemesh,e.models.faceiris]=await km(e.config))),e.config.hand.enabled&&(!e.models.handpose&&((r=(s=e.config.hand.detector)==null?void 0:s.modelPath)==null?void 0:r.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await cx(e.config)),!e.models.handskeleton&&e.config.hand.landmarks&&((o=(a=e.config.hand.detector)==null?void 0:a.modelPath)==null?void 0:o.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await cx(e.config))),e.config.hand.enabled&&!e.models.handtrack&&((l=(i=e.config.hand.detector)==null?void 0:i.modelPath)==null?void 0:l.includes("handtrack"))&&(e.models.handtrack=cI(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((c=(u=e.config.hand.detector)==null?void 0:u.modelPath)==null?void 0:c.includes("handtrack"))&&(e.models.handskeleton=dI(e.config)),e.config.body.enabled&&!e.models.posenet&&((p=(d=e.config.body)==null?void 0:d.modelPath)==null?void 0:p.includes("posenet"))&&(e.models.posenet=U8(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((f=(h=e.config.body)==null?void 0:h.modelPath)==null?void 0:f.includes("efficientpose"))&&(e.models.efficientpose=fI(e.config)),e.config.body.enabled&&!e.models.blazepose&&((g=(m=e.config.body)==null?void 0:m.modelPath)==null?void 0:g.includes("blazepose"))&&(e.models.blazepose=mx(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((y=(A=e.config.body)==null?void 0:A.modelPath)==null?void 0:y.includes("efficientpose"))&&(e.models.efficientpose=mx(e.config)),e.config.body.enabled&&!e.models.movenet&&((b=(x=e.config.body)==null?void 0:x.modelPath)==null?void 0:b.includes("movenet"))&&(e.models.movenet=gI(e.config)),e.config.object.enabled&&!e.models.nanodet&&((k=(v=e.config.object)==null?void 0:v.modelPath)==null?void 0:k.includes("nanodet"))&&(e.models.nanodet=AI(e.config)),e.config.object.enabled&&!e.models.centernet&&((C=(S=e.config.object)==null?void 0:S.modelPath)==null?void 0:C.includes("centernet"))&&(e.models.centernet=yI(e.config)),e.config.face.enabled&&((D=e.config.face.emotion)==null?void 0:D.enabled)&&!e.models.emotion&&(e.models.emotion=$8(e.config)),e.config.face.enabled&&((O=e.config.face.description)==null?void 0:O.enabled)&&!e.models.faceres&&(e.models.faceres=D8(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=Ex(e.config)),e.config.face.enabled&&((E=e.config.face.agegenderrace)==null?void 0:E.enabled)&&!e.models.agegenderrace&&(e.models.agegenderrace=bI(e.config));for await(let R of Object.keys(e.models))e.models[R]&&typeof e.models[R]!="undefined"&&(e.models[R]=await e.models[R])}async function wI(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&&re("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 u=l.op.toLowerCase();a.includes(u)||a.push(u)}else!o&&e.config.debug&&re("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&&re("model validation:",n,i)}}}var Zle=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]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Yle=(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 y=g[0]-A[0],x=g[1]-A[1],b=g[2]-A[2];return[y,x,b]},r=(g,A)=>{let y=g[1]*A[2]-g[2]*A[1],x=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[y,x,b]},a=g=>{let[A,y,x,b,v,k,S,C,D]=g,O,E,R;return b<1?b>-1?(R=Math.asin(b),E=Math.atan2(-S,A),O=Math.atan2(-k,v)):(R=-Math.PI/2,E=-Math.atan2(C,D),O=0):(R=Math.PI/2,E=Math.atan2(C,D),O=0),isNaN(O)&&(O=0),isNaN(E)&&(E=0),isNaN(R)&&(R=0),{pitch:2*-O,yaw:2*-E,roll:2*-R}},o=g=>{let A=(x,b,v,k)=>Math.atan2(k-b,v-x);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,u=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),c=n(s(u[1],u[0])),d=n(s(u[3],u[2])),p=n(r(d,c));d=r(c,p);let h=[d[0],d[1],d[2],c[0],c[1],c[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?Zle(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}},Rx=async(e,t)=>{var d,p,h,f;let n,s,r,a,o,i,l,u=[];e.state="run:face",n=et();let c=await T8(t,e.config);if(e.performance.face=Math.trunc(et()-n),!t.shape||t.shape.length!==4)return[];if(!c)return[];for(let m=0;m<c.length;m++){if(e.analyze("Get Face"),!c[m].tensor||c[m].tensor.isDisposedInternal){re("Face object is disposed:",c[m].tensor);continue}let g=Yle(c[m],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?Y2(c[m].tensor||ln([]),e.config,m,c.length):{}:(e.state="run:emotion",n=et(),o=e.config.face.emotion.enabled?await Y2(c[m].tensor||ln([]),e.config,m,c.length):{},e.performance.emotion=Math.trunc(et()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?X2(c[m].tensor||ln([]),e.config,m,c.length):[]:(e.state="run:description",n=et(),l=e.config.face.description.enabled?await X2(c[m].tensor||ln([]),e.config,m,c.length):[],e.performance.embedding=Math.trunc(et()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,r]=await Promise.all([s,a,o,i,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=c[m])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((f=(h=c[m])==null?void 0:h.annotations)==null?void 0:f.rightEyeIris)&&(delete c[m].annotations.leftEyeIris,delete c[m].annotations.rightEyeIris);let A=c[m].annotations&&c[m].annotations.leftEyeIris&&c[m].annotations.rightEyeIris&&c[m].annotations.leftEyeIris.length>0&&c[m].annotations.rightEyeIris.length>0&&c[m].annotations.leftEyeIris[0]!==null&&c[m].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[m].annotations.leftEyeIris[3][0]-c[m].annotations.leftEyeIris[1][0]),Math.abs(c[m].annotations.rightEyeIris[4][1]-c[m].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y=e.config.face.detector.return?st(c[m].tensor):null;Z(c[m].tensor),c[m].tensor&&delete c[m].tensor,u.push({...c[m],id:m,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:g,tensor:y}),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),u};var kI=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.y<a.position.y&&r.position.y<a.position.y?t.push({body:n,gesture:"i give up"}):a&&s&&s.position.y<a.position.y?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position.y<a.position.y&&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&&t.push({body:n,gesture:`leaning ${o.position.y>i.position.y?"left":"right"}`})}return t},II=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?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 o=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]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},SI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(u=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(u=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},CI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=aI(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var qr={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},Si=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("invalid canvas")},Pm=e=>Math.round(e*180/Math.PI);function Dx(e,t,n,s=0,r){e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Hd(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function _x(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Gd(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){_x(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function Fx(e,t,n){let s=Vt(qr,n);if(!t||!e)return;let r=Si(e);r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let u=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${u}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(c,6,0+a*s.lineHeight),a+=1}}}async function $x(e,t,n){var a,o,i,l;let s=Vt(qr,n);if(!t||!e)return;let r=Si(e);for(let u of t){r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Hd(r,u.box[0],u.box[1],u.box[2],u.box[3],s);let c=[];if(c.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&c.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&c.push(`age: ${u.age||""}`),u.iris&&c.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let d=u.emotion.map(p=>`${Math.trunc(100*p.score)}% ${p.emotion}`);d.length>3&&(d.length=3),c.push(d.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&c.push(`roll: ${Pm(u.rotation.angle.roll)}\xB0 yaw:${Pm(u.rotation.angle.yaw)}\xB0 pitch:${Pm(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&c.push(`gaze: ${Pm(u.rotation.gaze.bearing)}\xB0`)),c.length===0&&c.push("face"),r.fillStyle=s.color;for(let d=c.length-1;d>=0;d--){let p=Math.max(u.box[0],0),h=d*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c[d],p+5,h+16)),r.fillStyle=s.labelColor,r.fillText(c[d],p+4,h+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)Dx(r,d[0],d[1],d[2],s);if(s.drawPolygons){r.lineWidth=1;for(let d=0;d<wi.length/3;d++){let p=[wi[d*3+0],wi[d*3+1],wi[d*3+2]].map(h=>u.mesh[h]);_x(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris){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){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&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();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]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(d[0],d[1]);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]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(p[0],p[1]),r.stroke()}}}}}async function Ox(e,t,n){var a;let s=Vt(qr,n);if(!t||!e)return;let r=Si(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&&(Hd(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 ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Dx(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&(r.font=s.font,t[o].keypoints))for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${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){let i,l=[];l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),Gd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&_x(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),Gd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),Gd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),Gd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),Gd(r,l,s)}}}async function Px(e,t,n){let s=Vt(qr,n);if(!t||!e)return;let r=Si(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Hd(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(`${a.label}:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`${a.label}:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:s.color,Dx(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${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+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Mx(e,t,n){let s=Vt(qr,n);if(!t||!e)return;let r=Si(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Hd(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function TI(e,t,n){let s=Vt(qr,n);if(!t||!e)return;let r=Si(e);r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Hd(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${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 NI(e,t){if(!e||!t)return;Si(t).drawImage(e,0,0)}async function EI(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=et(),r=Vt(qr,n),a=Promise.all([$x(e,t.face,r),Ox(e,t.body,r),Px(e,t.hand,r),Mx(e,t.object,r),Fx(e,t.gesture,r)]);return t.performance.draw=Math.trunc(et()-s),a}function RI(e,t,n,s,r){var i,l,u,c,d,p,h,f,m,g,A,y,x,b,v,k;let a=0,o=[];for(let S of e){let C={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let P of t)S.box[0]>P.box[0]&&S.box[0]<P.box[0]+P.box[2]&&S.box[1]+S.box[3]>P.box[1]&&S.box[1]+S.box[3]<P.box[1]+P.box[3]&&(C.body=P);if(C.body)for(let P of n)P.box[0]+P.box[2]>C.body.box[0]&&P.box[0]+P.box[2]<C.body.box[0]+C.body.box[2]&&P.box[1]+P.box[3]>C.body.box[1]&&P.box[1]+P.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=P),P.box[0]<C.body.box[0]+C.body.box[2]&&P.box[0]>C.body.box[0]&&P.box[1]+P.box[3]>C.body.box[1]&&P.box[1]+P.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=P);for(let P of s)P.face!==void 0&&P.face===S.id?(i=C.gestures)==null||i.push(P):P.iris!==void 0&&P.iris===S.id?(l=C.gestures)==null||l.push(P):P.body!==void 0&&P.body===((u=C.body)==null?void 0:u.id)?(c=C.gestures)==null||c.push(P):P.hand!==void 0&&P.hand===((p=(d=C.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=C.gestures)==null||h.push(P):P.hand!==void 0&&P.hand===((m=(f=C.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=C.gestures)==null||g.push(P));let D=[],O=[],E=P=>{P&&P.length===4&&(D.push(P[0],P[0]+P[2]),O.push(P[1],P[1]+P[3]))};E((A=C.face)==null?void 0:A.box),E((y=C.body)==null?void 0:y.box),E((b=(x=C.hands)==null?void 0:x.left)==null?void 0:b.box),E((k=(v=C.hands)==null?void 0:v.right)==null?void 0:k.box);let R=Math.min(...D),T=Math.min(...O);C.box=[R,T,Math.max(...D)-R,Math.max(...O)-T],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),o.push(C)}return o}var $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function DI(e){var s,r,a,o,i,l,u,c,d,p,h,f,m,g,A,y,x,b,v,k,S;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if($e.canvas=e.canvas,!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C<e.body.length;C++){let D=e.body[C].box.map((R,T)=>((n-1)*$e.body[C].box[T]+R)/n),O=e.body[C].boxRaw.map((R,T)=>((n-1)*$e.body[C].boxRaw[T]+R)/n),E=e.body[C].keypoints.map((R,T)=>({score:R.score,part:R.part,position:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[0]+R.position[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[1]+R.position[1])/n:R.position[1]],positionRaw:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[0]+R.positionRaw[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[1]+R.positionRaw[1])/n:R.position[1]]}));$e.body[C]={...e.body[C],box:D,boxRaw:O,keypoints:E}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let C=0;C<e.hand.length;C++){let D=e.hand[C].box.map((T,P)=>((n-1)*$e.hand[C].box[P]+T)/n),O=e.hand[C].boxRaw.map((T,P)=>((n-1)*$e.hand[C].boxRaw[P]+T)/n);$e.hand[C].keypoints.length!==e.hand[C].keypoints.length&&($e.hand[C].keypoints=e.hand[C].keypoints);let E=e.hand[C].keypoints&&e.hand[C].keypoints.length>0?e.hand[C].keypoints.map((T,P)=>T.map((U,j)=>((n-1)*$e.hand[C].keypoints[P][j]+U)/n)):[],R={};if(Object.keys($e.hand[C].annotations).length!==Object.keys(e.hand[C].annotations).length&&($e.hand[C].annotations=e.hand[C].annotations),e.hand[C].annotations)for(let T of Object.keys(e.hand[C].annotations))R[T]=e.hand[C].annotations[T]&&e.hand[C].annotations[T][0]?e.hand[C].annotations[T].map((P,U)=>P.map((j,q)=>((n-1)*$e.hand[C].annotations[T][U][q]+j)/n)):null;$e.hand[C]={...e.hand[C],box:D,boxRaw:O,keypoints:E,annotations:R}}if(!$e.face||e.face.length!==$e.face.length)$e.face=JSON.parse(JSON.stringify(e.face));else for(let C=0;C<e.face.length;C++){let D=e.face[C].box.map((R,T)=>((n-1)*$e.face[C].box[T]+R)/n),O=e.face[C].boxRaw.map((R,T)=>((n-1)*$e.face[C].boxRaw[T]+R)/n),E={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};E.matrix=(s=e.face[C].rotation)==null?void 0:s.matrix,E.angle={roll:((n-1)*(((a=(r=$e.face[C].rotation)==null?void 0:r.angle)==null?void 0:a.roll)||0)+(((i=(o=e.face[C].rotation)==null?void 0:o.angle)==null?void 0:i.roll)||0))/n,yaw:((n-1)*(((u=(l=$e.face[C].rotation)==null?void 0:l.angle)==null?void 0:u.yaw)||0)+(((d=(c=e.face[C].rotation)==null?void 0:c.angle)==null?void 0:d.yaw)||0))/n,pitch:((n-1)*(((h=(p=$e.face[C].rotation)==null?void 0:p.angle)==null?void 0:h.pitch)||0)+(((m=(f=e.face[C].rotation)==null?void 0:f.angle)==null?void 0:m.pitch)||0))/n},E.gaze={bearing:((n-1)*(((A=(g=$e.face[C].rotation)==null?void 0:g.gaze)==null?void 0:A.bearing)||0)+(((x=(y=e.face[C].rotation)==null?void 0:y.gaze)==null?void 0:x.bearing)||0))/n,strength:((n-1)*(((v=(b=$e.face[C].rotation)==null?void 0:b.gaze)==null?void 0:v.strength)||0)+(((S=(k=e.face[C].rotation)==null?void 0:k.gaze)==null?void 0:S.strength)||0))/n},$e.face[C]={...e.face[C],rotation:E,box:D,boxRaw:O}}if(!$e.object||e.object.length!==$e.object.length)$e.object=JSON.parse(JSON.stringify(e.object));else for(let C=0;C<e.object.length;C++){let D=e.object[C].box.map((E,R)=>((n-1)*$e.object[C].box[R]+E)/n),O=e.object[C].boxRaw.map((E,R)=>((n-1)*$e.object[C].boxRaw[R]+E)/n);$e.object[C]={...e.object[C],box:D,boxRaw:O}}if(e.persons){let C=e.persons;if(!$e.persons||C.length!==$e.persons.length)$e.persons=JSON.parse(JSON.stringify(C));else for(let D=0;D<C.length;D++)$e.persons[D].box=C[D].box.map((O,E)=>((n-1)*$e.persons[D].box[E]+O)/n)}return e.gesture&&($e.gesture=e.gesture),e.performance&&($e.performance=e.performance),$e}var zx="2.2.3";var Mm=`
|
|
/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==`,zm=`
|
|
/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 Qle(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(Mm);break;case"body":case"full":n=await t(zm);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function eue(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Mm;break;case"full":case"body":n="data:image/jpeg;base64,"+zm;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:le.Image&&(s=new le.Image),s.onload=async()=>{let r=ps(s.naturalWidth,s.naturalHeight);if(!r)re("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function tue(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(Mm)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(zm)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&re("Warmup tfjs-node not loaded");return s}async function _I(e,t){let n=et();if(e.state="warmup",t&&(e.config=Vt(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await Qle(e):typeof Image!="undefined"||le.Canvas!==void 0?s=await eue(e):s=await tue(e);let a=et();e.config.debug&&re("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Lu,jd,qd,Lm,$I=class{constructor(t){xe(this,"version");xe(this,"config");xe(this,"result");xe(this,"state");xe(this,"process");xe(this,"tf");xe(this,"env");xe(this,"draw");xe(this,"models");xe(this,"events");xe(this,"faceTriangulation");xe(this,"faceUVMap");xe(this,"performance");Ju(this,Lu,void 0);Ju(this,jd,void 0);Ju(this,qd,void 0);xe(this,"gl");xe(this,"analyze",(...t)=>{if(!Yu(this,jd))return;let n=this.tf.engine().state.numTensors,s=Yu(this,Lu);Qu(this,Lu,n);let r=n-s;r!==0&&re(...t,r)});Ju(this,Lm,t=>{if(!Yu(this,qd))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ge))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});xe(this,"emit",t=>{var n;return(n=this.events)==null?void 0:n.dispatchEvent(new Event(t))});vm(),this.env=le,ea.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${mh}/dist/`,ea.modelBasePath=this.env.browser?"../models/":"file://models/",ea.backend=this.env.browser?"humangl":"tensorflow",this.version=zx,Object.defineProperty(this,"version",{value:zx}),this.config=JSON.parse(JSON.stringify(ea)),Object.seal(this.config),t&&(this.config=Vt(this.config,t)),this.tf=vi,this.state="idle",Qu(this,Lu,0),Qu(this,jd,!1),Qu(this,qd,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=new EventTarget,this.models=new Ud,this.draw={options:qr,canvas:(n,s)=>NI(n,s),face:(n,s,r)=>$x(n,s,r),body:(n,s,r)=>Ox(n,s,r),hand:(n,s,r)=>Px(n,s,r),gesture:(n,s,r)=>Fx(n,s,r),object:(n,s,r)=>Mx(n,s,r),person:(n,s,r)=>TI(n,s,r),all:(n,s,r)=>EI(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=N8,this.faceUVMap=E8,this.gl=$t,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ea)),this.config.backend=t}validate(t){return hg(ea,t||this.config)}image(t){return Fu(t,this.config)}similarity(t,n){return j2(t,n)}async segmentation(t,n){return xI(t,n,this.config)}enhance(t){return q2(t)}match(t,n,s=0){return _8(t,n,s)}async init(){await Dm(this,!0),await this.tf.ready(),S8(this.env)}async load(t){this.state="load";let n=et(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=Vt(this.config,t)),le.initial&&(this.config.debug&&re(`version: ${this.version}`),this.config.debug&&re(`tfjs version: ${this.tf.version_core}`),await Dm(this)||re("error: backend check failed"),await Ah(),this.env.browser&&(this.config.debug&&re("configuration:",this.config),this.config.debug&&re("tf flags:",this.tf.ENV.flags))),await vI(this),le.initial&&this.config.debug&&re("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),le.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await wI(this),this.emit("load"));let a=Math.trunc(et()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return DI(t)}async warmup(t){return _I(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var A,y,x,b,v,k,S,C,D,O,E,R,T,P,U,j,q,X,te,ne,se,ae;this.state="config";let r,a;this.config=Vt(this.config,n),this.state="check";let o=Yu(this,Lm).call(this,t);o&&(re(o,t),s({error:o}));let i=et();await Dm(this),await this.load(),r=et(),this.state="image";let l=Fu(t,this.config);if(this.process=l,this.performance.image=Math.trunc(et()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&re("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=et(),this.config.skipFrame=await I8(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(et()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],p=[];this.state="detect:face",this.config.async?(u=this.config.face.enabled?Rx(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=et(),u=this.config.face.enabled?await Rx(this,l.tensor):[],a=Math.trunc(et()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(u=await u),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?Vt(this.config,{body:{maxDetected:1*u.length}}):this.config;this.config.async?(((A=this.config.body.modelPath)==null?void 0:A.includes("posenet"))?c=this.config.body.enabled?sx(l.tensor,h):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?gx(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?bx(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(c=this.config.body.enabled?kx(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=et(),((v=this.config.body.modelPath)==null?void 0:v.includes("posenet"))?c=this.config.body.enabled?await sx(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await gx(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?c=this.config.body.enabled?await bx(l.tensor,h):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await kx(l.tensor,h):[]),a=Math.trunc(et()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?Vt(this.config,{hand:{maxDetected:2*u.length}}):this.config;this.config.async?(((O=(D=this.config.hand.detector)==null?void 0:D.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?ux(l.tensor,f):[]:((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handtrack"))&&(d=this.config.hand.enabled?fx(l.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=et(),((P=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?await ux(l.tensor,f):[]:((j=(U=this.config.hand.detector)==null?void 0:U.modelPath)==null?void 0:j.includes("handtrack"))&&(d=this.config.hand.enabled?await fx(l.tensor,f):[]),a=Math.trunc(et()-r),a>0&&(this.performance.hand=a)),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"))?p=this.config.object.enabled?Sx(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?Tx(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=et(),((te=this.config.object.modelPath)==null?void 0:te.includes("nanodet"))?p=this.config.object.enabled?await Sx(l.tensor,this.config):[]:((ne=this.config.object.modelPath)==null?void 0:ne.includes("centernet"))&&(p=this.config.object.enabled?await Tx(l.tensor,this.config):[]),a=Math.trunc(et()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([u,c,d,p]=await Promise.all([u,c,d,p])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=et(),m=[...II(u),...kI(c),...CI(d),...SI(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(et()-r)),this.performance.total=Math.trunc(et()-i);let g=((ae=(se=this.process)==null?void 0:se.tensor)==null?void 0:ae.shape)||[];this.result={face:u,body:c,hand:d,gesture:m,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return RI(u,c,d,m,g)}},Z(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Lu=new WeakMap,jd=new WeakMap,qd=new WeakMap,Lm=new WeakMap;return nue;})();
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* 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 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. */
|