mirror of https://github.com/vladmandic/human
5188 lines
1.3 MiB
5188 lines
1.3 MiB
|
|
/*
|
|
Human library
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
|
|
var BI=Object.defineProperty;var zm=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var b5=(e,t)=>{for(var n in t)BI(e,n,{get:t[n],enumerable:!0})};var v5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var pn=(e,t,n)=>(v5(e,t,"read from private field"),n?n.call(e):t.get(e)),ra=(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)},Ia=(e,t,n,a)=>(v5(e,t,"write to private field"),a?a.call(e,n):t.set(e,n),n);function ft(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(`Human: ModelPath Error: ${r} Expecting JSON file`);return r}function de(...e){let t=new Date,n=`${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 Je=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Ln(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,a)=>(Object.keys(a||{}).forEach(r=>{let s=n[r],i=a[r];Array.isArray(s)&&Array.isArray(i)?n[r]=s.concat(...i):t(s)&&t(i)?n[r]=Ln(s,i):n[r]=i}),n),{})}var w5={backend:"webgl",modelBasePath:"../models/",wasmPath:"../node_modules/@tensorflow/tfjs-backend-wasm/dist/",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:.1,iouThreshold:.1,maxDetected:2,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"}};function k5(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let a=n[0].match(/\(([^()]+)\)/g);e=a?a[0].replace(/\(|\)/g,""):"",t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/ /g," ")}}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var dp={};b5(dp,{Abs:()=>fo,Acos:()=>mo,Acosh:()=>go,AdadeltaOptimizer:()=>wh,AdagradOptimizer:()=>kh,AdamOptimizer:()=>Ih,AdamaxOptimizer:()=>Sh,Add:()=>Or,AddN:()=>xs,All:()=>yo,Any:()=>Ao,ArgMax:()=>bs,ArgMin:()=>Fu,Asin:()=>xo,Asinh:()=>bo,Atan:()=>vo,Atan2:()=>ko,Atanh:()=>wo,AvgPool:()=>vs,AvgPool3D:()=>$u,AvgPool3DGrad:()=>Kp,AvgPoolGrad:()=>Xp,BackendWasm:()=>i4,BatchMatMul:()=>ws,BatchToSpaceND:()=>Du,Bincount:()=>Zp,BroadcastTo:()=>mb,Callback:()=>Y8,CallbackList:()=>j4,Cast:()=>ks,Ceil:()=>Is,ClipByValue:()=>zr,Complex:()=>Yp,ComplexAbs:()=>Ou,Concat:()=>Io,Conv2D:()=>Ss,Conv2DBackpropFilter:()=>Jp,Conv2DBackpropInput:()=>Ns,Conv3D:()=>zu,Conv3DBackpropFilterV2:()=>Qp,Conv3DBackpropInputV2:()=>ec,Cos:()=>Ts,Cosh:()=>So,CropAndResize:()=>No,Cumsum:()=>Cs,CustomCallback:()=>H4,DataStorage:()=>Up,DenseBincount:()=>tc,DepthToSpace:()=>To,DepthwiseConv2dNative:()=>Es,DepthwiseConv2dNativeBackpropFilter:()=>nc,DepthwiseConv2dNativeBackpropInput:()=>ac,Diag:()=>rc,Dilation2D:()=>_u,Dilation2DBackpropFilter:()=>ic,Dilation2DBackpropInput:()=>sc,ENV:()=>sa,EarlyStopping:()=>Q8,Einsum:()=>oc,Elu:()=>Co,EluGrad:()=>lc,Environment:()=>hb,Equal:()=>Ro,Erf:()=>Eo,Exp:()=>Ms,ExpandDims:()=>Mo,Expm1:()=>Fo,FFT:()=>uc,Fill:()=>Pu,FlipLeftRight:()=>$o,Floor:()=>Fs,FloorDiv:()=>$s,FromPixels:()=>Rc,FusedBatchNorm:()=>Ds,FusedConv2D:()=>fi,FusedDepthwiseConv2D:()=>mi,GPGPUContext:()=>Vh,GatherNd:()=>Oo,GatherV2:()=>Do,GraphModel:()=>Mk,Greater:()=>zo,GreaterEqual:()=>Os,History:()=>U4,IFFT:()=>dc,Identity:()=>zs,Imag:()=>pc,InputSpec:()=>zt,IsFinite:()=>_o,IsInf:()=>Po,IsNan:()=>Lo,KernelBackend:()=>Eu,LRN:()=>Bu,LRNGrad:()=>hc,LayerVariable:()=>P4,LayersModel:()=>kr,LeakyRelu:()=>_s,Less:()=>Wo,LessEqual:()=>Bo,LinSpace:()=>cc,Log:()=>Ps,Log1p:()=>Vo,LogSoftmax:()=>gb,LogicalAnd:()=>jo,LogicalNot:()=>Lu,LogicalOr:()=>Wu,MathBackendCPU:()=>Eh,MathBackendWebGL:()=>Xl,Max:()=>Ls,MaxPool:()=>Bs,MaxPool3D:()=>Vu,MaxPool3DGrad:()=>mc,MaxPoolGrad:()=>fc,MaxPoolWithArgmax:()=>gc,Maximum:()=>Ws,Mean:()=>Vs,Min:()=>js,Minimum:()=>Us,MirrorPad:()=>Hs,Mod:()=>Uo,MomentumOptimizer:()=>Nh,Multinomial:()=>yc,Multiply:()=>Gs,Neg:()=>Ho,NonMaxSuppressionV3:()=>qo,NonMaxSuppressionV4:()=>Xo,NonMaxSuppressionV5:()=>Ko,NotEqual:()=>Go,OP_SCOPE_SUFFIX:()=>Mb,OneHot:()=>qs,OnesLike:()=>Zo,Optimizer:()=>xr,Pack:()=>Yo,PadV2:()=>Xs,Pool:()=>jS,Pow:()=>Ks,Prelu:()=>Zs,Prod:()=>Jo,RMSPropOptimizer:()=>Th,RNN:()=>ar,Range:()=>ju,Rank:()=>Jm,Real:()=>Ac,RealDiv:()=>Rs,Reciprocal:()=>Qo,Reduction:()=>yn,Relu:()=>Ys,Relu6:()=>Qs,Reshape:()=>el,ResizeBilinear:()=>Js,ResizeBilinearGrad:()=>bc,ResizeNearestNeighbor:()=>Uu,ResizeNearestNeighborGrad:()=>xc,Reverse:()=>ei,RotateWithOffset:()=>fl,Round:()=>ti,Rsqrt:()=>ni,SGDOptimizer:()=>kd,ScatterNd:()=>tl,Select:()=>nl,Selu:()=>al,Sequential:()=>au,Sigmoid:()=>ri,Sign:()=>il,Sin:()=>ai,Sinh:()=>sl,Slice:()=>rl,Softmax:()=>oi,Softplus:()=>ol,SpaceToBatchND:()=>Hu,SparseFillEmptyRows:()=>vc,SparseReshape:()=>wc,SparseSegmentMean:()=>kc,SparseSegmentSum:()=>Ic,SparseToDense:()=>Sc,SplitV:()=>ll,Sqrt:()=>si,Square:()=>Gu,SquaredDifference:()=>li,Step:()=>Pr,StridedSlice:()=>ul,StringNGrams:()=>Nc,StringSplit:()=>Tc,StringToHashBucketFast:()=>Cc,Sub:()=>ui,Sum:()=>ii,SymbolicTensor:()=>Da,Tan:()=>di,Tanh:()=>pi,Tensor:()=>Be,TensorBuffer:()=>Lt,Tile:()=>_r,TopK:()=>dl,Transform:()=>pl,Transpose:()=>ci,Unique:()=>Ec,Unpack:()=>cl,UnsortedSegmentSum:()=>qu,Variable:()=>td,ZerosLike:()=>hl,_FusedMatMul:()=>hi,abs:()=>Wt,acos:()=>S1,acosh:()=>N1,add:()=>ie,addN:()=>jc,all:()=>Uc,any:()=>id,argMax:()=>ki,argMin:()=>T1,asin:()=>C1,asinh:()=>E1,atan:()=>R1,atan2:()=>M1,atanh:()=>F1,avgPool:()=>ld,avgPool3d:()=>O1,backend:()=>c3,backend_util:()=>F,basicLSTMCell:()=>TC,batchNorm:()=>Ni,batchNorm2d:()=>g3,batchNorm3d:()=>y3,batchNorm4d:()=>A3,batchToSpaceND:()=>ud,bincount:()=>z1,booleanMaskAsync:()=>$M,broadcastTo:()=>Sl,browser:()=>oa,buffer:()=>Ve,callbacks:()=>Tie,cast:()=>ge,ceil:()=>_1,clipByValue:()=>Mn,clone:()=>Ha,complex:()=>Wr,concat:()=>lt,concat1d:()=>x3,concat2d:()=>Nl,concat3d:()=>b3,concat4d:()=>v3,constraints:()=>y4,conv1d:()=>Gc,conv2d:()=>mr,conv2dTranspose:()=>qc,conv3d:()=>L1,conv3dTranspose:()=>k3,copyRegisteredKernels:()=>GS,cos:()=>dd,cosh:()=>Xc,cosineWindow:()=>cg,cumsum:()=>Kc,customGrad:()=>qa,data:()=>Fk,denseBincount:()=>I3,deprecationWarn:()=>k1,depthToSpace:()=>W1,depthwiseConv2d:()=>Tl,deregisterOp:()=>Eie,device_util:()=>ad,diag:()=>nE,dilation2d:()=>B1,disableDeprecationWarnings:()=>WT,dispose:()=>he,disposeVariables:()=>BT,div:()=>me,divNoNan:()=>V1,dot:()=>S3,dropout:()=>G3,einsum:()=>N3,elu:()=>Cl,enableDebugMode:()=>LT,enableProdMode:()=>PT,enclosingPowerOfTwo:()=>q3,engine:()=>fr,env:()=>te,equal:()=>Hr,erf:()=>j1,exp:()=>la,expandDims:()=>mn,expm1:()=>U1,eye:()=>H1,fft:()=>bd,fill:()=>El,findBackend:()=>I1,findBackendFactory:()=>XT,floor:()=>Rl,floorDiv:()=>Vc,forceHalfFloat:()=>yw,fused:()=>Kr,gather:()=>Ti,gatherND:()=>H3,gather_util:()=>g1,getBackend:()=>GT,getGradient:()=>Xm,getKernel:()=>Mc,getKernelsForBackend:()=>gl,gpgpu_util:()=>Wv,grad:()=>FE,grads:()=>$E,greater:()=>Wn,greaterEqual:()=>qr,ifft:()=>Dl,imag:()=>Zc,image:()=>De,inTopKAsync:()=>UM,initializers:()=>I4,input:()=>f8,io:()=>En,irfft:()=>ch,isFinite:()=>T3,isInf:()=>C3,isNaN:()=>G1,keep:()=>Kt,kernel_impls:()=>Za,layers:()=>O4,leakyRelu:()=>pd,less:()=>Yc,lessEqual:()=>Xr,linalg:()=>s7,linspace:()=>E3,loadGraphModel:()=>ct,loadLayersModel:()=>_re,localResponseNormalization:()=>q1,log:()=>Bn,log1p:()=>Jc,logSigmoid:()=>M3,logSoftmax:()=>eh,logSumExp:()=>Z1,logicalAnd:()=>xa,logicalNot:()=>cd,logicalOr:()=>th,logicalXor:()=>O3,losses:()=>I$,matMul:()=>je,math:()=>qb,max:()=>Vn,maxPool:()=>hd,maxPool3d:()=>Y1,maxPoolWithArgmax:()=>z3,maximum:()=>Xa,mean:()=>Nt,memory:()=>Bc,meshgrid:()=>tR,metrics:()=>X8,min:()=>fd,minimum:()=>Ml,mirrorPad:()=>J1,mod:()=>Q1,model:()=>Ore,models:()=>K8,moments:()=>nh,movingAverage:()=>zM,mul:()=>B,multiRNNCell:()=>uR,multinomial:()=>_3,neg:()=>St,nextFrame:()=>Ch,norm:()=>gh,notEqual:()=>Ri,oneHot:()=>vl,ones:()=>jn,onesLike:()=>Un,op:()=>L,outerProduct:()=>fR,pad:()=>gr,pad1d:()=>yR,pad2d:()=>xR,pad3d:()=>vR,pad4d:()=>kR,pool:()=>P3,pow:()=>yr,prelu:()=>gd,print:()=>Bb,prod:()=>ah,profile:()=>VT,rand:()=>FR,randomGamma:()=>zR,randomNormal:()=>L3,randomUniform:()=>Fl,range:()=>$l,ready:()=>HT,real:()=>yd,reciprocal:()=>ng,registerBackend:()=>kl,registerCallbackConstructor:()=>Pre,registerGradient:()=>yb,registerKernel:()=>gi,registerOp:()=>Cie,regularizers:()=>Z8,relu:()=>Ka,relu6:()=>rh,removeBackend:()=>qT,reshape:()=>q,reverse:()=>Hn,reverse1d:()=>HR,reverse2d:()=>qR,reverse3d:()=>KR,reverse4d:()=>YR,rfft:()=>vd,round:()=>sh,rsqrt:()=>ih,scalar:()=>ke,scatterND:()=>U3,scatter_util:()=>y1,selu:()=>oh,separableConv2d:()=>ag,sequential:()=>zre,serialization:()=>re,setBackend:()=>UT,setPlatform:()=>KT,setWasmPath:()=>Bee,setWasmPaths:()=>Vee,setWebGLContext:()=>Oh,setdiff1dAsync:()=>W3,shared:()=>yg,sigmoid:()=>Rn,sign:()=>rg,signal:()=>k$,sin:()=>lh,sinh:()=>uh,slice:()=>Re,slice1d:()=>dh,slice2d:()=>sg,slice3d:()=>ph,slice4d:()=>Ad,slice_util:()=>fn,softmax:()=>xd,softplus:()=>Ci,spaceToBatchND:()=>md,sparse:()=>wd,sparseToDense:()=>pg,spectral:()=>w$,split:()=>Zt,sqrt:()=>an,square:()=>ot,squaredDifference:()=>hh,squeeze:()=>Vt,stack:()=>gn,step:()=>Ol,stridedSlice:()=>ig,string:()=>vh,sub:()=>ye,sum:()=>Se,sumOutType:()=>zc,tan:()=>og,tanh:()=>Si,tensor:()=>ln,tensor1d:()=>Dt,tensor2d:()=>Ta,tensor3d:()=>Lc,tensor4d:()=>kM,tensor5d:()=>IM,tensor6d:()=>SM,tensor_util:()=>Sa,test_util:()=>u3,tidy:()=>V,tile:()=>Gr,time:()=>jT,topk:()=>lg,train:()=>Fi,transpose:()=>Qe,truncatedNormal:()=>fh,unique:()=>mh,unregisterGradient:()=>HS,unregisterKernel:()=>US,unsortedSegmentSum:()=>ug,unstack:()=>Gn,upcastType:()=>Aa,util:()=>k,valueAndGrad:()=>DE,valueAndGrads:()=>OE,variable:()=>B3,variableGrads:()=>R3,version:()=>Ale,version_converter:()=>Foe,version_core:()=>_T,version_cpu:()=>q7,version_layers:()=>Dy,version_wasm:()=>l4,version_webgl:()=>gw,webgl:()=>CV,webgl_util:()=>fv,where:()=>un,whereAsync:()=>dg,zeros:()=>$t,zerosLike:()=>Ge});var VI=Object.create,jp=Object.defineProperty,jI=Object.getOwnPropertyDescriptor,UI=Object.getOwnPropertyNames,HI=Object.getPrototypeOf,GI=Object.prototype.hasOwnProperty,qI=e=>jp(e,"__esModule",{value:!0}),po=e=>{if(typeof zm!="undefined")return zm(e);throw new Error('Dynamic require of "'+e+'" is not supported')},xt=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Fe=(e,t)=>{for(var n in t)jp(e,n,{get:t[n],enumerable:!0})},XI=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of UI(t))!GI.call(e,a)&&a!=="default"&&jp(e,a,{get:()=>t[a],enumerable:!(n=jI(t,a))||n.enumerable});return e},gs=e=>XI(qI(jp(e!=null?VI(HI(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),KI=xt((e,t)=>{t.exports=a;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(S){}function a(S,z,O){this.low=S|0,this.high=z|0,this.unsigned=!!O}a.prototype.__isLong__,Object.defineProperty(a.prototype,"__isLong__",{value:!0});function r(S){return(S&&S.__isLong__)===!0}a.isLong=r;var s={},i={};function o(S,z){var O,W,G;return z?(S>>>=0,(G=0<=S&&S<256)&&(W=i[S],W)?W:(O=l(S,(S|0)<0?-1:0,!0),G&&(i[S]=O),O)):(S|=0,(G=-128<=S&&S<128)&&(W=s[S],W)?W:(O=l(S,S<0?-1:0,!1),G&&(s[S]=O),O))}a.fromInt=o;function u(S,z){if(isNaN(S))return z?v:x;if(z){if(S<0)return v;if(S>=g)return E}else{if(S<=-y)return _;if(S+1>=y)return C}return S<0?u(-S,z).neg():l(S%f|0,S/f|0,z)}a.fromNumber=u;function l(S,z,O){return new a(S,z,O)}a.fromBits=l;var d=Math.pow;function p(S,z,O){if(S.length===0)throw Error("empty string");if(S==="NaN"||S==="Infinity"||S==="+Infinity"||S==="-Infinity")return x;if(typeof z=="number"?(O=z,z=!1):z=!!z,O=O||10,O<2||36<O)throw RangeError("radix");var W;if((W=S.indexOf("-"))>0)throw Error("interior hyphen");if(W===0)return p(S.substring(1),z,O).neg();for(var G=u(d(O,8)),H=x,J=0;J<S.length;J+=8){var K=Math.min(8,S.length-J),ne=parseInt(S.substring(J,J+K),O);if(K<8){var Q=u(d(O,K));H=H.mul(Q).add(u(ne))}else H=H.mul(G),H=H.add(u(ne))}return H.unsigned=z,H}a.fromString=p;function c(S,z){return typeof S=="number"?u(S,z):typeof S=="string"?p(S,z):l(S.low,S.high,typeof z=="boolean"?z:S.unsigned)}a.fromValue=c;var h=1<<16,m=1<<24,f=h*h,g=f*f,y=g/2,A=o(m),x=o(0);a.ZERO=x;var v=o(0,!0);a.UZERO=v;var b=o(1);a.ONE=b;var w=o(1,!0);a.UONE=w;var N=o(-1);a.NEG_ONE=N;var C=l(4294967295|0,2147483647|0,!1);a.MAX_VALUE=C;var E=l(4294967295|0,4294967295|0,!0);a.MAX_UNSIGNED_VALUE=E;var _=l(0,2147483648|0,!1);a.MIN_VALUE=_;var $=a.prototype;$.toInt=function(){return this.unsigned?this.low>>>0:this.low},$.toNumber=function(){return this.unsigned?(this.high>>>0)*f+(this.low>>>0):this.high*f+(this.low>>>0)},$.toString=function(S){if(S=S||10,S<2||36<S)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(_)){var z=u(S),O=this.div(z),W=O.mul(z).sub(this);return O.toString(S)+W.toInt().toString(S)}else return"-"+this.neg().toString(S);for(var G=u(d(S,6),this.unsigned),H=this,J="";;){var K=H.div(G),ne=H.sub(K.mul(G)).toInt()>>>0,Q=ne.toString(S);if(H=K,H.isZero())return Q+J;for(;Q.length<6;)Q="0"+Q;J=""+Q+J}},$.getHighBits=function(){return this.high},$.getHighBitsUnsigned=function(){return this.high>>>0},$.getLowBits=function(){return this.low},$.getLowBitsUnsigned=function(){return this.low>>>0},$.getNumBitsAbs=function(){if(this.isNegative())return this.eq(_)?64:this.neg().getNumBitsAbs();for(var S=this.high!=0?this.high:this.low,z=31;z>0&&(S&1<<z)==0;z--);return this.high!=0?z+33:z+1},$.isZero=function(){return this.high===0&&this.low===0},$.eqz=$.isZero,$.isNegative=function(){return!this.unsigned&&this.high<0},$.isPositive=function(){return this.unsigned||this.high>=0},$.isOdd=function(){return(this.low&1)==1},$.isEven=function(){return(this.low&1)==0},$.equals=function(S){return r(S)||(S=c(S)),this.unsigned!==S.unsigned&&this.high>>>31==1&&S.high>>>31==1?!1:this.high===S.high&&this.low===S.low},$.eq=$.equals,$.notEquals=function(S){return!this.eq(S)},$.neq=$.notEquals,$.ne=$.notEquals,$.lessThan=function(S){return this.comp(S)<0},$.lt=$.lessThan,$.lessThanOrEqual=function(S){return this.comp(S)<=0},$.lte=$.lessThanOrEqual,$.le=$.lessThanOrEqual,$.greaterThan=function(S){return this.comp(S)>0},$.gt=$.greaterThan,$.greaterThanOrEqual=function(S){return this.comp(S)>=0},$.gte=$.greaterThanOrEqual,$.ge=$.greaterThanOrEqual,$.compare=function(S){if(r(S)||(S=c(S)),this.eq(S))return 0;var z=this.isNegative(),O=S.isNegative();return z&&!O?-1:!z&&O?1:this.unsigned?S.high>>>0>this.high>>>0||S.high===this.high&&S.low>>>0>this.low>>>0?-1:1:this.sub(S).isNegative()?-1:1},$.comp=$.compare,$.negate=function(){return!this.unsigned&&this.eq(_)?_:this.not().add(b)},$.neg=$.negate,$.add=function(S){r(S)||(S=c(S));var z=this.high>>>16,O=this.high&65535,W=this.low>>>16,G=this.low&65535,H=S.high>>>16,J=S.high&65535,K=S.low>>>16,ne=S.low&65535,Q=0,se=0,Z=0,le=0;return le+=G+ne,Z+=le>>>16,le&=65535,Z+=W+K,se+=Z>>>16,Z&=65535,se+=O+J,Q+=se>>>16,se&=65535,Q+=z+H,Q&=65535,l(Z<<16|le,Q<<16|se,this.unsigned)},$.subtract=function(S){return r(S)||(S=c(S)),this.add(S.neg())},$.sub=$.subtract,$.multiply=function(S){if(this.isZero())return x;if(r(S)||(S=c(S)),n){var z=n.mul(this.low,this.high,S.low,S.high);return l(z,n.get_high(),this.unsigned)}if(S.isZero())return x;if(this.eq(_))return S.isOdd()?_:x;if(S.eq(_))return this.isOdd()?_:x;if(this.isNegative())return S.isNegative()?this.neg().mul(S.neg()):this.neg().mul(S).neg();if(S.isNegative())return this.mul(S.neg()).neg();if(this.lt(A)&&S.lt(A))return u(this.toNumber()*S.toNumber(),this.unsigned);var O=this.high>>>16,W=this.high&65535,G=this.low>>>16,H=this.low&65535,J=S.high>>>16,K=S.high&65535,ne=S.low>>>16,Q=S.low&65535,se=0,Z=0,le=0,oe=0;return oe+=H*Q,le+=oe>>>16,oe&=65535,le+=G*Q,Z+=le>>>16,le&=65535,le+=H*ne,Z+=le>>>16,le&=65535,Z+=W*Q,se+=Z>>>16,Z&=65535,Z+=G*ne,se+=Z>>>16,Z&=65535,Z+=H*K,se+=Z>>>16,Z&=65535,se+=O*Q+W*ne+G*K+H*J,se&=65535,l(le<<16|oe,se<<16|Z,this.unsigned)},$.mul=$.multiply,$.divide=function(S){if(r(S)||(S=c(S)),S.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&S.low===-1&&S.high===-1)return this;var z=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,S.low,S.high);return l(z,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?v:x;var O,W,G;if(this.unsigned){if(S.unsigned||(S=S.toUnsigned()),S.gt(this))return v;if(S.gt(this.shru(1)))return w;G=v}else{if(this.eq(_)){if(S.eq(b)||S.eq(N))return _;if(S.eq(_))return b;var H=this.shr(1);return O=H.div(S).shl(1),O.eq(x)?S.isNegative()?b:N:(W=this.sub(S.mul(O)),G=O.add(W.div(S)),G)}else if(S.eq(_))return this.unsigned?v:x;if(this.isNegative())return S.isNegative()?this.neg().div(S.neg()):this.neg().div(S).neg();if(S.isNegative())return this.div(S.neg()).neg();G=x}for(W=this;W.gte(S);){O=Math.max(1,Math.floor(W.toNumber()/S.toNumber()));for(var J=Math.ceil(Math.log(O)/Math.LN2),K=J<=48?1:d(2,J-48),ne=u(O),Q=ne.mul(S);Q.isNegative()||Q.gt(W);)O-=K,ne=u(O,this.unsigned),Q=ne.mul(S);ne.isZero()&&(ne=b),G=G.add(ne),W=W.sub(Q)}return G},$.div=$.divide,$.modulo=function(S){if(r(S)||(S=c(S)),n){var z=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,S.low,S.high);return l(z,n.get_high(),this.unsigned)}return this.sub(this.div(S).mul(S))},$.mod=$.modulo,$.rem=$.modulo,$.not=function(){return l(~this.low,~this.high,this.unsigned)},$.and=function(S){return r(S)||(S=c(S)),l(this.low&S.low,this.high&S.high,this.unsigned)},$.or=function(S){return r(S)||(S=c(S)),l(this.low|S.low,this.high|S.high,this.unsigned)},$.xor=function(S){return r(S)||(S=c(S)),l(this.low^S.low,this.high^S.high,this.unsigned)},$.shiftLeft=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?l(this.low<<S,this.high<<S|this.low>>>32-S,this.unsigned):l(0,this.low<<S-32,this.unsigned)},$.shl=$.shiftLeft,$.shiftRight=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?l(this.low>>>S|this.high<<32-S,this.high>>S,this.unsigned):l(this.high>>S-32,this.high>=0?0:-1,this.unsigned)},$.shr=$.shiftRight,$.shiftRightUnsigned=function(S){if(r(S)&&(S=S.toInt()),S&=63,S===0)return this;var z=this.high;if(S<32){var O=this.low;return l(O>>>S|z<<32-S,z>>>S,this.unsigned)}else return S===32?l(z,0,this.unsigned):l(z>>>S-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.toSigned=function(){return this.unsigned?l(this.low,this.high,!1):this},$.toUnsigned=function(){return this.unsigned?this:l(this.low,this.high,!0)},$.toBytes=function(S){return S?this.toBytesLE():this.toBytesBE()},$.toBytesLE=function(){var S=this.high,z=this.low;return[z&255,z>>>8&255,z>>>16&255,z>>>24,S&255,S>>>8&255,S>>>16&255,S>>>24]},$.toBytesBE=function(){var S=this.high,z=this.low;return[S>>>24,S>>>16&255,S>>>8&255,S&255,z>>>24,z>>>16&255,z>>>8&255,z&255]},a.fromBytes=function(S,z,O){return O?a.fromBytesLE(S,z):a.fromBytesBE(S,z)},a.fromBytesLE=function(S,z){return new a(S[0]|S[1]<<8|S[2]<<16|S[3]<<24,S[4]|S[5]<<8|S[6]<<16|S[7]<<24,z)},a.fromBytesBE=function(S,z){return new a(S[4]<<24|S[5]<<16|S[6]<<8|S[7],S[0]<<24|S[1]<<16|S[2]<<8|S[3],z)}}),ZI=xt(()=>{}),YI=xt((e,t)=>{(function(n,a,r){function s(l){var d=this,p=u();d.next=function(){var c=2091639*d.s0+d.c*23283064365386963e-26;return d.s0=d.s1,d.s1=d.s2,d.s2=c-(d.c=c|0)},d.c=1,d.s0=p(" "),d.s1=p(" "),d.s2=p(" "),d.s0-=p(l),d.s0<0&&(d.s0+=1),d.s1-=p(l),d.s1<0&&(d.s1+=1),d.s2-=p(l),d.s2<0&&(d.s2+=1),p=null}function i(l,d){return d.c=l.c,d.s0=l.s0,d.s1=l.s1,d.s2=l.s2,d}function o(l,d){var p=new s(l),c=d&&d.state,h=p.next;return h.int32=function(){return p.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,p),h.state=function(){return i(p,{})}),h}function u(){var l=4022871197,d=function(p){p=p.toString();for(var c=0;c<p.length;c++){l+=p.charCodeAt(c);var h=.02519603282416938*l;l=h>>>0,h-=l,h*=l,l=h>>>0,h-=l,l+=h*4294967296}return(l>>>0)*23283064365386963e-26};return d}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),JI=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.x=0,l.y=0,l.z=0,l.w=0,l.next=function(){var c=l.x^l.x<<11;return l.x=l.y,l.y=l.z,l.z=l.w,l.w^=l.w>>>19^c^c>>>8},u===(u|0)?l.x=u:d+=u;for(var p=0;p<d.length+64;p++)l.x^=d.charCodeAt(p)|0,l.next()}function i(u,l){return l.x=u.x,l.y=u.y,l.z=u.z,l.w=u.w,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),QI=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.next=function(){var c=l.x^l.x>>>2;return l.x=l.y,l.y=l.z,l.z=l.w,l.w=l.v,(l.d=l.d+362437|0)+(l.v=l.v^l.v<<4^(c^c<<1))|0},l.x=0,l.y=0,l.z=0,l.w=0,l.v=0,u===(u|0)?l.x=u:d+=u;for(var p=0;p<d.length+64;p++)l.x^=d.charCodeAt(p)|0,p==d.length&&(l.d=l.x<<10^l.x>>>4),l.next()}function i(u,l){return l.x=u.x,l.y=u.y,l.z=u.z,l.w=u.w,l.v=u.v,l.d=u.d,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),eS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this;l.next=function(){var p=l.x,c=l.i,h,m,f;return h=p[c],h^=h>>>7,m=h^h<<24,h=p[c+1&7],m^=h^h>>>10,h=p[c+3&7],m^=h^h>>>3,h=p[c+4&7],m^=h^h<<7,h=p[c+7&7],h=h^h<<13,m^=h^h<<9,p[c]=m,l.i=c+1&7,m};function d(p,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h<c.length;++h)f[h&7]=f[h&7]<<15^c.charCodeAt(h)+f[h+1&7]<<13;for(;f.length<8;)f.push(0);for(h=0;h<8&&f[h]===0;++h);for(h==8?m=f[7]=-1:m=f[h],p.x=f,p.i=0,h=256;h>0;--h)p.next()}d(l,u)}function i(u,l){return l.x=u.x.slice(),l.i=u.i,l}function o(u,l){u==null&&(u=+new Date);var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.x&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),tS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this;l.next=function(){var p=l.w,c=l.X,h=l.i,m,f;return l.w=p=p+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,l.i=h,f+(p^p>>>16)|0};function d(p,c){var h,m,f,g,y,A=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,g=-32;g<x;++g)c&&(m^=c.charCodeAt((g+32)%c.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=A[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(A[(c&&c.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=A[f+34&127],h=A[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,A[f]=m^h;p.w=y,p.X=A,p.i=f}d(l,u)}function i(u,l){return l.i=u.i,l.w=u.w,l.X=u.X.slice(),l}function o(u,l){u==null&&(u=+new Date);var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.X&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),nS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.next=function(){var c=l.b,h=l.c,m=l.d,f=l.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,l.b=c=c<<20^c>>>12^h,l.c=h=h-m|0,l.d=m<<16^h>>>16^f,l.a=f-c|0},l.a=0,l.b=0,l.c=2654435769|0,l.d=1367130551,u===Math.floor(u)?(l.a=u/4294967296|0,l.b=u|0):d+=u;for(var p=0;p<d.length+20;p++)l.b^=d.charCodeAt(p)|0,l.next()}function i(u,l){return l.a=u.a,l.b=u.b,l.c=u.c,l.d=u.d,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),I5=xt(()=>{}),aS=xt((e,t)=>{(function(n,a){var r=this,s=256,i=6,o=52,u="random",l=a.pow(s,i),d=a.pow(2,o),p=d*2,c=s-1,h;function m(b,w,N){var C=[];w=w==!0?{entropy:!0}:w||{};var E=A(y(w.entropy?[b,v(n)]:b==null?x():b,3),C),_=new f(C),$=function(){for(var S=_.g(i),z=l,O=0;S<d;)S=(S+O)*s,z*=s,O=_.g(1);for(;S>=p;)S/=2,z/=2,O>>>=1;return(S+O)/z};return $.int32=function(){return _.g(4)|0},$.quick=function(){return _.g(4)/4294967296},$.double=$,A(v(_.S),n),(w.pass||N||function(S,z,O,W){return W&&(W.S&&g(W,_),S.state=function(){return g(_,{})}),O?(a[u]=S,z):S})($,E,"global"in w?w.global:this==a,w.state)}a["seed"+u]=m;function f(b){var w,N=b.length,C=this,E=0,_=C.i=C.j=0,$=C.S=[];for(N||(b=[N++]);E<s;)$[E]=E++;for(E=0;E<s;E++)$[E]=$[_=c&_+b[E%N]+(w=$[E])],$[_]=w;(C.g=function(S){for(var z,O=0,W=C.i,G=C.j,H=C.S;S--;)z=H[W=c&W+1],O=O*s+H[c&(H[W]=H[G=c&G+z])+(H[G]=z)];return C.i=W,C.j=G,O})(s)}function g(b,w){return w.i=b.i,w.j=b.j,w.S=b.S.slice(),w}function y(b,w){var N=[],C=typeof b,E;if(w&&C=="object")for(E in b)try{N.push(y(b[E],w-1))}catch(_){}return N.length?N:C=="string"?b:b+"\0"}function A(b,w){for(var N=b+"",C,E=0;E<N.length;)w[c&E]=c&(C^=w[c&E]*19)+N.charCodeAt(E++);return v(w)}function x(){try{var b;return h&&(b=h.randomBytes)?b=b(s):(b=new Uint8Array(s),(r.crypto||r.msCrypto).getRandomValues(b)),v(b)}catch(C){var w=r.navigator,N=w&&w.plugins;return[+new Date,r,N,r.screen,v(n)]}}function v(b){return String.fromCharCode.apply(0,b)}if(A(a.random(),n),typeof t=="object"&&t.exports){t.exports=m;try{h=I5()}catch(b){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),S5=xt((e,t)=>{var n=YI(),a=JI(),r=QI(),s=eS(),i=tS(),o=nS(),u=aS();u.alea=n,u.xor128=a,u.xorwow=r,u.xorshift7=s,u.xor4096=i,u.tychei=o,t.exports=u}),Cu=xt(()=>{}),rS=xt(()=>{}),sS=xt(()=>{}),iS=xt((e,t)=>{var n=function(){var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};function s(){return Z.buffer!=Ue&&tn(Z.buffer),In}function i(){return Z.buffer!=Ue&&tn(Z.buffer),kt}function o(){return Z.buffer!=Ue&&tn(Z.buffer),Sn}function u(){return Z.buffer!=Ue&&tn(Z.buffer),na}function l(){return Z.buffer!=Ue&&tn(Z.buffer),dn}var d=typeof r!="undefined"?r:{},p,c;d.ready=new Promise(function(T,R){p=T,c=R});var h={},m;for(m in d)d.hasOwnProperty(m)&&(h[m]=d[m]);var f=[],g="./this.program",y=function(T,R){throw R},A=!1,x=!1,v=!1,b=!1;A=typeof window=="object",x=typeof importScripts=="function",v=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",b=!A&&!v&&!x;var w=d.ENVIRONMENT_IS_PTHREAD||!1;w&&(Ue=d.buffer);var N="";function C(T){return d.locateFile?d.locateFile(T,N):N+T}var E,_,$,S,z,O;if(v){x?N=Cu().dirname(N)+"/":N=__dirname+"/",E=function(T,R){return z||(z=po("fs")),O||(O=Cu()),T=O.normalize(T),z.readFileSync(T,R?null:"utf8")},$=function(T){var R=E(T,!0);return R.buffer||(R=new Uint8Array(R)),fe(R.buffer),R},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),f=process.argv.slice(2),process.on("uncaughtException",function(T){if(!(T instanceof Tu))throw T}),process.on("unhandledRejection",ur),y=function(T){process.exit(T)},d.inspect=function(){return"[Emscripten Module object]"};var W;try{W=rS()}catch(T){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),T}global.Worker=W.Worker}else b?(typeof read!="undefined"&&(E=function(T){return read(T)}),$=function(T){var R;return typeof readbuffer=="function"?new Uint8Array(readbuffer(T)):(R=read(T,"binary"),fe(typeof R=="object"),R)},typeof scriptArgs!="undefined"?f=scriptArgs:typeof arguments!="undefined"&&(f=arguments),typeof quit=="function"&&(y=function(T){quit(T)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(A||x)&&(x?N=self.location.href:typeof document!="undefined"&&document.currentScript&&(N=document.currentScript.src),typeof a!="undefined"&&a&&(N=a),N.indexOf("blob:")!==0?N=N.substr(0,N.lastIndexOf("/")+1):N="",v?(E=function(T,R){return z||(z=po("fs")),O||(O=Cu()),T=O.normalize(T),z.readFileSync(T,R?null:"utf8")},$=function(T){var R=E(T,!0);return R.buffer||(R=new Uint8Array(R)),fe(R.buffer),R}):(E=function(T){var R=new XMLHttpRequest;return R.open("GET",T,!1),R.send(null),R.responseText},x&&($=function(T){var R=new XMLHttpRequest;return R.open("GET",T,!1),R.responseType="arraybuffer",R.send(null),new Uint8Array(R.response)}),_=function(T,R,j){var X=new XMLHttpRequest;X.open("GET",T,!0),X.responseType="arraybuffer",X.onload=function(){if(X.status==200||X.status==0&&X.response){R(X.response);return}j()},X.onerror=j,X.send(null)}),S=function(T){document.title=T});v&&typeof performance=="undefined"&&(global.performance=sS().performance);var G=d.print||console.log.bind(console),H=d.printErr||console.warn.bind(console);for(m in h)h.hasOwnProperty(m)&&(d[m]=h[m]);h=null,d.arguments&&(f=d.arguments),d.thisProgram&&(g=d.thisProgram),d.quit&&(y=d.quit);var J=Atomics.load,K=Atomics.store,ne=Atomics.compareExchange,Q;d.wasmBinary&&(Q=d.wasmBinary);var se=d.noExitRuntime||!0;typeof WebAssembly!="object"&&ur("no native wasm support detected");var Z,le,oe=!1,xe;function fe(T,R){T||ur("Assertion failed: "+R)}function Ne(T){var R=d["_"+T];return fe(R,"Cannot call unknown function "+T+", make sure it is exported"),R}function Te(T,R,j,X,ce){var ue={string:function(Cn){var uo=0;if(Cn!=null&&Cn!==0){var x5=(Cn.length<<2)+1;uo=io(x5),nt(Cn,uo,x5)}return uo},array:function(Cn){var uo=io(Cn.length);return Ze(Cn,uo),uo}};function pe(Cn){return R==="string"?ze(Cn):R==="boolean"?Boolean(Cn):Cn}var ve=Ne(T),at=[],Gt=0;if(X)for(var Pt=0;Pt<X.length;Pt++){var Fr=ue[j[Pt]];Fr?(Gt===0&&(Gt=Nu()),at[Pt]=Fr(X[Pt])):at[Pt]=X[Pt]}var lo=ve.apply(null,at);return lo=pe(lo),Gt!==0&&so(Gt),lo}function Oe(T,R,j,X){j=j||[];var ce=j.every(function(pe){return pe==="number"}),ue=R!=="string";return ue&&ce&&!X?Ne(T):function(){return Te(T,R,j,arguments,X)}}function Pe(T,R,j){for(var X=R+j,ce="";!(R>=X);){var ue=T[R++];if(!ue)return ce;if(!(ue&128)){ce+=String.fromCharCode(ue);continue}var pe=T[R++]&63;if((ue&224)==192){ce+=String.fromCharCode((ue&31)<<6|pe);continue}var ve=T[R++]&63;if((ue&240)==224?ue=(ue&15)<<12|pe<<6|ve:ue=(ue&7)<<18|pe<<12|ve<<6|T[R++]&63,ue<65536)ce+=String.fromCharCode(ue);else{var at=ue-65536;ce+=String.fromCharCode(55296|at>>10,56320|at&1023)}}return ce}function ze(T,R){return T?Pe(i(),T,R):""}function tt(T,R,j,X){if(!(X>0))return 0;for(var ce=j,ue=j+X-1,pe=0;pe<T.length;++pe){var ve=T.charCodeAt(pe);if(ve>=55296&&ve<=57343){var at=T.charCodeAt(++pe);ve=65536+((ve&1023)<<10)|at&1023}if(ve<=127){if(j>=ue)break;R[j++]=ve}else if(ve<=2047){if(j+1>=ue)break;R[j++]=192|ve>>6,R[j++]=128|ve&63}else if(ve<=65535){if(j+2>=ue)break;R[j++]=224|ve>>12,R[j++]=128|ve>>6&63,R[j++]=128|ve&63}else{if(j+3>=ue)break;R[j++]=240|ve>>18,R[j++]=128|ve>>12&63,R[j++]=128|ve>>6&63,R[j++]=128|ve&63}}return R[j]=0,j-ce}function nt(T,R,j){return tt(T,i(),R,j)}function it(T){for(var R=0,j=0;j<T.length;++j){var X=T.charCodeAt(j);X>=55296&&X<=57343&&(X=65536+((X&1023)<<10)|T.charCodeAt(++j)&1023),X<=127?++R:X<=2047?R+=2:X<=65535?R+=3:R+=4}return R}function Ze(T,R){s().set(T,R)}function ht(T,R){return T%R>0&&(T+=R-T%R),T}var Ue,In,kt,ta,en,Sn,na,Pn,dn;function tn(T){Ue=T,d.HEAP8=In=new Int8Array(T),d.HEAP16=ta=new Int16Array(T),d.HEAP32=Sn=new Int32Array(T),d.HEAPU8=kt=new Uint8Array(T),d.HEAPU16=en=new Uint16Array(T),d.HEAPU32=na=new Uint32Array(T),d.HEAPF32=Pn=new Float32Array(T),d.HEAPF64=dn=new Float64Array(T)}var Ba=d.INITIAL_MEMORY||16777216;if(w)Z=d.wasmMemory,Ue=d.buffer;else if(d.wasmMemory)Z=d.wasmMemory;else if(Z=new WebAssembly.Memory({initial:Ba/65536,maximum:2147483648/65536,shared:!0}),!(Z.buffer instanceof SharedArrayBuffer))throw H("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),v&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Z&&(Ue=Z.buffer),Ba=Ue.byteLength,tn(Ue);var fa,ma=[],Nr=[],or=[],Tr=[],Qi=[],Va=!1,vp=!1;w||Nr.push({func:function(){zp()}});function cf(){if(!w){if(d.preRun)for(typeof d.preRun=="function"&&(d.preRun=[d.preRun]);d.preRun.length;)kp(d.preRun.shift());to(ma)}}function yu(){Va=!0,!w&&to(Nr)}function hf(){w||to(or)}function wp(){w||(vp=!0)}function Nn(){if(!w){if(d.postRun)for(typeof d.postRun=="function"&&(d.postRun=[d.postRun]);d.postRun.length;)ff(d.postRun.shift());to(Qi)}}function kp(T){ma.unshift(T)}function ff(T){Qi.unshift(T)}var lr=0,Cr=null,hs=null;function mf(T){fe(!w,"addRunDependency cannot be used in a pthread worker"),lr++,d.monitorRunDependencies&&d.monitorRunDependencies(lr)}function gf(T){if(lr--,d.monitorRunDependencies&&d.monitorRunDependencies(lr),lr==0&&(Cr!==null&&(clearInterval(Cr),Cr=null),hs)){var R=hs;hs=null,R()}}d.preloadedImages={},d.preloadedAudios={};function ur(T){d.onAbort&&d.onAbort(T),w&&console.error("Pthread aborting at "+new Error().stack),T+="",H(T),oe=!0,xe=1,T="abort("+T+"). Build with -s ASSERTIONS=1 for more info.";var R=new WebAssembly.RuntimeError(T);throw c(R),R}function Ip(T,R){return String.prototype.startsWith?T.startsWith(R):T.indexOf(R)===0}var eo="data:application/octet-stream;base64,";function Sp(T){return Ip(T,eo)}var yf="file://";function Np(T){return Ip(T,yf)}var Tn="tfjs-backend-wasm-threaded-simd.wasm";Sp(Tn)||(Tn=C(Tn));function Tp(T){try{if(T==Tn&&Q)return new Uint8Array(Q);if($)return $(T);throw"both async and sync fetching of the wasm failed"}catch(R){ur(R)}}function Af(){if(!Q&&(A||x)){if(typeof fetch=="function"&&!Np(Tn))return fetch(Tn,{credentials:"same-origin"}).then(function(T){if(!T.ok)throw"failed to load wasm binary file at '"+Tn+"'";return T.arrayBuffer()}).catch(function(){return Tp(Tn)});if(_)return new Promise(function(T,R){_(Tn,function(j){T(new Uint8Array(j))},R)})}return Promise.resolve().then(function(){return Tp(Tn)})}function xf(){var T={a:um};function R(pe,ve){var at=pe.exports;if(d.asm=at,fa=d.asm.F,le=ve,!w){var Gt=Ie.unusedWorkers.length;Ie.unusedWorkers.forEach(function(Pt){Ie.loadWasmModuleToWorker(Pt,function(){--Gt||gf("wasm-instantiate")})})}}w||mf("wasm-instantiate");function j(pe){R(pe.instance,pe.module)}function X(pe){return Af().then(function(ve){return WebAssembly.instantiate(ve,T)}).then(pe,function(ve){H("failed to asynchronously prepare wasm: "+ve),ur(ve)})}function ce(){return!Q&&typeof WebAssembly.instantiateStreaming=="function"&&!Sp(Tn)&&!Np(Tn)&&typeof fetch=="function"?fetch(Tn,{credentials:"same-origin"}).then(function(pe){var ve=WebAssembly.instantiateStreaming(pe,T);return ve.then(j,function(at){return H("wasm streaming compile failed: "+at),H("falling back to ArrayBuffer instantiation"),X(j)})}):X(j)}if(d.instantiateWasm)try{var ue=d.instantiateWasm(T,R);return ue}catch(pe){return H("Module.instantiateWasm callback failed with error: "+pe),!1}return ce().catch(c),{}}var bf={9816:function(){throw"Canceled!"},9834:function(T,R){setTimeout(function(){h5(T,R)},0)}};function Cp(){Ie.initRuntime()}function to(T){for(;T.length>0;){var R=T.shift();if(typeof R=="function"){R(d);continue}var j=R.func;typeof j=="number"?R.arg===void 0?fa.get(j)():fa.get(j)(R.arg):j(R.arg===void 0?null:R.arg)}}function Au(T,R){if(T<=0||T>s().length||T&!0||R<0)return-28;if(R==0)return 0;R>=2147483647&&(R=Infinity);var j=Atomics.load(o(),oo>>2),X=0;if(j==T){var ce=Atomics.compareExchange(o(),oo>>2,j,0);if(ce==j&&(--R,X=1,R<=0))return 1}var ue=Atomics.notify(o(),T>>2,R);if(ue>=0)return ue+X;throw"Atomics.notify returned an unexpected value "+ue}d._emscripten_futex_wake=Au;function vf(T){if(w)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in killThread!";o()[T+12>>2]=0;var R=Ie.pthreads[T];R.worker.terminate(),Ie.freeThreadData(R),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(R.worker),1),R.worker.pthread=void 0}function wf(T){if(w)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in cancelThread!";var R=Ie.pthreads[T];R.worker.postMessage({cmd:"cancel"})}function kf(T){if(w)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in cleanupThread!";var R=Ie.pthreads[T];if(R){o()[T+12>>2]=0;var j=R.worker;Ie.returnWorkerToPool(j)}}var Ie={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var T=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),R=0;R<T;++R)Ie.allocateUnusedWorker()},initRuntime:function(){for(var T=ms(228),R=0;R<228/4;++R)u()[T/4+R]=0;o()[T+12>>2]=T;var j=T+152;o()[j>>2]=j;for(var X=ms(512),R=0;R<128;++R)u()[X/4+R]=0;Atomics.store(u(),T+100>>2,X),Atomics.store(u(),T+40>>2,T),Dm(T,!x,1),c5(T)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ie.threadExitHandlers.length>0;)Ie.threadExitHandlers.pop()();w&&ro()&&p5()},runExitHandlersAndDeinitThread:function(T,R){Atomics.store(u(),T+56>>2,1),Atomics.store(u(),T+60>>2,0),Ie.runExitHandlers(),Atomics.store(u(),T+4>>2,R),Atomics.store(u(),T+0>>2,1),Au(T+0,2147483647),Dm(0,0,0)},threadExit:function(T){var R=ro();R&&(Ie.runExitHandlersAndDeinitThread(R,T),w&&postMessage({cmd:"exit"}))},threadCancel:function(){Ie.runExitHandlersAndDeinitThread(ro(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var T in Ie.pthreads){var R=Ie.pthreads[T];R&&R.worker&&Ie.returnWorkerToPool(R.worker)}Ie.pthreads={};for(var j=0;j<Ie.unusedWorkers.length;++j){var X=Ie.unusedWorkers[j];X.terminate()}Ie.unusedWorkers=[];for(var j=0;j<Ie.runningWorkers.length;++j){var X=Ie.runningWorkers[j],R=X.pthread;Ie.freeThreadData(R),X.terminate()}Ie.runningWorkers=[]},freeThreadData:function(T){if(T){if(T.threadInfoStruct){var R=o()[T.threadInfoStruct+100>>2];o()[T.threadInfoStruct+100>>2]=0,Su(R),Su(T.threadInfoStruct)}T.threadInfoStruct=0,T.allocatedOwnStack&&T.stackBase&&Su(T.stackBase),T.stackBase=0,T.worker&&(T.worker.pthread=null)}},returnWorkerToPool:function(T){Ie.runWithoutMainThreadQueuedCalls(function(){delete Ie.pthreads[T.pthread.threadInfoStruct],Ie.unusedWorkers.push(T),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(T),1),Ie.freeThreadData(T.pthread),T.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(T){o()[A5>>2]=0;try{T()}finally{o()[A5>>2]=1}},receiveObjectTransfer:function(T){},loadWasmModuleToWorker:function(T,R){T.onmessage=function(j){var X=j.data,ce=X.cmd;if(T.pthread&&(Ie.currentProxiedOperationCallerThread=T.pthread.threadInfoStruct),X.targetThread&&X.targetThread!=ro()){var ue=Ie.pthreads[X.targetThread];ue?ue.worker.postMessage(j.data,X.transferList):console.error('Internal error! Worker sent a message "'+ce+'" to target pthread '+X.targetThread+", but that thread no longer exists!"),Ie.currentProxiedOperationCallerThread=void 0;return}if(ce==="processQueuedMainThreadWork")Fm();else if(ce==="spawnThread")Dp(j.data);else if(ce==="cleanupThread")kf(X.thread);else if(ce==="killThread")vf(X.thread);else if(ce==="cancelThread")wf(X.thread);else if(ce==="loaded")T.loaded=!0,R&&R(T),T.runPthread&&(T.runPthread(),delete T.runPthread);else if(ce==="print")G("Thread "+X.threadId+": "+X.text);else if(ce==="printErr")H("Thread "+X.threadId+": "+X.text);else if(ce==="alert")alert("Thread "+X.threadId+": "+X.text);else if(ce==="exit"){var pe=T.pthread&&Atomics.load(u(),T.pthread.threadInfoStruct+64>>2);pe&&Ie.returnWorkerToPool(T)}else if(ce==="exitProcess")try{WI(X.returnCode)}catch(ve){if(ve instanceof Tu)return;throw ve}else ce==="cancelDone"?Ie.returnWorkerToPool(T):ce==="objectTransfer"?Ie.receiveObjectTransfer(j.data):j.data.target==="setimmediate"?T.postMessage(j.data):H("worker sent an unknown command "+ce);Ie.currentProxiedOperationCallerThread=void 0},T.onerror=function(j){H("pthread sent an error! "+j.filename+":"+j.lineno+": "+j.message)},v&&(T.on("message",function(j){T.onmessage({data:j})}),T.on("error",function(j){T.onerror(j)}),T.on("exit",function(j){})),T.postMessage({cmd:"load",urlOrBlob:d.mainScriptUrlOrBlob||a,wasmMemory:Z,wasmModule:le})},allocateUnusedWorker:function(){var T=C("tfjs-backend-wasm-threaded-simd.worker.js");Ie.unusedWorkers.push(new Worker(T))},getNewWorker:function(){return Ie.unusedWorkers.length==0&&(Ie.allocateUnusedWorker(),Ie.loadWasmModuleToWorker(Ie.unusedWorkers[0])),Ie.unusedWorkers.length>0?Ie.unusedWorkers.pop():null},busySpinWait:function(T){for(var R=performance.now()+T;performance.now()<R;);}};function If(T,R){g5(T,R),so(T)}d.establishStackSpace=If;function Sf(){return se}d.getNoExitRuntime=Sf;function Nf(T,R){return fa.get(T)(R)}d.invokeEntryPoint=Nf;function Tf(T,R,j,X){ur("Assertion failed: "+ze(T)+", at: "+[R?ze(R):"unknown filename",j,X?ze(X):"unknown function"])}function Cf(T,R){var j=_main(T,R)}var fs;v?fs=function(){var T=process.hrtime();return T[0]*1e3+T[1]/1e6}:w?fs=function(){return performance.now()-d.__performance_now_clock_drift}:typeof dateNow!="undefined"?fs=dateNow:fs=function(){return performance.now()};function Ef(T){return o()[u5()>>2]=T,T}function Rf(T,R){if(w)return Er(1,1,T,R)}function Mf(T,R){if(T==R)postMessage({cmd:"processQueuedMainThreadWork"});else if(w)postMessage({targetThread:T,cmd:"processThreadQueue"});else{var j=Ie.pthreads[T],X=j&&j.worker;if(!X)return;X.postMessage({cmd:"processThreadQueue"})}return 1}function Ff(){ur()}function $f(T,R,j){var X=Pf(R,j);return bf[T].apply(null,X)}function Df(T,R){}function Of(T,R,j){if(T<=0||T>s().length||T&!0)return-28;if(A){if(Atomics.load(o(),T>>2)!=R)return-6;for(var X=performance.now(),ce=X+j,ue=Atomics.exchange(o(),oo>>2,T);;){if(X=performance.now(),X>ce)return ue=Atomics.exchange(o(),oo>>2,0),-73;if(ue=Atomics.exchange(o(),oo>>2,0),ue==0)break;if(Fm(),Atomics.load(o(),T>>2)!=R)return-6;ue=Atomics.exchange(o(),oo>>2,T)}return 0}else{var pe=Atomics.wait(o(),T>>2,R,j);if(pe==="timed-out")return-73;if(pe==="not-equal")return-6;if(pe==="ok")return 0;throw"Atomics.wait returned an unexpected value "+pe}}function zf(T,R,j){i().copyWithin(T,R,R+j)}function _f(){return v?po("os").cpus().length:navigator.hardwareConcurrency}function Er(T,R){for(var j=arguments.length-2,X=Nu(),ce=j,ue=io(ce*8),pe=ue>>3,ve=0;ve<j;ve++){var at=arguments[2+ve];l()[pe+ve]=at}var Gt=m5(T,ce,ue,R);return so(X),Gt}var xu=[],bu=[];function Pf(T,R){bu.length=0;var j;for(R>>=2;j=i()[T++];){var X=j<105;X&&R&1&&R++,bu.push(X?l()[R++>>1]:o()[R]),++R}return bu}function Lf(T,R,j){xu.length=R;for(var X=j>>3,ce=0;ce<R;ce++)xu[ce]=l()[X+ce];var ue=T<0,pe=ue?bf[-T-1]:lm[T];return pe.apply(null,xu)}function Wf(){return i().length}function Bf(T){try{return Z.grow(T-Ue.byteLength+65535>>>16),tn(Z.buffer),1}catch(R){}}function Vf(T){var R=Wf();if(T<=R)return!1;var j=2147483648;if(T>j)return!1;for(var X=1;X<=4;X*=2){var ce=R*(1+.2/X);ce=Math.min(ce,T+100663296);var ue=Math.min(j,ht(Math.max(T,ce),65536)),pe=Bf(ue);if(pe)return!0}return!1}var We={inEventHandler:0,removeAllEventListeners:function(){for(var T=We.eventHandlers.length-1;T>=0;--T)We._removeHandler(T);We.eventHandlers=[],We.deferredCalls=[]},registerRemoveEventListeners:function(){We.removeEventListenersRegistered||(Tr.push(We.removeAllEventListeners),We.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(T,R,j){function X(pe,ve){if(pe.length!=ve.length)return!1;for(var at in pe)if(pe[at]!=ve[at])return!1;return!0}for(var ce in We.deferredCalls){var ue=We.deferredCalls[ce];if(ue.targetFunction==T&&X(ue.argsList,j))return}We.deferredCalls.push({targetFunction:T,precedence:R,argsList:j}),We.deferredCalls.sort(function(pe,ve){return pe.precedence<ve.precedence})},removeDeferredCalls:function(T){for(var R=0;R<We.deferredCalls.length;++R)We.deferredCalls[R].targetFunction==T&&(We.deferredCalls.splice(R,1),--R)},canPerformEventHandlerRequests:function(){return We.inEventHandler&&We.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(We.canPerformEventHandlerRequests())for(var T=0;T<We.deferredCalls.length;++T){var R=We.deferredCalls[T];We.deferredCalls.splice(T,1),--T,R.targetFunction.apply(null,R.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(T,R){for(var j=0;j<We.eventHandlers.length;++j)We.eventHandlers[j].target==T&&(!R||R==We.eventHandlers[j].eventTypeString)&&We._removeHandler(j--)},_removeHandler:function(T){var R=We.eventHandlers[T];R.target.removeEventListener(R.eventTypeString,R.eventListenerFunc,R.useCapture),We.eventHandlers.splice(T,1)},registerOrRemoveHandler:function(T){var R=function(X){++We.inEventHandler,We.currentEventHandler=T,We.runDeferredCalls(),T.handlerFunc(X),We.runDeferredCalls(),--We.inEventHandler};if(T.callbackfunc)T.eventListenerFunc=R,T.target.addEventListener(T.eventTypeString,R,T.useCapture),We.eventHandlers.push(T),We.registerRemoveEventListeners();else for(var j=0;j<We.eventHandlers.length;++j)We.eventHandlers[j].target==T.target&&We.eventHandlers[j].eventTypeString==T.eventTypeString&&We._removeHandler(j--)},queueEventHandlerOnThread_iiii:function(T,R,j,X,ce){var ue=Nu(),pe=io(12);o()[pe>>2]=j,o()[pe+4>>2]=X,o()[pe+8>>2]=ce,$m(0,T,637534208,R,X,pe),so(ue)},getTargetThreadForEventCallback:function(T){switch(T){case 1:return 0;case 2:return Ie.currentProxiedOperationCallerThread;default:return T}},getNodeNameForTarget:function(T){return T?T==window?"#window":T==screen?"#screen":T&&T.nodeName?T.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function jf(T){var R=it(T)+1,j=ms(R);return nt(T,j,R),j}function Uf(T,R,j,X){var ce=Nu(),ue=io(12),pe=0;R&&(pe=jf(R)),o()[ue>>2]=pe,o()[ue+4>>2]=j,o()[ue+8>>2]=X,$m(0,T,657457152,0,pe,ue),so(ce)}function Hf(T,R,j,X){R=R?ze(R):"",Uf(T,R,j,X)}function Gf(T){return T>2?ze(T):T}var qf=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function Xf(T){T=Gf(T);var R=qf[T]||(typeof document!="undefined"?document.querySelector(T):void 0);return R}function vu(T){return Xf(T)}function Ep(T,R,j){var X=vu(T);if(!X)return-4;if(X.canvasSharedPtr&&(o()[X.canvasSharedPtr>>2]=R,o()[X.canvasSharedPtr+4>>2]=j),X.offscreenCanvas||!X.controlTransferredOffscreen){X.offscreenCanvas&&(X=X.offscreenCanvas);var ce=!1;if(X.GLctxObject&&X.GLctxObject.GLctx){var ue=X.GLctxObject.GLctx.getParameter(2978);ce=ue[0]===0&&ue[1]===0&&ue[2]===X.width&&ue[3]===X.height}X.width=R,X.height=j,ce&&X.GLctxObject.GLctx.viewport(0,0,R,j)}else if(X.canvasSharedPtr){var pe=o()[X.canvasSharedPtr+8>>2];return Hf(pe,T,R,j),1}else return-4;return 0}function Rp(T,R,j){return w?Er(2,1,T,R,j):Ep(T,R,j)}function Kf(T,R,j){var X=vu(T);return X?Ep(T,R,j):Rp(T,R,j)}function Zf(T){}function Yf(T,R){}function Jf(T){var R=T.getExtension("ANGLE_instanced_arrays");if(R)return T.vertexAttribDivisor=function(j,X){R.vertexAttribDivisorANGLE(j,X)},T.drawArraysInstanced=function(j,X,ce,ue){R.drawArraysInstancedANGLE(j,X,ce,ue)},T.drawElementsInstanced=function(j,X,ce,ue,pe){R.drawElementsInstancedANGLE(j,X,ce,ue,pe)},1}function Qf(T){var R=T.getExtension("OES_vertex_array_object");if(R)return T.createVertexArray=function(){return R.createVertexArrayOES()},T.deleteVertexArray=function(j){R.deleteVertexArrayOES(j)},T.bindVertexArray=function(j){R.bindVertexArrayOES(j)},T.isVertexArray=function(j){return R.isVertexArrayOES(j)},1}function em(T){var R=T.getExtension("WEBGL_draw_buffers");if(R)return T.drawBuffers=function(j,X){R.drawBuffersWEBGL(j,X)},1}function tm(T){return!!(T.multiDrawWebgl=T.getExtension("WEBGL_multi_draw"))}var et={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(T){et.lastError||(et.lastError=T)},getNewId:function(T){for(var R=et.counter++,j=T.length;j<R;j++)T[j]=null;return R},getSource:function(T,R,j,X){for(var ce="",ue=0;ue<R;++ue){var pe=X?o()[X+ue*4>>2]:-1;ce+=ze(o()[j+ue*4>>2],pe<0?void 0:pe)}return ce},createContext:function(T,R){var j=T.getContext("webgl",R);if(!j)return 0;var X=et.registerContext(j,R);return X},registerContext:function(T,R){var j=ms(8);o()[j+4>>2]=ro();var X={handle:j,attributes:R,version:R.majorVersion,GLctx:T};return T.canvas&&(T.canvas.GLctxObject=X),et.contexts[j]=X,(typeof R.enableExtensionsByDefault=="undefined"||R.enableExtensionsByDefault)&&et.initExtensions(X),j},makeContextCurrent:function(T){return et.currentContext=et.contexts[T],d.ctx=Rr=et.currentContext&&et.currentContext.GLctx,!(T&&!Rr)},getContext:function(T){return et.contexts[T]},deleteContext:function(T){et.currentContext===et.contexts[T]&&(et.currentContext=null),typeof We=="object"&&We.removeAllHandlersOnTarget(et.contexts[T].GLctx.canvas),et.contexts[T]&&et.contexts[T].GLctx.canvas&&(et.contexts[T].GLctx.canvas.GLctxObject=void 0),Su(et.contexts[T].handle),et.contexts[T]=null},initExtensions:function(T){if(T||(T=et.currentContext),!T.initExtensionsDone){T.initExtensionsDone=!0;var R=T.GLctx;Jf(R),Qf(R),em(R),R.disjointTimerQueryExt=R.getExtension("EXT_disjoint_timer_query"),tm(R);var j=R.getSupportedExtensions()||[];j.forEach(function(X){X.indexOf("lose_context")<0&&X.indexOf("debug")<0&&R.getExtension(X)})}},populateUniformTable:function(T){for(var R=et.programs[T],j=et.programInfos[T]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},X=j.uniforms,ce=Rr.getProgramParameter(R,35718),ue=0;ue<ce;++ue){var pe=Rr.getActiveUniform(R,ue),ve=pe.name;j.maxUniformLength=Math.max(j.maxUniformLength,ve.length+1),ve.slice(-1)=="]"&&(ve=ve.slice(0,ve.lastIndexOf("[")));var at=Rr.getUniformLocation(R,ve);if(at){var Gt=et.getNewId(et.uniforms);X[ve]=[pe.size,Gt],et.uniforms[Gt]=at;for(var Pt=1;Pt<pe.size;++Pt){var Fr=ve+"["+Pt+"]";at=Rr.getUniformLocation(R,Fr),Gt=et.getNewId(et.uniforms),et.uniforms[Gt]=at}}}}},nm=["default","low-power","high-performance"];function am(T,R){var j=R>>2,X=o()[j+(24>>2)],ce={alpha:!!o()[j+(0>>2)],depth:!!o()[j+(4>>2)],stencil:!!o()[j+(8>>2)],antialias:!!o()[j+(12>>2)],premultipliedAlpha:!!o()[j+(16>>2)],preserveDrawingBuffer:!!o()[j+(20>>2)],powerPreference:nm[X],failIfMajorPerformanceCaveat:!!o()[j+(28>>2)],majorVersion:o()[j+(32>>2)],minorVersion:o()[j+(36>>2)],enableExtensionsByDefault:o()[j+(40>>2)],explicitSwapControl:o()[j+(44>>2)],proxyContextToMainThread:o()[j+(48>>2)],renderViaOffscreenBackBuffer:o()[j+(52>>2)]},ue=vu(T);if(!ue||ce.explicitSwapControl)return 0;var pe=et.createContext(ue,ce);return pe}function rm(T,R){return am(T,R)}var no={mappings:{},buffers:[null,[],[]],printChar:function(T,R){var j=no.buffers[T];R===0||R===10?((T===1?G:H)(Pe(j,0)),j.length=0):j.push(R)},varargs:void 0,get:function(){no.varargs+=4;var T=o()[no.varargs-4>>2];return T},getStr:function(T){var R=ze(T);return R},get64:function(T,R){return T}};function Mp(T){return w?Er(3,1,T):0}function Fp(T,R,j,X,ce){if(w)return Er(4,1,T,R,j,X,ce)}function $p(T,R,j,X){if(w)return Er(5,1,T,R,j,X);for(var ce=0,ue=0;ue<j;ue++){for(var pe=o()[R+ue*8>>2],ve=o()[R+(ue*8+4)>>2],at=0;at<ve;at++)no.printChar(T,i()[pe+at]);ce+=ve}return o()[X>>2]=ce,0}function sm(T){var R=Ie.threadExitHandlers.pop();T&&R()}function im(T,R){Ie.threadExitHandlers.push(function(){fa.get(T)(R)})}function Dp(T){if(w)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var R=Ie.getNewWorker();if(R.pthread!==void 0)throw"Internal error!";if(!T.pthread_ptr)throw"Internal error, no pthread ptr!";Ie.runningWorkers.push(R);for(var j=ms(128*4),X=0;X<128;++X)o()[j+X*4>>2]=0;var ce=T.stackBase+T.stackSize,ue=Ie.pthreads[T.pthread_ptr]={worker:R,stackBase:T.stackBase,stackSize:T.stackSize,allocatedOwnStack:T.allocatedOwnStack,threadInfoStruct:T.pthread_ptr},pe=ue.threadInfoStruct>>2;Atomics.store(u(),pe+(64>>2),T.detached),Atomics.store(u(),pe+(100>>2),j),Atomics.store(u(),pe+(40>>2),ue.threadInfoStruct),Atomics.store(u(),pe+(80>>2),T.stackSize),Atomics.store(u(),pe+(76>>2),ce),Atomics.store(u(),pe+(104>>2),T.stackSize),Atomics.store(u(),pe+(104+8>>2),ce),Atomics.store(u(),pe+(104+12>>2),T.detached);var ve=d5(),at=ve+40;Atomics.store(u(),pe+(172>>2),at),R.pthread=ue;var Gt={cmd:"run",start_routine:T.startRoutine,arg:T.arg,threadInfoStruct:T.pthread_ptr,stackBase:T.stackBase,stackSize:T.stackSize};R.runPthread=function(){Gt.time=performance.now(),R.postMessage(Gt,T.transferList)},R.loaded&&(R.runPthread(),delete R.runPthread)}function om(T,R,j,X){if(typeof SharedArrayBuffer=="undefined")return H("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!T)return H("pthread_create called with a null thread pointer!"),28;var ce=[],ue=0;if(w&&(ce.length===0||ue))return f5(687865856,T,R,j,X);if(ue)return ue;var pe=0,ve=0,at=0;R&&R!=-1?(pe=o()[R>>2],pe+=81920,ve=o()[R+8>>2],at=o()[R+12>>2]!==0):pe=2097152;var Gt=ve==0;Gt?ve=y5(16,pe):(ve-=pe,fe(ve>0));for(var Pt=ms(228),Fr=0;Fr<228>>2;++Fr)u()[(Pt>>2)+Fr]=0;o()[T>>2]=Pt,o()[Pt+12>>2]=Pt;var lo=Pt+152;o()[lo>>2]=lo;var Cn={stackBase:ve,stackSize:pe,allocatedOwnStack:Gt,detached:at,startRoutine:j,pthread_ptr:Pt,arg:X,transferList:ce};return w?(Cn.cmd="spawnThread",postMessage(Cn,ce)):Dp(Cn),0}function Op(T){if(w)return Er(6,1,T);switch(T){case 30:return 16384;case 85:var R=2147483648;return R/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 Ef(28),-1}w||Ie.initMainThreadBlock();var Rr,lm=[null,Rf,Rp,Mp,Fp,$p,Op],um={e:Tf,r:Cf,x:Mf,b:Ff,y:$f,j:Df,c:Of,d:Au,f:fs,p:zf,z:_f,u:Lf,q:Vf,v:Kf,i:Zf,t:Yf,w:rm,m:Mp,n:Fp,g:$p,o:Cp,a:Z||d.wasmMemory,k:sm,l:im,h:om,s:Op},l5=xf(),zp=d.___wasm_call_ctors=function(){return(zp=d.___wasm_call_ctors=d.asm.A).apply(null,arguments)},dm=d._init=function(){return(dm=d._init=d.asm.B).apply(null,arguments)},pm=d._register_tensor=function(){return(pm=d._register_tensor=d.asm.C).apply(null,arguments)},cm=d._dispose_data=function(){return(cm=d._dispose_data=d.asm.D).apply(null,arguments)},hm=d._dispose=function(){return(hm=d._dispose=d.asm.E).apply(null,arguments)},fm=d._Abs=function(){return(fm=d._Abs=d.asm.G).apply(null,arguments)},mm=d._Add=function(){return(mm=d._Add=d.asm.H).apply(null,arguments)},gm=d._AddN=function(){return(gm=d._AddN=d.asm.I).apply(null,arguments)},ym=d._All=function(){return(ym=d._All=d.asm.J).apply(null,arguments)},Am=d._Any=function(){return(Am=d._Any=d.asm.K).apply(null,arguments)},xm=d._ArgMax=function(){return(xm=d._ArgMax=d.asm.L).apply(null,arguments)},bm=d._AvgPool=function(){return(bm=d._AvgPool=d.asm.M).apply(null,arguments)},vm=d._BatchMatMul=function(){return(vm=d._BatchMatMul=d.asm.N).apply(null,arguments)},wm=d._Ceil=function(){return(wm=d._Ceil=d.asm.O).apply(null,arguments)},km=d._ClipByValue=function(){return(km=d._ClipByValue=d.asm.P).apply(null,arguments)},Im=d._Conv2D=function(){return(Im=d._Conv2D=d.asm.Q).apply(null,arguments)},Sm=d._Conv2DBackpropInput=function(){return(Sm=d._Conv2DBackpropInput=d.asm.R).apply(null,arguments)},Nm=d._Cos=function(){return(Nm=d._Cos=d.asm.S).apply(null,arguments)},Tm=d._CropAndResize=function(){return(Tm=d._CropAndResize=d.asm.T).apply(null,arguments)},Cm=d._Cumsum=function(){return(Cm=d._Cumsum=d.asm.U).apply(null,arguments)},Em=d._DepthToSpace=function(){return(Em=d._DepthToSpace=d.asm.V).apply(null,arguments)},Rm=d._DepthwiseConv2dNative=function(){return(Rm=d._DepthwiseConv2dNative=d.asm.W).apply(null,arguments)},_p=d._Equal=function(){return(_p=d._Equal=d.asm.X).apply(null,arguments)},Pp=d._Exp=function(){return(Pp=d._Exp=d.asm.Y).apply(null,arguments)},Lp=d._FlipLeftRight=function(){return(Lp=d._FlipLeftRight=d.asm.Z).apply(null,arguments)},wu=d._Floor=function(){return(wu=d._Floor=d.asm._).apply(null,arguments)},ao=d._FloorDiv=function(){return(ao=d._FloorDiv=d.asm.$).apply(null,arguments)},Mm=d._FusedBatchNorm=function(){return(Mm=d._FusedBatchNorm=d.asm.aa).apply(null,arguments)},ku=d._FusedConv2D=function(){return(ku=d._FusedConv2D=d.asm.ba).apply(null,arguments)},Y=d._FusedDepthwiseConv2D=function(){return(Y=d._FusedDepthwiseConv2D=d.asm.ca).apply(null,arguments)},ae=d._Gather=function(){return(ae=d._Gather=d.asm.da).apply(null,arguments)},Ce=d._GatherNd=function(){return(Ce=d._GatherNd=d.asm.ea).apply(null,arguments)},Ye=d._Greater=function(){return(Ye=d._Greater=d.asm.fa).apply(null,arguments)},Ct=d._GreaterEqual=function(){return(Ct=d._GreaterEqual=d.asm.ga).apply(null,arguments)},At=d._LeakyRelu=function(){return(At=d._LeakyRelu=d.asm.ha).apply(null,arguments)},He=d._Less=function(){return(He=d._Less=d.asm.ia).apply(null,arguments)},qe=d._LessEqual=function(){return(qe=d._LessEqual=d.asm.ja).apply(null,arguments)},nn=d._Log=function(){return(nn=d._Log=d.asm.ka).apply(null,arguments)},dr=d._LogicalAnd=function(){return(dr=d._LogicalAnd=d.asm.la).apply(null,arguments)},pr=d._Max=function(){return(pr=d._Max=d.asm.ma).apply(null,arguments)},Wp=d._MaxPool=function(){return(Wp=d._MaxPool=d.asm.na).apply(null,arguments)},Iu=d._Maximum=function(){return(Iu=d._Maximum=d.asm.oa).apply(null,arguments)},aa=d._Mean=function(){return(aa=d._Mean=d.asm.pa).apply(null,arguments)},Mr=d._Min=function(){return(Mr=d._Min=d.asm.qa).apply(null,arguments)},Bp=d._Minimum=function(){return(Bp=d._Minimum=d.asm.ra).apply(null,arguments)},Q9=d._MirrorPad=function(){return(Q9=d._MirrorPad=d.asm.sa).apply(null,arguments)},eI=d._Multiply=function(){return(eI=d._Multiply=d.asm.ta).apply(null,arguments)},tI=d._Neg=function(){return(tI=d._Neg=d.asm.ua).apply(null,arguments)},nI=d._NonMaxSuppressionV3=function(){return(nI=d._NonMaxSuppressionV3=d.asm.va).apply(null,arguments)},aI=d._NonMaxSuppressionV4=function(){return(aI=d._NonMaxSuppressionV4=d.asm.wa).apply(null,arguments)},rI=d._NonMaxSuppressionV5=function(){return(rI=d._NonMaxSuppressionV5=d.asm.xa).apply(null,arguments)},sI=d._NotEqual=function(){return(sI=d._NotEqual=d.asm.ya).apply(null,arguments)},iI=d._OneHot=function(){return(iI=d._OneHot=d.asm.za).apply(null,arguments)},oI=d._PadV2=function(){return(oI=d._PadV2=d.asm.Aa).apply(null,arguments)},lI=d._Pow=function(){return(lI=d._Pow=d.asm.Ba).apply(null,arguments)},uI=d._Prelu=function(){return(uI=d._Prelu=d.asm.Ca).apply(null,arguments)},dI=d._Prod=function(){return(dI=d._Prod=d.asm.Da).apply(null,arguments)},pI=d._RealDiv=function(){return(pI=d._RealDiv=d.asm.Ea).apply(null,arguments)},cI=d._Relu=function(){return(cI=d._Relu=d.asm.Fa).apply(null,arguments)},hI=d._Relu6=function(){return(hI=d._Relu6=d.asm.Ga).apply(null,arguments)},fI=d._ResizeBilinear=function(){return(fI=d._ResizeBilinear=d.asm.Ha).apply(null,arguments)},mI=d._Reverse=function(){return(mI=d._Reverse=d.asm.Ia).apply(null,arguments)},gI=d._RotateWithOffset=function(){return(gI=d._RotateWithOffset=d.asm.Ja).apply(null,arguments)},yI=d._Round=function(){return(yI=d._Round=d.asm.Ka).apply(null,arguments)},AI=d._Rsqrt=function(){return(AI=d._Rsqrt=d.asm.La).apply(null,arguments)},xI=d._ScatterNd=function(){return(xI=d._ScatterNd=d.asm.Ma).apply(null,arguments)},bI=d._SelectV2=function(){return(bI=d._SelectV2=d.asm.Na).apply(null,arguments)},vI=d._Sigmoid=function(){return(vI=d._Sigmoid=d.asm.Oa).apply(null,arguments)},wI=d._Sin=function(){return(wI=d._Sin=d.asm.Pa).apply(null,arguments)},kI=d._Softmax=function(){return(kI=d._Softmax=d.asm.Qa).apply(null,arguments)},II=d._Sqrt=function(){return(II=d._Sqrt=d.asm.Ra).apply(null,arguments)},SI=d._Square=function(){return(SI=d._Square=d.asm.Sa).apply(null,arguments)},NI=d._SquaredDifference=function(){return(NI=d._SquaredDifference=d.asm.Ta).apply(null,arguments)},TI=d._Step=function(){return(TI=d._Step=d.asm.Ua).apply(null,arguments)},CI=d._StridedSlice=function(){return(CI=d._StridedSlice=d.asm.Va).apply(null,arguments)},EI=d._Sub=function(){return(EI=d._Sub=d.asm.Wa).apply(null,arguments)},RI=d._Sum=function(){return(RI=d._Sum=d.asm.Xa).apply(null,arguments)},MI=d._Tan=function(){return(MI=d._Tan=d.asm.Ya).apply(null,arguments)},FI=d._Tanh=function(){return(FI=d._Tanh=d.asm.Za).apply(null,arguments)},$I=d._Tile=function(){return($I=d._Tile=d.asm._a).apply(null,arguments)},DI=d._TopK=function(){return(DI=d._TopK=d.asm.$a).apply(null,arguments)},OI=d._Transform=function(){return(OI=d._Transform=d.asm.ab).apply(null,arguments)},zI=d._Transpose=function(){return(zI=d._Transpose=d.asm.bb).apply(null,arguments)},_I=d.__FusedMatMul=function(){return(_I=d.__FusedMatMul=d.asm.cb).apply(null,arguments)},ms=d._malloc=function(){return(ms=d._malloc=d.asm.db).apply(null,arguments)},Su=d._free=function(){return(Su=d._free=d.asm.eb).apply(null,arguments)},u5=d.___errno_location=function(){return(u5=d.___errno_location=d.asm.fb).apply(null,arguments)},d5=d._emscripten_get_global_libc=function(){return(d5=d._emscripten_get_global_libc=d.asm.gb).apply(null,arguments)},ro=d._pthread_self=function(){return(ro=d._pthread_self=d.asm.hb).apply(null,arguments)},p5=d.___pthread_tsd_run_dtors=function(){return(p5=d.___pthread_tsd_run_dtors=d.asm.ib).apply(null,arguments)},Fm=d._emscripten_main_thread_process_queued_calls=function(){return(Fm=d._emscripten_main_thread_process_queued_calls=d.asm.jb).apply(null,arguments)},PI=d._emscripten_current_thread_process_queued_calls=function(){return(PI=d._emscripten_current_thread_process_queued_calls=d.asm.kb).apply(null,arguments)},c5=d._emscripten_register_main_browser_thread_id=function(){return(c5=d._emscripten_register_main_browser_thread_id=d.asm.lb).apply(null,arguments)},h5=d.__emscripten_do_dispatch_to_thread=function(){return(h5=d.__emscripten_do_dispatch_to_thread=d.asm.mb).apply(null,arguments)},f5=d._emscripten_sync_run_in_main_thread_4=function(){return(f5=d._emscripten_sync_run_in_main_thread_4=d.asm.nb).apply(null,arguments)},m5=d._emscripten_run_in_main_runtime_thread_js=function(){return(m5=d._emscripten_run_in_main_runtime_thread_js=d.asm.ob).apply(null,arguments)},$m=d.__emscripten_call_on_thread=function(){return($m=d.__emscripten_call_on_thread=d.asm.pb).apply(null,arguments)},LI=d._emscripten_tls_init=function(){return(LI=d._emscripten_tls_init=d.asm.qb).apply(null,arguments)},Dm=d.__emscripten_thread_init=function(){return(Dm=d.__emscripten_thread_init=d.asm.rb).apply(null,arguments)},Nu=d.stackSave=function(){return(Nu=d.stackSave=d.asm.sb).apply(null,arguments)},so=d.stackRestore=function(){return(so=d.stackRestore=d.asm.tb).apply(null,arguments)},io=d.stackAlloc=function(){return(io=d.stackAlloc=d.asm.ub).apply(null,arguments)},g5=d._emscripten_stack_set_limits=function(){return(g5=d._emscripten_stack_set_limits=d.asm.vb).apply(null,arguments)},y5=d._memalign=function(){return(y5=d._memalign=d.asm.wb).apply(null,arguments)},A5=d.__emscripten_allow_main_runtime_queued_calls=9808,oo=d.__emscripten_main_thread_futex=11432;d.cwrap=Oe,d.PThread=Ie,d.PThread=Ie,d.wasmMemory=Z,d.ExitStatus=Tu;var Vp;function Tu(T){this.name="ExitStatus",this.message="Program terminated with exit("+T+")",this.status=T}hs=function T(){Vp||Om(),Vp||(hs=T)};function Om(T){if(T=T||f,lr>0)return;if(w){p(d),yu(),postMessage({cmd:"loaded"});return}if(cf(),lr>0)return;function R(){Vp||(Vp=!0,d.calledRun=!0,!oe&&(yu(),hf(),p(d),d.onRuntimeInitialized&&d.onRuntimeInitialized(),Nn()))}d.setStatus?(d.setStatus("Running..."),setTimeout(function(){setTimeout(function(){d.setStatus("")},1),R()},1)):R()}d.run=Om;function WI(T,R){if(!(R&&se&&T===0)){if(!R&&w)throw postMessage({cmd:"exitProcess",returnCode:T}),new Tu(T);se||(Ie.terminateAllThreads(),xe=T,wp(),d.onExit&&d.onExit(T),oe=!0),y(T,new Tu(T))}}if(d.preInit)for(typeof d.preInit=="function"&&(d.preInit=[d.preInit]);d.preInit.length>0;)d.preInit.pop()();return w&&(se=!1,Ie.initWorker()),Om(),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)}),oS=xt((e,t)=>{var n=function(){var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};var s=typeof r!="undefined"?r:{},i,o;s.ready=new Promise(function(Y,ae){i=Y,o=ae});var u={},l;for(l in s)s.hasOwnProperty(l)&&(u[l]=s[l]);var d=[],p="./this.program",c=function(Y,ae){throw ae},h=!1,m=!1,f=!1,g=!1;h=typeof window=="object",m=typeof importScripts=="function",f=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g=!h&&!f&&!m;var y="";function A(Y){return s.locateFile?s.locateFile(Y,y):y+Y}var x,v,b,w,N,C;f?(m?y=Cu().dirname(y)+"/":y=__dirname+"/",x=function(Y,ae){return N||(N=po("fs")),C||(C=Cu()),Y=C.normalize(Y),N.readFileSync(Y,ae?null:"utf8")},b=function(Y){var ae=x(Y,!0);return ae.buffer||(ae=new Uint8Array(ae)),G(ae.buffer),ae},process.argv.length>1&&(p=process.argv[1].replace(/\\/g,"/")),d=process.argv.slice(2),process.on("uncaughtException",function(Y){if(!(Y instanceof Mm))throw Y}),process.on("unhandledRejection",Va),c=function(Y){process.exit(Y)},s.inspect=function(){return"[Emscripten Module object]"}):g?(typeof read!="undefined"&&(x=function(Y){return read(Y)}),b=function(Y){var ae;return typeof readbuffer=="function"?new Uint8Array(readbuffer(Y)):(ae=read(Y,"binary"),G(typeof ae=="object"),ae)},typeof scriptArgs!="undefined"?d=scriptArgs:typeof arguments!="undefined"&&(d=arguments),typeof quit=="function"&&(c=function(Y){quit(Y)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(h||m)&&(m?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),a&&(y=a),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",x=function(Y){var ae=new XMLHttpRequest;return ae.open("GET",Y,!1),ae.send(null),ae.responseText},m&&(b=function(Y){var ae=new XMLHttpRequest;return ae.open("GET",Y,!1),ae.responseType="arraybuffer",ae.send(null),new Uint8Array(ae.response)}),v=function(Y,ae,Ce){var Ye=new XMLHttpRequest;Ye.open("GET",Y,!0),Ye.responseType="arraybuffer",Ye.onload=function(){if(Ye.status==200||Ye.status==0&&Ye.response){ae(Ye.response);return}Ce()},Ye.onerror=Ce,Ye.send(null)},w=function(Y){document.title=Y});var E=s.print||console.log.bind(console),_=s.printErr||console.warn.bind(console);for(l in u)u.hasOwnProperty(l)&&(s[l]=u[l]);u=null,s.arguments&&(d=s.arguments),s.thisProgram&&(p=s.thisProgram),s.quit&&(c=s.quit);var $;s.wasmBinary&&($=s.wasmBinary);var S=s.noExitRuntime||!0;typeof WebAssembly!="object"&&Va("no native wasm support detected");var z,O=!1,W;function G(Y,ae){Y||Va("Assertion failed: "+ae)}function H(Y){var ae=s["_"+Y];return G(ae,"Cannot call unknown function "+Y+", make sure it is exported"),ae}function J(Y,ae,Ce,Ye,Ct){var At={string:function(aa){var Mr=0;if(aa!=null&&aa!==0){var Bp=(aa.length<<2)+1;Mr=wu(Bp),le(aa,Mr,Bp)}return Mr},array:function(aa){var Mr=wu(aa.length);return oe(aa,Mr),Mr}};function He(aa){return ae==="string"?se(aa):ae==="boolean"?Boolean(aa):aa}var qe=H(Y),nn=[],dr=0;if(Ye)for(var pr=0;pr<Ye.length;pr++){var Wp=At[Ce[pr]];Wp?(dr===0&&(dr=Pp()),nn[pr]=Wp(Ye[pr])):nn[pr]=Ye[pr]}var Iu=qe.apply(null,nn);return Iu=He(Iu),dr!==0&&Lp(dr),Iu}function K(Y,ae,Ce,Ye){Ce=Ce||[];var Ct=Ce.every(function(He){return He==="number"}),At=ae!=="string";return At&&Ct&&!Ye?H(Y):function(){return J(Y,ae,Ce,arguments,Ye)}}var ne=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function Q(Y,ae,Ce){for(var Ye=ae+Ce,Ct=ae;Y[Ct]&&!(Ct>=Ye);)++Ct;if(Ct-ae>16&&Y.subarray&&ne)return ne.decode(Y.subarray(ae,Ct));for(var At="";ae<Ct;){var He=Y[ae++];if(!(He&128)){At+=String.fromCharCode(He);continue}var qe=Y[ae++]&63;if((He&224)==192){At+=String.fromCharCode((He&31)<<6|qe);continue}var nn=Y[ae++]&63;if((He&240)==224?He=(He&15)<<12|qe<<6|nn:He=(He&7)<<18|qe<<12|nn<<6|Y[ae++]&63,He<65536)At+=String.fromCharCode(He);else{var dr=He-65536;At+=String.fromCharCode(55296|dr>>10,56320|dr&1023)}}return At}function se(Y,ae){return Y?Q(Te,Y,ae):""}function Z(Y,ae,Ce,Ye){if(!(Ye>0))return 0;for(var Ct=Ce,At=Ce+Ye-1,He=0;He<Y.length;++He){var qe=Y.charCodeAt(He);if(qe>=55296&&qe<=57343){var nn=Y.charCodeAt(++He);qe=65536+((qe&1023)<<10)|nn&1023}if(qe<=127){if(Ce>=At)break;ae[Ce++]=qe}else if(qe<=2047){if(Ce+1>=At)break;ae[Ce++]=192|qe>>6,ae[Ce++]=128|qe&63}else if(qe<=65535){if(Ce+2>=At)break;ae[Ce++]=224|qe>>12,ae[Ce++]=128|qe>>6&63,ae[Ce++]=128|qe&63}else{if(Ce+3>=At)break;ae[Ce++]=240|qe>>18,ae[Ce++]=128|qe>>12&63,ae[Ce++]=128|qe>>6&63,ae[Ce++]=128|qe&63}}return ae[Ce]=0,Ce-Ct}function le(Y,ae,Ce){return Z(Y,Te,ae,Ce)}function oe(Y,ae){Ne.set(Y,ae)}function xe(Y,ae){return Y%ae>0&&(Y+=ae-Y%ae),Y}var fe,Ne,Te,Oe,Pe,ze,tt,nt,it;function Ze(Y){fe=Y,s.HEAP8=Ne=new Int8Array(Y),s.HEAP16=Oe=new Int16Array(Y),s.HEAP32=ze=new Int32Array(Y),s.HEAPU8=Te=new Uint8Array(Y),s.HEAPU16=Pe=new Uint16Array(Y),s.HEAPU32=tt=new Uint32Array(Y),s.HEAPF32=nt=new Float32Array(Y),s.HEAPF64=it=new Float64Array(Y)}var ht=s.INITIAL_MEMORY||16777216,Ue,In=[],kt=[],ta=[],en=[],Sn=!1;kt.push({func:function(){Cp()}});function na(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Ba(s.preRun.shift());Cr(In)}function Pn(){Sn=!0,Cr(kt)}function dn(){Cr(ta)}function tn(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)fa(s.postRun.shift());Cr(en)}function Ba(Y){In.unshift(Y)}function fa(Y){en.unshift(Y)}var ma=0,Nr=null,or=null;function Tr(Y){ma++,s.monitorRunDependencies&&s.monitorRunDependencies(ma)}function Qi(Y){if(ma--,s.monitorRunDependencies&&s.monitorRunDependencies(ma),ma==0&&(Nr!==null&&(clearInterval(Nr),Nr=null),or)){var ae=or;or=null,ae()}}s.preloadedImages={},s.preloadedAudios={};function Va(Y){s.onAbort&&s.onAbort(Y),Y+="",_(Y),O=!0,W=1,Y="abort("+Y+"). Build with -s ASSERTIONS=1 for more info.";var ae=new WebAssembly.RuntimeError(Y);throw o(ae),ae}function vp(Y,ae){return String.prototype.startsWith?Y.startsWith(ae):Y.indexOf(ae)===0}var cf="data:application/octet-stream;base64,";function yu(Y){return vp(Y,cf)}var hf="file://";function wp(Y){return vp(Y,hf)}var Nn="tfjs-backend-wasm.wasm";yu(Nn)||(Nn=A(Nn));function kp(Y){try{if(Y==Nn&&$)return new Uint8Array($);if(b)return b(Y);throw"both async and sync fetching of the wasm failed"}catch(ae){Va(ae)}}function ff(){if(!$&&(h||m)){if(typeof fetch=="function"&&!wp(Nn))return fetch(Nn,{credentials:"same-origin"}).then(function(Y){if(!Y.ok)throw"failed to load wasm binary file at '"+Nn+"'";return Y.arrayBuffer()}).catch(function(){return kp(Nn)});if(v)return new Promise(function(Y,ae){v(Nn,function(Ce){Y(new Uint8Array(Ce))},ae)})}return Promise.resolve().then(function(){return kp(Nn)})}function lr(){var Y={a:xf};function ae(He,qe){var nn=He.exports;s.asm=nn,z=s.asm.i,Ze(z.buffer),Ue=s.asm.o,Qi("wasm-instantiate")}Tr("wasm-instantiate");function Ce(He){ae(He.instance)}function Ye(He){return ff().then(function(qe){return WebAssembly.instantiate(qe,Y)}).then(He,function(qe){_("failed to asynchronously prepare wasm: "+qe),Va(qe)})}function Ct(){return!$&&typeof WebAssembly.instantiateStreaming=="function"&&!yu(Nn)&&!wp(Nn)&&typeof fetch=="function"?fetch(Nn,{credentials:"same-origin"}).then(function(He){var qe=WebAssembly.instantiateStreaming(He,Y);return qe.then(Ce,function(nn){return _("wasm streaming compile failed: "+nn),_("falling back to ArrayBuffer instantiation"),Ye(Ce)})}):Ye(Ce)}if(s.instantiateWasm)try{var At=s.instantiateWasm(Y,ae);return At}catch(He){return _("Module.instantiateWasm callback failed with error: "+He),!1}return Ct().catch(o),{}}function Cr(Y){for(;Y.length>0;){var ae=Y.shift();if(typeof ae=="function"){ae(s);continue}var Ce=ae.func;typeof Ce=="number"?ae.arg===void 0?Ue.get(Ce)():Ue.get(Ce)(ae.arg):Ce(ae.arg===void 0?null:ae.arg)}}function hs(){Va()}function mf(Y,ae,Ce){Te.copyWithin(Y,ae,ae+Ce)}function gf(){return Te.length}function ur(Y){try{return z.grow(Y-fe.byteLength+65535>>>16),Ze(z.buffer),1}catch(ae){}}function Ip(Y){var ae=gf(),Ce=2147483648;if(Y>Ce)return!1;for(var Ye=1;Ye<=4;Ye*=2){var Ct=ae*(1+.2/Ye);Ct=Math.min(Ct,Y+100663296);var At=Math.min(Ce,xe(Math.max(Y,Ct),65536)),He=ur(At);if(He)return!0}return!1}var eo={mappings:{},buffers:[null,[],[]],printChar:function(Y,ae){var Ce=eo.buffers[Y];ae===0||ae===10?((Y===1?E:_)(Q(Ce,0)),Ce.length=0):Ce.push(ae)},varargs:void 0,get:function(){eo.varargs+=4;var Y=ze[eo.varargs-4>>2];return Y},getStr:function(Y){var ae=se(Y);return ae},get64:function(Y,ae){return Y}};function Sp(Y){return 0}function yf(Y,ae,Ce,Ye,Ct){}function Np(Y,ae,Ce,Ye){for(var Ct=0,At=0;At<Ce;At++){for(var He=ze[ae+At*8>>2],qe=ze[ae+(At*8+4)>>2],nn=0;nn<qe;nn++)eo.printChar(Y,Te[He+nn]);Ct+=qe}return ze[Ye>>2]=Ct,0}function Tn(){return 6}function Tp(Y){return ze[_p()>>2]=Y,Y}function Af(Y){switch(Y){case 30:return 16384;case 85:var ae=2147483648;return ae/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 Tp(28),-1}var xf={a:hs,d:mf,e:Ip,f:Sp,c:yf,b:Np,g:Tn,h:Af},bf=lr(),Cp=s.___wasm_call_ctors=function(){return(Cp=s.___wasm_call_ctors=s.asm.j).apply(null,arguments)},to=s._init=function(){return(to=s._init=s.asm.k).apply(null,arguments)},Au=s._register_tensor=function(){return(Au=s._register_tensor=s.asm.l).apply(null,arguments)},vf=s._dispose_data=function(){return(vf=s._dispose_data=s.asm.m).apply(null,arguments)},wf=s._dispose=function(){return(wf=s._dispose=s.asm.n).apply(null,arguments)},kf=s._Abs=function(){return(kf=s._Abs=s.asm.p).apply(null,arguments)},Ie=s._Add=function(){return(Ie=s._Add=s.asm.q).apply(null,arguments)},If=s._AddN=function(){return(If=s._AddN=s.asm.r).apply(null,arguments)},Sf=s._All=function(){return(Sf=s._All=s.asm.s).apply(null,arguments)},Nf=s._Any=function(){return(Nf=s._Any=s.asm.t).apply(null,arguments)},Tf=s._ArgMax=function(){return(Tf=s._ArgMax=s.asm.u).apply(null,arguments)},Cf=s._AvgPool=function(){return(Cf=s._AvgPool=s.asm.v).apply(null,arguments)},fs=s._BatchMatMul=function(){return(fs=s._BatchMatMul=s.asm.w).apply(null,arguments)},Ef=s._Ceil=function(){return(Ef=s._Ceil=s.asm.x).apply(null,arguments)},Rf=s._ClipByValue=function(){return(Rf=s._ClipByValue=s.asm.y).apply(null,arguments)},Mf=s._Conv2D=function(){return(Mf=s._Conv2D=s.asm.z).apply(null,arguments)},Ff=s._Conv2DBackpropInput=function(){return(Ff=s._Conv2DBackpropInput=s.asm.A).apply(null,arguments)},$f=s._Cos=function(){return($f=s._Cos=s.asm.B).apply(null,arguments)},Df=s._CropAndResize=function(){return(Df=s._CropAndResize=s.asm.C).apply(null,arguments)},Of=s._Cumsum=function(){return(Of=s._Cumsum=s.asm.D).apply(null,arguments)},zf=s._DepthToSpace=function(){return(zf=s._DepthToSpace=s.asm.E).apply(null,arguments)},_f=s._DepthwiseConv2dNative=function(){return(_f=s._DepthwiseConv2dNative=s.asm.F).apply(null,arguments)},Er=s._Equal=function(){return(Er=s._Equal=s.asm.G).apply(null,arguments)},xu=s._Exp=function(){return(xu=s._Exp=s.asm.H).apply(null,arguments)},bu=s._FlipLeftRight=function(){return(bu=s._FlipLeftRight=s.asm.I).apply(null,arguments)},Pf=s._Floor=function(){return(Pf=s._Floor=s.asm.J).apply(null,arguments)},Lf=s._FloorDiv=function(){return(Lf=s._FloorDiv=s.asm.K).apply(null,arguments)},Wf=s._FusedBatchNorm=function(){return(Wf=s._FusedBatchNorm=s.asm.L).apply(null,arguments)},Bf=s._FusedConv2D=function(){return(Bf=s._FusedConv2D=s.asm.M).apply(null,arguments)},Vf=s._FusedDepthwiseConv2D=function(){return(Vf=s._FusedDepthwiseConv2D=s.asm.N).apply(null,arguments)},We=s._Gather=function(){return(We=s._Gather=s.asm.O).apply(null,arguments)},jf=s._GatherNd=function(){return(jf=s._GatherNd=s.asm.P).apply(null,arguments)},Uf=s._Greater=function(){return(Uf=s._Greater=s.asm.Q).apply(null,arguments)},Hf=s._GreaterEqual=function(){return(Hf=s._GreaterEqual=s.asm.R).apply(null,arguments)},Gf=s._LeakyRelu=function(){return(Gf=s._LeakyRelu=s.asm.S).apply(null,arguments)},qf=s._Less=function(){return(qf=s._Less=s.asm.T).apply(null,arguments)},Xf=s._LessEqual=function(){return(Xf=s._LessEqual=s.asm.U).apply(null,arguments)},vu=s._Log=function(){return(vu=s._Log=s.asm.V).apply(null,arguments)},Ep=s._LogicalAnd=function(){return(Ep=s._LogicalAnd=s.asm.W).apply(null,arguments)},Rp=s._Max=function(){return(Rp=s._Max=s.asm.X).apply(null,arguments)},Kf=s._MaxPool=function(){return(Kf=s._MaxPool=s.asm.Y).apply(null,arguments)},Zf=s._Maximum=function(){return(Zf=s._Maximum=s.asm.Z).apply(null,arguments)},Yf=s._Mean=function(){return(Yf=s._Mean=s.asm._).apply(null,arguments)},Jf=s._Min=function(){return(Jf=s._Min=s.asm.$).apply(null,arguments)},Qf=s._Minimum=function(){return(Qf=s._Minimum=s.asm.aa).apply(null,arguments)},em=s._MirrorPad=function(){return(em=s._MirrorPad=s.asm.ba).apply(null,arguments)},tm=s._Multiply=function(){return(tm=s._Multiply=s.asm.ca).apply(null,arguments)},et=s._Neg=function(){return(et=s._Neg=s.asm.da).apply(null,arguments)},nm=s._NonMaxSuppressionV3=function(){return(nm=s._NonMaxSuppressionV3=s.asm.ea).apply(null,arguments)},am=s._NonMaxSuppressionV4=function(){return(am=s._NonMaxSuppressionV4=s.asm.fa).apply(null,arguments)},rm=s._NonMaxSuppressionV5=function(){return(rm=s._NonMaxSuppressionV5=s.asm.ga).apply(null,arguments)},no=s._NotEqual=function(){return(no=s._NotEqual=s.asm.ha).apply(null,arguments)},Mp=s._OneHot=function(){return(Mp=s._OneHot=s.asm.ia).apply(null,arguments)},Fp=s._PadV2=function(){return(Fp=s._PadV2=s.asm.ja).apply(null,arguments)},$p=s._Pow=function(){return($p=s._Pow=s.asm.ka).apply(null,arguments)},sm=s._Prelu=function(){return(sm=s._Prelu=s.asm.la).apply(null,arguments)},im=s._Prod=function(){return(im=s._Prod=s.asm.ma).apply(null,arguments)},Dp=s._RealDiv=function(){return(Dp=s._RealDiv=s.asm.na).apply(null,arguments)},om=s._Relu=function(){return(om=s._Relu=s.asm.oa).apply(null,arguments)},Op=s._Relu6=function(){return(Op=s._Relu6=s.asm.pa).apply(null,arguments)},Rr=s._ResizeBilinear=function(){return(Rr=s._ResizeBilinear=s.asm.qa).apply(null,arguments)},lm=s._Reverse=function(){return(lm=s._Reverse=s.asm.ra).apply(null,arguments)},um=s._RotateWithOffset=function(){return(um=s._RotateWithOffset=s.asm.sa).apply(null,arguments)},l5=s._Round=function(){return(l5=s._Round=s.asm.ta).apply(null,arguments)},zp=s._Rsqrt=function(){return(zp=s._Rsqrt=s.asm.ua).apply(null,arguments)},dm=s._ScatterNd=function(){return(dm=s._ScatterNd=s.asm.va).apply(null,arguments)},pm=s._SelectV2=function(){return(pm=s._SelectV2=s.asm.wa).apply(null,arguments)},cm=s._Sigmoid=function(){return(cm=s._Sigmoid=s.asm.xa).apply(null,arguments)},hm=s._Sin=function(){return(hm=s._Sin=s.asm.ya).apply(null,arguments)},fm=s._Softmax=function(){return(fm=s._Softmax=s.asm.za).apply(null,arguments)},mm=s._Sqrt=function(){return(mm=s._Sqrt=s.asm.Aa).apply(null,arguments)},gm=s._Square=function(){return(gm=s._Square=s.asm.Ba).apply(null,arguments)},ym=s._SquaredDifference=function(){return(ym=s._SquaredDifference=s.asm.Ca).apply(null,arguments)},Am=s._Step=function(){return(Am=s._Step=s.asm.Da).apply(null,arguments)},xm=s._StridedSlice=function(){return(xm=s._StridedSlice=s.asm.Ea).apply(null,arguments)},bm=s._Sub=function(){return(bm=s._Sub=s.asm.Fa).apply(null,arguments)},vm=s._Sum=function(){return(vm=s._Sum=s.asm.Ga).apply(null,arguments)},wm=s._Tan=function(){return(wm=s._Tan=s.asm.Ha).apply(null,arguments)},km=s._Tanh=function(){return(km=s._Tanh=s.asm.Ia).apply(null,arguments)},Im=s._Tile=function(){return(Im=s._Tile=s.asm.Ja).apply(null,arguments)},Sm=s._TopK=function(){return(Sm=s._TopK=s.asm.Ka).apply(null,arguments)},Nm=s._Transform=function(){return(Nm=s._Transform=s.asm.La).apply(null,arguments)},Tm=s._Transpose=function(){return(Tm=s._Transpose=s.asm.Ma).apply(null,arguments)},Cm=s.__FusedMatMul=function(){return(Cm=s.__FusedMatMul=s.asm.Na).apply(null,arguments)},Em=s._malloc=function(){return(Em=s._malloc=s.asm.Oa).apply(null,arguments)},Rm=s._free=function(){return(Rm=s._free=s.asm.Pa).apply(null,arguments)},_p=s.___errno_location=function(){return(_p=s.___errno_location=s.asm.Qa).apply(null,arguments)},Pp=s.stackSave=function(){return(Pp=s.stackSave=s.asm.Ra).apply(null,arguments)},Lp=s.stackRestore=function(){return(Lp=s.stackRestore=s.asm.Sa).apply(null,arguments)},wu=s.stackAlloc=function(){return(wu=s.stackAlloc=s.asm.Ta).apply(null,arguments)};s.cwrap=K;var ao;function Mm(Y){this.name="ExitStatus",this.message="Program terminated with exit("+Y+")",this.status=Y}or=function Y(){ao||ku(),ao||(or=Y)};function ku(Y){if(Y=Y||d,ma>0||(na(),ma>0))return;function ae(){ao||(ao=!0,s.calledRun=!0,!O&&(Pn(),dn(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),tn()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ae()},1)):ae()}if(s.run=ku,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return ku(),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)}),lS=xt((e,t)=>{(function(n,a,r){function s(l){var d=this,p=u();d.next=function(){var c=2091639*d.s0+d.c*23283064365386963e-26;return d.s0=d.s1,d.s1=d.s2,d.s2=c-(d.c=c|0)},d.c=1,d.s0=p(" "),d.s1=p(" "),d.s2=p(" "),d.s0-=p(l),d.s0<0&&(d.s0+=1),d.s1-=p(l),d.s1<0&&(d.s1+=1),d.s2-=p(l),d.s2<0&&(d.s2+=1),p=null}function i(l,d){return d.c=l.c,d.s0=l.s0,d.s1=l.s1,d.s2=l.s2,d}function o(l,d){var p=new s(l),c=d&&d.state,h=p.next;return h.int32=function(){return p.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,p),h.state=function(){return i(p,{})}),h}function u(){var l=4022871197,d=function(p){p=String(p);for(var c=0;c<p.length;c++){l+=p.charCodeAt(c);var h=.02519603282416938*l;l=h>>>0,h-=l,h*=l,l=h>>>0,h-=l,l+=h*4294967296}return(l>>>0)*23283064365386963e-26};return d}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),uS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.x=0,l.y=0,l.z=0,l.w=0,l.next=function(){var c=l.x^l.x<<11;return l.x=l.y,l.y=l.z,l.z=l.w,l.w^=l.w>>>19^c^c>>>8},u===(u|0)?l.x=u:d+=u;for(var p=0;p<d.length+64;p++)l.x^=d.charCodeAt(p)|0,l.next()}function i(u,l){return l.x=u.x,l.y=u.y,l.z=u.z,l.w=u.w,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),dS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.next=function(){var c=l.x^l.x>>>2;return l.x=l.y,l.y=l.z,l.z=l.w,l.w=l.v,(l.d=l.d+362437|0)+(l.v=l.v^l.v<<4^(c^c<<1))|0},l.x=0,l.y=0,l.z=0,l.w=0,l.v=0,u===(u|0)?l.x=u:d+=u;for(var p=0;p<d.length+64;p++)l.x^=d.charCodeAt(p)|0,p==d.length&&(l.d=l.x<<10^l.x>>>4),l.next()}function i(u,l){return l.x=u.x,l.y=u.y,l.z=u.z,l.w=u.w,l.v=u.v,l.d=u.d,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),pS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this;l.next=function(){var p=l.x,c=l.i,h,m,f;return h=p[c],h^=h>>>7,m=h^h<<24,h=p[c+1&7],m^=h^h>>>10,h=p[c+3&7],m^=h^h>>>3,h=p[c+4&7],m^=h^h<<7,h=p[c+7&7],h=h^h<<13,m^=h^h<<9,p[c]=m,l.i=c+1&7,m};function d(p,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h<c.length;++h)f[h&7]=f[h&7]<<15^c.charCodeAt(h)+f[h+1&7]<<13;for(;f.length<8;)f.push(0);for(h=0;h<8&&f[h]===0;++h);for(h==8?m=f[7]=-1:m=f[h],p.x=f,p.i=0,h=256;h>0;--h)p.next()}d(l,u)}function i(u,l){return l.x=u.x.slice(),l.i=u.i,l}function o(u,l){u==null&&(u=+new Date);var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.x&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),cS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this;l.next=function(){var p=l.w,c=l.X,h=l.i,m,f;return l.w=p=p+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,l.i=h,f+(p^p>>>16)|0};function d(p,c){var h,m,f,g,y,A=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,g=-32;g<x;++g)c&&(m^=c.charCodeAt((g+32)%c.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=A[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(A[(c&&c.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=A[f+34&127],h=A[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,A[f]=m^h;p.w=y,p.X=A,p.i=f}d(l,u)}function i(u,l){return l.i=u.i,l.w=u.w,l.X=u.X.slice(),l}function o(u,l){u==null&&(u=+new Date);var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.X&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),hS=xt((e,t)=>{(function(n,a,r){function s(u){var l=this,d="";l.next=function(){var c=l.b,h=l.c,m=l.d,f=l.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,l.b=c=c<<20^c>>>12^h,l.c=h=h-m|0,l.d=m<<16^h>>>16^f,l.a=f-c|0},l.a=0,l.b=0,l.c=2654435769|0,l.d=1367130551,u===Math.floor(u)?(l.a=u/4294967296|0,l.b=u|0):d+=u;for(var p=0;p<d.length+20;p++)l.b^=d.charCodeAt(p)|0,l.next()}function i(u,l){return l.a=u.a,l.b=u.b,l.c=u.c,l.d=u.d,l}function o(u,l){var d=new s(u),p=l&&l.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),fS=xt((e,t)=>{(function(n,a,r){var s=256,i=6,o=52,u="random",l=r.pow(s,i),d=r.pow(2,o),p=d*2,c=s-1,h;function m(b,w,N){var C=[];w=w==!0?{entropy:!0}:w||{};var E=A(y(w.entropy?[b,v(a)]:b==null?x():b,3),C),_=new f(C),$=function(){for(var S=_.g(i),z=l,O=0;S<d;)S=(S+O)*s,z*=s,O=_.g(1);for(;S>=p;)S/=2,z/=2,O>>>=1;return(S+O)/z};return $.int32=function(){return _.g(4)|0},$.quick=function(){return _.g(4)/4294967296},$.double=$,A(v(_.S),a),(w.pass||N||function(S,z,O,W){return W&&(W.S&&g(W,_),S.state=function(){return g(_,{})}),O?(r[u]=S,z):S})($,E,"global"in w?w.global:this==r,w.state)}function f(b){var w,N=b.length,C=this,E=0,_=C.i=C.j=0,$=C.S=[];for(N||(b=[N++]);E<s;)$[E]=E++;for(E=0;E<s;E++)$[E]=$[_=c&_+b[E%N]+(w=$[E])],$[_]=w;(C.g=function(S){for(var z,O=0,W=C.i,G=C.j,H=C.S;S--;)z=H[W=c&W+1],O=O*s+H[c&(H[W]=H[G=c&G+z])+(H[G]=z)];return C.i=W,C.j=G,O})(s)}function g(b,w){return w.i=b.i,w.j=b.j,w.S=b.S.slice(),w}function y(b,w){var N=[],C=typeof b,E;if(w&&C=="object")for(E in b)try{N.push(y(b[E],w-1))}catch(_){}return N.length?N:C=="string"?b:b+"\0"}function A(b,w){for(var N=b+"",C,E=0;E<N.length;)w[c&E]=c&(C^=w[c&E]*19)+N.charCodeAt(E++);return v(w)}function x(){try{var b;return h&&(b=h.randomBytes)?b=b(s):(b=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(b)),v(b)}catch(C){var w=n.navigator,N=w&&w.plugins;return[+new Date,n,N,n.screen,v(a)]}}function v(b){return String.fromCharCode.apply(0,b)}if(A(r.random(),a),typeof t=="object"&&t.exports){t.exports=m;try{h=I5()}catch(b){}}else typeof define=="function"&&define.amd?define(function(){return m}):r["seed"+u]=m})(typeof self!="undefined"?self:e,[],Math)}),N5=xt((e,t)=>{var n=lS(),a=uS(),r=dS(),s=pS(),i=cS(),o=hS(),u=fS();u.alea=n,u.xor128=a,u.xorwow=r,u.xorshift7=s,u.xor4096=i,u.tychei=o,t.exports=u}),mS=xt(()=>{}),_m={};Fe(_m,{bin:()=>_5,browser:()=>j5,default:()=>gS,dependencies:()=>V5,description:()=>E5,devDependencies:()=>W5,jsdelivr:()=>$5,license:()=>L5,main:()=>M5,miniprogram:()=>z5,module:()=>F5,name:()=>T5,private:()=>R5,repository:()=>P5,scripts:()=>B5,types:()=>O5,unpkg:()=>D5,version:()=>C5});var T5="@tensorflow/tfjs",C5="3.7.0",E5="An open-source machine learning framework.",R5=!1,M5="dist/tf.node.js",F5="dist/index.js",$5="dist/tf.min.js",D5="dist/tf.min.js",O5="dist/index.d.ts",z5="dist/miniprogram",_5={"tfjs-custom-module":"dist/tools/custom_module/cli.js"},P5={type:"git",url:"https://github.com/tensorflow/tfjs.git"},L5="Apache-2.0",W5={"@babel/core":"^7.9.0","@babel/polyfill":"^7.10.4","@babel/preset-env":"^7.9.5","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@types/argparse":"^1.0.38","@types/jasmine":"2.8.7","@types/node":"~10.17.50","@types/shelljs":"^0.8.4","@types/yargs":"^15.0.7","clang-format":"~1.2.2",commander:"~2.14.1",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-babel":"^4.4.0","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~4.2.2",shelljs:"~0.8.1","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"1.0.0-pre.50"},B5={build:"tsc && yarn build-cli && yarn bundle","build-ci":"tsc && yarn build-cli && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-converter":"cd ../tfjs-converter && yarn && yarn build","build-converter-ci":"cd ../tfjs-converter && yarn && yarn build-ci","build-data":"cd ../tfjs-data && yarn && yarn build","build-data-ci":"cd ../tfjs-data && yarn && yarn build-ci","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-converter && yarn build-data && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-converter-ci && yarn build-data-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-cli":"tsc --project ./tools/custom_module/tsconfig.json && chmod +x ./dist/tools/custom_module/cli.js","run-custom-build":"ts-node -s ./tools/custom_module/cli.ts","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && yarn build && karma start","test-dev":"karma start","test-tools":"ts-node --project ./tools/custom_module/tsconfig.json run_tools_tests.ts","test-ci":"./scripts/test-ci.sh"},V5={"@tensorflow/tfjs-backend-cpu":"3.7.0","@tensorflow/tfjs-backend-webgl":"3.7.0","@tensorflow/tfjs-converter":"3.7.0","@tensorflow/tfjs-core":"3.7.0","@tensorflow/tfjs-data":"3.7.0","@tensorflow/tfjs-layers":"3.7.0",argparse:"^1.0.10",chalk:"^4.1.0","core-js":"3","regenerator-runtime":"^0.13.5",yargs:"^16.0.3"},j5={"node-fetch":!1,util:!1,crypto:!1},gS={name:T5,version:C5,description:E5,private:R5,main:M5,module:F5,jsdelivr:$5,unpkg:D5,types:O5,miniprogram:z5,bin:_5,repository:P5,license:L5,devDependencies:W5,scripts:B5,dependencies:V5,browser:j5},Pm={};Fe(Pm,{browser:()=>ox,default:()=>yS,dependencies:()=>ix,description:()=>G5,devDependencies:()=>rx,engines:()=>tx,jsdelivr:()=>K5,"jsnext:main":()=>J5,license:()=>ax,main:()=>X5,miniprogram:()=>ex,module:()=>Q5,name:()=>U5,private:()=>q5,repository:()=>nx,scripts:()=>sx,sideEffects:()=>lx,types:()=>Y5,unpkg:()=>Z5,version:()=>H5});var U5="@tensorflow/tfjs-core",H5="3.7.0",G5="Hardware-accelerated JavaScript library for machine intelligence",q5=!1,X5="dist/tf-core.node.js",K5="dist/tf-core.min.js",Z5="dist/tf-core.min.js",Y5="dist/index.d.ts",J5="dist/index.js",Q5="dist/index.js",ex="dist/miniprogram",tx={yarn:">= 1.3.2"},nx={type:"git",url:"https://github.com/tensorflow/tfjs-core.git"},ax="Apache-2.0",rx={"@bazel/bazelisk":"^1.3.0","@bazel/typescript":"^0.27.8","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"link:../tfjs-backend-cpu","@types/jasmine":"~3.0.0","@types/node":"~9.6.0","@types/node-fetch":"~2.1.2","clang-format":"~1.2.4",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~3.1.0","karma-jasmine":"~4.0.1","karma-typescript":"~5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~5.3.0","rollup-plugin-visualizer":"~3.3.2",shelljs:"~0.8.3","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"~1.0.0-pre.21",yargs:"~13.2.2"},sx={"build-ci":"./scripts/enumerate-tests.js --ci && tsc && yarn bundle-ci && yarn build-test-snippets",build:"node ./scripts/enumerate-tests.js && tsc && yarn bundle",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-npm":"./scripts/build-npm.sh","build-deps":"yarn build && yarn build-cpu-backend","build-cpu-backend":"cd ../tfjs-backend-cpu && yarn && yarn build","build-cpu-backend-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build:bazel":"bazelisk build //...","build-test-snippets":"yarn tsc --project ./scripts/test_snippets/tsconfig.json","format-all":"clang-format -i -style=Google --glob=src/**/*.ts","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build && rollup -c && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-webworker":"karma start --worker","run-browserstack":"karma start --browserstack","test-bundle-size":"./scripts/test-bundle-size.js","test-node":"rimraf dist/ && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-dev":"tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_node.js","test-async-backends":"rimraf dist/ && yarn build && ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-async-backends-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-snippets":"yarn build && yarn build-cpu-backend && ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts","test-snippets-ci":"ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts"},ix={"@types/long":"^4.0.1","@types/offscreencanvas":"~2019.3.0","@types/seedrandom":"2.4.27","@types/webgl-ext":"0.0.30",long:"4.0.0","node-fetch":"~2.6.1",seedrandom:"2.4.3"},ox={"node-fetch":!1,util:!1,crypto:!1,worker_threads:!1},lx=["./dist/index.js","./dist/engine.js","./dist/tensor.js","./dist/base_side_effects.js","./dist/flags.js","./dist/platforms/*.js","./dist/register_all_gradients.js","./dist/public/chained_ops/*.js","./dist/io/*.js"],yS={name:U5,version:H5,description:G5,private:q5,main:X5,jsdelivr:K5,unpkg:Z5,types:Y5,"jsnext:main":J5,module:Q5,miniprogram:ex,engines:tx,repository:nx,license:ax,devDependencies:rx,scripts:sx,dependencies:ix,browser:ox,sideEffects:lx},Lm={};Fe(Lm,{browser:()=>Sx,default:()=>AS,dependencies:()=>Ix,description:()=>px,devDependencies:()=>vx,jsdelivr:()=>fx,"jsnext:main":()=>yx,license:()=>bx,main:()=>hx,miniprogram:()=>xx,module:()=>Ax,name:()=>ux,peerDependencies:()=>kx,private:()=>cx,scripts:()=>wx,types:()=>gx,unpkg:()=>mx,version:()=>dx});var ux="@tensorflow/tfjs-data",dx="3.7.0",px="TensorFlow Data API in JavaScript",cx=!1,hx="dist/tf-data.node.js",fx="dist/tf-data.min.js",mx="dist/tf-data.min.js",gx="dist/index.d.ts",yx="dist/index.js",Ax="dist/index.js",xx="dist/miniprogram",bx="Apache-2.0",vx={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.7.0","@tensorflow/tfjs-core":"3.7.0","@tensorflow/tfjs-layers":"3.7.0","@types/jasmine":"~2.5.53","@types/seedrandom":"^2.4.27","@types/utf8":"~2.1.6","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",nyc:"^15.1.0",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~7.0.0",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"^1.0.0-pre.50"},wx={build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-backend-cpu-ci","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-dev":"tsc && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-browsers":"karma start --browsers='Chrome,Firefox'","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts",coverage:"yarn nyc yarn ts-node --transpile-only -P tsconfig.test.json src/test_node.ts",lint:"tslint -p . -t verbose"},kx={"@tensorflow/tfjs-core":"3.7.0",seedrandom:"~2.4.3"},Ix={"@types/node-fetch":"^2.1.2","node-fetch":"~2.6.1"},Sx={fs:!1,"node-fetch":!1,string_decoder:!1,crypto:!1},AS={name:ux,version:dx,description:px,private:cx,main:hx,jsdelivr:fx,unpkg:mx,types:gx,"jsnext:main":yx,module:Ax,miniprogram:xx,license:bx,devDependencies:vx,scripts:wx,peerDependencies:kx,dependencies:Ix,browser:Sx},Wm={};Fe(Wm,{default:()=>xS,description:()=>Cx,devDependencies:()=>Px,jsdelivr:()=>Ox,"jsnext:main":()=>$x,license:()=>Ex,main:()=>Mx,miniprogram:()=>_x,module:()=>Dx,name:()=>Nx,peerDependencies:()=>Wx,private:()=>Rx,scripts:()=>Lx,types:()=>Fx,unpkg:()=>zx,version:()=>Tx});var Nx="@tensorflow/tfjs-layers",Tx="3.7.0",Cx="TensorFlow layers API in JavaScript",Ex="Apache-2.0 AND MIT",Rx=!1,Mx="dist/tf-layers.node.js",Fx="dist/index.d.ts",$x="dist/index.js",Dx="dist/index.js",Ox="dist/tf-layers.min.js",zx="dist/tf-layers.min.js",_x="dist/miniprogram",Px={"@babel/polyfill":"^7.8.7","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.7.0","@tensorflow/tfjs-backend-webgl":"3.7.0","@tensorflow/tfjs-core":"3.7.0","@types/jasmine":"~2.5.53","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},Lx={prep:"yarn install && yarn build-ci",build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-npm":"./scripts/build-npm.sh",format:"./tools/clang_format_ts.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts","run-browserstack":"karma start --browsers='bs_chrome_mac' --singleRun --reporters='dots,karma-typescript'",lint:"tslint -p . -t verbose"},Wx={"@tensorflow/tfjs-core":"3.7.0"},xS={name:Nx,version:Tx,description:Cx,license:Ex,private:Rx,main:Mx,types:Fx,"jsnext:main":$x,module:Dx,jsdelivr:Ox,unpkg:zx,miniprogram:_x,devDependencies:Px,scripts:Lx,peerDependencies:Wx},Bm={};Fe(Bm,{default:()=>bS,description:()=>jx,devDependencies:()=>eb,jsdelivr:()=>Kx,"jsnext:main":()=>Hx,license:()=>Jx,main:()=>Ux,miniprogram:()=>Zx,module:()=>Gx,name:()=>Bx,peerDependencies:()=>Qx,repository:()=>Yx,scripts:()=>tb,types:()=>qx,unpkg:()=>Xx,version:()=>Vx});var Bx="@tensorflow/tfjs-converter",Vx="3.7.0",jx="Tensorflow model converter for javascript",Ux="dist/tf-converter.node.js",Hx="dist/index.js",Gx="dist/index.js",qx="dist/index.d.ts",Xx="dist/tf-converter.min.js",Kx="dist/tf-converter.min.js",Zx="dist/miniprogram",Yx={type:"git",url:"https://github.com/tensorflow/tfjs-converter.git"},Jx="Apache-2.0",Qx={"@tensorflow/tfjs-core":"3.7.0"},eb={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-replace":"^2.3.3","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.7.0","@tensorflow/tfjs-core":"3.7.0","@types/argparse":"^1.0.38","@types/deep-equal":"^1.0.1","@types/jasmine":"~2.8.6","@types/long":"~3.0.32","@types/node-fetch":"1.6.9",ajv:"~6.3.0",argparse:"^1.0.10","babel-core":"~6.26.3","babel-plugin-external-helpers":"~6.22.0","babel-preset-env":"~1.7.0","clang-format":"~1.2.2",copyfiles:"~1.2.0","deep-equal":"^1.0.1","jasmine-core":"~3.5.0","node-fetch":"~2.6.1",opn:"~5.1.0",protobufjs:"~6.8.6",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-morph":"^7.1.3","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"~0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},tb={build:"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle","build-ci":"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && yarn gen-json --test && yarn gen-kernel2ops && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts","test-dev":"tsc && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose","make-version":"sh -c ./scripts/make-version","gen-doc":"ts-node -s ./scripts/gen_doc.ts","gen-json":"ts-node -s ./scripts/gen_json.ts","model-summary":"ts-node -s ./tools/model_summary.ts",pb2json:"ts-node -s ./tools/pb2json_converter.ts","build-pip-package":"yarn gen-json --test && cd python && ./build-pip-package.sh --test /tmp/tfjs-pips","run-python-tests":"yarn gen-json --test && cd python && ./run-python-tests.sh","gen-kernel2ops":"ts-node -s scripts/kernels_to_ops.ts --out metadata/kernel2op.json"},bS={name:Bx,version:Vx,description:jx,main:Ux,"jsnext:main":Hx,module:Gx,types:qx,unpkg:Xx,jsdelivr:Kx,miniprogram:Zx,repository:Yx,license:Jx,peerDependencies:Qx,devDependencies:eb,scripts:tb},vS=1e-7,wS=1e-4,Up=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}},Eu=class{refCount(e){return ga("refCount")}incRef(e){return ga("incRef")}timerAvailable(){return!0}time(e){return ga("time")}read(e){return ga("read")}readSync(e){return ga("readSync")}numDataIds(){return ga("numDataIds")}disposeData(e,t){return ga("disposeData")}write(e,t,n){return ga("write")}move(e,t,n,a,r){return ga("move")}memory(){return ga("memory")}floatPrecision(){return ga("floatPrecision")}epsilon(){return this.floatPrecision()===32?vS:wS}dispose(){return ga("dispose")}};function ga(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 nb(e){let t=e.length,n=0,a=0;for(;t>0;)a=Math.random()*t|0,t--,n=e[t],e[t]=e[a],e[a]=n}function kS(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,a,r,s=0;for(;n>0;)s=Math.random()*n|0,n--,a=e[n],r=t[n],e[n]=e[s],t[n]=t[s],e[s]=a,t[s]=r}function Ru(e,t,n){return Math.max(e,Math.min(t,n))}function IS(e){return e%2==0?e:e+1}function SS(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function NS(e,t){let n=Math.random();return t*n+(1-n)*e}function TS(e,t){let n=0;for(let a=0;a<e.length;a++){let r=Number(e[a])-Number(t[a]);n+=r*r}return n}function D(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function cn(e,t,n=""){D(cr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ys(e){D(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function As(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||on(e)&&!n)for(let a=0;a<e.length;++a)As(e[a],t,n);else t.push(e);return t}function Mt(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 CS(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 qt(e){return e%1==0}function ES(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function RS(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function MS(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return nb(t),t}function Mu(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function FS(e,t=a=>0,n){return new Promise((a,r)=>{let s=0,i=()=>{if(e()){a();return}s++;let o=t(s);if(n!=null&&s>=n){r();return}setTimeout(i,o)};i()})}function $S(e,t){let n=1,a=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(a!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${a} and dim ${s}`);a=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(a===-1){if(t>0&&t!==n)throw Error(`Size(${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} / ${n}`);let r=e.slice();return r[a]=t/n,r}function ya(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),D(e.every(a=>a>=-n&&a<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),D(e.every(a=>qt(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function ab(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:ya(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),a.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),a.push(o))}return{newShape:n,keptDims:a}}function rb(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 sb(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 ib(e,t){for(let n=0;n<e.length;n++){let a=e[n];if(isNaN(a)||!isFinite(a))throw Error(`A tensor of type ${t} being uploaded contains ${a}.`)}}function ob(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function DS(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function on(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function Vm(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 lb(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function $r(e){return typeof e=="string"||e instanceof String}function ub(e){return typeof e=="boolean"}function db(e){return typeof e=="number"}function Hp(e){return Array.isArray(e)?Hp(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":db(e)?"float32":$r(e)?"string":ub(e)?"bool":"float32"}function Dr(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Gp(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function co(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let a=t-3;a>=0;--a)n[a]=n[a+1]*e[a+1];return n}function pb(e,t,n,a=!1){let r=new Array;if(t.length===1){let s=t[0]*(a?2:1);for(let i=0;i<s;i++)r[i]=n[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((u,l)=>u*l)*(a?2:1);for(let u=0;u<s;u++)r[u]=pb(e+u*o,i,n,a)}return r}function ho(e,t,n=!1){if(e.length===0)return t[0];let a=e.reduce((r,s)=>r*s)*(n?2:1);if(a===0)return[];if(a!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return pb(0,e,t,n)}function jm(e,t){let n=qp(e,t);for(let a=0;a<n.length;a++)n[a]=1;return n}function qp(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 OS(e,t){let n=e.reduce((a,r)=>a*r,1);if(t==null||t==="float32")return ho(e,new Float32Array(n));if(t==="int32")return ho(e,new Int32Array(n));if(t==="bool")return ho(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Um(e){e.forEach(t=>{D(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function zS(e,t,n){if(t===0)return 0;if(t===1)return e[0];let a=e[e.length-1];for(let r=0;r<e.length-1;++r)a+=n[r]*e[r];return a}function _S(e,t,n){if(t===0)return[];if(t===1)return[e];let a=new Array(t);for(let r=0;r<a.length-1;++r)a[r]=Math.floor(e/n[r]),e-=a[r]*n[r];return a[a.length-1]=e,a}function Hm(e){return e&&e.then&&typeof e.then=="function"}var cb="tfjsflags",hb=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=PS,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let a=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${a}.`),this.set(e,a)}}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(Hm(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);cb in e&&e[cb].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=WS(n,a)})}};function PS(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(LS(t,a[0],a[1]),a.join("="))),t}function LS(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function WS(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 ${e}.`)}function te(){return sa}var sa=null;function BS(e){sa=e}var Gm;function fb(){if(Gm==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Gm=e}return Gm}function VS(){let e=fb();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function qm(e,t){let n=VS();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var fo="Abs",mo="Acos",go="Acosh",Or="Add",xs="AddN",yo="All",Ao="Any",bs="ArgMax",Fu="ArgMin",xo="Asin",bo="Asinh",vo="Atan",wo="Atanh",ko="Atan2",vs="AvgPool",Xp="AvgPoolGrad",$u="AvgPool3D",Kp="AvgPool3DGrad",ws="BatchMatMul",Du="BatchToSpaceND",Zp="Bincount",mb="BroadcastTo",ks="Cast",Is="Ceil",zr="ClipByValue",Yp="Complex",Ou="ComplexAbs",Io="Concat",Ss="Conv2D",Jp="Conv2DBackpropFilter",Ns="Conv2DBackpropInput",zu="Conv3D",Qp="Conv3DBackpropFilterV2",ec="Conv3DBackpropInputV2",Ts="Cos",So="Cosh",Cs="Cumsum",No="CropAndResize",tc="DenseBincount",To="DepthToSpace",Es="DepthwiseConv2dNative",nc="DepthwiseConv2dNativeBackpropFilter",ac="DepthwiseConv2dNativeBackpropInput",rc="Diag",_u="Dilation2D",sc="Dilation2DBackpropInput",ic="Dilation2DBackpropFilter",Rs="RealDiv",oc="Einsum",Co="Elu",lc="EluGrad",Eo="Erf",Ro="Equal",Ms="Exp",Mo="ExpandDims",Fo="Expm1",uc="FFT",Pu="Fill",$o="FlipLeftRight",Fs="Floor",$s="FloorDiv",Ds="FusedBatchNorm",Do="GatherV2",Oo="GatherNd",zo="Greater",Os="GreaterEqual",zs="Identity",dc="IFFT",pc="Imag",_o="IsFinite",Po="IsInf",Lo="IsNan",_s="LeakyRelu",Wo="Less",Bo="LessEqual",cc="LinSpace",Ps="Log",Vo="Log1p",jo="LogicalAnd",Lu="LogicalNot",Wu="LogicalOr",gb="LogSoftmax",Bu="LRN",hc="LRNGrad",Ls="Max",Ws="Maximum",Bs="MaxPool",fc="MaxPoolGrad",Vu="MaxPool3D",mc="MaxPool3DGrad",gc="MaxPoolWithArgmax",Vs="Mean",js="Min",Us="Minimum",Hs="MirrorPad",Uo="Mod",yc="Multinomial",Gs="Multiply",Ho="Neg",Go="NotEqual",qo="NonMaxSuppressionV3",Xo="NonMaxSuppressionV4",Ko="NonMaxSuppressionV5",Zo="OnesLike",qs="OneHot",Yo="Pack",Xs="PadV2",jS="Pool",Ks="Pow",Zs="Prelu",Jo="Prod",ju="Range",Ac="Real",Qo="Reciprocal",Ys="Relu",el="Reshape",Uu="ResizeNearestNeighbor",xc="ResizeNearestNeighborGrad",Js="ResizeBilinear",bc="ResizeBilinearGrad",Qs="Relu6",ei="Reverse",ti="Round",ni="Rsqrt",tl="ScatterNd",nl="Select",al="Selu",rl="Slice",ai="Sin",sl="Sinh",il="Sign",ri="Sigmoid",ol="Softplus",si="Sqrt",ii="Sum",Hu="SpaceToBatchND",ll="SplitV",oi="Softmax",vc="SparseFillEmptyRows",wc="SparseReshape",kc="SparseSegmentMean",Ic="SparseSegmentSum",Sc="SparseToDense",li="SquaredDifference",Gu="Square",ul="StridedSlice",Nc="StringNGrams",Tc="StringSplit",Cc="StringToHashBucketFast",ui="Sub",di="Tan",pi="Tanh",_r="Tile",dl="TopK",pl="Transform",ci="Transpose",Ec="Unique",cl="Unpack",qu="UnsortedSegmentSum",hl="ZerosLike",Pr="Step",Rc="FromPixels",fl="RotateWithOffset",hi="_FusedMatMul",fi="FusedConv2D",mi="FusedDepthwiseConv2D",ml=qm("kernelRegistry",()=>new Map),Xu=qm("gradRegistry",()=>new Map);function Mc(e,t){let n=Km(e,t);return ml.get(n)}function Xm(e){return Xu.get(e)}function gl(e){let t=ml.entries(),n=[];for(;;){let{done:a,value:r}=t.next();if(a)break;let[s,i]=r,[o]=s.split("_");o===e&&n.push(i)}return n}function gi(e){let{kernelName:t,backendName:n}=e,a=Km(t,n);ml.has(a)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),ml.set(a,e)}function yb(e){let{kernelName:t}=e;Xu.has(t)&&te().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Xu.set(t,e)}function US(e,t){let n=Km(e,t);if(!ml.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);ml.delete(n)}function HS(e){if(!Xu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Xu.delete(e)}function GS(e,t){gl(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});gi(a)})}function Km(e,t){return`${t}_${e}`}var k={};Fe(k,{arraysEqual:()=>cr,assert:()=>D,assertNonNegativeIntegerDimensions:()=>Um,assertNonNull:()=>ys,assertShapesMatch:()=>cn,bytesFromStringArray:()=>lb,bytesPerElement:()=>Vm,checkConversionForErrors:()=>ib,clamp:()=>Ru,computeStrides:()=>co,createScalarValue:()=>JS,createShuffledIndices:()=>MS,decodeString:()=>Dc,distSquared:()=>TS,encodeString:()=>Yu,fetch:()=>eN,fingerPrint64:()=>YS,flatten:()=>As,getArrayFromDType:()=>sb,getTypedArrayFromDType:()=>rb,hasEncodingLoss:()=>DS,hexToLong:()=>Ku,indexToLoc:()=>_S,inferDtype:()=>Hp,inferFromImplicitShape:()=>$S,isBoolean:()=>ub,isFunction:()=>Dr,isInt:()=>qt,isNumber:()=>db,isPromise:()=>Hm,isScalarShape:()=>CS,isString:()=>$r,isTypedArray:()=>on,isValidDtype:()=>ob,locToIndex:()=>zS,makeOnesTypedArray:()=>jm,makeZerosNestedTypedArray:()=>OS,makeZerosTypedArray:()=>qp,nearestDivisor:()=>Gp,nearestLargerEven:()=>IS,now:()=>Zu,parseAxisParam:()=>ya,randUniform:()=>NS,repeatedTry:()=>FS,rightPad:()=>Mu,shuffle:()=>nb,shuffleCombo:()=>kS,sizeFromShape:()=>Mt,sizeToSquarishShape:()=>RS,squeezeShape:()=>ab,sum:()=>SS,tanh:()=>ES,toNestedArray:()=>ho,toTypedArray:()=>$c});var Ab=gs(KI()),yi=Ab.default||Ab;function Ku(e){return yi.fromString(e,!0,16)}var xb=Ku("c3a5c85c97cb3127"),Ai=Ku("b492b66fbe98f273"),hn=Ku("9ae16a3b2f90404f");function Zm(e){return e.xor(e.shru(47))}function bb(e,t,n){let a=e.slice(t,t+n);return yi.fromBytes(Array.from(a),!0,!0)}function pt(e,t){return bb(e,t,8)}function vb(e,t){return bb(e,t,4)}function Xt(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Lr(e,t,n=Ku("9ddfea08eb382d69")){let a=e.xor(t).mul(n);a=a.xor(a.shru(47));let r=t.xor(a).mul(n);return r=r.xor(r.shru(47)),r=r.mul(n),r}function qS(e,t,n,a,r,s){r=r.add(e),s=Xt(s.add(r).add(a),21);let i=r;return r=r.add(t),r=r.add(n),s=s.add(Xt(r,44)),[r.add(a),s.add(i)]}function Fc(e,t,n,a){return qS(pt(e,t),pt(e,t+8),pt(e,t+16),pt(e,t+24),n,a)}function XS(e,t=e.length){if(t>=8){let n=hn.add(t*2),a=pt(e,0).add(hn),r=pt(e,t-8),s=Xt(r,37).mul(n).add(a),i=Xt(a,25).add(r).mul(n);return Lr(s,i,n)}if(t>=4){let n=hn.add(t*2),a=vb(e,0);return Lr(a.shl(3).add(t),vb(e,t-4),n)}if(t>0){let n=e[0],a=e[t>>1],r=e[t-1],s=n+(a<<8),i=t+(r<<2);return Zm(hn.mul(s).xor(xb.mul(i))).mul(hn)}return hn}function KS(e,t=e.length){let n=hn.add(t*2),a=pt(e,0).mul(Ai),r=pt(e,8),s=pt(e,t-8).mul(n),i=pt(e,t-16).mul(hn);return Lr(Xt(a.add(r),43).add(Xt(s,30)).add(i),a.add(Xt(r.add(hn),18)).add(s),n)}function ZS(e,t=e.length){let n=hn.add(t*2),a=pt(e,0).mul(hn),r=pt(e,8),s=pt(e,t-8).mul(n),i=pt(e,t-16).mul(hn),o=Xt(a.add(r),43).add(Xt(s,30)).add(i),u=Lr(o,a.add(Xt(r.add(hn),18)).add(s),n),l=pt(e,16).mul(n),d=pt(e,24),p=o.add(pt(e,t-32)).mul(n),c=u.add(pt(e,t-24)).mul(n);return Lr(Xt(l.add(d),43).add(Xt(p,30)).add(c),l.add(Xt(d.add(a),18)).add(p),n)}function YS(e,t=e.length){let n=yi.fromNumber(81,!0);if(t<=32)return t<=16?XS(e,t):KS(e,t);if(t<=64)return ZS(e,t);let a=n,r=n.mul(Ai).add(113),s=Zm(r.mul(hn).add(113)).mul(hn),i=[yi.UZERO,yi.UZERO],o=[yi.UZERO,yi.UZERO];a=a.mul(hn).add(pt(e,0));let u=0,l=(t-1>>6)*64,d=l+(t-1&63)-63;do a=Xt(a.add(r).add(i[0]).add(pt(e,u+8)),37).mul(Ai),r=Xt(r.add(i[1]).add(pt(e,u+48)),42).mul(Ai),a=a.xor(o[1]),r=r.add(i[0]).add(pt(e,u+40)),s=Xt(s.add(o[0]),33).mul(Ai),i=Fc(e,u,i[1].mul(Ai),a.add(o[0])),o=Fc(e,u+32,s.add(o[1]),r.add(pt(e,u+16))),[s,a]=[a,s],u+=64;while(u!==l);let p=Ai.add(s.and(255).shl(1));return u=d,o[0]=o[0].add(t-1&63),i[0]=i[0].add(o[0]),o[0]=o[0].add(i[0]),a=Xt(a.add(r).add(i[0]).add(pt(e,u+8)),37).mul(p),r=Xt(r.add(i[1]).add(pt(e,u+48)),42).mul(p),a=a.xor(o[1].mul(9)),r=r.add(i[0].mul(9).add(pt(e,u+40))),s=Xt(s.add(o[0]),33).mul(p),i=Fc(e,u,i[1].mul(p),a.add(o[0])),o=Fc(e,u+32,s.add(o[1]),r.add(pt(e,u+16))),[s,a]=[a,s],Lr(Lr(i[0],o[0],p).add(Zm(r).mul(xb)).add(s),Lr(i[1],o[1],p).add(a),p)}function JS(e,t){return t==="string"?Yu(e):$c([e],t)}function QS(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function $c(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=As(e)),te().getBool("DEBUG")&&ib(e,t),QS(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 a=0;a<n.length;++a)Math.round(e[a])!==0&&(n[a]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Zu(){return te().platform.now()}function eN(e,t){return te().platform.fetch(e,t)}function Yu(e,t="utf-8"){return t=t||"utf-8",te().platform.encode(e,t)}function Dc(e,t="utf-8"){return t=t||"utf-8",te().platform.decode(e,t)}var tN=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new aN)}profileKernel(e,t,n){let a,r=()=>{a=n()},s,i=Zu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(r);else{r();for(let o of a)o.dataSync();s=Promise.resolve({kernelMs:Zu()-i})}if(te().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<a.length;o++){let u=a[o];u.data().then(l=>{nN(l,u.dtype,e)})}return{kernelName:e,outputs:a,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:a,inputs:r,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),a,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],r,o[2])})})}};function nN(e,t,n){if(t!=="float32")return!1;for(let a=0;a<e.length;a++){let r=e[a];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var aN=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?Mu(`${a}ms`,9):a.error,o=Mu(e,25),u=t.rank,l=t.size,d=Mu(t.shape.toString(),14),p="";for(let c in r){let h=r[c];if(h!=null){let m=h.shape||t.shape,f=m.length;p+=`${c}: ${f}D ${f>0?m:""} `}}console.log(`%c${o} %c${i} %c${u}D ${d} %c${l} %c${p} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function rN(e,t,n){let a={},r={};for(let u=0;u<t.length;u++)a[t[u].id]=!0;for(let u=0;u<e.length;u++){let l=e[u],d=l.inputs;for(let p in d){let c=d[p],h=!1;for(let m=0;m<t.length;m++)if(a[c.id]){l.outputs.forEach(f=>a[f.id]=!0),h=!0,r[l.id]=!0;break}if(h)break}}let s={};s[n.id]=!0;let i={};for(let u=e.length-1;u>=0;u--){let l=e[u],d=l.inputs;for(let p=0;p<l.outputs.length;p++)if(s[l.outputs[p].id]){for(let c in d)s[d[c].id]=!0,i[l.id]=!0;break}}let o=[];for(let u=0;u<e.length;u++){let l=e[u];if(r[l.id]&&i[l.id]){let d={};for(let c in l.inputs){let h=l.inputs[c];a[h.id]&&(d[c]=h)}let p=Object.assign({},l);p.inputs=d,p.outputs=l.outputs,o.push(p)}}return o}function sN(e,t,n,a){for(let r=t.length-1;r>=0;r--){let s=t[r],i=[];if(s.outputs.forEach(u=>{let l=e[u.id];l!=null?i.push(l):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let u in s.inputs){if(!(u in o))throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(o)}.`);let l=n(()=>o[u]());if(l.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`);let d=s.inputs[u];if(!cr(l.shape,d.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${d.shape}'`);if(e[d.id]==null)e[d.id]=l;else{let p=e[d.id];e[d.id]=a(p,l),p.dispose()}}}}var wb=20,Ju=3,Ym=7;function iN(e,t,n,a){let r=co(t),s=oN(e,t,n,r),i=t.length,o=Oc(e,t,n,r,s),u=["Tensor"];return a&&(u.push(` dtype: ${n}`),u.push(` rank: ${i}`),u.push(` shape: [${t}]`),u.push(" values:")),u.push(o.map(l=>" "+l).join(`
|
|
`)),u.join(`
|
|
`)}function oN(e,t,n,a){let r=Mt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,u=n==="complex64"?ed(e):e;if(o>1)for(let l=0;l<r/s;l++){let d=l*s;for(let p=0;p<s;p++)i[p]=Math.max(i[p],Qu(u[d+p],0,n).length)}return i}function Qu(e,t,n){let a;return Array.isArray(e)?a=`${parseFloat(e[0].toFixed(Ym))} + ${parseFloat(e[1].toFixed(Ym))}j`:$r(e)?a=`'${e}'`:n==="bool"?a=kb(e):a=parseFloat(e.toFixed(Ym)).toString(),Mu(a,t)}function kb(e){return e===0?"false":"true"}function Oc(e,t,n,a,r,s=!0){let i=n==="complex64"?2:1,o=t[0],u=t.length;if(u===0){if(n==="complex64"){let f=ed(e);return[Qu(f[0],0,n)]}return n==="bool"?[kb(e[0])]:[e[0].toString()]}if(u===1){if(o>wb){let g=Ju*i,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((o-Ju)*i,o*i));return n==="complex64"&&(y=ed(y),A=ed(A)),["["+y.map((x,v)=>Qu(x,r[v],n)).join(", ")+", ..., "+A.map((x,v)=>Qu(x,r[o-Ju+v],n)).join(", ")+"]"]}let f=n==="complex64"?ed(e):Array.from(e);return["["+f.map((g,y)=>Qu(g,r[y],n)).join(", ")+"]"]}let l=t.slice(1),d=a.slice(1),p=a[0]*i,c=[];if(o>wb){for(let f=0;f<Ju;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),l,n,d,r,!1))}c.push("...");for(let f=o-Ju;f<o;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),l,n,d,r,f===o-1))}}else for(let f=0;f<o;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),l,n,d,r,f===o-1))}let h=u===2?",":"";c[0]="["+c[0]+h;for(let f=1;f<c.length-1;f++)c[f]=" "+c[f]+h;let m=`,
|
|
`;for(let f=2;f<u;f++)m+=`
|
|
`;return c[c.length-1]=" "+c[c.length-1]+"]"+(s?"":m),c}function ed(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Lt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Mt(e),n!=null){let a=n.length;D(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||sb(t,this.size),this.strides=co(e)}set(e,...t){t.length===0&&(t=[0]),D(t.length===this.rank,()=>`The number of provided coordinates (${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 a of e){if(a<0||a>=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 a=0;a<e.length-1;++a)n+=this.strides[a]*e[a];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 ja().makeTensor(this.values,this.shape,this.dtype)}},ja=null,yl=null,lN=null;function uN(e){ja=e}function dN(e){yl=e}function pN(e){lN=e}var Be=class{constructor(e,t,n,a){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Mt(e),this.strides=co(e),this.dataId=n,this.id=a,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return yl.buffer(this.shape,this.dtype,e)}bufferSync(){return yl.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ho(this.shape,e,this.dtype==="complex64")}arraySync(){return ho(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=ja().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Dc(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=ja().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Dc(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 ja().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ja().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return yl.print(this,e)}clone(){return this.throwIfDisposed(),yl.clone(this)}toString(e=!1){let t=this.dataSync();return iN(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),yl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),ja().makeVariable(this,e,t,n)}};Object.defineProperty(Be,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function ee(){return qm("Tensor",()=>Be)}ee();var td=class extends Be{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);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`);ja().disposeTensor(this),this.dataId=e.dataId,ja().incRef(this,null)}dispose(){ja().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(td,Symbol.hasInstance,{value:e=>e instanceof Be&&e.assign!=null&&e.assign instanceof Function});var Sa={};Fe(Sa,{assertTypesMatch:()=>Ib,getTensorsInContainer:()=>a1,isTensorInList:()=>hN,makeTypesMatch:()=>It});var Jm;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Jm||(Jm={}));var Qm;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Qm||(Qm={}));var e1;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(e1||(e1={}));var t1;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(t1||(t1={}));var n1;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(n1||(n1={}));var cN={float32:t1,int32:Qm,bool:e1,complex64:n1};function Aa(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return cN[e][t]}function zc(e){return Aa(e,"int32")}function It(e,t){if(e.dtype===t.dtype)return[e,t];let n=Aa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Ib(e,t){D(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function hN(e,t){return t.some(n=>n.id===e.id)}function a1(e){let t=[],n=new Set;return Sb(e,t,n),t}function Sb(e,t,n){if(e==null)return;if(e instanceof Be){t.push(e);return}if(!fN(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),Sb(s,t,n))}}function fN(e){return Array.isArray(e)||typeof e=="object"}function r1(e){return e.kernelName!=null}var Nb=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},nd=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Nb}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new tN(this.backendInstance),!0}setupRegisteredKernels(){gl(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){gl(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Eu)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)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),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return nd.nextTensorId++}nextVariableId(){return nd.nextVariableId++}clone(e){let t=P.runKernel(zs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},u={dtype:i};return P.runKernel(ks,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(Mc(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=r1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(r1(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Mc(h,this.backendName);D(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:N}=v;return this.makeTensorFromDataId(b,w,N)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,f,g),g}}let{inputs:l,attrs:d}=e,p=r1(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(u,l,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Xm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>s[l]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&$r(e[0])&&(r=e.map(o=>Yu(o)));let s=a.write(r,t,n),i=new Be(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),u=lb(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Be(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new td(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*Vm(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 td||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*Vm(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Xm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=u=>(u=u.map((l,d)=>{if(l==null){let p=n[d],c=qp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return l}),a(u.length>1?u:u[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=a1(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=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===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(D(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));D(r instanceof Be,()=>"The result y returned by f() must be a tensor.");let s=rN(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?mN(r.shape):n,sN(i,s,u=>this.tidy(u),gN);let o=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let l of u.saved)l.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return D(Dr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{D(t.every(i=>i instanceof Be),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),D(n.value instanceof Be,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),D(Dr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let u=n.gradFunc(i,o),l=Array.isArray(u)?u:[u];D(l.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(...)."),D(l.every(p=>p instanceof Be),()=>"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 d={};return l.forEach((p,c)=>{d[c]=()=>p}),d};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Zu(),n=await this.backend.time(e);return n.wallMs=Zu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Nb;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}};nd.nextTensorId=0;nd.nextVariableId=0;function mN(e){let t=jm(Mt(e),"float32");return P.makeTensor(t,e,"float32")}function Tb(){let e=fb();if(e._tfengine==null){let t=new hb(e);e._tfengine=new nd(t)}return BS(e._tfengine.ENV),uN(()=>e._tfengine),e._tfengine}var P=Tb();function gN(e,t){let n={a:e,b:t};return P.runKernel(Or,n)}var ad={};Fe(ad,{isBrowser:()=>Cb,isMobile:()=>AN});function yN(){return typeof navigator!="undefined"&&navigator!=null}function AN(e){if(e||yN()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function Cb(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Na=te();Na.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.")});Na.registerFlag("IS_BROWSER",()=>Cb());Na.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Na.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Na.registerFlag("PROD",()=>!1);Na.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Na.getBool("DEBUG"));Na.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Na.registerFlag("IS_TEST",()=>!1);Na.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Na.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Ua(e,t){let n=e;if(on(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||on(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&te().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Eb(e,a,[]),a}function Eb(e,t,n){if(n=n||[],!Array.isArray(e)&&!on(e)){D(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}D(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),D(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)Eb(e[r],a,n.concat(r))}function Rb(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function M(e,t,n,a="numeric"){if(e instanceof Be)return Rb(a,e.dtype,t,n),e;let r=Hp(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),Rb(a,r,t,n),e==null||!on(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Ua(e,r);!on(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?$c(e,r):As(e,[],!0);return P.makeTensor(i,s,r)}function rd(e,t,n,a="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((r,s)=>M(r,`${t}[${s}]`,n,a))}var Mb="__op";function L(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],a=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+Mb;let r=(...s)=>{P.startScope(n);try{let i=a(...s);return Hm(i)&&console.error("Cannot return a Promise inside of tidy."),P.endScope(i),i}catch(i){throw P.endScope(null),i}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function xN(e,t){let n=M(e,"real","complex"),a=M(t,"imag","complex");cn(n.shape,a.shape,`real and imag shapes, ${n.shape} and ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return P.runKernel(Yp,r)}var Wr=L({complex_:xN});function Br(e,t,n,a){if(a==null&&(a=Hp(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!on(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Um(t);let r=Mt(t),s=Mt(n);D(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],u=i===n.length-1?o!==Mt(t.slice(i)):!0;D(n[i]===t[i]||!u,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!on(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?$c(e,a):As(e,[],!0),P.makeTensor(e,t,a)}function ln(e,t,n){let a=Ua(e,n);return Br(e,t,a,n)}var s1={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},_c=4;async function bN(e,t){let n=[],a=[],r=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<r.length;++i){let o=r[i],u=Array.isArray(e)?e[i].tensor:e[o];if(u.dtype!=="float32"&&u.dtype!=="int32"&&u.dtype!=="bool"&&u.dtype!=="string"&&u.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${u.dtype}`);let l={name:o,shape:u.shape,dtype:u.dtype};if(u.dtype==="string"){let d=new Promise(async p=>{let c=await u.bytes(),h=c.reduce((g,y)=>g+y.length,0)+_c*c.length,m=new Uint8Array(h),f=0;for(let g=0;g<c.length;g++){let y=c[g],A=new Uint8Array(new Uint32Array([y.length]).buffer);m.set(A,f),f+=_c,m.set(y,f),f+=y.length}p(m)});a.push(d)}else a.push(u.data());t!=null&&(l.group=t),n.push(l)}let s=await Promise.all(a);return{data:vN(s),specs:n}}function Fb(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,u=s.shape,l=Mt(u),d;if("quantization"in s){let p=s.quantization;if(p.dtype==="uint8"||p.dtype==="uint16"){if(!("min"in p&&"scale"in p))throw new Error(`Weight ${s.name} with quantization ${p.dtype} doesn't have corresponding metadata min and scale.`)}else if(p.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let c=s1[p.dtype],h=e.slice(r,r+l*c),m=p.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(o==="float32")if(p.dtype==="uint8"||p.dtype==="uint16"){d=new Float32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];d[f]=g*p.scale+p.min}}else if(p.dtype==="float16")a===void 0&&(a=TN()),d=a(m);else throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`);else if(o==="int32"){if(p.dtype!=="uint8"&&p.dtype!=="uint16")throw new Error(`Unsupported quantization type ${p.dtype} for weight type int32.`);d=new Int32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];d[f]=Math.round(g*p.scale+p.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=l*c}else if(o==="string"){let p=Mt(s.shape);d=[];for(let c=0;c<p;c++){let h=new Uint32Array(e.slice(r,r+_c))[0];r+=_c;let m=new Uint8Array(e.slice(r,r+h));d.push(m),r+=h}}else{let p=s1[o],c=e.slice(r,r+l*p);if(o==="float32")d=new Float32Array(c);else if(o==="int32")d=new Int32Array(c);else if(o==="bool")d=new Uint8Array(c);else if(o==="complex64"){d=new Float32Array(c);let h=new Float32Array(d.length/2),m=new Float32Array(d.length/2);for(let y=0;y<h.length;y++)h[y]=d[y*2],m[y]=d[y*2+1];let f=ln(h,u,"float32"),g=ln(m,u,"float32");n[i]=Wr(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=l*p}o!=="complex64"&&(n[i]=ln(d,u,o))}return n}function vN(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(s=>{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let a=new Uint8Array(t),r=0;return n.forEach(s=>{a.set(new Uint8Array(s.buffer),r),r+=s.byteLength}),a.buffer}var i1=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function $b(e){return i1?Buffer.byteLength(e):new Blob([e]).size}function wN(e){if(i1)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let a=0,r=t.length;a<r;a++)n+=String.fromCharCode(t[a]);return btoa(n)}function kN(e){if(i1){let a=Buffer.from(e,"base64");return a.buffer.slice(a.byteOffset,a.byteOffset+a.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let a=0;a<t.length;++a)n.set([t.charCodeAt(a)],a);return n.buffer}function o1(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),a=0;return e.forEach(r=>{n.set(new Uint8Array(r),a),a+=r.byteLength}),n.buffer}function Db(e){let t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);let n=e.split(t);return n[n.length-1]}function sd(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:$b(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:$b(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function IN(){let e=n=>{let a=n<<13,r=0;for(;(a&8388608)==0;)r-=8388608,a<<=1;return a&=~8388608,r+=947912704,a|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 SN(){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 NN(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function TN(){let e=IN(),t=SN(),n=NN();return a=>{let r=new ArrayBuffer(4*a.length),s=new Uint32Array(r);for(let i=0;i<a.length;i++){let o=a[i],u=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=u}return new Float32Array(r)}}var Et=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Et.instance==null&&(Et.instance=new Et),Et.instance}static registerSaveRouter(e){Et.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Et.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Et.getHandlers(e,"save")}static getLoadHandlers(e,t){return Et.getHandlers(e,"load",t)}static getHandlers(e,t,n){let a=[];return(t==="load"?Et.getInstance().loadRouters:Et.getInstance().saveRouters).forEach(r=>{let s=r(e,n);s!==null&&a.push(s)}),a}},CN=e=>Et.registerSaveRouter(e),EN=e=>Et.registerLoadRouter(e),RN=e=>Et.getSaveHandlers(e),MN=(e,t)=>Et.getLoadHandlers(e,t),l1="tensorflowjs",u1=1,xi="models_store",Vr="model_info_store";function Ob(){if(!te().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 d1(e){let t=e.result;t.createObjectStore(xi,{keyPath:"modelPath"}),t.createObjectStore(Vr,{keyPath:"modelPath"})}var bi=class{constructor(e){if(this.indexedDB=Ob(),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,a)=>{let r=this.indexedDB.open(l1,u1);r.onupgradeneeded=()=>d1(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(xi,"readonly"),o=i.objectStore(xi).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=u=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=sd(t),o=s.transaction(Vr,"readwrite"),u=o.objectStore(Vr),l=u.put({modelPath:this.modelPath,modelArtifactsInfo:i}),d;l.onsuccess=()=>{d=s.transaction(xi,"readwrite");let p=d.objectStore(xi).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});p.onsuccess=()=>n({modelArtifactsInfo:i}),p.onerror=c=>{u=o.objectStore(Vr);let h=u.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(p.error)),h.onerror=m=>(s.close(),a(p.error))}},l.onerror=p=>(s.close(),a(l.error)),o.oncomplete=()=>{d==null?s.close():d.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};bi.URL_SCHEME="indexeddb://";var zb=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bi.URL_SCHEME)?FN(e.slice(bi.URL_SCHEME.length)):null;Et.registerSaveRouter(zb);Et.registerLoadRouter(zb);function FN(e){return new bi(e)}function $N(e){return e.startsWith(bi.URL_SCHEME)?e.slice(bi.URL_SCHEME.length):e}var DN=class{constructor(){this.indexedDB=Ob()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(l1,u1);n.onupgradeneeded=()=>d1(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(Vr,"readonly"),s=r.objectStore(Vr).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=$N(e),new Promise((t,n)=>{let a=this.indexedDB.open(l1,u1);a.onupgradeneeded=()=>d1(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(Vr,"readwrite"),i=s.objectStore(Vr),o=i.get(e),u;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let l=i.delete(e),d=()=>{u=r.transaction(xi,"readwrite");let p=u.objectStore(xi).delete(e);p.onsuccess=()=>t(o.result.modelArtifactsInfo),p.onerror=c=>n(o.error)};l.onsuccess=d,l.onerror=p=>(d(),r.close(),n(o.error))}},o.onerror=l=>(r.close(),n(o.error)),s.oncomplete=()=>{u==null?r.close():u.oncomplete=()=>r.close()}},a.onerror=r=>n(a.error)})}},hr="/",Al="tensorflowjs_models",_b="info",ON="model_topology",zN="weight_specs",_N="weight_data",PN="model_metadata";function Pb(e){return{info:[Al,e,_b].join(hr),topology:[Al,e,ON].join(hr),weightSpecs:[Al,e,zN].join(hr),weightData:[Al,e,_N].join(hr),modelMetadata:[Al,e,PN].join(hr)}}function LN(e){let t=e.split(hr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(hr)}function WN(e){return e.startsWith(vi.URL_SCHEME)?e.slice(vi.URL_SCHEME.length):e}var vi=class{constructor(e){if(!te().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Pb(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=sd(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,wN(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${a.modelTopologyBytes}, weightSpecsBytes=${a.weightSpecsBytes}, weightDataBytes=${a.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let a=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(a==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=a;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let i=JSON.parse(r);t.format=i.format,t.generatedBy=i.generatedBy,t.convertedBy=i.convertedBy,i.signature!=null&&(t.signature=i.signature),i.userDefinedMetadata!=null&&(t.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(t.modelInitializer=i.modelInitializer)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=kN(s),t}};vi.URL_SCHEME="localstorage://";var Lb=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(vi.URL_SCHEME)?BN(e.slice(vi.URL_SCHEME.length)):null;Et.registerSaveRouter(Lb);Et.registerLoadRouter(Lb);function BN(e){return new vi(e)}var VN=class{constructor(){D(te().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),D(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Al+hr,n=hr+_b;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=LN(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=WN(e);let t=Pb(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},xl="://",ia=class{constructor(){this.managers={}}static getInstance(){return ia.instance==null&&(ia.instance=new ia),ia.instance}static registerManager(e,t){D(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(xl)&&(e=e.slice(0,e.indexOf(xl))),D(e.length>0,()=>"scheme must not be an empty string.");let n=ia.getInstance();D(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 Pc(e){if(e.indexOf(xl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ia.getSchemes().join(",")}`);return{scheme:e.split(xl)[0],path:e.split(xl)[1]}}async function Wb(e,t,n=!1){D(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=Et.getLoadHandlers(e);D(a.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),D(a.length<2,()=>`Copying failed because more than one (${a.length}) load handlers for source URL ${e}.`);let r=a[0],s=Et.getSaveHandlers(t);D(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),D(s.length<2,()=>`Copying failed because more than one (${a.length}) save handlers for destination URL ${t}.`);let i=s[0],o=Pc(e).scheme,u=Pc(e).path,l=o===Pc(e).scheme,d=await r.load();n&&l&&await ia.getManager(o).removeModel(u);let p=await i.save(d);return n&&!l&&await ia.getManager(o).removeModel(u),p.modelArtifactsInfo}async function jN(){let e=ia.getSchemes(),t={};for(let n of e){let a=await ia.getManager(n).listModels();for(let r in a){let s=n+xl+r;t[s]=a[r]}}return t}async function UN(e){let t=Pc(e);return ia.getManager(t.scheme).removeModel(t.path)}async function HN(e,t){return Wb(e,t,!1)}async function GN(e,t){return Wb(e,t,!0)}var qN=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(te().get("IS_BROWSER")){te().setPlatform("browser",new qN);try{ia.registerManager(vi.URL_SCHEME,new VN)}catch(e){}try{ia.registerManager(bi.URL_SCHEME,new DN)}catch(e){}}var XN={importFetch:()=>ZI()},p1,KN=class{constructor(){this.util=po("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return te().global.fetch!=null?te().global.fetch(e,t):(p1==null&&(p1=XN.importFetch()),p1(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)}};te().get("IS_NODE")&&te().setPlatform("node",new KN);function Ve(e,t="float32",n){return t=t||"float32",Um(e),new Lt(e,t,n)}function ZN(e,t){let n=M(e,"x","cast");if(!ob(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 a={x:n},r={dtype:t};return P.runKernel(ks,a,r)}var ge=L({cast_:ZN});function YN(e){let t={x:M(e,"x","clone","string_or_numeric")};return P.runKernel(zs,t)}var Ha=L({clone_:YN});function Bb(e,t=!1){console.log(e.toString(t))}Tb();var JN={buffer:Ve,cast:ge,clone:Ha,print:Bb};dN(JN);var En={};Fe(En,{browserFiles:()=>sT,browserHTTPRequest:()=>dT,concatenateArrayBuffers:()=>o1,copyModel:()=>HN,decodeWeights:()=>Fb,encodeWeights:()=>bN,fromMemory:()=>cT,getLoadHandlers:()=>MN,getModelArtifactsInfoForJSON:()=>sd,getSaveHandlers:()=>RN,http:()=>f1,isHTTPScheme:()=>h1,listModels:()=>jN,loadWeights:()=>iT,moveModel:()=>GN,registerLoadRouter:()=>EN,registerSaveRouter:()=>CN,removeModel:()=>UN,weightsLoaderFactory:()=>Hb,withSaveHandler:()=>hT});var QN="model",eT=".json",tT=".weights.bin";function Vb(e){return new Promise(t=>setTimeout(t)).then(e)}var bl=class{constructor(e){if(!te().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(bl.URL_SCHEME)&&(e=e.slice(bl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=QN),this.modelTopologyFileName=e+eT,this.weightDataFileName=e+tT}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}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer);let r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=r,await Vb(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await Vb(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:sd(e)}}}};bl.URL_SCHEME="downloads://";var nT=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,a)=>{let r=new FileReader;r.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){a(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let u=i.weightsManifest;if(u==null){a(new Error(`weightManifest field is missing from file ${e.name}`));return}let l;try{l=this.checkManifestAndWeightFiles(u,t)}catch(h){a(h);return}let d=[],p=[],c=[];u.forEach(h=>{h.paths.forEach(m=>{p.push(m),c.push(null)}),d.push(...h.weights)}),u.forEach(h=>{h.paths.forEach(m=>{let f=new FileReader;f.onload=g=>{let y=g.target.result,A=p.indexOf(m);if(c[A]=y,c.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:d,weightData:o1(c),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},f.onerror=g=>a(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(l[m])})})},r.onerror=s=>a(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>Db(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=Db(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),a.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);r[i]=t[a.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return r}},aT=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bl.URL_SCHEME)?rT(e.slice(bl.URL_SCHEME.length)):null;Et.registerSaveRouter(aT);function rT(e="model"){return new bl(e)}function sT(e){return new nT(e)}function jb(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=u=>(u.then(l=>{let d=n+ ++r/e.length*(a-n);return t(d),l}),u);function i(u){D(u!=null&&Array.isArray(u)&&u.length>0,()=>"promises must be a none empty array")}function o(u,l){D(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${u}`),D(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${l}`),D(l>=u,()=>`startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`)}return Promise.all(e.map(s))}async function Ub(e,t){t==null&&(t={});let n=t.fetchFunc==null?te().platform.fetch:t.fetchFunc,a=e.map(l=>n(l,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await jb(a,t.onProgress,r,s)).map(l=>l.arrayBuffer()),o=.5,u=1;return t.onProgress==null?await Promise.all(i):await jb(i,t.onProgress,o,u)}async function iT(e,t="",n,a){return Hb(r=>Ub(r,{requestInit:a}))(e,t,n)}function Hb(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,A=s1[y]*Mt(g.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:g,groupOffset:f,sizeBytes:A})};a!=null?a.forEach((v,b)=>{v===g.name&&(x(),i[b]=!0)}):x(),o.push(g.name),f+=A})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
|
|
Manifest JSON has weights with names: ${o.join(", ")}.`)}let u=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),l=[];u.forEach(h=>{t[h].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;l.push(f)})});let d=await e(l),p={},c=0;return u.forEach(h=>{let m=t[h].paths.length,f=0;for(let x=0;x<m;x++)f+=d[c+x].byteLength;let g=new ArrayBuffer(f),y=new Uint8Array(g),A=0;for(let x=0;x<m;x++){let v=new Uint8Array(d[c+x]);y.set(v,A),A+=v.byteLength}s[h].forEach(x=>{let v=g.slice(x.groupOffset,x.groupOffset+x.sizeBytes),b=Fb(v,[x.manifestEntry]);for(let w in b)p[w]=b[w]}),c+=m}),p}}var oT="application/octet-stream",lT="application/json",c1=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?(D(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=te().platform.fetch,D(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&D(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}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(a)],{type:lT}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:oT}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:sd(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(h){let m=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?m+=" 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.":m+=" Please make sure the server is serving valid JSON for this request.",new Error(m)}let n=t.modelTopology,a=t.weightsManifest,r=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,u=t.userDefinedMetadata;if(n==null&&a==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let l,d;a!=null&&([l,d]=await this.loadWeights(a));let p={modelTopology:n,weightSpecs:l,weightData:d,generatedBy:r,convertedBy:s,format:i};o!=null&&(p.signature=o),u!=null&&(p.userDefinedMetadata=u);let c=t.modelInitializer;return c&&(p.modelInitializer=c),p}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,a]=uT(t),r=this.weightPathPrefix||n,s=[];for(let l of e)s.push(...l.weights);let i=[],o=[];for(let l of e)for(let d of l.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(d)):i.push(r+d+a);this.weightUrlConverter&&i.push(...await Promise.all(o));let u=await Ub(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,o1(u)]}};c1.URL_SCHEME_REGEX=/^https?:\/\//;function uT(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function h1(e){return e.match(c1.URL_SCHEME_REGEX)!=null}var Gb=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>h1(a)):n=h1(e),n)return f1(e,t)}return null};Et.registerSaveRouter(Gb);Et.registerLoadRouter(Gb);function f1(e,t){return new c1(e,t)}function dT(e,t){return f1(e,t)}var m1=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},pT=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function cT(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new m1(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 m1({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 m1({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function hT(e){return new pT(e)}var qb={};Fe(qb,{confusionMatrix:()=>AT});function fT(e,t,n=!1,a=!1){let r=M(e,"a","matMul"),s=M(t,"b","matMul");[r,s]=It(r,s);let i={a:r,b:s},o={transposeA:n,transposeB:a};return P.runKernel(ws,i,o)}var je=L({matMul_:fT});function mT(e,t,n=1,a=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let r={indices:M(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:a};return P.runKernel(qs,r,s)}var vl=L({oneHot_:mT});function gT(e,t){let n=M(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),D(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{D(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let a={x:n},r={perm:t};return P.runKernel(ci,a,r)}var Qe=L({transpose_:gT});function yT(e,t,n){let a=M(e,"labels","confusionMatrix"),r=M(t,"predictions","confusionMatrix");D(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),D(a.rank===1,()=>`Expected the rank of labels to be 1, but got ${a.rank}`),D(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),D(a.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${a.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),D(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=vl(ge(a,"int32"),n),i=vl(ge(r,"int32"),n),o=Qe(s),u=je(o,i);return ge(u,"int32")}var AT=L({confusionMatrix_:yT}),oa={};Fe(oa,{fromPixels:()=>ST,fromPixelsAsync:()=>kT,toPixels:()=>IT});function Lc(e,t,n){if(ys(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=Ua(e,n);if(a.length!==3&&a.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Br(e,t,a,n)}var wl;function Xb(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,a=!1,r=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)a=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)s=!0;else if(e.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)o=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(r){let c=2;if(r&&e.readyState<c)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(Mc(Rc,P.backendName)!=null){let c={pixels:e},h={numChannels:t};return P.runKernel(Rc,c,h)}let[u,l]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;i?d=e.getContext("2d").getImageData(0,0,u,l).data:a||n?d=e.data:(s||r||o)&&(wl==null&&(wl=document.createElement("canvas").getContext("2d")),wl.canvas.width=u,wl.canvas.height=l,wl.drawImage(e,0,0,u,l),d=wl.getImageData(0,0,u,l).data);let p;if(t===4)p=new Int32Array(d);else{let c=u*l;p=new Int32Array(c*t);for(let h=0;h<c;h++)for(let m=0;m<t;++m)p[h*t+m]=d[h*4+m]}return Lc(p,[l,u,t],"int32")}function xT(e){return e!=null&&e.data instanceof Uint8Array}function bT(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function vT(e){return e!=null&&e.width!==0&&e.height!==0}function wT(e){return bT()&&!(e instanceof ImageBitmap)&&vT(e)&&!xT(e)}async function kT(e,t=3){let n=null;if(te().getBool("WRAP_TO_IMAGEBITMAP")&&wT(e)){let a;try{a=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){a=null}a!=null&&a.width===e.width&&a.height===e.height?n=a:n=e}else n=e;return Xb(n,t)}async function IT(e,t){let n=M(e,"img","toPixels");if(!(e instanceof Be)){let l=n;n=ge(l,"int32"),l.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[a,r]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,u=new Uint8ClampedArray(r*a*4);for(let l=0;l<a*r;++l){let d=[0,0,0,255];for(let c=0;c<s;c++){let h=i[l*s+c];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}.`);s===1?(d[0]=h*o,d[1]=h*o,d[2]=h*o):d[c]=h*o}let p=l*4;u[p+0]=Math.round(d[0]),u[p+1]=Math.round(d[1]),u[p+2]=Math.round(d[2]),u[p+3]=Math.round(d[3])}if(t!=null){t.width=r,t.height=a;let l=t.getContext("2d"),d=new ImageData(u,r,a);l.putImageData(d,0,0)}return n!==e&&n.dispose(),u}var ST=L({fromPixels_:Xb}),g1={};Fe(g1,{prepareAndValidate:()=>Kb});function Kb(e,t){let n=e.shape.length,a=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(a<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${a}.`);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[a-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[a-1]} vs. ${n}`);if(Mt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,s=r[r.length-1],i=1;for(let p=0;p<r.length-1;++p)i*=r[p];let o=e.shape,u=r.slice();u.pop();let l=1;for(let p=s;p<n;++p)l*=o[p],u.push(o[p]);let d=[...co(e.shape).map(p=>p/l),1].slice(0,s);return[u,i,l,d]}var y1={};Fe(y1,{calculateShapes:()=>Zb,validateInput:()=>x1,validateUpdateShape:()=>A1});function A1(e,t,n){let a=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${a}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(s+` update.rank < ${r}. `);if(e.length<a+(n.rank-r))throw new Error(s+` Output shape length < ${a+(n.rank-r)}`);if(n.rank!==r+e.length-a)throw new Error(s+` update.rank != ${r+e.length-a}`);for(let i=0;i<r;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-r;++i)if(n.shape[i+r]!==e[i+a])throw new Error(s+` updates.shape[${i+r}] (${n.shape[i+r]}) != shape[${i+r}] (${e[i+r]})`)}function x1(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: ${e.shape}`)}A1(n,t,e)}function Zb(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let p=r;p<s;++p)i*=n[p];let o=r<1?1:r,u=Mt(t.shape)/o,l=[...co(n.slice(0,r)),1],d=Mt(n);return{sliceRank:r,numUpdates:u,sliceSize:i,strides:l,outputSize:d}}var fn={};Fe(fn,{assertParamsValid:()=>NT,computeFlatOffset:()=>CT,computeOutShape:()=>Yb,getNormalizedAxes:()=>t3,isSliceContinous:()=>TT,maskToAxes:()=>Wc,parseSliceParams:()=>o3,sliceInfo:()=>ET,startForAxis:()=>s3,startIndicesWithElidedDims:()=>n3,stopForAxis:()=>i3,stopIndicesWithElidedDims:()=>a3,stridesForAxis:()=>r3,stridesWithElidedDims:()=>Jb});function NT(e,t,n){let a=e.shape.length;D(a===t.length,()=>`Error in slice${a}D: Length of begin ${t} must match the rank of the array (${a}).`),D(a===n.length,()=>`Error in slice${a}D: Length of size ${n} must match the rank of the array (${a}).`);for(let r=0;r<a;++r)D(t[r]+n[r]<=e.shape[r],()=>`Error in slice${a}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function Wc(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function Yb(e,t,n){let a=[];for(let r=0;r<e.length;r++)a[r]=Math.ceil((t[r]-e[r])/n[r]);return a}function Jb(e,t,n,a){let r=[...e];for(let s=r.length;s<a.length;s++)r.push(1);for(let s=0;s<n;s++)s===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function Qb(e,t,n){return n<=e?n:n-(t-1)}function e3(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function t3(e,t,n,a,r,s,i,o,u){let l=e.length,d=new Array(l),p=new Array(l),c=new Array(l);if(t.length&&n>0){let h=t[0],m=n+1;d=n3(i,h,m,a,e),p=a3(o,h,m,r,e),c=Jb(s,h,m,e)}else for(let h=0;h<l;h++)d[h]=s3(i,a,s,e,h,u),p[h]=i3(o,r,s,e,h,u),c[h]=r3(s,h,u);return{begin:d,end:p,strides:c}}function n3(e,t,n,a,r){let s=[...r],i=e3(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let u=Qb(t,n,o),l=a[u];e&1<<u&&(l=0),s[o]=l}return s}function a3(e,t,n,a,r){let s=[...r],i=e3(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let u=Qb(t,n,o),l=a[u];e&1<<u&&(l=Number.MAX_SAFE_INTEGER),s[o]=l}for(let o=0;o<s.length;o++){let u=r[o];s[o]<0&&(s[o]+=u),s[o]=Ru(0,s[o],r[o])}return s}function r3(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}function s3(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let u=a[r];return i<0&&(i+=u),i=Ru(0,i,u-1),i}function i3(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let u=a[r];return i<0&&(i+=u),o>0?i=Ru(0,i,u):i=Ru(-1,i,u-1),i}function TT(e,t,n){let a=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){a=r;break}for(let r=a+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function CT(e,t){let n=e.length>0?e[e.length-1]:1;for(let a=0;a<e.length-1;a++)n+=e[a]*t[a];return n}function o3(e,t,n){let a,r=e.shape.length;typeof t=="number"?a=[t,...new Array(r-1).fill(0)]:t.length<r?a=t.concat(new Array(r-t.length).fill(0)):a=t.slice(),a.forEach(i=>{D(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(r).fill(-1):typeof n=="number"?s=[n,...new Array(r-1).fill(-1)]:n.length<r?s=n.concat(new Array(r-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(D(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-a[o])),[a,s]}function ET(e,t,n,a,r,s,i,o,u){let l=t.slice(),d=n.slice(),p=a;a==null&&(p=new Array(l.length));let c=Wc(i);if(c.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&u!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-l.length,m=Wc(o),f=e.slice();m.forEach(w=>{l[w]=0,d[w]=1,f.splice(w,0,1)});let{begin:g,end:y,strides:A}=t3(f,c,h,l,d,p,r,s,i);l=g,d=y,p=A;let x=Wc(u);x.forEach(w=>{d[w]=l[w]+1,p[w]=1});let v=Yb(l,d,p),b=v.filter((w,N)=>x.indexOf(N)===-1);return{nonStrided:p.every(w=>w===1),$begin:l,$end:d,$strides:p,size:v,newShape:f,outShape:b}}var re={};Fe(re,{Serializable:()=>l3,SerializationMap:()=>wi,registerClass:()=>jr});var l3=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},wi=class{constructor(){this.classNameMap={}}static getMap(){return wi.instance==null&&(wi.instance=new wi),wi.instance}static register(e){wi.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function jr(e){D(e.className!=null,()=>"Class being registered does not have the static className property defined."),D(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),D(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),wi.register(e)}var u3={};Fe(u3,{TEST_EPSILON_FLOAT16:()=>d3,encodeStrings:()=>p3,expectArrayBuffersEqual:()=>zT,expectArraysClose:()=>MT,expectArraysEqual:()=>$T,expectNumbersClose:()=>DT,expectPromiseToFail:()=>FT,expectValuesInRange:()=>OT,testEpsilon:()=>b1});var RT=.001,d3=.1;function MT(e,t,n){return n==null&&(n=b1()),v1(e,t,(a,r)=>w1(a,r,n))}function b1(){return P.backend.floatPrecision()===32?RT:d3}function v1(e,t,n){let a=!0;if((on(e)||on(t))&&(a=!1),on(e)&&on(t)&&(a=!0),a){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Ua(e),o=Ua(t);if(!cr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=on(e)?e:As(e),s=on(t)?t:As(t);if(r.length!==s.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${s.length}.
|
|
Actual: ${r}.
|
|
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],u=s[i];if(!n(o,u))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${u}.
|
|
Actual: ${r}.
|
|
Expected: ${s}.`)}}function FT(e,t){e().then(()=>t.fail(),()=>t())}function $T(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return $r(e)||$r(e[0])||$r(t)||$r(t[0])?v1(e,n,(a,r)=>a==r):v1(e,t,(a,r)=>w1(a,r,0))}function DT(e,t,n){if(n==null&&(n=b1()),!w1(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function w1(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function OT(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function zT(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function p3(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?p3(n):e[t]=Yu(n)}return e}var _T="3.7.0";function PT(){te().set("PROD",!0)}function LT(){te().set("DEBUG",!0)}function WT(){te().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function k1(e){te().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}pN(k1);function BT(){P.disposeVariables()}function fr(){return P}function Bc(){return P.memory()}function VT(e){return P.profile(e)}function V(e,t){return P.tidy(e,t)}function he(e){a1(e).forEach(t=>t.dispose())}function Kt(e){return P.keep(e)}function jT(e){return P.time(e)}function UT(e){return P.setBackend(e)}function HT(){return P.ready()}function GT(){return P.backendName}function qT(e){P.removeBackend(e)}function I1(e){return P.findBackend(e)}function XT(e){return P.findBackendFactory(e)}function kl(e,t,n=1){return P.registerBackend(e,t,n)}function c3(){return P.backend}function KT(e,t){te().setPlatform(e,t)}function ZT(e,t){let n=M(e,"a","add"),a=M(t,"b","add");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(Or,r)}var ie=L({add_:ZT});function YT(e,t){let n=M(e,"a","floorDiv"),a=M(t,"b","floorDiv");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel($s,r)}var Vc=L({floorDiv_:YT});function JT(e,t){let n=M(e,"a","div"),a=M(t,"b","div");if([n,a]=It(n,a),n.dtype==="int32"&&a.dtype==="int32")return Vc(n,a);let r={a:n,b:a},s={};return P.runKernel(Rs,r,s)}var me=L({div_:JT});function QT(e,t){let n=M(e,"a","mul"),a=M(t,"b","mul");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(Gs,r)}var B=L({mul_:QT});function eC(e){let t=M(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return P.runKernel(Ou,n)}else{let n={x:t};return P.runKernel(fo,n)}}var Wt=L({abs_:eC});function tC(e){let t={x:M(e,"x","acos")};return P.runKernel(mo,t)}var S1=L({acos_:tC});function nC(e){let t={x:M(e,"x","acosh")};return P.runKernel(go,t)}var N1=L({acosh_:nC});function aC(e){D(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),D(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,s)=>M(r,`tensors${s}`,"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 a=t;return P.runKernel(xs,a)}var jc=L({addN_:aC});function rC(e,t=null,n=!1){let a={x:M(e,"x","all","bool")},r={axis:t,keepDims:n};return P.runKernel(yo,a,r)}var Uc=L({all_:rC});function sC(e,t=null,n=!1){let a={x:M(e,"x","any","bool")},r={axis:t,keepDims:n};return P.runKernel(Ao,a,r)}var id=L({any_:sC});function iC(e,t=0){let n={x:M(e,"x","argMax")},a={axis:t};return P.runKernel(bs,n,a)}var ki=L({argMax_:iC});function oC(e,t=0){let n={x:M(e,"x","argMin")},a={axis:t};return P.runKernel(Fu,n,a)}var T1=L({argMin_:oC});function lC(e){let t={x:M(e,"x","asin")};return P.runKernel(xo,t)}var C1=L({asin_:lC});function uC(e){let t={x:M(e,"x","asinh")};return P.runKernel(bo,t)}var E1=L({asinh_:uC});function dC(e){let t={x:M(e,"x","atan")};return P.runKernel(vo,t)}var R1=L({atan_:dC});function pC(e,t){let n=M(e,"a","atan2"),a=M(t,"b","atan2");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(ko,r)}var M1=L({atan2_:pC});function cC(e){let t={x:M(e,"x","atanh")};return P.runKernel(wo,t)}var F1=L({atanh_:cC});function hC(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],u=m3(r);return od(e,o,n,s,a,null,null,u)}function h3(e,t,n,a,r,s,i="channelsLast"){let[o,u]=Hc(t),l;if(i==="channelsLast")l=[o,u,e[3],e[3]];else if(i==="channelsFirst")l=[o,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return od(e,l,n,a,r,s,!1,i)}function fC(e,t,n,a,r,s,i="NDHWC"){let[o,u,l]=D1(t),d,p;if(i==="NDHWC")p="channelsLast",d=[o,u,l,e[4],e[4]];else if(i==="NCDHW")p="channelsFirst",d=[o,u,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return f3(e,d,n,a,r,!1,p,s)}function od(e,t,n,a,r,s,i=!1,o="channelsLast"){let[u,l,d,p]=[-1,-1,-1,-1];if(o==="channelsLast")[u,l,d,p]=e;else if(o==="channelsFirst")[u,p,l,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[c,h,,m]=t,[f,g]=Hc(n),[y,A]=Hc(a),x=Il(c,y),v=Il(h,A),{padInfo:b,outHeight:w,outWidth:N}=yC(r,l,d,f,g,x,v,s,o),C=i?m*p:m,E;return o==="channelsFirst"?E=[u,C,w,N]:o==="channelsLast"&&(E=[u,w,N,C]),{batchSize:u,dataFormat:o,inHeight:l,inWidth:d,inChannels:p,outHeight:w,outWidth:N,outChannels:C,padInfo:b,strideHeight:f,strideWidth:g,filterHeight:c,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:A,inShape:e,outShape:E,filterShape:t}}function f3(e,t,n,a,r,s=!1,i="channelsLast",o){let[u,l,d,p,c]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[u,l,d,p,c]=e;else if(i==="channelsFirst")[u,c,l,d,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,m,f,,g]=t,[y,A,x]=D1(n),[v,b,w]=D1(a),N=Il(h,v),C=Il(m,b),E=Il(f,w),{padInfo:_,outDepth:$,outHeight:S,outWidth:z}=AC(r,l,d,p,y,A,x,N,C,E,o),O=s?g*c:g,W;return i==="channelsFirst"?W=[u,O,$,S,z]:i==="channelsLast"&&(W=[u,$,S,z,O]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:d,inWidth:p,inChannels:c,outDepth:$,outHeight:S,outWidth:z,outChannels:O,padInfo:_,strideDepth:y,strideHeight:A,strideWidth:x,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:N,effectiveFilterHeight:C,effectiveFilterWidth:E,dilationDepth:v,dilationHeight:b,dilationWidth:w,inShape:e,outShape:W,filterShape:t}}function mC(e,t,n,a,r){a==null&&(a=$1(e,t,n));let s=e[0],i=e[1],o=Ii((s-t+2*a)/n+1,r),u=Ii((i-t+2*a)/n+1,r);return[o,u]}function gC(e,t,n,a,r,s){r==null&&(r=$1(e,t,a));let i=e[0],o=e[1],u=e[2],l=Ii((i-t+2*r)/a+1,s),d=Ii((o-t+2*r)/a+1,s),p=Ii((u-t+2*r)/a+1,s);return[l,d,p,n]}function $1(e,t,n,a=1){let r=Il(t,a);return Math.floor((e[0]*(n-1)-n+r)/2)}function Hc(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function D1(e){return typeof e=="number"?[e,e,e]:e}function Il(e,t){return t<=1?e:e+(e-1)*(t-1)}function yC(e,t,n,a,r,s,i,o,u){let l,d,p;if(typeof e=="number"){l={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let c=mC([t,n],s,a,e,o);d=c[0],p=c[1]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/r);let c=Math.max(0,(d-1)*a+s-t),h=Math.max(0,(p-1)*r+i-n),m=Math.floor(c/2),f=c-m,g=Math.floor(h/2),y=h-g;l={top:m,bottom:f,left:g,right:y,type:"SAME"}}else if(e==="valid")l={top:0,bottom:0,left:0,right:0,type:"VALID"},d=Math.ceil((t-s+1)/a),p=Math.ceil((n-i+1)/r);else if(typeof e=="object"){let c=u==="channelsLast"?e[1][0]:e[2][0],h=u==="channelsLast"?e[1][1]:e[2][1],m=u==="channelsLast"?e[2][0]:e[3][0],f=u==="channelsLast"?e[2][1]:e[3][1];l={top:c,bottom:h,left:m,right:f,type:c===0&&h===0&&m===0&&f===0?"VALID":"EXPLICIT"},d=Ii((t-s+c+h)/a+1,o),p=Ii((n-i+m+f)/r+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:l,outHeight:d,outWidth:p}}function AC(e,t,n,a,r,s,i,o,u,l,d){let p,c,h,m;if(typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let f=gC([t,n,a,1],o,1,r,e,d);c=f[0],h=f[1],m=f[2]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/s),m=Math.ceil(a/i);let f=(c-1)*r+o-t,g=(h-1)*s+u-n,y=(m-1)*i+l-a,A=Math.floor(f/2),x=f-A,v=Math.floor(g/2),b=g-v,w=Math.floor(y/2),N=y-w;p={top:v,bottom:b,left:w,right:N,front:A,back:x,type:"SAME"}}else if(e==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},c=Math.ceil((t-o+1)/r),h=Math.ceil((n-u+1)/s),m=Math.ceil((a-l+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:c,outHeight:h,outWidth:m}}function Ii(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 Ur(e){let[t,n,a]=Hc(e);return t===1&&n===1&&a===1}function Ga(e,t){return Ur(e)||Ur(t)}function m3(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function xC(e,t){let n={x:M(e,"x","reshape","string_or_numeric")},a={shape:t};return P.runKernel(el,n,a)}var q=L({reshape_:xC});function bC(e,t,n,a,r){let s=M(e,"x","avgPool","float32"),i=1;D(Ga(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,u=!1;s.rank===3&&(u=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),D(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),r!=null&&D(qt(a),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let l={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(vs,l,d);return p=ge(p,s.dtype),u?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ld=L({avgPool_:bC});function vC(e,t,n,a,r,s="NDHWC"){let i=M(e,"x","avgPool3d","float32"),o=i,u=!1;i.rank===4&&(u=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),D(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),D(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),r!=null&&D(qt(a),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let l={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=P.runKernel($u,l,d);return p=ge(p,o.dtype),u?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var O1=L({avgPool3d_:vC});function wC(e,t=0){D(e.length>=1,()=>"Pass at least one tensor to concat");let n=rd(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
|
|
with dtype ${s.dtype}. `)}),n.length===1)return Ha(n[0]);let a=n,r={axis:t};return P.runKernel(Io,a,r)}var lt=L({concat_:wC});function kC(e){let t={x:M(e,"x","sigmoid")};return P.runKernel(ri,t)}var Rn=L({sigmoid_:kC});function IC(e,t,n){let a=M(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return P.runKernel(rl,r,s)}var Re=L({slice_:IC});function SC(e){let t={x:M(e,"x","tanh")};return P.runKernel(pi,t)}var Si=L({tanh_:SC});function NC(e,t,n,a,r,s){let i=M(e,"forgetBias","basicLSTMCell"),o=M(t,"lstmKernel","basicLSTMCell"),u=M(n,"lstmBias","basicLSTMCell"),l=M(a,"data","basicLSTMCell"),d=M(r,"c","basicLSTMCell"),p=M(s,"h","basicLSTMCell"),c=lt([l,p],1),h=je(c,o),m=ie(h,u),f=m.shape[0],g=m.shape[1]/4,y=[f,g],A=Re(m,[0,0],y),x=Re(m,[0,g],y),v=Re(m,[0,g*2],y),b=Re(m,[0,g*3],y),w=ie(B(Rn(A),Si(x)),B(d,Rn(ie(i,v)))),N=B(Si(w),Rn(b));return[w,N]}var TC=L({basicLSTMCell_:NC});function CC(e,t,n){let a=M(e,"x","batchToSpaceND"),r=t.reduce((o,u)=>o*u);D(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),D(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),D(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return P.runKernel(Du,s,i)}var ud=L({batchToSpaceND_:CC});function EC(e){let t;return e.rank===0||e.rank===1?t=q(e,[1,1,1,e.size]):e.rank===2?t=q(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function RC(e,t,n,a,r,s){s==null&&(s=.001);let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),u=M(n,"variance","batchNorm"),l;r!=null&&(l=M(r,"scale","batchNorm"));let d;a!=null&&(d=M(a,"offset","batchNorm")),D(o.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),D(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),D(l==null||o.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:EC(i),scale:l,offset:d,mean:o,variance:u},c={varianceEpsilon:s},h=P.runKernel(Ds,p,c);return q(h,i.shape)}var Ni=L({batchNorm_:RC});function MC(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),u=M(n,"variance","batchNorm"),l;r!=null&&(l=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),D(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),D(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),D(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&D(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),d!=null&&D(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),Ni(i,o,u,d,l,s)}var g3=L({batchNorm2d_:MC});function FC(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),u=M(n,"variance","batchNorm"),l;r!=null&&(l=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),D(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),D(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),D(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&D(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),d!=null&&D(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),Ni(i,o,u,d,l,s)}var y3=L({batchNorm3d_:FC});function $C(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),u=M(n,"variance","batchNorm"),l;r!=null&&(l=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),D(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),D(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),D(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&D(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),d!=null&&D(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Ni(i,o,u,d,l,s)}var A3=L({batchNorm4d_:$C});function DC(e,t,n){let a=M(e,"x","bincount"),r=M(t,"weights","bincount");D(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),D(n>=0,()=>`size must be non-negative, but got ${n}.`),D(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return P.runKernel(Zp,s,i)}var z1=L({bincount_:DC});function OC(e,t){let n=M(e,"broadcastTo","x"),a=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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=q(n,u)}let r=n.shape,s=Array.from(t);for(let u=t.length-1;u>=0;u--)if(r[u]===t[u])s[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((u,l)=>u>1?l:-1).filter(u=>u>=0).length===0)return Ha(n);let i={x:n},o={reps:s};return P.runKernel(_r,i,o)}var Sl=L({broadcastTo_:OC});function zC(e){let t={x:M(e,"x","ceil")};return P.runKernel(Is,t)}var _1=L({ceil_:zC});function _C(e,t,n){let a=M(e,"x","clipByValue");D(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return P.runKernel(zr,r,s)}var Mn=L({clipByValue_:_C});function PC(e){return lt(e,0)}var x3=L({concat1d_:PC});function LC(e,t){return lt(e,t)}var Nl=L({concat2d_:LC});function WC(e,t){return lt(e,t)}var b3=L({concat3d_:WC});function BC(e,t){return lt(e,t)}var v3=L({concat4d_:BC});function VC(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","conv2d"),u=M(t,"filter","conv2d"),l=o,d=!1;o.rank===3&&(d=!0,l=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),D(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),D(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),i!=null&&D(qt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?l.shape[3]:l.shape[1];D(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),D(Ga(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let c={x:l,filter:u},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=P.runKernel(Ss,c,h);return d?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var mr=L({conv2d_:VC});function jC(e,t,n,a,r="NWC",s=1,i){let o=M(e,"x","conv1d"),u=M(t,"filter","conv1d"),l=o,d=!1;o.rank===2&&(d=!0,l=q(o,[1,o.shape[0],o.shape[1]])),D(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),D(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),i!=null&&D(qt(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),D(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),D(Ga(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),D(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=q(u,[1,u.shape[0],u.shape[1],u.shape[2]]),c=q(l,[l.shape[0],1,l.shape[1],l.shape[2]]),h=mr(c,p,[1,n],a,"NHWC",[1,s],i);return d?q(h,[h.shape[2],h.shape[3]]):q(h,[h.shape[0],h.shape[2],h.shape[3]])}var Gc=L({conv1d_:jC});function UC(e,t,n,a,r,s="NHWC",i){D(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,u=t,l=!1;t.rank===3&&(l=!0,u=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),D(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),D(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),D(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let d=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?u.shape[3]:u.shape[1];D(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),D(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),i!=null&&D(qt(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let c={dy:u,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=P.runKernel(Ns,c,h);return l?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var P1=L({conv2DBackpropInput_:UC});function HC(e,t,n,a,r,s){let i=M(e,"x","conv2dTranspose"),o=M(t,"filter","conv2dTranspose");return P1(n,i,o,a,r,"NHWC",s)}var qc=L({conv2dTranspose_:HC});function GC(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=M(e,"x","conv3d"),o=M(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),D(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),D(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),D(u.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`),D(Ga(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),D(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let d={x:u,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},c=P.runKernel(zu,d,p);return l?q(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var L1=L({conv3d_:GC});function qC(e,t,n,a,r){D(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let u=s[4],l=i.shape[4];D(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),D(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),D(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),D(u===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`),D(l===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);let d={dy:i,filter:n},p={pad:r,strides:a,inputShape:s},c=P.runKernel(ec,d,p);return o?q(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var w3=L({conv3DBackpropInput_:qC});function XC(e,t,n,a,r){let s=M(e,"x","conv3dTranspose"),i=M(t,"filter","conv3dTranspose");return w3(n,s,i,a,r)}var k3=L({conv3dTranspose_:XC});function KC(e){let t={x:M(e,"x","cos")};return P.runKernel(Ts,t)}var dd=L({cos_:KC});function ZC(e){let t={x:M(e,"x","cosh")};return P.runKernel(So,t)}var Xc=L({cosh_:ZC});function YC(e,t=0,n=!1,a=!1){let r={x:M(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return P.runKernel(Cs,r,s)}var Kc=L({cumsum_:YC});function JC(e,t,n,a=!1){let r=M(e,"x","denseBincount"),s=M(t,"weights","denseBincount");D(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),D(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),D(n>=0,()=>`size must be non-negative, but got ${n}.`),D(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return P.runKernel(tc,i,o)}var I3=L({denseBincount_:JC});function QC(e,t,n="NHWC"){let a=M(e,"x","depthToSpace"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];D(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${r} and ${t} for depthToSpace with input shape
|
|
${a.shape}`),D(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${a.shape}`),D(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},u={blockSize:t,dataFormat:n};return P.runKernel(To,o,u)}var W1=L({depthToSpace_:QC});function eE(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","depthwiseConv2d"),u=M(t,"filter","depthwiseConv2d"),l=o,d=!1;o.rank===3&&(d=!0,l=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),D(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),D(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`),D(l.shape[3]===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),i!=null&&D(qt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:l,filter:u},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=P.runKernel(Es,p,c);return d?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Tl=L({depthwiseConv2d_:eE});function tE(e){let t={x:M(e,"x","diag")};return P.runKernel(rc,t)}var nE=L({diag_:tE});function aE(e,t,n,a,r=[1,1],s="NHWC"){let i=M(e,"x","dilation2d"),o=M(t,"filter","dilation2d");D(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),D(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),D(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0);let d={x:u,filter:o},p={strides:n,pad:a,dilations:r},c=P.runKernel(_u,d,p);return l?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var B1=L({dilation2d_:aE});function rE(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function Bt(e,t){let n=[];for(let a=0;a<t.length;a++){let r=e[e.length-a-1],s=t.length-a-1,i=t[s];(r==null||r===1&&i>1)&&n.unshift(s)}return n}function mt(e,t){let n=[],a=Math.max(e.length,t.length);for(let r=0;r<a;r++){let s=e[e.length-r-1];s==null&&(s=1);let i=t[t.length-r-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function sE(e,t){let n=M(e,"a","equal","string_or_numeric"),a=M(t,"b","equal","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ro,r)}var Hr=L({equal_:sE});function iE(e,t,n){let a=M(t,"a","where"),r=M(n,"b","where"),s=M(e,"condition","where","bool"),i=mt(mt(s.shape,a.shape),r.shape),o=Sl(s,i),u=Sl(a,i),l=Sl(r,i),d={condition:o,t:u,e:l};return P.runKernel(nl,d)}var un=L({where_:iE});function oE(e){let t={x:M(e,"x","zerosLike")};return P.runKernel(hl,t)}var Ge=L({zerosLike_:oE});function lE(e,t){let n=M(e,"a","div"),a=M(t,"b","div");[n,a]=It(n,a);let r=me(n,a),s=Ge(r),i=Hr(a,s);return un(i,s,r)}var V1=L({divNoNan_:lE});function uE(e,t){let n=M(e,"t1","dot"),a=M(t,"t2","dot");D((n.rank===1||n.rank===2)&&(a.rank===1||a.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(D(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=q(n,[1,-1]),o=q(a,[-1,1]),u=je(i,o);return q(u,[])}else if(n.rank===1&&a.rank===2){let i=q(n,[1,-1]),o=q(a,[a.shape[0],a.shape[1]]),u=je(i,o);return q(u,[u.size])}else if(n.rank===2&&a.rank===1){let i=q(a,[-1,1]),o=je(n,i);return q(o,[o.size])}else{let i=q(a,[a.shape[0],a.shape[1]]);return je(n,i)}}var S3=L({dot_:uE});function dE(e,...t){let n=t.map((r,s)=>M(r,`tensors${s}`,"einsum")),a={equation:e};return P.runKernel(oc,n,a)}var N3=L({einsum_:dE});function pE(e){let t={x:M(e,"x","elu")};return P.runKernel(Co,t)}var Cl=L({elu_:pE});function cE(e){let t=M(e,"x","erf");D(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ge(t,"float32"));let n={x:t};return P.runKernel(Eo,n)}var j1=L({erf_:cE});function hE(e){let t={x:M(e,"x","exp")};return P.runKernel(Ms,t)}var la=L({exp_:hE});function fE(e,t=0){let n=M(e,"x","expandDims","string_or_numeric");D(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return P.runKernel(Mo,a,r)}var mn=L({expandDims_:fE});function mE(e){let t={x:M(e,"x","expm1")};return P.runKernel(Fo,t)}var U1=L({expm1_:mE});function gE(e,t){let n=M(e,"x","tile","string_or_numeric");D(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return P.runKernel(_r,a,r)}var Gr=L({tile_:gE});function yE(e,t,n,a="float32"){t==null&&(t=e);let r=Ve([e,t],a),s=e<=t?e:t;for(let o=0;o<s;++o)r.set(1,o,o);let i=q(r.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return Gr(mn(i,0),[n[0],1,1]);if(n.length===2)return Gr(mn(mn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Gr(mn(mn(mn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var H1=L({eye_:yE});function El(e,t,n){let a={shape:e,value:t,dtype:n};return P.runKernel(Pu,{},a)}function AE(e){let t={x:M(e,"x","floor")};return P.runKernel(Fs,t)}var Rl=L({floor_:AE});function xE(e,t,n=0,a=0){let r=M(e,"x","gather"),s=M(t,"indices","gather","int32"),i={x:r,indices:s},o={axis:n,batchDims:a};return P.runKernel(Do,i,o)}var Ti=L({gather_:xE});function bE(e,t){let n=M(e,"a","greater","string_or_numeric"),a=M(t,"b","greater","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(zo,r)}var Wn=L({greater_:bE});function vE(e,t){let n=M(e,"a","greaterEqual","string_or_numeric"),a=M(t,"b","greaterEqual","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Os,r)}var qr=L({greaterEqual_:vE});function wE(e){let t={input:M(e,"input","imag")};return P.runKernel(pc,t)}var Zc=L({imag_:wE});function kE(e){let t={x:M(e,"x","isFinite")};return P.runKernel(_o,t)}var T3=L({isFinite_:kE});function IE(e){let t={x:M(e,"x","isInf")};return P.runKernel(Po,t)}var C3=L({isInf_:IE});function SE(e){let t={x:M(e,"x","isNaN")};return P.runKernel(Lo,t)}var G1=L({isNaN_:SE});function NE(e,t=.2){let n={x:M(e,"x","leakyRelu")},a={alpha:t};return P.runKernel(_s,n,a)}var pd=L({leakyRelu_:NE});function TE(e,t){let n=M(e,"a","less","string_or_numeric"),a=M(t,"b","less","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Wo,r)}var Yc=L({less_:TE});function CE(e,t){let n=M(e,"a","lessEqual","string_or_numeric"),a=M(t,"b","lessEqual","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Bo,r)}var Xr=L({lessEqual_:CE});function E3(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let a={start:e,stop:t,num:n};return P.runKernel(cc,{},a)}function EE(e,t=5,n=1,a=1,r=.5){let s=M(e,"x","localResponseNormalization");D(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
|
|
rank ${s.rank}.`),D(qt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=q(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let u={x:i},l={depthRadius:t,bias:n,alpha:a,beta:r},d=P.runKernel(Bu,u,l);return o?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var q1=L({localResponseNormalization_:EE});function RE(e){let t={x:M(e,"x","log")};return P.runKernel(Ps,t)}var Bn=L({log_:RE});function ME(e){let t={x:M(e,"x","log1p")};return P.runKernel(Vo,t)}var Jc=L({log1p_:ME});function FE(e){return D(Dr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=M(t,"x","tf.grad","string_or_numeric"),r=n!=null?M(n,"dy","tf.grad"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(a),[a],r);return r!=null&&cn(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Qc(i),i[0]})}}function $E(e){return D(Dr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{D(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=rd(t,"args","tf.grads","string_or_numeric"),r=n!=null?M(n,"dy","tf.grads"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(...a),a,r);return r!=null&&cn(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Qc(i),i})}}function DE(e){return D(Dr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{D(t instanceof Be,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),D(n==null||n instanceof Be,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=P.gradients(()=>e(t),[t],n);return Qc(a),{grad:a[0],value:r}}}function OE(e){return D(Dr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{D(Array.isArray(t)&&t.every(r=>r instanceof Be),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),D(n==null||n instanceof Be,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=P.gradients(()=>e(...t),t,n);return n!=null&&cn(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Qc(a.grads),a}}function R3(e,t){D(Dr(e),()=>"The f passed in variableGrads(f) must be a function"),D(t==null||Array.isArray(t)&&t.every(l=>l instanceof td),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let l in P.registeredVariables)t.push(P.registeredVariables[l])}let a=n?t.filter(l=>!l.trainable):null,r=t.length;t=t.filter(l=>l.trainable),D(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=P.gradients(e,t,null,s);D(o.some(l=>l!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),D(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let u={};return t.forEach((l,d)=>{o[d]!=null&&(u[l.name]=o[d])}),a!=null&&a.forEach(l=>u[l.name]=null),{value:i,grads:u}}function qa(e){return P.customGrad(e)}function Qc(e){if(e.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
|
|
the f you passed encloses all operations that lead from x to y.`)}function zE(e){let t={x:M(e,"x","neg")};return P.runKernel(Ho,t)}var St=L({neg_:zE});function _E(e){let t={x:M(e,"x","softplus")};return P.runKernel(ol,t)}var Ci=L({softplus_:_E});function PE(e){let t=M(e,"x","logSigmoid");return qa(n=>({value:St(Ci(St(n))),gradFunc:a=>B(a,Rn(St(n)))}))(t)}var M3=L({logSigmoid_:PE});function LE(e,t=null,n=!1){let a={x:M(e,"x","max")},r={reductionIndices:t,keepDims:n};return P.runKernel(Ls,a,r)}var Vn=L({max_:LE});function WE(e,t){let n=M(e,"a","sub"),a=M(t,"b","sub");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(ui,r)}var ye=L({sub_:WE});function BE(e,t=null,n=!1){let a=M(e,"x","sum");a.dtype==="bool"&&(a=ge(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return P.runKernel(ii,r,s)}var Se=L({sum_:BE});function VE(e,t=-1){let n=M(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 qa((a,r)=>{let s=!0,i=Vn(a,t,!0),o=ye(a,i),u=ye(ge(o,"float32"),Bn(Se(la(o),t,s)));return r([u]),{value:u,gradFunc:(l,d)=>{let[p]=d,c=!0,h=la(p);return ye(l,B(Se(l,t,c),h))}}})(n)}var eh=L({logSoftmax_:VE});function X1(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function F3(e,t,n){let a=e.length+t.length,r=[],s=0,i=0;for(let o=0;o<a;o++)n.indexOf(o)===-1?r.push(e[s++]):r.push(t[i++]);return r}function $3(e,t){let n=[],a=e.length;for(let s=0;s<a;s++)t.indexOf(s)===-1&&n.push(e[s]);let r=t.map(s=>e[s]);return[n,r]}function Ei(e,t){let n=t.map(a=>1);return F3(e,n,t)}function jE(e,t,n){D(X1(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function D3(e,t){if(X1(e,t))return null;let n=[];for(let a=0;a<t;++a)e.indexOf(a)===-1&&n.push(a);return e.forEach(a=>n.push(a)),n}function K1(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function UE(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function HE(e,t=null,n=!1){let a=M(e,"x","logSumExp"),r=ya(t,a.shape),s=Vn(a,r,!0),i=ye(a,s),o=la(i),u=Se(o,r),l=Bn(u),d=ie(q(s,l.shape),l);if(n){let p=Ei(d.shape,r);return q(d,p)}return d}var Z1=L({logSumExp_:HE});function GE(e,t){let n=M(e,"a","logicalAnd","bool"),a=M(t,"b","logicalAnd","bool");mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(jo,r)}var xa=L({logicalAnd_:GE});function qE(e){let t={x:M(e,"x","logicalNot","bool")};return P.runKernel(Lu,t)}var cd=L({logicalNot_:qE});function XE(e,t){let n=M(e,"a","logicalOr","bool"),a=M(t,"b","logicalOr","bool");mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Wu,r)}var th=L({logicalOr_:XE});function KE(e,t){let n=M(e,"a","logicalXor","bool"),a=M(t,"b","logicalXor","bool");return mt(n.shape,a.shape),xa(th(e,t),cd(xa(e,t)))}var O3=L({logicalXor_:KE});function ZE(e,t,n,a,r){let s=M(e,"x","maxPool"),i=1,o=s,u=!1;s.rank===3&&(u=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),D(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),D(Ga(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&D(qt(a),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let l={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(Bs,l,d);return u?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var hd=L({maxPool_:ZE});function YE(e,t=[1,1,1],n,a,r,s="NDHWC"){let i=M(e,"x","maxPool3d"),o=i,u=!1;i.rank===4&&(u=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),D(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),D(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),r!=null&&D(qt(a),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let l={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=P.runKernel(Vu,l,d);return u?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Y1=L({maxPool3d_:YE});function JE(e,t,n,a,r=!1){let s={x:M(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=P.runKernel(gc,s,i);return{result:o[0],indexes:o[1]}}var z3=L({maxPoolWithArgmax_:JE});function QE(e,t){let n=M(e,"a","maximum"),a=M(t,"b","maximum");[n,a]=It(n,a),n.dtype==="bool"&&(n=ge(n,"int32"),a=ge(a,"int32")),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ws,r)}var Xa=L({maximum_:QE});function eR(e,t=null,n=!1){let a={x:M(e,"x","mean")},r={axis:t,keepDims:n};return P.runKernel(Vs,a,r)}var Nt=L({mean_:eR});function $t(e,t="float32"){if(t==="complex64"){let a=$t(e,"float32"),r=$t(e,"float32");return Wr(a,r)}let n=qp(Mt(e),t);return P.makeTensor(n,e,t)}function jn(e,t="float32"){if(t==="complex64"){let a=jn(e,"float32"),r=$t(e,"float32");return Wr(a,r)}let n=jm(Mt(e),t);return P.makeTensor(n,e,t)}function tR(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 a=M(e,"x","meshgrid",e instanceof Be?e.dtype:"float32");if(t===void 0)return[a];let r=M(t,"y","meshgrid",t instanceof Be?t.dtype:"float32"),s=Mt(a.shape),i=Mt(r.shape);return n==="xy"?(a=q(a,[1,-1]),r=q(r,[-1,1]),[je(jn([i,1],a.dtype),a),je(r,jn([1,s],r.dtype))]):(a=q(a,[-1,1]),r=q(r,[1,-1]),[je(a,jn([1,i],a.dtype)),je(jn([s,1],r.dtype),r)])}function nR(e,t=null,n=!1){let a={x:M(e,"x","min")},r={axis:t,keepDims:n};return P.runKernel(js,a,r)}var fd=L({min_:nR});function aR(e,t){let n=M(e,"a","minimum"),a=M(t,"b","minimum");[n,a]=It(n,a),n.dtype==="bool"&&(n=ge(n,"int32"),a=ge(a,"int32")),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Us,r)}var Ml=L({minimum_:aR});function rR(e,t,n){D(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let a=M(e,"x","mirrorPad");if(a.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");D(t.length===a.rank,()=>`Padding doesn't match input. Must be ${a.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let o=0;o<a.rank;o++)D(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),D(t[o][0]>=0&&t[o][0]<=a.shape[o]-r&&t[o][1]>=0&&t[o][1]<=a.shape[o]-r,()=>`Padding in dimension ${o} cannot be greater than or equal to ${a.shape[o]-r} or less than 0 for input of shape ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return P.runKernel(Hs,i,s)}var J1=L({mirrorPad_:rR});function sR(e,t){let n=M(e,"a","mod"),a=M(t,"b","mod");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(Uo,r)}var Q1=L({mod_:sR});function iR(e){let t=M(e,"x","square"),n={};return P.runKernel("Square",{x:t},n)}var ot=L({square_:iR});function oR(e,t=null,n=!1){e=M(e,"x","moments");let a=ya(t,e.shape),r=Nt(e,a,n),s=r.shape;n||(s=Ei(r.shape,a));let i=ot(ye(ge(e,"float32"),q(r,s))),o=Nt(i,a,n);return{mean:r,variance:o}}var nh=L({moments_:oR});function lR(e,t,n,a){let r=M(t,"data","multiRNNCell"),s=rd(n,"c","multiRNNCell"),i=rd(a,"h","multiRNNCell"),o=r,u=[];for(let p=0;p<e.length;p++){let c=e[p](o,s[p],i[p]);u.push(c[0]),u.push(c[1]),o=c[1]}let l=[],d=[];for(let p=0;p<u.length;p+=2)l.push(u[p]),d.push(u[p+1]);return[l,d]}var uR=L({multiRNNCell_:lR});function dR(e,t,n,a=!1){let r=M(e,"logits","multinomial"),s=r.size,i=r.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?q(r,[1,-1]):r},u={numSamples:t,seed:n,normalized:a},l=P.runKernel(yc,o,u);return i===1?q(l,[l.size]):l}var _3=L({multinomial_:dR});function pR(e,t){let n=M(e,"a","notEqual","string_or_numeric"),a=M(t,"b","notEqual","string_or_numeric");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Go,r)}var Ri=L({notEqual_:pR});function cR(e){let t={x:M(e,"x","onesLike")};return P.runKernel(Zo,t)}var Un=L({onesLike_:cR});function hR(e,t){let n=M(e,"v1","outerProduct"),a=M(t,"v2","outerProduct");D(n.rank===1&&a.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${a.rank}.`);let r=q(n,[-1,1]),s=q(a,[1,-1]);return je(r,s)}var fR=L({outerProduct_:hR});function mR(e,t,n=0){let a=M(e,"x","pad");if(a.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},s={x:a};return P.runKernel(Xs,s,r)}var gr=L({pad_:mR});function gR(e,t,n=0){return D(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),gr(e,[t],n)}var yR=L({pad1d_:gR});function AR(e,t,n=0){return D(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),gr(e,t,n)}var xR=L({pad2d_:AR});function bR(e,t,n=0){return D(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."),gr(e,t,n)}var vR=L({pad3d_:bR});function wR(e,t,n=0){return D(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."),gr(e,t,n)}var kR=L({pad4d_:wR});function IR(e,t,n){let a=M(e,"x","spaceToBatchND");D(a.rank>=1+t.length,()=>`input rank ${a.rank} should be > than [blockShape] ${t.length}`),D(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),D(a.shape.reduce((i,o,u)=>u>0&&u<=t.length?i&&(o+n[u-1][0]+n[u-1][1])%t[u-1]==0:i,!0),()=>`input spatial dimensions ${a.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:a},s={blockShape:t,paddings:n};return P.runKernel(Hu,r,s)}var md=L({spaceToBatchND_:IR});function SR(e,t,n,a,r,s){r==null&&(r=[1,1]),s==null&&(s=1),a===0&&(a="valid");let i=M(e,"x","maxPool"),o=i,u=!1;i.rank===3&&(u=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]])),D(Ga(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let l=h3(o.shape,t,s,r,a),d=[l.dilationHeight,l.dilationWidth],p;a==="same"?p=TR([l.filterHeight,l.filterWidth],d):p=[[0,0],[0,0]];let c=d[0]===1&&d[1]===1,[h,m]=NR([l.inHeight,l.inWidth],d,p),f=c?a:"valid",g=c?o:md(o,d,h),y=(n==="avg"?()=>ld(g,t,s,f):()=>hd(g,t,s,f))(),A=c?y:ud(y,d,m);return u?q(A,[A.shape[1],A.shape[2],A.shape[3]]):A}function NR(e,t,n){let a=n.map(d=>d[0]),r=n.map(d=>d[1]),s=e.concat(a,r),i=t.map((d,p)=>(d-s[p]%d)%d),o=r.map((d,p)=>d+i[p]),u=t.map((d,p)=>[a[p],o[p]]),l=t.map((d,p)=>[0,i[p]]);return[u,l]}function TR(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),a=n.map(s=>Math.floor(s/2)),r=n.map((s,i)=>s-a[i]);return n.map((s,i)=>[a[i],r[i]])}var P3=L({pool_:SR});function CR(e,t){let n=M(e,"base","pow"),a=M(t,"exp","pow");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(Ks,r)}var yr=L({pow_:CR});function ER(e,t){let n=M(e,"x","prelu"),a=M(t,"alpha","prelu"),r={x:n,alpha:a};return P.runKernel(Zs,r)}var gd=L({prelu_:ER});function RR(e,t=null,n=!1){let a=M(e,"x","prod");a.dtype==="bool"&&(a=ge(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return P.runKernel(Jo,r,s)}var ah=L({prod_:RR});function MR(e,t,n){let a=Mt(e),r=null;if(n==null||n==="float32")r=new Float32Array(a);else if(n==="int32")r=new Int32Array(a);else if(n==="bool")r=new Uint8Array(a);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<a;s++)r[s]=t();return P.makeTensor(r,e,n)}var FR=L({rand_:MR}),eg=gs(S5()),tg=class{constructor(e,t,n,a,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=a,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=r||Math.random();this.random=eg.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let a=this.nextVal;return this.nextVal=NaN,a}let e,t,n=!1;for(;!n;){let a,r,s;do a=2*this.random()-1,r=2*this.random()-1,s=a*a+r*r;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*a*i,t=this.mean+this.stdDev*r*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},$R=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=eg.alea(r.toString()),this.randn=new tg(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,a,r,s;for(;;){do a=this.randn.nextValue(),s=1+this.c*a;while(s<=0);if(s*=s*s,e=a*a,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),r=this.randu(),r<t||Math.log(r)<n)break}return s=1/this.beta*this.d*s,this.alpha<1&&(s*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(s)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},DR=class{constructor(e=0,t=1,n,a){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,a==null&&(a=Math.random()),typeof a=="number"&&(a=a.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=eg.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function OR(e,t,n=1,a="float32",r){if(n==null&&(n=1),a==null&&(a="float32"),a!=="float32"&&a!=="int32")throw new Error(`Unsupported data type ${a}`);let s=new $R(t,n,a,r),i=Ve(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var zR=L({randomGamma_:OR});function _R(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error(`Unsupported data type ${a}`);let s=new tg(t,n,a,!1,r),i=Ve(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var L3=L({randomNormal_:_R});function PR(e,t=0,n=1,a="float32",r){let s=Ve(e,a),i=new DR(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Fl=L({randomUniform_:PR});function $l(e,t,n=1,a="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:a};return P.runKernel(ju,{},r)}function LR(e){let t={input:M(e,"input","real")};return P.runKernel(Ac,t)}var yd=L({real_:LR});function WR(e){let t={x:M(e,"x","reciprocal")};return P.runKernel(Qo,t)}var ng=L({reciprocal_:WR});function BR(e){let t={x:M(e,"x","relu")};return P.runKernel(Ys,t)}var Ka=L({relu_:BR});function VR(e){let t={x:M(e,"x","relu6")};return P.runKernel(Qs,t)}var rh=L({relu6_:VR});function jR(e,t){let n={x:M(e,"x","reverse")},a={dims:t};return P.runKernel(ei,n,a)}var Hn=L({reverse_:jR});function UR(e){let t=M(e,"x","reverse");return D(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Hn(t,0)}var HR=L({reverse1d_:UR});function GR(e,t){let n=M(e,"x","reverse");return D(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Hn(n,t)}var qR=L({reverse2d_:GR});function XR(e,t){let n=M(e,"x","reverse");return D(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Hn(n,t)}var KR=L({reverse3d_:XR});function ZR(e,t){let n=M(e,"x","reverse");return D(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Hn(n,t)}var YR=L({reverse4d_:ZR});function JR(e){let t={x:M(e,"x","round")};return P.runKernel(ti,t)}var sh=L({round_:JR});function QR(e){let t={x:M(e,"x","rsqrt")};return P.runKernel(ni,t)}var ih=L({rsqrt_:QR});function ke(e,t){if((on(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"&&on(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Br(e,[],[],t)}function eM(e){let t={x:M(e,"x","selu")};return P.runKernel(al,t)}var oh=L({selu_:eM});function tM(e,t,n,a,r,s=[1,1],i="NHWC"){let o=M(e,"x","separableConv2d"),u=M(t,"depthwiseFilter","separableConv2d"),l=M(n,"pointwiseFilter","separableConv2d"),d=o,p=!1;if(o.rank===3&&(p=!0,d=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");D(d.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${d.rank}.`),D(u.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`),D(l.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`),D(l.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${l.shape[0]}.`),D(l.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);let c=u.shape[2],h=u.shape[3];D(l.shape[2]===c*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${c*h}, but got ${l.shape[2]}.`);let m=Tl(d,u,a,r,i,s),f=mr(m,l,1,"valid",i);return p?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ag=L({separableConv2d_:tM});async function nM(e,t){let n=M(e,"x","setdiff1d"),a=M(t,"y","setdiff1d");D(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${a.dtype}).`),D(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),D(a.rank===1,()=>`y should be 1D tensor, but got y (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let d=0;d<r.length;d++)i.has(r[d])||o++;let u=new Lt([o],n.dtype),l=new Lt([o],"int32");for(let d=0,p=0;d<r.length;d++)i.has(r[d])||(u.values[p]=r[d],l.values[p]=d,p++);return[u.toTensor(),l.toTensor()]}var W3=nM;function aM(e){let t={x:M(e,"x","sign")};return P.runKernel(il,t)}var rg=L({sign_:aM});function rM(e){let t={x:M(e,"x","sin")};return P.runKernel(ai,t)}var lh=L({sin_:rM});function sM(e){let t={x:M(e,"x","sinh")};return P.runKernel(sl,t)}var uh=L({sinh_:sM});function iM(e,t,n){let a=M(e,"x","slice1d");return D(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),Re(a,[t],[n])}var dh=L({slice1d_:iM});function oM(e,t,n){let a=M(e,"x","slice2d");return D(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var sg=L({slice2d_:oM});function lM(e,t,n){let a=M(e,"x","slice3d");return D(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var ph=L({slice3d_:lM});function uM(e,t,n){let a=M(e,"x","slice4d");return D(a.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var Ad=L({slice4d_:uM});function dM(e,t=-1){let n=M(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 a={logits:n},r={dim:t};return P.runKernel(oi,a,r)}var xd=L({softmax_:dM});function pM(e){D(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(uc,t)}var bd=L({fft_:pM});function cM(e){D(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(dc,t)}var Dl=L({ifft_:cM});function hM(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=q(e,[n,t]);a=Dl(r)}else{let r=[n,2*(t-1)],s=q(yd(e),[n,t]),i=q(Zc(e),[n,t]),o=Hn(Re(s,[0,1],[n,t-2]),1),u=B(Hn(Re(i,[0,1],[n,t-2]),1),ke(-1)),l=lt([s,o],1),d=lt([i,u],1),p=q(Wr(l,d),[r[0],r[1]]);a=Dl(p)}if(a=yd(a),e.rank===3&&e.shape[0]!==0){let r=a,s=e.shape[0];a=q(a,[s,a.shape[0]/s,a.shape[1]]),r.dispose()}return a}var ch=L({irfft_:hM});function fM(e,t,n=0){let a={x:M(e,"x","split")},r={numOrSizeSplits:t,axis:n};return P.runKernel(ll,a,r)}var Zt=L({split_:fM});function mM(e,t){D(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&t<n){let m=e.shape.map(g=>0),f=e.shape.map(g=>g);f[e.shape.length-1]=t,r=Re(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,r=lt([e,$t(m)],e.shape.length-1),n=t}else r=e;let s=Ge(r),i=q(Wr(r,s),[a,n]),o=bd(i),u=Math.floor(n/2)+1,l=yd(o),d=Zc(o),p=Zt(l,[u,n-u],l.shape.length-1),c=Zt(d,[u,n-u],d.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=u,q(Wr(p[0],c[0]),h)}var vd=L({rfft_:mM});function gM(e){let t={x:M(e,"x","sqrt")};return P.runKernel(si,t)}var an=L({sqrt_:gM});function yM(e,t){let n=M(e,"a","squaredDifference"),a=M(t,"b","squaredDifference");[n,a]=It(n,a),mt(n.shape,a.shape);let r={a:n,b:a},s={};return P.runKernel(li,r,s)}var hh=L({squaredDifference_:yM});function AM(e,t){let n=M(e,"x","squeeze");return q(n,ab(n.shape,t).newShape)}var Vt=L({squeeze_:AM});function xM(e,t=0){let n=rd(e,"tensors","stack","string_or_numeric");D(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&D(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let a=n,r={axis:t};return P.runKernel(Yo,a,r)}var gn=L({stack_:xM});function bM(e,t=0){let n={x:M(e,"x","step")},a={alpha:t};return P.runKernel(Pr,n,a)}var Ol=L({step_:bM});function vM(e,t,n,a,r=0,s=0,i=0,o=0,u=0){let l={x:M(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:a,beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:u};return P.runKernel(ul,l,d)}var ig=L({stridedSlice_:vM});function wM(e){let t={x:M(e,"x","tan")};return P.runKernel(di,t)}var og=L({tan_:wM});function Dt(e,t){ys(e);let n=Ua(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Br(e,null,n,t)}function Ta(e,t,n){if(ys(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=Ua(e,n);if(a.length!==2&&a.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Br(e,t,a,n)}function kM(e,t,n){if(ys(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=Ua(e,n);if(a.length!==4&&a.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Br(e,t,a,n)}function IM(e,t,n){if(ys(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=Ua(e,n);if(a.length!==5&&a.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Br(e,t,a,n)}function SM(e,t,n){if(ys(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=Ua(e,n);if(a.length!==6&&a.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||a,Br(e,t,a,n)}function NM(e,t=1,n=!0){let a=M(e,"x","topk");if(a.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=a.shape[a.shape.length-1];if(t>r)throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);let s={x:a},i={k:t,sorted:n},[o,u]=P.runKernel(dl,s,i);return{values:o,indices:u}}var lg=L({topk_:NM});function TM(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new tg(t,n,a,!0,r),i=Ve(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var fh=L({truncatedNormal_:TM});function CM(e,t=0){let n=M(e,"x","unique","string_or_numeric");D(n.rank>0,()=>"The input tensor must be at least 1D");let a={x:n},r={axis:t},[s,i]=P.runKernel(Ec,a,r);return{values:s,indices:i}}var mh=L({unique_:CM});function EM(e,t,n){let a=M(e,"x","unsortedSegmentSum"),r=M(t,"segmentIds","unsortedSegmentSum","int32");D(qt(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return P.runKernel(qu,s,i)}var ug=L({unsortedSegmentSum_:EM});function RM(e,t=0){let n=M(e,"x","unstack","string_or_numeric");D(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let a={value:n},r={axis:t};return P.runKernel(cl,a,r)}var Gn=L({unstack_:RM});function B3(e,t=!0,n,a){return P.makeVariable(e,t,n,a)}function V3(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let a=Ve(e,"int32"),r=Ve([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=a.indexToLoc(n[s]),o=s*e.length;r.values.set(i,o)}return r.toTensor()}async function MM(e){let t=M(e,"condition","whereAsync","bool"),n=await t.data(),a=V3(t.shape,n);return e!==t&&t.dispose(),a}var dg=MM;async function FM(e,t,n){let a=M(e,"tensor","boolMask"),r=M(t,"mask","boolMask","bool"),s=n==null?0:n,i=r.rank,o=a.shape;D(i>0,()=>"mask cannot be scalar"),cn(o.slice(s,s+i),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let u=1;for(let f=s;f<s+i;f++)u*=o[f];let l=o.slice(0,s).concat([u],o.slice(s+i)),d=q(a,l),p=q(r,[-1]),c=await dg(p),h=Vt(c,[1]),m=Ti(d,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),d.dispose(),p.dispose(),c.dispose(),m}var $M=FM;function DM(e,t="euclidean",n=null,a=!1){e=M(e,"x","norm");let r=j3(e,t,n),s=r.shape;if(a){let i=ya(n,e.shape);s=Ei(r.shape,i)}return q(r,s)}function j3(e,t,n=null){if(e.rank===0)return Wt(e);if(e.rank!==1&&n===null)return j3(q(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Se(Wt(e),n);if(t===Infinity)return Vn(Wt(e),n);if(t===-Infinity)return fd(Wt(e),n);if(t==="euclidean"||t===2)return an(Se(yr(Wt(e),ke(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Vn(Se(Wt(e),n[0]),n[1]-1);if(t===Infinity)return Vn(Se(Wt(e),n[1]),n[0]);if(t===-Infinity)return fd(Se(Wt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return an(Se(ot(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var gh=L({norm_:DM});function OM(e,t,n,a,r=!0){let s=M(e,"v","movingAverage"),i=M(t,"x","movingAverage"),o=M(n,"decay","movingAverage");Ib(s,i),D(cr(s.shape,i.shape),()=>"Shape mismatch in v and x");let u=ke(1),l=ye(u,o),d=B(ye(i,s),l);if(r){D(a!=null,()=>"When using zeroDebias: true, step is required.");let p=M(a,"step","movingAverage");d=me(d,ye(u,yr(o,p)))}return ie(s,d)}var zM=L({movingAverage_:OM});function _M(e,t,n){let a=M(e,"indices","scatterND","int32"),r=M(t,"updates","scatterND");x1(r,a,n);let s={indices:a,updates:r},i={shape:n};return P.runKernel(tl,s,i)}var U3=L({scatterND_:_M});function PM(e,t,n,a){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,s=e.rank>1?e.shape[1]:1;if(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==a.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function LM(e,t,n,a=0){let r=M(e,"sparseIndices","sparseToDense","int32"),s=M(t,"sparseValues","sparseToDense"),i=M(a,"defaultValue","sparseToDense",s.dtype);PM(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},u={outputShape:n};return P.runKernel(Sc,o,u)}var pg=L({sparseToDense_:LM});function WM(e,t){let n=M(t,"indices","gatherND","int32"),a={params:M(e,"x","gatherND","string_or_numeric"),indices:n};return P.runKernel(Oo,a)}var H3=L({gatherND_:WM});function BM(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 a=0;a<e.shape.length;a++)t[a]==null&&e.shape[a]!=null?n.push(e.shape[a]):n.push(t[a]);return n}return t}function VM(e,t,n,a){let r=M(e,"x","dropout");if(D(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.`),D(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Be?r.clone():r;let s=BM(r,n),i=1-t,o=me(Rl(ie(Fl(s,0,1,"float32",a),i)),i);return B(r,o)}var G3=L({dropout_:VM});function q3(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function cg(e,t,n){let a=1-e%2,r=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+a-1);r[s]=t-n*Math.cos(i)}return Dt(r,"float32")}async function jM(e,t,n=1){let a=M(e,"predictions","inTopK"),r=M(t,"targets","inTopK");D(a.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${a.rank}`),D(a.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${a.rank} and targets rank ${r.rank}`),cn(a.shape.slice(0,a.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=a.shape[a.shape.length-1];D(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await a.data(),o=await r.data(),[u,l]=[i.length/s,s],d=rb("bool",u);for(let p=0;p<u;p++){let c=p*l,h=i.subarray(c,c+l),m=[];for(let f=0;f<h.length;f++)m.push({value:h[f],index:f});m.sort((f,g)=>g.value-f.value),d[p]=0;for(let f=0;f<n;f++)if(m[f].index===o[p]){d[p]=1;break}}return e!==a&&a.dispose(),t!==r&&r.dispose(),ln(d,r.shape,"bool")}var UM=jM,Kr={};Fe(Kr,{conv2d:()=>qM,depthwiseConv2d:()=>YM,matMul:()=>QM});function HM(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u=t;u.rank===3&&(u=q(t,[1,t.shape[0],t.shape[1],t.shape[2]])),D(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),D(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),D(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let l=s==="NHWC"?o.shape[3]:o.shape[1],d=s==="NHWC"?u.shape[3]:u.shape[1];D(l===n[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`),D(d===n[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${n[3]}).`),i!=null&&D(qt(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let p={x:o,dy:u},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(Jp,p,c)}var hg=L({conv2DBackpropFilter_:HM});function yh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return B(e,Ol(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Ah(e,t){let n=t,a=Bt(e.shape,t.shape);return a.length>0&&(n=Se(n,a)),q(n,e.shape)}function xh(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Ka(e);if(t==="elu")return Cl(e);if(t==="relu6")return rh(e);if(t==="prelu")return gd(e,n);if(t==="leakyrelu")return pd(e,a);if(t==="sigmoid")return Rn(e);throw new Error(`Unknown fused activation ${t}.`)}var bh=(e,t)=>!(e>0)||t==="linear";function GM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:d}){if(u=u||"linear",bh(P.state.gradientDepth,u)===!1){let b=mr(e,t,n,a,r,s,i);return o!=null&&(b=ie(b,o)),xh(b,u,l,d)}let p=M(e,"x","conv2d"),c=M(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=q(p,[1,p.shape[0],p.shape[1],p.shape[2]])),D(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),D(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),i!=null&&D(qt(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),D(h.shape[3]===c.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${c.shape[2]}.`),D(Ga(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),D(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let f=od(h.shape,c.shape,n,s,a,i),g;o!=null&&(g=M(o,"bias","fused conv2d"),[g]=It(g,p),mt(f.outShape,g.shape));let y;l!=null&&(y=M(l,"prelu weights","fused conv2d"));let A=(b,w)=>{let[N,C,E,_]=w,$=yh(b,E,u);D(Ur(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let S=P1(C.shape,$,N,n,a),z=hg(C,$,N.shape,n,a),O=[S,z];if(_!=null){let W=Ah(_,$);O.push(W)}return O},x={x:h,filter:c,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:d};return o==null?qa((b,w,N)=>{let C=P.runKernel(fi,x,v);return N([w,b,C]),m&&(C=q(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:A}})(h,c):qa((b,w,N,C)=>{let E=P.runKernel(fi,x,v);return C([w,b,E,N]),m&&(E=q(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(h,c,g)}var qM=L({fusedConv2d_:GM});function XM(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u=t;u.rank===3&&(u=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={x:o,dy:u},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return P.runKernel(nc,l,d)}var X3=L({depthwiseConv2dNativeBackpropFilter_:XM});function KM(e,t,n,a,r,s=[1,1],i){let o=t,u=!1;t.rank===3&&(u=!0,o=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={dy:o,filter:n},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},p=P.runKernel(ac,l,d);return u?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var K3=L({depthwiseConv2dNativeBackpropInput_:KM});function ZM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:d}){if(bh(P.state.gradientDepth,u)===!1){let b=Tl(e,t,n,a,r,s,i);return o!=null&&(b=ie(b,o)),xh(b,u,l,d)}let p=M(e,"x","depthwiseConv2d"),c=M(t,"filter","depthwiseConv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=q(p,[1,p.shape[0],p.shape[1],p.shape[2]])),D(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),D(c.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${c.rank}.`),D(h.shape[3]===c.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),s==null&&(s=[1,1]),D(Ga(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&D(qt(a),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${a}.`);let f=od(h.shape,c.shape,n,s,a,i,!0),g;o!=null&&(g=M(o,"bias","fused conv2d"),[g]=It(g,p),mt(f.outShape,g.shape));let y;l!=null&&(y=M(l,"prelu weights","fused depthwiseConv2d"));let A=(b,w)=>{D(Ur(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,C,E,_]=w,$=yh(b,E,u),S=K3(C.shape,$,N,n,a,s,i),z=X3(C,$,N.shape,n,a,s,i);if(_!=null){let O=Ah(g,$);return[S,z,O]}return[S,z]},x={x:h,filter:c,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:d};return o==null?qa((b,w,N)=>{let C=P.runKernel(mi,x,v);return N([w,b,C]),m&&(C=q(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:A}})(h,c):qa((b,w,N,C)=>{let E=P.runKernel(mi,x,v);return C([w,b,E,N]),m&&(E=q(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(h,c,g)}var YM=L({fusedDepthwiseConv2d_:ZM});function JM({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(bh(P.state.gradientDepth,s)===!1){let _=je(e,t,n,a);return r!=null&&(_=ie(_,r)),xh(_,s,i,o)}let u=M(e,"a","fused matMul"),l=M(t,"b","fused matMul");[u,l]=It(u,l);let d=n?u.shape[u.rank-2]:u.shape[u.rank-1],p=a?l.shape[l.rank-1]:l.shape[l.rank-2],c=n?u.shape[u.rank-1]:u.shape[u.rank-2],h=a?l.shape[l.rank-2]:l.shape[l.rank-1],m=u.shape.slice(0,-2),f=l.shape.slice(0,-2),g=Mt(m),y=Mt(f);D(u.rank>=2&&l.rank>=2&&u.rank===l.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${u.rank} and ${l.rank}.`),D(cr(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${u.shape} and ${l.shape} must match.`),D(d===p,()=>`Error in fused matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${a} must match.`);let A=u.shape.slice(0,-2).concat([c,h]),x=n?q(u,[g,d,c]):q(u,[g,c,d]),v=a?q(l,[y,h,p]):q(l,[y,p,h]),b;r!=null&&(b=M(r,"bias","fused matMul"),[b]=It(b,u),mt(A,b.shape));let w;i!=null&&(w=M(i,"prelu weights","fused matMul"));let N=(_,$)=>{let[S,z,O,W]=$,G=yh(q(_,O.shape),O,s),H,J;if(!n&&!a?(H=je(G,z,!1,!0),J=je(S,G,!0,!1)):!n&&a?(H=je(G,z,!1,!1),J=je(G,S,!0,!1)):n&&!a?(H=je(z,G,!1,!0),J=je(S,G,!1,!1)):(H=je(z,G,!0,!0),J=je(G,S,!0,!0)),r!=null){let K=Ah(W,G);return[H,J,K]}else return[H,J]},C={a:x,b:v,bias:b,preluActivationWeights:w},E={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?qa((_,$,S)=>{let z=P.runKernel(hi,C,E);return S([_,$,z]),{value:q(z,A),gradFunc:N}})(x,v):qa((_,$,S,z)=>{let O=P.runKernel(hi,C,E);return z([_,$,O,S]),{value:q(O,A),gradFunc:N}})(x,v,b)}var QM=L({fusedMatMul_:JM});function eF(e){return cg(e,.54,.46)}var tF=L({hammingWindow_:eF});function nF(e){return cg(e,.5,.5)}var Z3=L({hannWindow_:nF});function aF(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Re(e,s,t)),s+=n;if(a)for(;s<e.size;){let o=s+t-e.size,u=lt([Re(e,s,t-o),El([o],r)]);i.push(u),s+=n}return i.length===0?Ta([],[0,t]):q(lt(i),[i.length,t])}var Y3=L({frame_:aF});function rF(e,t,n,a,r=Z3){a==null&&(a=q3(t));let s=Y3(e,t,n),i=B(s,r(t));return vd(i,a)}var sF=L({stft_:rF});function iF(e,t,n,a,r="bilinear",s=0){let i=M(e,"image","cropAndResize"),o=M(t,"boxes","cropAndResize","float32"),u=M(n,"boxInd","cropAndResize","int32"),l=o.shape[0];D(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),D(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`),D(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`),D(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),D(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),D(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let d={image:i,boxes:o,boxInd:u},p={method:r,extrapolationValue:s,cropSize:a};return P.runKernel(No,d,p)}var oF=L({cropAndResize_:iF});function lF(e){let t=M(e,"image","flipLeftRight","float32");D(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return P.runKernel($o,n,{})}var uF=L({flipLeftRight_:lF});function dF(e,t,n=0,a=.5){let r=M(e,"image","rotateWithOffset","float32");D(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(fl,s,i)}var pF=L({rotateWithOffset_:dF});function zl(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),D(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),D(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),D(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),D(t.rank===1,()=>"scores must be a 1D tensor"),D(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),D(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s}}function cF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppression"),i=M(t,"scores","nonMaxSuppression"),o=zl(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let u={maxOutputSize:n,iouThreshold:a,scoreThreshold:r};return P.runKernel(qo,{boxes:s,scores:i},u)}var hF=L({nonMaxSuppression_:cF});function fF(e,t,n){let a=mF(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function mF(e,t,n){return yF(e,t,n||gF)}function gF(e,t){return e>t?1:e<t?-1:0}function yF(e,t,n){let a=0,r=e.length,s=0,i=!1;for(;a<r;){s=a+(r-a>>>1);let o=n(t,e[s]);o>0?a=s+1:(r=s,i=!o)}return i?a:-a-1}function J3(e,t,n,a,r){return fg(e,t,n,a,r,0)}function Q3(e,t,n,a,r,s){return fg(e,t,n,a,r,0,!1,s,!0)}function e7(e,t,n,a,r,s){return fg(e,t,n,a,r,s,!0)}function fg(e,t,n,a,r,s,i=!1,o=!1,u=!1){let l=[];for(let g=0;g<t.length;g++)t[g]>r&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(t7);let d=s>0?-.5/s:0,p=[],c=[];for(;p.length<n&&l.length>0;){let g=l.pop(),{score:y,boxIndex:A,suppressBeginIndex:x}=g;if(y<r)break;let v=!1;for(let b=p.length-1;b>=x;--b){let w=AF(e,A,p[b]);if(w>=a){v=!0;break}if(g.score=g.score*xF(a,d,w),g.score<=r)break}g.suppressBeginIndex=p.length,v||(g.score===y?(p.push(A),c.push(g.score)):g.score>r&&fF(l,g,t7))}let h=p.length,m=n-h;o&&m>0&&(p.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:p};return i&&(f.selectedScores=c),u&&(f.validOutputs=h),f}function AF(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),u=Math.max(a[1],a[3]),l=Math.min(r[0],r[2]),d=Math.min(r[1],r[3]),p=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(u-i),m=(p-l)*(c-d);if(h<=0||m<=0)return 0;let f=Math.max(s,l),g=Math.max(i,d),y=Math.min(o,p),A=Math.min(u,c),x=Math.max(y-f,0)*Math.max(A-g,0);return x/(h+m-x)}function xF(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function t7(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function bF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppressionAsync"),i=M(t,"scores","nonMaxSuppressionAsync"),o=zl(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],d=u[1],{selectedIndices:p}=J3(l,d,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),Dt(p,"int32")}var vF=bF;function wF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),u=zl(i,o,n,a,r,s);n=u.maxOutputSize,a=u.iouThreshold,r=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:o},d={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},p=P.runKernel(Ko,l,d);return{selectedIndices:p[0],selectedScores:p[1]}}var kF=L({nonMaxSuppressionWithScore_:wF});async function IF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),u=zl(i,o,n,a,r,s);n=u.maxOutputSize,a=u.iouThreshold,r=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),o.data()]),d=l[0],p=l[1],{selectedIndices:c,selectedScores:h}=e7(d,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Dt(c,"int32"),selectedScores:Dt(h)}}var SF=IF;function NF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),u=zl(i,o,n,a,r,null),l=u.maxOutputSize,d=u.iouThreshold,p=u.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:l,iouThreshold:d,scoreThreshold:p,padToMaxOutputSize:s},m=P.runKernel(Xo,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var TF=L({nonMaxSuppressionPadded_:NF});async function CF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),u=zl(i,o,n,a,r,null),l=u.maxOutputSize,d=u.iouThreshold,p=u.scoreThreshold,[c,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=Q3(c,h,l,d,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Dt(m,"int32"),validOutputs:ke(f,"int32")}}var EF=CF;function RF(e,t,n=!1,a=!1){let r=M(e,"images","resizeBilinear");D(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),D(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),D(a===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=q(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},u={alignCorners:n,halfPixelCenters:a,size:t},l=P.runKernel(Js,o,u);return i?q(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var n7=L({resizeBilinear_:RF});function MF(e,t,n=!1,a=!1){let r=M(e,"images","resizeNearestNeighbor");D(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),D(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),D(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),D(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=q(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},u={alignCorners:n,halfPixelCenters:a,size:t},l=P.runKernel(Uu,o,u);return i?q(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var a7=L({resizeNearestNeighbor_:MF});function FF(e,t="binary",n=!1,a=.5){let r=M(e,"image","threshold"),s=.2989,i=.587,o=.114,u=r.shape[0]*r.shape[1],l=B(Dt([a]),255),d,p,c,h;if(D(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),D(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]}.`),D(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),D(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[d,p,c]=Zt(r,[1,1,1],-1);let f=B(d,s),g=B(p,i),y=B(c,o);h=ie(ie(f,g),y)}else h=e;if(t==="otsu"){let f=z1(ge(sh(h),"int32"),ln([]),256);l=$F(f,u)}let m=n?Xr(h,l):Wn(h,l);return ge(B(m,255),"int32")}function $F(e,t){let n=Dt([-1]),a=Dt([0]),r=Dt([0]),s,i,o,u,l,d;for(let p=0;p<e.size-1;p++){s=Re(e,0,p+1),i=Re(e,p+1),l=me(Se(s),t),d=me(Se(i),t);let c=Se(B(s,$l(0,s.size)));o=me(c,Se(s));let h=El(i.shape,s.size),m=ie($l(0,i.size),h),f=B(i,m);u=me(Se(f),Se(i));let g=ye(o,u),y=ye(o,u),A=B(l,d);r=B(B(A,g),y);let x=Wn(r,a);a=un(x,r,a),n=un(x,Dt([p]),n)}return n}var DF=L({threshold_:FF});function OF(e,t,n="nearest",a="constant",r=0,s){let i=M(e,"image","transform","float32"),o=M(t,"transforms","transform","float32");D(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),D(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),D(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let u={image:i,transforms:o},l={interpolation:n,fillMode:a,fillValue:r,outputShape:s};return P.runKernel(pl,u,l)}var zF=L({transform_:OF});function _F(e,t,n){D(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),D(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=M(e,"a","bandPart");D(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=q($l(0,s,1,"int32"),[-1,1]),u=$l(0,i,1,"int32"),l=ye(o,u),d=xa(Xr(l,ke(+t,"int32")),qr(l,ke(-n,"int32"))),p=$t([s,i],a.dtype);return q(gn(Gn(q(a,[-1,s,i])).map(c=>un(d,c,p))),r)}var PF=L({bandPart_:_F});function LF(e){let t;if(Array.isArray(e)){t=!1,D(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s<e.length;++s)D(e[s].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=Zt(e,e.shape[0],0).map(r=>Vt(r,[0]));D(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r<e.length;++r)n.push(P.tidy(()=>{let s=a[r];if(r>0)for(let i=0;i<r;++i){let o=B(Se(B(n[i],s)),n[i]);s=ye(s,o)}return me(s,gh(s,"euclidean"))}));return t?gn(n,0):n}var WF=L({gramSchmidt_:LF});function BF(e,t=!1){if(D(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return r7(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((u,l)=>u*l),a=Gn(q(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(u=>{let[l,d]=r7(u,t);r.push(l),s.push(d)});let i=q(gn(r,0),e.shape),o=q(gn(s,0),e.shape);return[i,o]}}function r7(e,t=!1){return P.tidy(()=>{D(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=H1(n),s=Ha(e),i=Ta([[1]],[1,1]),o=Ha(i),u=n>=a?a:n;for(let l=0;l<u;++l){let d=s,p=o,c=r;[o,s,r]=P.tidy(()=>{let h=Re(s,[l,l],[n-l,1]),m=gh(h),f=Re(s,[l,l],[1,1]),g=un(Wn(f,0),Ta([[-1]]),Ta([[1]])),y=ye(f,B(g,m)),A=me(h,y);A.shape[0]===1?o=Ha(i):o=lt([i,Re(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=St(me(je(g,y),m)),v=Re(s,[l,0],[n-l,a]),b=B(x,o),w=Qe(o);if(l===0)s=ye(v,je(b,je(w,v)));else{let E=ye(v,je(b,je(w,v)));s=lt([Re(s,[0,0],[l,a]),E],0)}let N=Qe(b),C=Re(r,[0,l],[n,r.shape[1]-l]);if(l===0)r=ye(C,je(je(C,o),N));else{let E=ye(C,je(je(C,o),N));r=lt([Re(r,[0,0],[n,l]),E],1)}return[o,s,r]}),he([d,p,c])}return!t&&n>a&&(r=Re(r,[0,0],[n,a]),s=Re(s,[0,0],[a,a])),[r,s]})}var VF=L({qr_:BF}),yn;(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"})(yn||(yn={}));function jF(e,t,n=yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=M(t,"weights","computeWeightedLoss"));let s=r==null?a:B(a,r);if(n===yn.NONE)return s;if(n===yn.SUM)return Se(s);if(n===yn.MEAN){if(r==null)return Nt(s);{let i=a.size/r.size,o=me(Se(s),Se(r));return i>1?me(o,ke(i)):o}}if(n===yn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return me(Se(s),ke(a.size));{let i=B(r,jn(a.shape)),o=ge(Se(Ri(i,ke(0))),"float32");return me(Se(s),o)}}throw Error(`Unknown reduction: ${n}`)}var Ar=L({computeWeightedLoss_:jF});function UF(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=M(n,"weights","absoluteDifference")),cn(r.shape,s.shape,"Error in absoluteDifference: ");let o=Wt(ye(r,s));return Ar(o,i,a)}var HF=L({absoluteDifference_:UF});function GF(e,t,n,a,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","cosineDistance"),i=M(t,"predictions","cosineDistance"),o=null;a!=null&&(o=M(a,"weights","cosineDistance")),cn(s.shape,i.shape,"Error in cosineDistance: ");let u=ke(1),l=ye(u,Se(B(s,i),n,!0));return Ar(l,o,r)}var qF=L({cosineDistance_:GF});function XF(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),cn(r.shape,s.shape,"Error in hingeLoss: ");let o=ke(1);r=ye(B(ke(2),r),o);let u=Ka(ye(o,B(r,s)));return Ar(u,i,a)}var KF=L({hingeLoss_:XF});function ZF(e,t,n,a=1,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),cn(s.shape,i.shape,"Error in huberLoss: ");let u=ke(a),l=Wt(ye(i,s)),d=Ml(l,u),p=ye(l,d),c=ie(B(ke(.5),ot(d)),B(u,p));return Ar(c,o,r)}var YF=L({huberLoss_:ZF});function JF(e,t,n,a=1e-7,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),cn(s.shape,i.shape,"Error in logLoss: ");let u=ke(1),l=ke(a),d=St(B(s,Bn(ie(i,l)))),p=B(ye(u,s),Bn(ie(ye(u,i),l))),c=ye(d,p);return Ar(c,o,r)}var QF=L({logLoss_:JF});function e$(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),cn(r.shape,s.shape,"Error in meanSquaredError: ");let o=hh(r,s);return Ar(o,i,a)}var t$=L({meanSquaredError_:e$});function n$(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),a=M(t,"logits","sigmoidCrossEntropyWithLogits");cn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ka(a),s=B(a,n),i=Jc(la(St(Wt(a))));return ie(ye(r,s),i)}function a$(e,t,n,a=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),cn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let l=ke(a),d=ke(1),p=ke(.5);s=ie(B(s,ye(d,l)),B(p,l))}let u=n$(s,i);return Ar(u,o,r)}var r$=L({sigmoidCrossEntropy_:a$});function s$(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 ${n}`);return qa((a,r,s)=>{let i=Z1(r,[n],!0),o=ye(ge(r,"float32"),i);s([a,o]);let u=St(B(o,a));return{value:Se(u,[n]),gradFunc:(l,d)=>{let[p,c]=d,h=Ei(l.shape,[n]);return[B(q(l,h),ye(ge(p,"float32"),la(c))),B(q(l,h),ye(la(c),ge(p,"float32")))]}}})(e,t)}function i$(e,t,n,a=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"onehotLabels","softmaxCrossEntropy"),i=M(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","softmaxCrossEntropy")),cn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let l=ke(a),d=ke(1),p=ke(s.shape[1]);s=ie(B(s,ye(d,l)),me(l,p))}let u=s$(s,i);return Ar(u,o,r)}var o$=L({softmaxCrossEntropy_:i$});function l$(e,t,n,a){let r=M(e,"indices","sparseFillEmptyRows"),s=M(t,"values","sparseFillEmptyRows"),i=M(n,"denseShape","sparseFillEmptyRows"),o=M(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
|
|
${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let u={indices:r,values:s,denseShape:i,defaultValue:o},l=P.runKernel(vc,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var u$=L({sparseFillEmptyRows_:l$});function d$(e,t,n){let a=M(e,"inputIndices","sparseReshape"),r=M(t,"inputShape","sparseReshape"),s=M(n,"newShape","sparseReshape");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
|
|
${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=P.runKernel(wc,i);return{outputIndices:o[0],outputShape:o[1]}}var p$=L({sparseReshape_:d$});function c$(e,t,n){let a=M(e,"data","sparseSegmentMean"),r=M(t,"indices","sparseSegmentMean"),s=M(n,"segmentIds","sparseSegmentMean");if(a.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(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
|
|
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(kc,i)}var h$=L({sparseSegmentMean_:c$});function f$(e,t,n){let a=M(e,"data","sparseSegmentSum"),r=M(t,"indices","sparseSegmentSum"),s=M(n,"segmentIds","sparseSegmentSum");if(a.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(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
|
|
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(Ic,i)}var m$=L({sparseSegmentSum_:f$});function g$(e,t,n,a,r,s,i,o){let u=M(e,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=M(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:n,nGramWidths:a,leftPad:r,rightPad:s,padWidth:i,preserveShortSequences:o},p={data:u,dataSplits:l},c=P.runKernel(Nc,p,d);return{nGrams:c[0],nGramsSplits:c[1]}}var y$=L({stringNGrams_:g$});function A$(e,t,n=!0){let a=M(e,"input","stringSplit","string"),r=M(t,"delimiter","stringSplit","string");if(a.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${a.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let s={skipEmpty:n},i={input:a,delimiter:r},o=P.runKernel(Tc,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var x$=L({stringSplit_:A$});function b$(e,t){let n=M(e,"input","stringToHashBucketFast","string"),a={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return P.runKernel(Cc,r,a)}var v$=L({stringToHashBucketFast_:b$}),w$={fft:bd,ifft:Dl,rfft:vd,irfft:ch},k$={hammingWindow:tF,hannWindow:Z3,frame:Y3,stft:sF},De={flipLeftRight:uF,resizeNearestNeighbor:a7,resizeBilinear:n7,rotateWithOffset:pF,cropAndResize:oF,nonMaxSuppression:hF,nonMaxSuppressionAsync:vF,nonMaxSuppressionWithScore:kF,nonMaxSuppressionWithScoreAsync:SF,nonMaxSuppressionPadded:TF,nonMaxSuppressionPaddedAsync:EF,threshold:DF,transform:zF},s7={bandPart:PF,gramSchmidt:WF,qr:VF},I$={absoluteDifference:HF,computeWeightedLoss:Ar,cosineDistance:qF,hingeLoss:KF,huberLoss:YF,logLoss:QF,meanSquaredError:t$,sigmoidCrossEntropy:r$,softmaxCrossEntropy:o$},wd={sparseFillEmptyRows:u$,sparseReshape:p$,sparseSegmentMean:h$,sparseSegmentSum:m$},vh={stringNGrams:y$,stringSplit:x$,stringToHashBucketFast:v$},xr=class extends l3{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return he(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return R3(e,t)}dispose(){this.iterations_!=null&&he(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(xr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var wh=class extends xr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;V(()=>{let u=ie(B(i,this.rho),B(ot(s),1-this.rho)),l=B(me(an(ie(o,this.epsilon)),an(ie(i,this.epsilon))),s),d=ie(B(o,this.rho),B(ot(l),1-this.rho));i.assign(u),o.assign(d);let p=ie(B(l,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(he(this.accumulatedGrads.map(e=>e.variable)),he(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};wh.className="Adadelta";jr(wh);var kh=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:V(()=>El(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;V(()=>{let i=ie(s,ot(r));s.assign(i);let o=ie(B(me(r,an(ie(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&he(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)}};kh.className="Adagrad";jr(kh);var Ih=class extends xr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:V(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:V(()=>Ge(i).variable(o))});let u=Array.isArray(e)?e[s].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=ie(B(l,this.beta1),B(u,1-this.beta1)),c=ie(B(d,this.beta2),B(ot(u),1-this.beta2)),h=me(p,n),m=me(c,a);l.assign(p),d.assign(c);let f=ie(B(me(h,ie(an(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&he(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&he(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),V(()=>{this.accBeta1.assign(yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(yr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};Ih.className="Adam";jr(Ih);var Sh=class extends xr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=me(-this.learningRate,ie(B(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ge(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ge(i).variable(o)});let u=Array.isArray(e)?e[s].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=ie(B(l,this.beta1),B(u,1-this.beta1)),c=B(d,this.beta2),h=Wt(u),m=Xa(c,h);l.assign(p),d.assign(m);let f=ie(B(me(a,n),me(p,ie(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&he(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&he(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)}};Sh.className="Adamax";jr(Sh);var kd=class extends xr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=P.registeredVariables[t];V(()=>{let s=ie(B(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Kt(ke(-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)}};kd.className="SGD";jr(kd);var Nh=class extends kd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&V(()=>{let i,o=ie(B(this.m,r),s);this.useNesterov?i=ie(B(this.c,ie(s,B(o,this.m))),a):i=ie(B(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&he(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Nh.className="Momentum";jr(Nh);var Th=class extends xr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;V(()=>{let u=ie(B(i,this.decay),B(ot(s),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,d=ie(B(l,this.decay),B(s,1-this.decay)),p=me(B(s,this.learningRate),an(ye(u,ie(ot(d),this.epsilon)))),c=ie(B(o,this.momentum),p);i.assign(u),l.assign(d),o.assign(c);let h=ye(a,c);a.assign(h)}else{let l=ie(B(i,this.decay),B(ot(s),1-this.decay)),d=ie(B(o,this.momentum),me(B(s,this.learningRate),an(ie(l,this.epsilon))));i.assign(l),o.assign(d);let p=ye(a,d);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&he(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&he(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&he(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(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.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)}};Th.className="RMSProp";jr(Th);var Mi=class{static sgd(e){return new kd(e)}static momentum(e,t,n=!1){return new Nh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new Th(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Ih(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new wh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Sh(e,t,n,a,r)}static adagrad(e,t=.1){return new kh(e,t)}},Fi={sgd:Mi.sgd,momentum:Mi.momentum,adadelta:Mi.adadelta,adagrad:Mi.adagrad,rmsprop:Mi.rmsprop,adamax:Mi.adamax,adam:Mi.adam},S$=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Ch(){return new Promise(e=>S$(()=>e()))}var F={};Fe(F,{ERF_A1:()=>z$,ERF_A2:()=>_$,ERF_A3:()=>P$,ERF_A4:()=>L$,ERF_A5:()=>W$,ERF_P:()=>O$,PARALLELIZE_THRESHOLD:()=>mg,SELU_SCALE:()=>o7,SELU_SCALEALPHA:()=>i7,applyActivation:()=>xh,assertAndGetBroadcastShape:()=>mt,assertAxesAreInnerMostDims:()=>jE,assertParamsConsistent:()=>N$,assignToTypedArray:()=>X$,axesAreInnerMostDims:()=>X1,calculateShapes:()=>Zb,checkEinsumDimSizes:()=>eD,combineLocations:()=>F3,complexWithEvenIndex:()=>H$,complexWithOddIndex:()=>G$,computeConv2DInfo:()=>od,computeConv3DInfo:()=>f3,computeDefaultPad:()=>$1,computeDilation2DInfo:()=>hC,computeOptimalWindowSize:()=>C$,computeOutAndReduceShapes:()=>$3,computeOutShape:()=>T$,computePool2DInfo:()=>h3,computePool3DInfo:()=>fC,convertConv2DDataFormat:()=>m3,decodeEinsumEquation:()=>J$,eitherStridesOrDilationsAreOne:()=>Ga,expandShapeToKeepDim:()=>Ei,exponent:()=>Z$,exponents:()=>K$,fromStringArrayToUint8:()=>uD,fromUint8ToStringArray:()=>lD,getAxesPermutation:()=>D3,getBroadcastDims:()=>rE,getComplexWithIndex:()=>q$,getEinsumComputePath:()=>tD,getEinsumPermutation:()=>Q$,getFusedBiasGradient:()=>Ah,getFusedDyActivation:()=>yh,getImageCenter:()=>E$,getInnerMostAxes:()=>UE,getPermuted:()=>M$,getReductionAxes:()=>Bt,getReshaped:()=>R$,getReshapedPermuted:()=>F$,getSliceBeginCoords:()=>$$,getSliceSize:()=>D$,getUndoAxesPermutation:()=>K1,isIdentityPermutation:()=>nD,log:()=>V$,mergeRealAndImagArrays:()=>j$,prepareAndValidate:()=>Kb,prepareSplitSize:()=>rD,segment_util:()=>d7,shouldFuse:()=>bh,slice_util:()=>fn,splitRealAndImagArrays:()=>U$,tupleValuesAreOne:()=>Ur,upcastType:()=>Aa,validateInput:()=>x1,validateUpdateShape:()=>A1,warn:()=>B$});function N$(e,t){let n=e[0].length;e.forEach((r,s)=>{D(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),D(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)D(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function T$(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var mg=30;function C$(e){return e<=mg?e:Gp(e,Math.floor(Math.sqrt(e)))}function E$(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function R$(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function M$(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function F$(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?a?r.push(t[s-1]*e[s]):r.push(e[s]/t[s-1]):r.push(e[s]);return r}function $$(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function D$(e,t,n){let a=e.slice(0,1);for(let r=0;r<n;++r)a.push(e[r+1]-t[r][0]-t[r][1]);return a}var i7=1.7580993408473768,o7=1.0507009873554805,O$=.3275911,z$=.254829592,_$=-.284496736,P$=1.421413741,L$=-1.453152027,W$=1.061405429;function B$(...e){te().getBool("IS_TEST")||console.warn(...e)}function V$(...e){te().getBool("IS_TEST")||console.log(...e)}function j$(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function U$(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let a=0;a<e.length;a+=2)t[a/2]=e[a],n[a/2]=e[a+1];return{real:t,imag:n}}function H$(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function G$(e){let t=Math.floor(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function q$(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function X$(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function K$(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function Z$(e,t,n){let a=(n?2:-2)*Math.PI*(e/t),r=Math.cos(a),s=Math.sin(a);return{real:r,imag:s}}var gg="->",Y$=/->/g,l7=",",u7="...";function J$(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(Y$,"").length)/gg.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${gg}").`);let[a,r]=e.split(gg);D(a.indexOf(u7)===-1,()=>`The ellipsis notation ("${u7}") is not supported yet.`);let s=a.split(l7),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let o=[];for(let c=0;c<r.length;++c){let h=r[c];if(!s.some(m=>m.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);o.indexOf(h)===-1&&o.push(h)}for(let c=0;c<a.length;++c){let h=a[c];o.indexOf(h)===-1&&h!==l7&&o.push(h)}let u=new Array(s.length);for(let c=0;c<i;++c){if(new Set(s[c].split("")).size!==s[c].length)throw new Error(`Found duplicate axes in input component ${s[c]}. Support for duplicate axes in input is not implemented yet.`);u[c]=[];for(let h=0;h<s[c].length;++h)u[c].push(o.indexOf(s[c][h]))}let l=o.length,d=r.length,p=[];for(let c=d;c<l;++c)p.push(c);return{allDims:o,summedDims:p,idDims:u}}function Q$(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let a=[];for(let r=0;r<e;++r)n[r]===-1&&a.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:a}}function eD(e,t,n){let a=new Array(e);for(let r=0;r<n.length;++r){let s=n[r].shape;for(let i=0;i<t[r].length;++i)a[t[r][i]]===void 0?a[t[r][i]]=s[i]:D(a[t[r][i]]===s[i],()=>`Expected dimension ${a[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function tD(e,t){let n=e,a=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let i=0;i<r;++i)a.push([]);let s=[];for(let i=0;i<n.length;++i){let o=n[i],u=aD(t,o);for(let l of u)s.indexOf(l)===-1&&(a[i].push(l),s.push(l))}return{path:n,steps:a}}function nD(e){return e.every((t,n)=>t===n)}function aD(e,t){let n=[];for(let a=0;a<e.length;++a)(e[a].length===0||e[a].indexOf(t)!==-1||t===-1)&&n.push(a);return n}function rD(e,t,n=0){let a=[];if(typeof t=="number")D(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);D(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,u)=>u>0?o+u:o);t[s]=e.shape[n]-i}D(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}var d7={};Fe(d7,{collectGatherOpShapeInfo:()=>oD,computeOutShape:()=>iD,segOpComputeOptimalWindowSize:()=>sD});function sD(e,t){let n=!1,a;for(e<=mg?(a=e,n=!0):a=Gp(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=Gp(e,a+1);return a}function iD(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function oD(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${a}`);if(a<0&&(a+=r),a>s)throw new Error(`batchDims (${a}) must be less than rank(x) (
|
|
${s}).`);if(n<a)throw new Error(`batchDims (${a}) must be less than or equal to axis (${n}).`);for(let p=0;p<a;++p)if(e.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=e.shape[n],o=[],u=1,l=1,d=1;for(let p=0;p<a;++p)o.push(e.shape[p]),u*=e.shape[p];for(let p=a;p<n;p++)o.push(e.shape[p]),l*=e.shape[p];for(let p=a;p<r;p++)o.push(t.shape[p]);for(let p=n+1;p<s;p++)o.push(e.shape[p]),d*=e.shape[p];return{batchSize:u,sliceSize:d,outerSize:l,dimSize:i,outputShape:o}}function lD(e){try{return e.map(t=>Dc(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function uD(e){return e.map(t=>Yu(t))}var Za={};Fe(Za,{nonMaxSuppressionV3Impl:()=>J3,nonMaxSuppressionV4Impl:()=>Q3,nonMaxSuppressionV5Impl:()=>e7,whereImpl:()=>V3});function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var dD=Za.whereImpl,Eh=class extends Eu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Up(this,fr())}nextDataId(){return Eh.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,te().get("IS_NODE")&&F.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 a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,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,a,r){this.data.set(e,{values:t,dtype:a,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 a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return F.mergeRealAndImagArrays(a,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(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return fr().makeTensorFromDataId(a,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=k.now();return e(),{kernelMs:k.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){we([e],"where");let t=this.readSync(e.dataId);return dD(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Eh.nextDataId=0;var yg={};Fe(yg,{addImpl:()=>c7,bincountImpl:()=>xg,bincountReduceImpl:()=>h7,ceilImpl:()=>f7,concatImpl:()=>bg,equalImpl:()=>m7,expImpl:()=>y7,expm1Impl:()=>x7,floorImpl:()=>b7,gatherNdImpl:()=>v7,gatherV2Impl:()=>w7,greaterEqualImpl:()=>I7,greaterImpl:()=>k7,lessEqualImpl:()=>N7,lessImpl:()=>S7,linSpaceImpl:()=>T7,logImpl:()=>C7,maxImpl:()=>E7,maximumImpl:()=>R7,minimumImpl:()=>M7,multiplyImpl:()=>vg,negImpl:()=>F7,notEqualImpl:()=>$7,prodImpl:()=>D7,rangeImpl:()=>kg,rsqrtImpl:()=>O7,simpleAbsImpl:()=>p7,sliceImpl:()=>Fh,sparseFillEmptyRowsImpl:()=>z7,sparseReshapeImpl:()=>_7,sparseSegmentReductionImpl:()=>Ig,squaredDifferenceImpl:()=>P7,stridedSliceImpl:()=>L7,stringNGramsImpl:()=>W7,stringSplitImpl:()=>B7,stringToHashBucketFastImpl:()=>V7,subImpl:()=>j7,tileImpl:()=>U7,topKImpl:()=>H7,transposeImpl:()=>wg,uniqueImpl:()=>G7});function p7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var pD=e=>{let{x:t}=e.inputs,n=e.backend;we(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=p7(r),n.makeOutput(a,t.shape,"float32")},cD={kernelName:fo,backendName:"cpu",kernelFunc:pD};function Ot(e){return(t,n,a,r,s)=>{let i=F.assertAndGetBroadcastShape(t,n),o=i.length,u=k.computeStrides(i),l=k.sizeFromShape(i),d=k.getTypedArrayFromDType(s,l),p=t.length,c=n.length,h=k.computeStrides(t),m=k.computeStrides(n),f=F.getBroadcastDims(t,i),g=F.getBroadcastDims(n,i);if(f.length+g.length===0)for(let y=0;y<d.length;++y)d[y]=e(a[y%a.length],r[y%r.length]);else for(let y=0;y<d.length;++y){let A=k.indexToLoc(y,o,u),x=A.slice(-p);f.forEach(N=>x[N]=0);let v=k.locToIndex(x,p,h),b=A.slice(-c);g.forEach(N=>b[N]=0);let w=k.locToIndex(b,c,m);d[y]=e(a[v],r[w])}return[d,i]}}function qn(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(a.shape,"complex64"),u=n.data.get(o.dataId);return u.complexTensorInfos={real:n.makeTensorInfo(a.shape,"float32",s),imag:n.makeTensorInfo(r.shape,"float32",i)},o}var hD={kernelName:Yp,backendName:"cpu",kernelFunc:qn};function Rh(e,t,n="float32"){if(n==="complex64"){let r=Rh(e,t,"float32"),s=Rh(e,t,"float32");return qn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function Ya(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var fD={kernelName:zs,backendName:"cpu",kernelFunc:Ya};function $i(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var mD={kernelName:Ac,backendName:"cpu",kernelFunc:$i};function Zr(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Ya({inputs:{x:r},backend:n});let i=Rh(n,r.shape,r.dtype),o=Zr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=qn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=$i({inputs:{input:r},backend:n}),o=Zr({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Ya({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(r.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(r.shape,"int32",o)}if(s==="bool"){let i=n.data.get(r.dataId).values,o=k.toTypedArray([0],r.dtype),[u,l]=Ot((d,p)=>d!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(l,"bool",u)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var gD={kernelName:ks,backendName:"cpu",kernelFunc:Zr};function Yt(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,u=s;we([i,o],e);let l=u.data.get(i.dataId).values,d=u.data.get(o.dataId).values,p=i.dtype==="string"?F.fromUint8ToStringArray(l):l,c=i.dtype==="string"?F.fromUint8ToStringArray(d):d,h=a||i.dtype,[m,f]=t(i.shape,o.shape,p,c,h);return u.makeTensorInfo(f,h,m)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,u=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let l=Zr({inputs:{x:i},backend:u,attrs:{dtype:"complex64"}}),d=u.data.get(l.dataId),p=d.complexTensorInfos.real,c=d.complexTensorInfos.imag,h=u.data.get(p.dataId).values,m=u.data.get(c.dataId).values,f=Zr({inputs:{x:o},backend:u,attrs:{dtype:"complex64"}}),g=u.data.get(f.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=u.data.get(y.dataId).values,v=u.data.get(A.dataId).values,[b,w,N]=n(i.shape,o.shape,h,m,x,v),C=u.makeTensorInfo(N,"float32",b),E=u.makeTensorInfo(N,"float32",w),_=qn({inputs:{real:C,imag:E},backend:u});return u.disposeIntermediateTensorInfo(l),u.disposeIntermediateTensorInfo(f),u.disposeIntermediateTensorInfo(C),u.disposeIntermediateTensorInfo(E),_}else{let l=u.data.get(i.dataId).values,d=u.data.get(o.dataId).values,p=a||i.dtype,[c,h]=t(i.shape,o.shape,l,d,p);return u.makeTensorInfo(h,p,c)}}}function Ag(e){return(t,n,a,r,s,i)=>{let o=F.assertAndGetBroadcastShape(t,n),u=k.sizeFromShape(o),l=o.length,d=k.computeStrides(o),p=k.getTypedArrayFromDType("float32",u),c=k.getTypedArrayFromDType("float32",u),h=F.getBroadcastDims(t,o),m=F.getBroadcastDims(n,o),f=F.mergeRealAndImagArrays(a,r),g=F.mergeRealAndImagArrays(s,i),y=t.length,A=k.computeStrides(t),x=n.length,v=k.computeStrides(n);if(h.length+m.length===0)for(let b=0;b<p.length;b++){let w=b%f.length,N=b%g.length,C=e(f[w*2],f[w*2+1],g[N*2],g[N*2+1]);p[b]=C.real,c[b]=C.imag}else for(let b=0;b<p.length;b++){let w=k.indexToLoc(b,l,d),N=w.slice(-y);h.forEach(S=>N[S]=0);let C=k.locToIndex(N,y,A),E=w.slice(-x);m.forEach(S=>E[S]=0);let _=k.locToIndex(E,x,v),$=e(f[C*2],f[C*2+1],g[_*2],g[_*2+1]);p[b]=$.real,c[b]=$.imag}return[p,c,o]}}var c7=Ot((e,t)=>e+t),yD=Ag((e,t,n,a)=>({real:e+n,imag:t+a})),Id=Yt(Or,c7,yD),AD={kernelName:Or,backendName:"cpu",kernelFunc:Id};function xg(e,t,n,a,r){let s=k.sizeFromShape(a),i=k.makeZerosTypedArray(r,n);for(let o=0;o<e.length;o++){let u=e[o];if(u<0)throw new Error("Input x must be non-negative!");u>=r||(s>0?i[u]+=t[o]:i[u]+=1)}return i}function h7(e,t,n,a=!1){let r=e.shape[0],s=e.shape[1],i=Ve([r,n],t.dtype);for(let o=0;o<r;o++)for(let u=0;u<s;u++){let l=e.get(o,u);if(l<0)throw new Error("Input x must be non-negative!");l>=n||(a?i.set(1,o,l):t.size>0?i.set(i.get(o,l)+t.get(o,u),o,l):i.set(i.get(o,l)+1,o,l))}return i}function _l(e){return(t,n,a)=>{let r=k.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)r[s]=e(t[s],a);return r}}function rt(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(we(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,u=o.data.get(i.dataId).values,l=k.sizeFromShape(i.shape),d=n||i.dtype,p=k.getArrayFromDType(d,l);for(let c=0;c<l;++c)p[c]=t(u[c],r);return o.makeTensorInfo(i.shape,d,p)}}function Pl(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(we(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,u=o.data.get(i.dataId).values,l=n||i.dtype,d=t(u,l,r);return o.makeTensorInfo(i.shape,l,d)}}var f7=_l(e=>Math.ceil(e)),xD=Pl(Is,f7),bD={kernelName:Is,backendName:"cpu",kernelFunc:xD};function bg(e,t,n,a){let r=k.getArrayFromDType(n,k.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?F.fromUint8ToStringArray(i.vals):i.vals,u=0;for(let l=0;l<i.shape[0];++l){let d=l*t[1]+s;for(let p=0;p<i.shape[1];++p)r[d+p]=o[u++]}s+=i.shape[1]})}return r}var m7=Ot((e,t)=>e===t?1:0),g7=Yt(Ro,m7,null,"bool"),vD={kernelName:Ro,backendName:"cpu",kernelFunc:g7},y7=_l(e=>Math.exp(e)),A7=Pl(Ms,y7),wD={kernelName:Ms,backendName:"cpu",kernelFunc:A7},x7=_l(e=>Math.expm1(e)),kD=Pl(Fo,x7),ID={kernelName:Fo,backendName:"cpu",kernelFunc:kD},b7=_l(e=>Math.floor(e)),SD=Pl(Fs,b7),ND={kernelName:Fs,backendName:"cpu",kernelFunc:SD};function v7(e,t,n,a,r,s,i,o,u){let l=Ve([a,s],n);for(let d=0;d<a;d++){let p=[],c=0;for(let h=0;h<r;h++){let m=e[d*r+h];c+=m*i[h],p.push(m)}if(c<0||c>=u/s)throw new Error(`Invalid indices: ${p} does not index into ${o}`);for(let h=0;h<s;h++)l.values[d*s+h]=t.get(...t.indexToLoc(c*s+h))}return l}function w7(e,t,n){let a=Ve(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],u=t.locToIndex([i,o]);s[2]=t.values[u];let l=e.locToIndex(s);a.values[r]=e.values[l]}return a}var k7=Ot((e,t)=>e>t?1:0),TD=Yt(zo,k7,null,"bool"),CD={kernelName:zo,backendName:"cpu",kernelFunc:TD},I7=Ot((e,t)=>e>=t?1:0),ED=Yt(Os,I7,null,"bool"),RD={kernelName:Os,backendName:"cpu",kernelFunc:ED},S7=Ot((e,t)=>e<t?1:0),MD=Yt(Wo,S7,null,"bool"),FD={kernelName:Wo,backendName:"cpu",kernelFunc:MD},N7=Ot((e,t)=>e<=t?1:0),$D=Yt(Bo,N7,null,"bool"),DD={kernelName:Bo,backendName:"cpu",kernelFunc:$D};function T7(e,t,n){let a=(t-e)/(n-1),r=k.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var C7=_l(e=>Math.log(e)),OD=Pl(Ps,C7),zD={kernelName:Ps,backendName:"cpu",kernelFunc:OD};function E7(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let u=0;u<t;++u){let l=e[i+u];(Number.isNaN(l)||l>o)&&(o=l)}r[s]=o}return r}var R7=Ot((e,t)=>Math.max(e,t)),_D=Yt(Ws,R7),PD={kernelName:Ws,backendName:"cpu",kernelFunc:_D},M7=Ot((e,t)=>Math.min(e,t)),LD=Yt(Us,M7),WD={kernelName:Us,backendName:"cpu",kernelFunc:LD},vg=Ot((e,t)=>e*t),BD=Ag((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),Mh=Yt(Gs,vg,BD),VD={kernelName:Gs,backendName:"cpu",kernelFunc:Mh};function F7(e,t,n){let a=k.createScalarValue(-1,n);return vg([],t,a,e,n)}function jD(e){let{inputs:t,backend:n}=e,{x:a}=t;we(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=F7(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var UD={kernelName:Ho,backendName:"cpu",kernelFunc:jD},$7=Ot((e,t)=>e!==t?1:0),HD=Yt(Go,$7,null,"bool"),GD={kernelName:Go,backendName:"cpu",kernelFunc:HD};function wg(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),u=k.computeStrides(r),l=k.getTypedArrayFromDType(n,k.sizeFromShape(r));for(let d=0;d<i;++d){let p=k.indexToLoc(d,s,o),c=new Array(p.length);for(let m=0;m<c.length;m++)c[m]=p[a[m]];let h=k.locToIndex(c,s,u);l[h]=e[d]}return l}function ua(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{perm:s}=n;we(r,"transpose");let i=r.shape.length,o=new Array(i);for(let d=0;d<o.length;d++)o[d]=r.shape[s[d]];let u=a.data.get(r.dataId).values,l=wg(u,r.shape,r.dtype,s,o);return{dataId:a.write(l,o,r.dtype),shape:o,dtype:r.dtype}}var qD={kernelName:ci,backendName:"cpu",kernelFunc:ua};function D7(e,t,n,a){let[r,s]=F.computeOutAndReduceShapes(e,a),i=Aa(t,"int32"),o=k.makeZerosTypedArray(k.sizeFromShape(r),i),u=k.sizeFromShape(s);for(let l=0;l<o.length;++l){let d=l*u,p=1;for(let c=0;c<u;++c)p*=n[d+c];o[l]=p}return{outVals:o,outShape:r,outDtype:i}}function XD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"prod");let o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=F.getAxesPermutation(u,o),d=u,p=r,c=[];l!=null&&(p=ua({inputs:{x:r},backend:n,attrs:{perm:l}}),c.push(p),d=F.getInnerMostAxes(d.length,o));let h=n.data.get(p.dataId).values,{outVals:m,outShape:f,outDtype:g}=D7(p.shape,p.dtype,h,d),y=f;return i&&(y=F.expandShapeToKeepDim(f,u)),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(y,g,m)}var KD={kernelName:Jo,backendName:"cpu",kernelFunc:XD};function kg(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return k.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),u=k.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),u[0]=e;for(let l=1;l<u.length;l++)u[l]=u[l-1]+n;return u}var O7=_l(e=>1/Math.sqrt(e)),ZD=Pl(ni,O7),YD={kernelName:ni,backendName:"cpu",kernelFunc:ZD};function Fh(e,t,n,a,r){let s=fn.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let p=fn.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let u=r==="string"?F.fromUint8ToStringArray(e):e,l=Ve(a,r,u),d=Ve(n,r);for(let p=0;p<d.size;++p){let c=d.indexToLoc(p),h=c.map((m,f)=>m+t[f]);d.set(l.get(...h),...c)}return r==="string"?F.fromStringArrayToUint8(d.values):d.values}function Di(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;we(r,"slice");let[o,u]=fn.parseSliceParams(r,s,i);fn.assertParamsValid(r,o,u);let l=n.data.get(r.dataId).values,d=Fh(l,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}var JD={kernelName:rl,backendName:"cpu",kernelFunc:Di};function z7(e,t,n,a,r,s,i){let o=t[0],u=s[0],l=new Array(u),d=new Array(o),p=t[1];if(u===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
|
|
indices.shape[0] = ${o}`);let g=k.getArrayFromDType(n,0),y=k.getArrayFromDType(r,0);return[g,[0,p],y,l,d]}let c=!0,h=0,m=new Array(u).fill(0);for(let g=0;g<o;++g){let y=e[g*p];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=u)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${u}`);++m[y],c=c&&y>=h,h=y}let f=!0;for(let g=0;g<u;++g){let y=m[g]===0;l[g]=y,f=f&&!y,m[g]=Math.max(m[g],1),g>0&&(m[g]+=m[g-1])}if(f&&c){let g=e,y=a;for(let A=0;A<o;++A)d[A]=A;return[g,[o,p],y,l,d]}else{let g=m[u-1],y=k.getArrayFromDType(n,g*p),A=k.getArrayFromDType(r,g),x=new Array(u).fill(0);for(let v=0;v<o;++v){let b=e[v*p],w=x[b],N=(b===0?0:m[b-1])+w;x[b]++;for(let C=0;C<p;++C)y[N*p+C]=e[v*p+C];A[N]=a[v],d[v]=N}for(let v=0;v<u;++v)if(x[v]===0){let b=v===0?0:m[v-1];y[b*p+0]=v;for(let w=1;w<p;++w)y[b*p+w]=0;A[b]=i}return[y,[g,p],A,l,d]}}function _7(e,t,n,a,r){let s=k.sizeFromShape(a),i=t[0],o=r.length,u=[],l=1,d=-1;for(let g=0;g<o;++g){let y=r[g];if(y===-1){if(d!==-1)throw new Error(`only one output dimension may be -1, not both ${d} and ${g}`);d=g,u.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);l*=y,u.push(y)}}if(d!==-1){if(l<=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(s/l);if(l*g!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
|
|
dense values, but the requested shape requires a multiple of ${l}. inputShape=${a} outputShape= ${u}`);u[d]=g}let p=k.sizeFromShape(u);if(p!==s)throw new Error(`Input to reshape is a tensor with ${s} dense values, but the requested shape has ${p}. inputShape=${a} outputShape=${u}`);let c=a.length,h=[];if(c>0){h[c-1]=1;for(let g=c-2;g>=0;--g)h[g]=h[g+1]*a[g+1]}let m=[];if(o>0){m[o-1]=1;for(let g=o-2;g>=0;--g)m[g]=m[g+1]*u[g+1]}let f=k.getArrayFromDType(n,i*o);for(let g=0;g<i;++g){let y=0;for(let A=0;A<c;++A)y+=e[g*c+A]*h[A];for(let A=0;A<o;++A)f[g*o+A]=Math.trunc(y/m[A]),y%=m[A]}return[f,[i,o],u]}function Ig(e,t,n,a,r,s=!1,i=0){let o=a.length;if(o!==r.length)throw new Error("segmentIds and indices should have same size.");let u=[t[0],e.length/t[0]],l=u[1],d=o>0?r[o-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let c=p.reduce((A,x)=>A*x,1),h=k.getArrayFromDType(n,c);if(o===0)return d>0&&h.fill(i),[h,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,f=1,g=0,y=r[m];for(;;){let A=0;if(f<o){if(A=r[f],y===A){++f;continue}if(y>=A)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>g&&h.fill(i,g*l,y*l);for(let x=m;x<f;++x){let v=a[x];if(v<0||v>=u[0])throw new Error(`Bad: indices[${x}] == ${a[x]} out of range [0, ${u[0]})`);for(let b=0;b<l;b++)h[y*l+b]+=e[v*l+b]}if(s)for(let x=0;x<l;x++)h[y*l+x]/=f-m;if(m=f,++f,g=y+1,y=A,f>o)break}return g<d&&h.fill(i,g*l,d*l),[h,p]}var P7=Ot((e,t)=>{let n=e-t;return n*n}),QD=Yt(li,P7),eO={kernelName:li,backendName:"cpu",kernelFunc:QD};function L7(e,t,n,a){let r=Ve(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let u=0;u<o.length;u++)o[u]=i[u]*n[u]+a[u];r.set(t.get(...o),...i)}return r}var tO=class{constructor(e,t,n,a,r,s){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(a),this.padWidth=r,this.preserveShort=s}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,a,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),u=Math.max(0,o-i),l=Math.max(0,o-(r-(i+1))),d=s-(u+l),p=t+(u>0?0:i-o),c=0;c+=u*this.leftPad.length;for(let g=0;g<d;++g)c+=e[p+g].length;c+=l*this.rightPad.length,c+=(u+l+d-1)*this.separator.length,n[a+i]=new Uint8Array(c);let h=n[a+i],m=0,f=g=>g.forEach(y=>h[m++]=y);for(let g=0;g<u;++g)f(this.leftPad),f(this.separator);for(let g=0;g<d-1;++g)f(e[p+g]),f(this.separator);if(d>0){f(e[p+d-1]);for(let g=0;g<l;++g)f(this.separator),f(this.rightPad)}else{for(let g=0;g<l-1;++g)f(this.rightPad),f(this.separator);f(this.rightPad)}}}compute(e,t){let n=e.length,a=t.length;if(a>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let u=1;u<a;++u){let l=t[u]>=o;if(l=l&&t[u]<=n,!l)throw new Error(`Invalid split value ${t[u]}, must be in [${o}, ${n}]`);o=t[u]}if(o!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`)}let r=a-1,s=k.getArrayFromDType("int32",a);if(n===0||a===0){let o=new Array(n);for(let u=0;u<=r;++u)s[u]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let u=t[o]-t[o-1],l=0;this.nGramWidths.forEach(d=>{l+=this.getNumNGrams(u,d)}),this.preserveShort&&u>0&&l===0&&(l=1),s[o]=s[o-1]+l}let i=new Array(s[r]);for(let o=0;o<r;++o){let u=t[o],l=s[o];if(this.nGramWidths.forEach(d=>{let p=t[o+1]-t[o],c=this.getNumNGrams(p,d);this.createNGrams(e,u,i,l,c,d),l+=c}),this.preserveShort&&l===s[o]){let d=t[o+1]-t[o];if(d===0)continue;let p=d+2*this.padWidth,c=1;this.createNGrams(e,u,i,l,c,p)}}return[i,s]}};function W7(e,t,n,a,r,s,i,o){return new tO(n,a,r,s,i,o).compute(e,t)}function nO(e,t,n){if(!e.length)return[];if(t.length===0){let s=new Array(e.length);for(let i=0;i<e.length;++i)s[i]=e.subarray(i,i+1);return s}if(t.length===1){let s=t[0],i=[],o=e.indexOf(s);for(;o!==-1;){let u=e.subarray(0,o);(!n||u.length!==0)&&i.push(u),e=e.subarray(o+1),o=e.indexOf(s)}return(!n||e.length!==0)&&i.push(e),i}let a=[],r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!n||i.length!==0)&&a.push(i),r=s+1}return a}function B7(e,t,n){let a=e.length,r=[],s=0,i=0,o=new Array(a);for(let c=0;c<a;++c){let h=nO(e[c],t,n),m=h.length;o[c]=m,s+=m,i=Math.max(i,m),r.push(...h)}let u=k.getArrayFromDType("int32",s*2),l=new Array(s),d=[a,i],p=0;for(let c=0;c<a;++c)for(let h=0;h<o[c];++h)u[p*2]=c,u[p*2+1]=h,l[p]=r[p],++p;return[u,l,d]}function V7(e,t){let n=k.getArrayFromDType("int32",e.length);for(let a=0;a<e.length;++a)n[a]=k.fingerPrint64(e[a]).modulo(t).getLowBitsUnsigned();return n}var j7=Ot((e,t)=>e-t),aO=Ag((e,t,n,a)=>({real:e-n,imag:t-a})),Sg=Yt(ui,j7,aO),rO={kernelName:ui,backendName:"cpu",kernelFunc:Sg};function U7(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=Ve(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let u=0;u<i.length;u++)i[u]=s[u]%e.shape[u];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}function H7(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],u=k.getTypedArrayFromDType(n,i*a),l=k.getTypedArrayFromDType("int32",i*a);for(let p=0;p<i;p++){let c=p*o,h=e.subarray(c,c+o),m=[];for(let A=0;A<h.length;A++)m.push({value:h[A],index:A});m.sort((A,x)=>x.value-A.value);let f=p*a,g=u.subarray(f,f+a),y=l.subarray(f,f+a);for(let A=0;A<a;A++)g[A]=m[A].value,y[A]=m[A].index}let d=t.slice();return d[d.length-1]=a,[Ve(d,n,u),Ve(d,"int32",l)]}function G7(e,t,n,a){let r=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),u=new Lt(s,a,e),l=[],d=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(d)f=e[m].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let A=0;A<s[2];A++)g.push(u.get(y,m,A));f=g.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let g=Object.keys(i).length;i[f]=g,o[m]=g,l.push(m)}}let p=s.slice();p[1]=Object.keys(i).length;let c=new Lt(p,a);l.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)c.set(u.get(g,m,y),g,f,y)});let h=n.slice();return h[r]=p[1],{outputValues:c.values,outputShape:h,indices:o}}var q7="3.7.0";kl("cpu",()=>new Eh,1);var X7=rt(Co,e=>e>=0?e:Math.exp(e)-1),sO={kernelName:Co,backendName:"cpu",kernelFunc:X7};function K7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;we([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,u=k.getTypedArrayFromDType("float32",i);for(let l=0;l<o.length;l++)u[l]=o[l]<0?s*o[l]:o[l];return n.makeTensorInfo(r.shape,"float32",u)}var iO={kernelName:_s,backendName:"cpu",kernelFunc:K7},oO=Ot((e,t)=>e<0?t*e:e);function Z7(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;we([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,u]=oO(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(u,a.dtype,o)}var lO={kernelName:Zs,backendName:"cpu",kernelFunc:Z7},Y7=rt(Ys,e=>Math.max(0,e)),uO={kernelName:Ys,backendName:"cpu",kernelFunc:Y7},J7=rt(Qs,e=>Math.min(Math.max(0,e),6)),dO={kernelName:Qs,backendName:"cpu",kernelFunc:J7},Q7=rt(ri,e=>1/(1+Math.exp(-e))),pO={kernelName:ri,backendName:"cpu",kernelFunc:Q7};function Ng(e,t,n,a,r){if(n==="linear")return Ya({inputs:{x:t},backend:e});if(n==="relu")return Y7({inputs:{x:t},backend:e});if(n==="elu")return X7({inputs:{x:t},backend:e});if(n==="relu6")return J7({inputs:{x:t},backend:e});if(n==="prelu")return Z7({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return K7({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return Q7({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),u=k.sizeFromShape(o);k.assert(i===u,()=>`The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let l=n.data.get(r.dataId);if(l.complexTensorInfos!=null){let d=l.complexTensorInfos.real,p=l.complexTensorInfos.imag;d.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var cO={kernelName:el,backendName:"cpu",kernelFunc:gt};function ev(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;we([r,s],"matMul");let u=r.shape.length,l=s.shape.length,d=i?r.shape[u-2]:r.shape[u-1],p=o?s.shape[l-1]:s.shape[l-2],c=i?r.shape[u-1]:r.shape[u-2],h=o?s.shape[l-2]:s.shape[l-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),A=g===y||g===1||y===1;k.assert(u>=2&&l>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,d,c]:[g,c,d],b=o?[y,h,p]:[y,p,h],w=gt({inputs:{x:r},backend:n,attrs:{shape:v}}),N=gt({inputs:{x:s},backend:n,attrs:{shape:b}}),C=i?w.shape[1]:w.shape[2],E=i?w.shape[2]:w.shape[1],_=o?N.shape[1]:N.shape[2],$=Math.max(g,y),S=n.data.get(w.dataId).values,z=n.data.get(N.dataId).values,O=k.computeStrides(w.shape),W=k.computeStrides(N.shape),[G,H,J]=i?[O[0],1,O[1]]:[O[0],O[1],1],[K,ne,Q]=o?[1,W[1],W[0]]:[W[1],1,W[0]],se=E*_,Z=Ve([$,E,_],w.dtype),le=Z.values,oe=n.blockSize;for(let xe=0;xe<$;xe++)for(let fe=0;fe<E;fe+=oe)for(let Ne=0;Ne<_;Ne+=oe)for(let Te=0;Te<C;Te+=oe){let Oe=Math.min(fe+oe,E),Pe=Math.min(Ne+oe,_),ze=Math.min(Te+oe,C);for(let tt=fe;tt<Oe;tt++)for(let nt=Ne;nt<Pe;nt++){let it=0;for(let Ze=Te;Ze<ze;Ze++){let ht=Math.min(xe,g-1)*G,Ue=Math.min(xe,y-1)*Q,In=S[ht+tt*H+Ze*J],kt=z[Ze*K+nt*ne+Ue];it+=In*kt}le[xe*se+(tt*_+nt)]+=it}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(x,Z.dtype,Z.values)}var hO={kernelName:ws,backendName:"cpu",kernelFunc:ev};function fO(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a,c,h,m,f=[];c=ev({inputs:{a:r,b:s},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=Id({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),d&&(m=Ng(n,c,d,o,p),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var mO={kernelName:hi,backendName:"cpu",kernelFunc:fO},gO=rt(mo,e=>Math.acos(e)),yO={kernelName:mo,backendName:"cpu",kernelFunc:gO},AO=rt(go,e=>Math.acosh(e)),xO={kernelName:go,backendName:"cpu",kernelFunc:AO};function bO(e){let{inputs:t,backend:n}=e,a=t;we(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Ve(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var vO={kernelName:xs,backendName:"cpu",kernelFunc:bO};function wO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"all");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("all",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*h,x=f[A];for(let v=0;v<h;++v){let b=f[A+v];x=x&&b}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),A=gt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var kO={kernelName:yo,backendName:"cpu",kernelFunc:wO};function IO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"any");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("any",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*h,x=f[A];for(let v=0;v<h;++v){let b=f[A+v];x=x||b}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),A=gt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var SO={kernelName:Ao,backendName:"cpu",kernelFunc:IO};function NO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;we(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMax",i,u.shape.length);let[d,p]=F.computeOutAndReduceShapes(u.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,A=f[y],x=0;for(let v=0;v<m;++v){let b=f[y+v];b>A&&(A=b,x=v)}h[g]=x}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var TO={kernelName:bs,backendName:"cpu",kernelFunc:NO};function CO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;we(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[d,p]=F.computeOutAndReduceShapes(u.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,A=f[y],x=0;for(let v=0;v<m;++v){let b=f[y+v];b<A&&(A=b,x=v)}h[g]=x}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var EO={kernelName:Fu,backendName:"cpu",kernelFunc:CO},RO=rt(xo,e=>Math.asin(e)),MO={kernelName:xo,backendName:"cpu",kernelFunc:RO},FO=rt(bo,e=>Math.asinh(e)),$O={kernelName:bo,backendName:"cpu",kernelFunc:FO},DO=rt(vo,e=>Math.atan(e)),OO={kernelName:vo,backendName:"cpu",kernelFunc:DO},zO=Ot((e,t)=>Math.atan2(e,t)),_O=Yt(ko,zO),PO={kernelName:ko,backendName:"cpu",kernelFunc:_O},LO=rt(wo,e=>Math.atanh(e)),WO={kernelName:wo,backendName:"cpu",kernelFunc:LO};function Tg(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,d=r.effectiveFilterHeight,p=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Ve(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],A=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;v<r.batchSize;++v){let b=v*y,w=v*a[0];for(let N=0;N<r.inChannels;++N)for(let C=0;C<r.outHeight;++C){let E=C*i-c,_=Math.max(0,E),$=Math.min(r.inHeight,d+E),S=b+C*A;for(let z=0;z<r.outWidth;++z){let O=z*o-h,W=Math.max(0,O),G=Math.min(r.inWidth,p+O),H=m,J=0,K=0;for(let Q=_;Q<$;Q+=u){let se=w+Q*a[1];for(let Z=W;Z<G;Z+=l){let le=se+Z*a[2],oe=e[le+N];s==="max"&&oe>H?H=oe:s==="avg"&&(J+=oe,K++)}if(isNaN(H))break}let ne=S+z*x+N;g[ne]=s==="avg"?J/K:H}}}return f}function tv(e,t,n,a,r=!1,s=!1){let i=Ve(a.outShape,"int32"),o=a.strideHeight,u=a.strideWidth,l=a.dilationHeight,d=a.dilationWidth,p=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Ve(t,n,e);for(let g=0;g<a.batchSize;++g)for(let y=0;y<a.inChannels;++y)for(let A=0;A<a.outHeight;++A){let x=A*o-h,v=x;for(;v<0;)v+=l;let b=Math.min(a.inHeight,p+x);for(let w=0;w<a.outWidth;++w){let N=w*u-m,C=N;for(;C<0;)C+=d;let E=Math.min(a.inWidth,c+N),_=Number.NEGATIVE_INFINITY,$=-1;for(let S=v;S<b;S+=l){let z=S-x;for(let O=C;O<E;O+=d){let W=O-N,G=f.get(g,S,O,y);G>_&&(_=G,r?$=s?((g*a.inHeight+S)*a.inWidth+O)*a.inChannels+y:(S*a.inWidth+O)*a.inChannels+y:$=z*c+W)}}i.set($,g,A,w,y)}}return i}function nv(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,u=r.strideWidth,l=r.dilationDepth,d=r.dilationHeight,p=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Ve(r.outShape,n),v=x.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],w=r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let E=0;E<r.batchSize;++E){let _=E*b,$=E*a[0];for(let S=0;S<r.inChannels;++S)for(let z=0;z<r.outDepth;++z){let O=z*i-f,W=O;for(;W<0;)W+=l;let G=Math.min(r.inDepth,c+O),H=_+z*w;for(let J=0;J<r.outHeight;++J){let K=J*o-g,ne=K;for(;ne<0;)ne+=d;let Q=Math.min(r.inHeight,h+K),se=H+J*N;for(let Z=0;Z<r.outWidth;++Z){let le=Z*u-y,oe=le;for(;oe<0;)oe+=p;let xe=Math.min(r.inWidth,m+le),fe=se+Z*C,Ne=A,Te=0,Oe=0;for(let ze=W;ze<G;ze+=l){let tt=$+ze*a[1];for(let nt=ne;nt<Q;nt+=d){let it=tt+nt*a[2];for(let Ze=oe;Ze<xe;Ze+=p){let ht=it+Ze*a[3],Ue=e[ht+S];if(s==="max"&&Ue>Ne?Ne=Ue:s==="avg"&&(Te+=Ue,Oe++),isNaN(Ne))break}if(isNaN(Ne))break}if(isNaN(Ne))break}let Pe=fe+S;v[Pe]=s==="avg"?Te/Oe:Ne}}}}return x}function BO(e,t){let n=Ve(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,d=t.effectiveFilterHeight,p=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*a-c,x=A;for(;x<0;)x+=i;let v=Math.min(t.inDepth,l+A);for(let b=0;b<t.outHeight;++b){let w=b*r-h,N=w;for(;N<0;)N+=o;let C=Math.min(t.inHeight,d+w);for(let E=0;E<t.outWidth;++E){let _=E*s-m,$=_;for(;$<0;)$+=u;let S=Math.min(t.inWidth,p+_),z=Number.NEGATIVE_INFINITY,O=-1;for(let W=x;W<v;W+=i){let G=W-A;for(let H=N;H<C;H+=o){let J=H-w;for(let K=$;K<S;K+=u){let ne=K-_,Q=e.get(f,W,H,K,g);Q>=z&&(z=Q,O=G*d*p+J*d+ne)}}}n.set(O,f,y,b,E,g)}}}return n}function VO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u),p;if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))p=Ya({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=Tg(c,r.shape,r.dtype,h,d,"avg");p=n.makeTensorInfo(d.outShape,r.dtype,m.values)}return p}var jO={kernelName:vs,backendName:"cpu",kernelFunc:VO};function UO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=a;we(r,"avgPool3d");let d=F.computePool3DInfo(r.shape,s,i,1,o,u,l),p=n.data.get(r.dataId).values,c=nv(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var HO={kernelName:$u,backendName:"cpu",kernelFunc:UO};function GO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=a;we([r,s],"avgPool3DGrad");let d=F.computePool3DInfo(s.shape,i,o,1,u,l),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,v=d.effectiveFilterDepth,b=d.effectiveFilterHeight,w=d.effectiveFilterWidth,N=v-1-d.padInfo.front,C=w-1-d.padInfo.left,E=b-1-d.padInfo.top,_=Ve(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let z=0;z<d.batchSize;++z)for(let O=0;O<d.inChannels;++O)for(let W=0;W<d.inDepth;++W)for(let G=0;G<d.inHeight;++G)for(let H=0;H<d.inWidth;++H){let J=W-N,K=G-E,ne=H-C,Q=0;for(let se=0;se<v;se+=y){let Z=(J+se)/p;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let le=0;le<b;le+=A){let oe=(K+le)/c;if(!(oe<0||oe>=d.outHeight||Math.floor(oe)!==oe))for(let xe=0;xe<w;xe+=x){let fe=(ne+xe)/h;fe<0||fe>=d.outWidth||Math.floor(fe)!==fe||(Q+=S.get(z,Z,oe,fe,O))}}}_.set(Q*$,z,W,G,H,O)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var qO={kernelName:Kp,backendName:"cpu",kernelFunc:GO};function XO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;we([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=F.computePool2DInfo(i.shape,o,u,1,l),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,v=y-1-d.padInfo.top,b=Ve(i.shape,"float32"),w=1/(h*m),N=n.data.get(r.dataId).values,C=Ve(r.shape,"float32",N);for(let E=0;E<d.batchSize;++E)for(let _=0;_<d.inChannels;++_)for(let $=0;$<d.inHeight;++$)for(let S=0;S<d.inWidth;++S){let z=$-v,O=S-x,W=0;for(let G=0;G<y;G+=f){let H=(z+G)/p;if(!(H<0||H>=d.outHeight||Math.floor(H)!==H))for(let J=0;J<A;J+=g){let K=(O+J)/c;K<0||K>=d.outWidth||Math.floor(K)!==K||(W+=C.get(E,H,K,_))}}b.set(W*w,E,$,S,_)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var KO={kernelName:Xp,backendName:"cpu",kernelFunc:XO};function ZO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:u}=t;k.assert(o.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([r,o,u,s,i],"batchNorm");let{varianceEpsilon:l}=a;l==null&&(l=.001);let d=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(u.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),g=m.length,y=h.length,A=c.length,x=p.length,v=0,b=0,w=0,N=0;for(let C=0;C<d.length;++C)f[C]=m[v++]+(d[C]-p[b++])*h[w++]/Math.sqrt(c[N++]+l),v>=g&&(v=0),b>=x&&(b=0),w>=y&&(w=0),N>=A&&(N=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var YO={kernelName:Ds,backendName:"cpu",kernelFunc:ZO};function JO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),u=F.getReshaped(r.shape,s,o),l=F.getPermuted(u.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=gt({inputs:{x:r},backend:n,attrs:{shape:u}}),m=ua({inputs:{x:h},backend:n,attrs:{perm:l}}),f=gt({inputs:{x:m},backend:n,attrs:{shape:d}}),g=Di({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var QO={kernelName:Du,backendName:"cpu",kernelFunc:JO};function ez(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,l=xg(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var tz={kernelName:Zp,backendName:"cpu",kernelFunc:ez},nz=rt(zr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),az={kernelName:zr,backendName:"cpu",kernelFunc:nz},rz=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let d=o[l],p=u[l];a[l]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},sz={kernelName:Ou,backendName:"cpu",kernelFunc:rz};function Ll(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var iz={kernelName:pc,backendName:"cpu",kernelFunc:Ll};function Wl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return Ya({inputs:{x:o[0]},backend:n});let u=o.map(f=>f.shape);if(F.assertParamsConsistent(u,s),o[0].dtype==="complex64"){let f=o.map(v=>$i({inputs:{input:v},backend:n})),g=o.map(v=>Ll({inputs:{input:v},backend:n})),y=Wl({inputs:f,backend:n,attrs:{axis:s}}),A=Wl({inputs:g,backend:n,attrs:{axis:s}}),x=qn({inputs:{real:y,imag:A},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),x}let l=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return gt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),d=l.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=F.computeOutShape(l.map(f=>f.shape),1);let p=l[0].shape[0]===1,c=bg(d,i,t[0].dtype,p),h=F.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var oz={kernelName:Io,backendName:"cpu",kernelFunc:Wl};function av(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:d}=a;we([r,s],"conv2d");let p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,s.shape,i,l,o,d,!1,p),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,A=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Lt(c.outShape,r.dtype),b=k.computeStrides(r.shape),w=k.computeStrides(s.shape),N=b[0],C=x?b[1]:b[2],E=x?b[2]:1,_=x?1:b[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],z=x?v.strides[2]:1,O=x?1:v.strides[1],W=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,H=v.values;for(let J=0;J<c.batchSize;++J){let K=J*N,ne=J*$;for(let Q=0;Q<c.outHeight;++Q){let se=ne+Q*S,Z=Q*c.strideHeight-A;for(let le=0;le<h;++le){let oe=Z+le*f;if(oe<0||oe>=c.inHeight)continue;let xe=le*w[0],fe=K+oe*C;for(let Ne=0;Ne<c.outWidth;++Ne){let Te=se+Ne*z,Oe=Ne*c.strideWidth-y;for(let Pe=0;Pe<m;++Pe){let ze=Oe+Pe*g;if(ze<0||ze>=c.inWidth)continue;let tt=xe+Pe*w[1],nt=fe+ze*E,it=tt;for(let Ze=0;Ze<c.inChannels;++Ze){let ht=W[nt+Ze*_];for(let Ue=0;Ue<c.outChannels;++Ue)H[Te+Ue*O]+=ht*G[it+Ue];it+=c.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,H)}var lz={kernelName:Ss,backendName:"cpu",kernelFunc:av};function uz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:d}=a;we([r,s],"conv2dBackpropFilter");let p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,d,i,1,o,l,!1,p),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=c,y=c.dataFormat==="channelsLast",A=new Lt(c.filterShape,"float32"),x=c.padInfo.left,v=c.padInfo.top,b=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,N=new Lt(r.shape,r.dtype,b),C=new Lt(s.shape,s.dtype,w);for(let E=0;E<f;++E){let _=Math.max(0,Math.ceil((v-E)/h)),$=Math.min(c.outHeight,(c.inHeight+v-E)/h);for(let S=0;S<g;++S){let z=Math.max(0,Math.ceil((x-S)/m)),O=Math.min(c.outWidth,(c.inWidth+x-S)/m);for(let W=0;W<c.inChannels;++W)for(let G=0;G<c.outChannels;++G){let H=0;for(let J=0;J<c.batchSize;++J)for(let K=_;K<$;++K){let ne=E+K*h-v;for(let Q=z;Q<O;++Q){let se=S+Q*m-x;y?H+=N.get(J,ne,se,W)*C.get(J,K,Q,G):H+=N.get(J,W,ne,se)*C.get(J,G,K,Q)}}A.set(H,E,S,W,G)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var dz={kernelName:Jp,backendName:"cpu",kernelFunc:uz};function pz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:d}=a;we([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),c=k.computeStrides(r.shape),h=F.convertConv2DDataFormat(l),m=F.computeConv2DInfo(i,s.shape,o,1,u,d,!1,h),f=new Lt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,A=n.data.get(s.dataId).values,[x,v,b]=p,{batchSize:w,filterHeight:N,filterWidth:C,inChannels:E,inHeight:_,inWidth:$,outChannels:S,outHeight:z,outWidth:O,strideHeight:W,strideWidth:G}=m;h=m.dataFormat;let H=N-1-m.padInfo.top,J=C-1-m.padInfo.left,K=h==="channelsLast",ne=f.strides[0],Q=K?f.strides[1]:f.strides[2],se=K?f.strides[2]:1,Z=K?1:f.strides[1],le=c[0],oe=K?c[1]:c[2],xe=K?c[2]:1,fe=K?1:c[1];for(let Ne=0;Ne<w;++Ne)for(let Te=0;Te<E;++Te)for(let Oe=0;Oe<_;++Oe){let Pe=Oe-H,ze=Math.max(0,Math.ceil(Pe/W)),tt=Math.min(z,(N+Pe)/W);for(let nt=0;nt<$;++nt){let it=nt-J,Ze=Math.max(0,Math.ceil(it/G)),ht=Math.min(O,(C+it)/G),Ue=0;for(let kt=ze;kt<tt;++kt){let ta=kt*W-Pe;for(let en=Ze;en<ht;++en){let Sn=en*G-it,na=le*Ne+oe*kt+xe*en,Pn=x*(N-1-ta)+v*(C-1-Sn)+b*Te;for(let dn=0;dn<S;++dn){let tn=y[na+fe*dn],Ba=A[Pn+dn];Ue+=tn*Ba}}}let In=ne*Ne+Q*Oe+se*nt+Z*Te;g[In]=Ue}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var cz={kernelName:Ns,backendName:"cpu",kernelFunc:pz};function hz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a;we([r,s],"conv3d");let l=F.computeConv3DInfo(r.shape,s.shape,i,u,o),{filterDepth:d,filterHeight:p,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=l,y=g.front,A=g.left,x=g.top,v=new Lt(l.outShape,r.dtype),b=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,N=v.values,C=k.computeStrides(r.shape),E=k.computeStrides(s.shape);for(let _=0;_<l.batchSize;++_){let $=_*C[0],S=_*v.strides[0];for(let z=0;z<l.outDepth;++z){let O=S+z*v.strides[1],W=z*l.strideDepth-y;for(let G=0;G<d;++G){let H=W+G*h;if(H<0||H>=l.inDepth)continue;let J=G*E[0],K=$+H*C[1];for(let ne=0;ne<l.outHeight;++ne){let Q=O+ne*v.strides[2],se=ne*l.strideHeight-x;for(let Z=0;Z<p;++Z){let le=se+Z*m;if(le<0||le>=l.inHeight)continue;let oe=J+Z*E[1],xe=K+le*C[2];for(let fe=0;fe<l.outWidth;++fe){let Ne=Q+fe*l.outChannels,Te=fe*l.strideWidth-A;for(let Oe=0;Oe<c;++Oe){let Pe=Te+Oe*f;if(Pe<0||Pe>=l.inWidth)continue;let ze=oe+Oe*E[2],tt=xe+Pe*l.inChannels,nt=ze;for(let it=0;it<l.inChannels;++it){let Ze=b[tt+it];for(let ht=0;ht<l.outChannels;++ht)N[Ne+ht]+=Ze*w[nt+ht];nt+=l.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var fz={kernelName:zu,backendName:"cpu",kernelFunc:hz};function mz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:u}=a;we([r,s],"conv3dBackpropFilterV2");let l=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(r.shape,u,i,1,o),c=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,A=new Lt(p.filterShape,"float32"),x=A.values,[v,b,w,N]=A.strides,C=n.data.get(s.dataId).values,[E,_,$,S]=d,z=n.data.get(r.dataId).values,[O,W,G,H]=l,J=p.padInfo.front,K=p.padInfo.left,ne=p.padInfo.top;for(let Q=0;Q<f;++Q){let se=Math.max(0,Math.ceil((J-Q)/c)),Z=Math.min(p.outDepth,(p.inDepth+J-Q)/c),le=Q*v;for(let oe=0;oe<g;++oe){let xe=Math.max(0,Math.ceil((ne-oe)/h)),fe=Math.min(p.outHeight,(p.inHeight+ne-oe)/h),Ne=oe*b+le;for(let Te=0;Te<y;++Te){let Oe=Math.max(0,Math.ceil((K-Te)/m)),Pe=Math.min(p.outWidth,(p.inWidth+K-Te)/m),ze=Te*w+Ne;for(let tt=0;tt<p.inChannels;++tt){let nt=tt*N+ze;for(let it=0;it<p.outChannels;++it){let Ze=0;for(let ht=0;ht<p.batchSize;++ht){let Ue=ht*O,In=ht*E;for(let kt=se;kt<Z;++kt){let ta=(Q+kt*c-J)*W+Ue,en=kt*_+In;for(let Sn=xe;Sn<fe;++Sn){let na=(oe+Sn*h-ne)*G+ta,Pn=Sn*$+en;for(let dn=Oe;dn<Pe;++dn){let tn=(Te+dn*m-K)*H+na,Ba=dn*S+Pn;Ze+=z[tn+tt]*C[Ba+it]}}}}x[nt+it]=Ze}}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var gz={kernelName:Qp,backendName:"cpu",kernelFunc:mz};function yz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:u}=a;we([r],"conv3dBackpropInputV2");let l=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(u,s.shape,o,1,i),c=new Lt(p.inShape,"float32"),h=c.values,[m,f,g,y]=c.strides,A=n.data.get(r.dataId).values,[x,v,b,w]=l,N=n.data.get(s.dataId).values,[C,E,_,$]=d,{batchSize:S,filterDepth:z,filterHeight:O,filterWidth:W,inChannels:G,inDepth:H,inHeight:J,inWidth:K,outChannels:ne,outDepth:Q,outHeight:se,outWidth:Z,strideDepth:le,strideHeight:oe,strideWidth:xe}=p,fe=z-1-p.padInfo.front,Ne=O-1-p.padInfo.top,Te=W-1-p.padInfo.left;for(let Oe=0;Oe<S;++Oe)for(let Pe=0;Pe<G;++Pe)for(let ze=0;ze<H;++ze){let tt=ze-fe,nt=Math.max(0,Math.ceil(tt/le)),it=Math.min(Q,(z+tt)/le);for(let Ze=0;Ze<J;++Ze){let ht=Ze-Ne,Ue=Math.max(0,Math.ceil(ht/oe)),In=Math.min(se,(O+ht)/oe);for(let kt=0;kt<K;++kt){let ta=kt-Te,en=Math.max(0,Math.ceil(ta/xe)),Sn=Math.min(Z,(W+ta)/xe),na=0;for(let Pn=nt;Pn<it;++Pn){let dn=Pn*le-tt;for(let tn=Ue;tn<In;++tn){let Ba=tn*oe-ht;for(let fa=en;fa<Sn;++fa){let ma=fa*xe-ta,Nr=x*Oe+v*Pn+b*tn+w*fa,or=C*(z-1-dn)+E*(O-1-Ba)+_*(W-1-ma)+$*Pe;for(let Tr=0;Tr<ne;++Tr){let Qi=A[Nr+Tr],Va=N[or+Tr];na+=Qi*Va}}}}h[m*Oe+f*ze+g*Ze+y*kt+Pe]=na}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var Az={kernelName:ec,backendName:"cpu",kernelFunc:yz},xz=rt(Ts,e=>Math.cos(e)),bz={kernelName:Ts,backendName:"cpu",kernelFunc:xz},vz=rt(So,e=>Math.cosh(e)),wz={kernelName:So,backendName:"cpu",kernelFunc:vz};function kz(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=a,[d,p,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Ve([m,f,g,h],"float32"),A=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,b=k.computeStrides(r.shape),w=k.computeStrides(y.shape);for(let N=0;N<m;N++){let C=N*4,E=A[C],_=A[C+1],$=A[C+2],S=A[C+3],z=x[N];if(z>=d)continue;let O=f>1?($-E)*(p-1)/(f-1):0,W=g>1?(S-_)*(c-1)/(g-1):0;for(let G=0;G<f;G++){let H=f>1?E*(p-1)+G*O:.5*(E+$)*(p-1);if(H<0||H>p-1){for(let J=0;J<g;J++)for(let K=0;K<h;K++){let ne=K+J*w[2]+G*w[1]+N*w[0];y.values[ne]=l}continue}if(u==="bilinear"){let J=Math.floor(H),K=Math.ceil(H),ne=H-J;for(let Q=0;Q<g;Q++){let se=g>1?_*(c-1)+Q*W:.5*(_+S)*(c-1);if(se<0||se>c-1){for(let xe=0;xe<h;xe++){let fe=xe+Q*w[2]+G*w[1]+N*w[0];y.values[fe]=l}continue}let Z=Math.floor(se),le=Math.ceil(se),oe=se-Z;for(let xe=0;xe<h;xe++){let fe=xe+Z*b[2]+J*b[1]+z*b[0],Ne=v[fe];fe=xe+le*b[2]+J*b[1]+z*b[0];let Te=v[fe];fe=xe+Z*b[2]+K*b[1]+z*b[0];let Oe=v[fe];fe=xe+le*b[2]+K*b[1]+z*b[0];let Pe=v[fe],ze=Ne+(Te-Ne)*oe,tt=Oe+(Pe-Oe)*oe;fe=xe+Q*w[2]+G*w[1]+N*w[0],y.values[fe]=ze+(tt-ze)*ne}}}else for(let J=0;J<g;++J){let K=g>1?_*(c-1)+J*W:.5*(_+S)*(c-1);if(K<0||K>c-1){for(let se=0;se<h;se++){let Z=se+J*w[2]+G*w[1]+N*w[0];y.values[Z]=l}continue}let ne=Math.round(K),Q=Math.round(H);for(let se=0;se<h;se++){let Z=se+ne*b[2]+Q*b[1]+z*b[0],le=se+J*w[2]+G*w[1]+N*w[0];y.values[le]=v[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Iz={kernelName:No,backendName:"cpu",kernelFunc:kz};function Sz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let u=F.getAxesPermutation([s],r.shape.length),l=r;u!=null&&(l=ua({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=F.getInnerMostAxes(1,r.shape.length)[0];if(d!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${d}`);let p=Aa(l.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<h.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)c[x]=i?0:h[x];else{let v=f(y,A-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(l.shape,p,c);if(u!=null){let y=F.getUndoAxesPermutation(u),A=ua({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),A}return g}var Nz={kernelName:Cs,backendName:"cpu",kernelFunc:Sz};function Tz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=xg(u,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(s),d=h7(u,l,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Cz={kernelName:tc,backendName:"cpu",kernelFunc:Tz};function Ez(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],u=r.shape[1],l=r.shape[2],d=r.shape[3],p=u*s,c=l*s,h=d/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*c*h),g=0;for(let y=0;y<o;++y)for(let A=0;A<p;++A){let x=Math.floor(A/s),v=A%s;for(let b=0;b<c;++b){let w=Math.floor(b/s),N=b%s,C=(v*s+N)*h;for(let E=0;E<h;++E){let _=E+C+d*(w+l*(x+u*y));f[g++]=m[_]}}}return n.makeTensorInfo([o,p,c,h],r.dtype,f)}var Rz={kernelName:To,backendName:"cpu",kernelFunc:Ez};function rv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=a;we([r,s],"depthwiseConv2DNative");let d=k.computeStrides(r.shape),p=k.computeStrides(s.shape),c=u;c==null&&(c=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=F.computeConv2DInfo(r.shape,s.shape,i,c,o,l,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:A}=h,x=A.left,v=A.top,b=h.outChannels/h.inChannels,w=new Lt(h.outShape,r.dtype),N=n.data.get(r.dataId).values,C=n.data.get(s.dataId).values,E=w.values;for(let _=0;_<h.batchSize;++_){let $=_*d[0],S=_*w.strides[0];for(let z=0;z<h.outHeight;++z){let O=S+z*w.strides[1],W=z*h.strideHeight-v;for(let G=0;G<m;++G){let H=W+G*g;if(H<0||H>=h.inHeight)continue;let J=G*p[0],K=$+H*d[1];for(let ne=0;ne<h.outWidth;++ne){let Q=O+ne*w.strides[2],se=ne*h.strideWidth-x;for(let Z=0;Z<f;++Z){let le=se+Z*y;if(le<0||le>=h.inWidth)continue;let oe=J+Z*p[1],xe=K+le*h.inChannels,fe=Q,Ne=oe;for(let Te=0;Te<h.inChannels;++Te){let Oe=N[xe+Te];for(let Pe=0;Pe<b;++Pe)E[fe+Pe]+=Oe*C[Ne+Pe];fe+=b,Ne+=b}}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var Mz={kernelName:Es,backendName:"cpu",kernelFunc:rv};function Fz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:d}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=F.computeConv2DInfo(r.shape,d,i,o,u,l,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Lt(p.filterShape,"float32"),y=p.padInfo.left,A=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,b=new Lt(r.shape,r.dtype,v),w=n.data.get(s.dataId).values,N=new Lt(s.shape,s.dtype,w);for(let C=0;C<m;++C){let E=Math.max(0,Math.ceil((A-C)/c)),_=Math.min(p.outHeight,(p.inHeight+A-C)/c);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/h)),z=Math.min(p.outWidth,(p.inWidth+y-$)/h);for(let O=0;O<p.outChannels;++O){let W=Math.trunc(O/x),G=O%x,H=0;for(let J=0;J<p.batchSize;++J)for(let K=E;K<_;++K){let ne=C+K*c-A;for(let Q=S;Q<z;++Q){let se=$+Q*h-y;H+=b.get(J,ne,se,W)*N.get(J,K,Q,O)}}g.set(H,C,$,W,G)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var $z={kernelName:nc,backendName:"cpu",kernelFunc:Fz};function Dz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=F.computeConv2DInfo(d,s.shape,i,o,u,l,!0),m=new Lt(h.inShape,"float32"),f=m.values,[g,y,A]=m.strides,x=n.data.get(r.dataId).values,[v,b,w]=p,N=n.data.get(s.dataId).values,[C,E,_]=c,{batchSize:$,filterHeight:S,filterWidth:z,inChannels:O,inHeight:W,inWidth:G,outChannels:H,outHeight:J,outWidth:K,strideHeight:ne,strideWidth:Q}=h,se=S-1-h.padInfo.top,Z=z-1-h.padInfo.left,le=H/O;for(let oe=0;oe<$;++oe)for(let xe=0;xe<O;++xe)for(let fe=0;fe<W;++fe){let Ne=fe-se,Te=Math.max(0,Math.ceil(Ne/ne)),Oe=Math.min(J,(S+Ne)/ne);for(let Pe=0;Pe<G;++Pe){let ze=Pe-Z,tt=Math.max(0,Math.ceil(ze/Q)),nt=Math.min(K,(z+ze)/Q),it=0;for(let Ze=Te;Ze<Oe;++Ze){let ht=Ze*ne-Ne;for(let Ue=tt;Ue<nt;++Ue){let In=Ue*Q-ze,kt=v*oe+b*Ze+w*Ue,ta=C*(S-1-ht)+E*(z-1-In)+_*xe;for(let en=0;en<le;++en){let Sn=xe*le+en,na=x[kt+Sn],Pn=N[ta+en];it+=na*Pn}}}f[g*oe+y*fe+A*Pe+xe]=it}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Oz={kernelName:ac,backendName:"cpu",kernelFunc:Dz};function zz(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Ve([r,r],a.dtype),o=i.values;for(let l=0;l<s.length;l++)o[l*r+l]=s[l];let u=[...a.shape,...a.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var _z={kernelName:rc,backendName:"cpu",kernelFunc:zz},Pz={kernelName:_u,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,u=t,l=u.data.get(a.dataId).values,d=a.shape.length,p=u.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:v,strideWidth:b,filterHeight:w,filterWidth:N,dilationHeight:C,dilationWidth:E,outShape:_}=F.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=k.sizeFromShape(_),S=_.length,z=k.getArrayFromDType(a.dtype,$);for(let O=0;O<h;++O)for(let W=0;W<y;++W){let G=W*v-x.top;for(let H=0;H<A;++H){let J=H*b-x.left;for(let K=0;K<g;++K){let ne=Number.MIN_SAFE_INTEGER;for(let se=0;se<w;++se){let Z=G+se*C;if(Z>=0&&Z<m)for(let le=0;le<N;++le){let oe=J+le*E;if(oe>=0&&oe<f){let xe=k.locToIndex([O,Z,oe,K],d,k.computeStrides(a.shape)),fe=k.locToIndex([se,le,K],c,k.computeStrides(r.shape)),Ne=l[xe]+p[fe];Ne>ne&&(ne=Ne)}}}let Q=k.locToIndex([O,W,H,K],S,k.computeStrides(_));z[Q]=ne}}}return{dataId:u.write(k.toTypedArray(z,a.dtype),_,a.dtype),shape:_,dtype:a.dtype}}},Lz={kernelName:ic,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:u}=n,l=t,d=k.toNestedArray(a.shape,l.data.get(a.dataId).values),p=k.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:v,filterHeight:b,filterWidth:w,dilationHeight:N,dilationWidth:C,outShape:E}=F.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",u);k.assert(s.rank===E.length,()=>`Error in ${ic}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let _=k.toNestedArray(E,l.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let z=0;z<g;++z){let O=z*x-A.top;for(let W=0;W<y;++W){let G=W*v-A.left;for(let H=0;H<f;++H){let J=Number.MIN_SAFE_INTEGER,K=0,ne=0;for(let Q=0;Q<b;++Q){let se=O+Q*N;if(se>=0&&se<h)for(let Z=0;Z<w;++Z){let le=G+Z*C;if(le>=0&&le<m){let oe=d[S][se][le][H]+p[Q][Z][H];oe>J&&(J=oe,K=Q,ne=Z)}}}$[K][ne][H]+=_[S][z][W][H]}}}return{dataId:l.write(k.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Wz={kernelName:sc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:u}=n,l=t,d=k.toNestedArray(a.shape,l.data.get(a.dataId).values),p=k.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:v,filterHeight:b,filterWidth:w,dilationHeight:N,dilationWidth:C,outShape:E}=F.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",u);k.assert(s.rank===E.length,()=>`Error in ${sc}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let _=k.toNestedArray(E,l.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S<c;++S)for(let z=0;z<g;++z){let O=z*x-A.top;for(let W=0;W<y;++W){let G=W*v-A.left;for(let H=0;H<f;++H){let J=Number.MIN_SAFE_INTEGER,K=O<0?0:O,ne=G<0?0:G;for(let Q=0;Q<b;++Q){let se=O+Q*N;if(se>=0&&se<h)for(let Z=0;Z<w;++Z){let le=G+Z*C;if(le>=0&&le<m){let oe=d[S][se][le][H]+p[Q][Z][H];oe>J&&(J=oe,K=se,ne=le)}}}$[S][K][ne][H]+=_[S][z][W][H]}}}return{dataId:l.write(k.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function Sd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"sum");let o;r.dtype==="bool"?o=Zr({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=Ya({inputs:{x:r},backend:n});let u=o.shape.length,l=k.parseAxisParam(s,o.shape),d=F.getAxesPermutation(l,u),p=l,c=o;d!=null&&(c=ua({inputs:{x:o},backend:n,attrs:{perm:d}}),p=F.getInnerMostAxes(p.length,u)),F.assertAxesAreInnerMostDims("sum",p,c.shape.length);let[h,m]=F.computeOutAndReduceShapes(c.shape,p),f=F.upcastType(c.dtype,"int32"),g=Rh(n,h,f),y=k.sizeFromShape(m),A=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let v=0;v<A.length;++v){let b=v*y,w=0;for(let N=0;N<y;++N)w+=x[b+N];A[v]=w}if(i){let v=F.expandShapeToKeepDim(g.shape,l),b=g;g=gt({inputs:{x:g},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(b)}return n.disposeIntermediateTensorInfo(o),d!=null&&n.disposeIntermediateTensorInfo(c),g}var Bz={kernelName:ii,backendName:"cpu",kernelFunc:Sd};function Vz(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:u}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:d}=F.getEinsumComputePath(o,u),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=F.getEinsumPermutation(h,u[g]),x;F.isIdentityPermutation(y)?x=s[g]:(x=ua({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let b=0;b<A.length;++b)v.splice(A[b],0,1);k.arraysEqual(x.shape,v)||(x=gt({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Mh({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(l[f]>=0&&(c=Sd({inputs:{x:c},backend:n,attrs:{axis:l[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var jz={kernelName:oc,backendName:"cpu",kernelFunc:Vz};function Uz(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;we([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let u=0;u<i.length;++u){let l=i[u];l>=1?s[u]=o[u]:s[u]=o[u]*(l+1)}return n.makeTensorInfo(r.shape,"float32",s)}var Hz={kernelName:lc,backendName:"cpu",kernelFunc:Uz},Gz=F.ERF_P,qz=F.ERF_A1,Xz=F.ERF_A2,Kz=F.ERF_A3,Zz=F.ERF_A4,Yz=F.ERF_A5,Jz=rt(Eo,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+Gz*n);return t*(1-((((Yz*a+Zz)*a+Kz)*a+Xz)*a+qz)*a*Math.exp(-n*n))}),Qz={kernelName:Eo,backendName:"cpu",kernelFunc:Jz};function $h(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),u=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),o.splice(u,0,1),gt({inputs:{x:r},backend:n,attrs:{shape:o}})}var e_={kernelName:Mo,backendName:"cpu",kernelFunc:$h},t_=Ot((e,t)=>e/t),Cg=Yt(Rs,t_),Eg={kernelName:Rs,backendName:"cpu",kernelFunc:Cg};function sv(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[r,s],d=k.sizeFromShape(l),p=k.getTypedArrayFromDType("float32",d),c=k.getTypedArrayFromDType("float32",d);for(let g=0;g<r;g++){let y=Di({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),A=Di({inputs:{x:u},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=qn({inputs:{real:y,imag:A},backend:n}),{real:v,imag:b}=n_(x,t,n),w=F.mergeRealAndImagArrays(v,b);for(let N=0;N<s;N++){let C=F.getComplexWithIndex(w,N);p[g*s+N]=C.real,c[g*s+N]=C.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(l,"float32",p),m=n.makeTensorInfo(l,"float32",c),f=qn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function n_(e,t,n){let a=k.sizeFromShape(e.shape),r=n.data.get(e.dataId),s=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(a_(a)){let o=Rg(s,i,a,t,n),u=[e.shape[0],e.shape[1]];if(t){let l=n.makeTensorInfo(u,"float32",o.real),d=n.makeTensorInfo(u,"float32",o.imag),p=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),c=Ya({inputs:{x:p},backend:n}),h=Eg.kernelFunc({inputs:{a:l,b:p},backend:n}),m=Eg.kernelFunc({inputs:{a:d,b:c},backend:n}),f=n.data.get(h.dataId).values,g=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return o}else{let o=F.mergeRealAndImagArrays(s,i),u=r_(o,a,t);return F.splitRealAndImagArrays(u)}}function a_(e){return(e&e-1)==0}function Rg(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=F.mergeRealAndImagArrays(e,t),i=n/2,o=F.complexWithEvenIndex(s),u=o.real,l=o.imag,d=[u.length],p=r.makeTensorInfo(d,"float32",u),c=r.makeTensorInfo(d,"float32",l),h=qn({inputs:{real:p,imag:c},backend:r}),m=F.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],A=r.makeTensorInfo(y,"float32",f),x=r.makeTensorInfo(y,"float32",g),v=qn({inputs:{real:A,imag:x},backend:r}),b=Rg(u,l,i,a,r),w=b.real,N=b.imag,C=[w.length],E=r.makeTensorInfo(C,"float32",w),_=r.makeTensorInfo(C,"float32",N),$=qn({inputs:{real:E,imag:_},backend:r}),S=Rg(f,g,i,a,r),z=S.real,O=S.imag,W=[z.length],G=r.makeTensorInfo(W,"float32",z),H=r.makeTensorInfo(W,"float32",O),J=qn({inputs:{real:G,imag:H},backend:r}),K=F.exponents(n,a),ne=[K.real.length],Q=r.makeTensorInfo(ne,"float32",K.real),se=r.makeTensorInfo(ne,"float32",K.imag),Z=qn({inputs:{real:Q,imag:se},backend:r}),le=Mh({inputs:{a:Z,b:J},backend:r}),oe=Id({inputs:{a:$,b:le},backend:r}),xe=Sg({inputs:{a:$,b:le},backend:r}),fe=$i({inputs:{input:oe},backend:r}),Ne=$i({inputs:{input:xe},backend:r}),Te=Ll({inputs:{input:oe},backend:r}),Oe=Ll({inputs:{input:xe},backend:r}),Pe=Wl({inputs:[fe,Ne],backend:r,attrs:{axis:0}}),ze=Wl({inputs:[Te,Oe],backend:r,attrs:{axis:0}}),tt=r.data.get(Pe.dataId).values,nt=r.data.get(ze.dataId).values;return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(E),r.disposeIntermediateTensorInfo(_),r.disposeIntermediateTensorInfo($),r.disposeIntermediateTensorInfo(G),r.disposeIntermediateTensorInfo(H),r.disposeIntermediateTensorInfo(J),r.disposeIntermediateTensorInfo(Q),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(Z),r.disposeIntermediateTensorInfo(le),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(xe),r.disposeIntermediateTensorInfo(fe),r.disposeIntermediateTensorInfo(Te),r.disposeIntermediateTensorInfo(Ne),r.disposeIntermediateTensorInfo(Oe),r.disposeIntermediateTensorInfo(Pe),r.disposeIntermediateTensorInfo(ze),{real:tt,imag:nt}}function r_(e,t,n){let a=new Float32Array(t*2);for(let r=0;r<t;r++){let s=0,i=0;for(let o=0;o<t;o++){let u=F.exponent(r*o,t,n),l=F.getComplexWithIndex(e,o);s+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(s/=t,i/=t),F.assignToTypedArray(a,s,i,r)}return a}function s_(e){let{inputs:t,backend:n}=e,{input:a}=t,r=k.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=gt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),u=sv(o,!1,n),l=gt({inputs:{x:u},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var i_={kernelName:uc,backendName:"cpu",kernelFunc:s_};function Mg(e){let{backend:t,attrs:n}=e,{shape:a,value:r,dtype:s}=n,i=s||k.inferDtype(r),o=k.getArrayFromDType(i,k.sizeFromShape(a));return l_(o,r,i),t.makeTensorInfo(a,i,o)}var o_={kernelName:Pu,backendName:"cpu",kernelFunc:Mg};function l_(e,t,n){e.fill(t)}var u_={kernelName:$o,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,u,l]=a.shape,d=r.data.get(a.dataId).values;for(let p=0;p<i;p++){let c=p*u*o*l;for(let h=0;h<o;h++){let m=h*(u*l);for(let f=0;f<u;f++){let g=f*l;for(let y=0;y<l;y++){let A=[i,h,f,y][2],x=Math.round(u-A),v=c+m+g+y,b=d[v];if(x>=0&&x<u){let w=x*l,N=c+m+w+y;b=d[N]}s[v]=b}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},d_=Ot((e,t)=>Math.floor(e/t)),p_=Yt($s,d_,null,"int32"),c_={kernelName:$s,backendName:"cpu",kernelFunc:p_};function h_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=av({inputs:{x:r,filter:s},backend:n,attrs:{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let g=f;f=Id({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Ng(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var f_={kernelName:fi,backendName:"cpu",kernelFunc:h_};function m_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=rv({inputs:{x:r,filter:s},backend:n,attrs:{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let g=f;f=Id({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Ng(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var g_={kernelName:mi,backendName:"cpu",kernelFunc:m_};function y_(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[u,l,d,p]=F.prepareAndValidate(a,r);if(l===0)return n.makeTensorInfo(u,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=v7(c,h,a.dtype,l,o,d,p,a.shape,s);return n.makeTensorInfo(u,a.dtype,m.values)}var A_={kernelName:Oo,backendName:"cpu",kernelFunc:y_};function x_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;we([r,s],"gatherV2");let u=o;o==null&&(u=0);let l=k.sizeFromShape(s.shape),d=k.parseAxisParam(i,r.shape)[0],p=F.segment_util.collectGatherOpShapeInfo(r,s,d,u),c=gt({inputs:{x:r},backend:n,attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]}}),h=gt({inputs:{x:s},backend:n,attrs:{shape:[p.batchSize,l/p.batchSize]}}),m=[p.batchSize,p.outerSize,l/p.batchSize,p.sliceSize],f=n.bufferSync(h),g=n.bufferSync(c),y=w7(g,f,m);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(p.outputShape,y.dtype,y.values)}var b_={kernelName:Do,backendName:"cpu",kernelFunc:x_};function v_(e){let{inputs:t,backend:n}=e,{input:a}=t,r=k.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=gt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),u=sv(o,!0,n),l=gt({inputs:{x:u},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var w_={kernelName:dc,backendName:"cpu",kernelFunc:v_},k_=rt(_o,e=>Number.isFinite(e)?1:0,"bool"),I_={kernelName:_o,backendName:"cpu",kernelFunc:k_},S_=rt(Po,e=>Math.abs(e)===Infinity?1:0,"bool"),N_={kernelName:Po,backendName:"cpu",kernelFunc:S_},T_=rt(Lo,e=>Number.isNaN(e)?1:0,"bool"),C_={kernelName:Lo,backendName:"cpu",kernelFunc:T_};function E_(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=T7(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var R_={kernelName:cc,backendName:"cpu",kernelFunc:E_},M_=rt(Vo,e=>Math.log1p(e)),F_={kernelName:Vo,backendName:"cpu",kernelFunc:M_},$_=Ot((e,t)=>e&&t),D_=Yt(jo,$_,null,"bool"),O_={kernelName:jo,backendName:"cpu",kernelFunc:D_},z_=rt(Lu,e=>e?0:1,"bool"),__={kernelName:Lu,backendName:"cpu",kernelFunc:z_},P_=Ot((e,t)=>e||t),L_=Yt(Wu,P_,null,"bool"),W_={kernelName:Wu,backendName:"cpu",kernelFunc:L_};function B_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:u}=a;we(r,"LRN");let l=r.shape[3],d=l-1,p=n.data.get(r.dataId).values,c=k.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%l,y=f-g+Math.max(0,g-s),A=f-g+Math.min(g+s,d),x=0;for(;y<=A;y++){let v=p[y];x+=v*v}return x}for(let f=0;f<c;f++){let g=m(f),y=p[f]*Math.pow(i+o*g,-u);h[f]=y}return n.makeTensorInfo(r.shape,r.dtype,h)}var V_={kernelName:Bu,backendName:"cpu",kernelFunc:B_};function j_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:d}=a;we(i,"LRNGrad");let p=k.sizeFromShape(i.shape),c=i.shape[3],h=n.data.get(i.dataId).values,m=n.data.get(r.dataId).values,f=n.data.get(s.dataId).values,g=new Float32Array(p),y=p;for(let A=0;A<y;A++){let x=A%c,v=A-x+Math.max(0,x-o),b=A-x+Math.min(c,x+o+1),w=0;for(let N=v;N<b;N++)w+=Math.pow(m[N],2);w=l*w+u;for(let N=v;N<b;N++){let C=-2*l*d*m[N]*f[A]/w;A===N&&(C+=Math.pow(w,-d)),C*=h[A],g[N]+=C}}return n.makeTensorInfo(i.shape,r.dtype,g)}var U_={kernelName:hc,backendName:"cpu",kernelFunc:j_};function iv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,u=r.shape,l=u.length,d=k.parseAxisParam(s,u),p=d,c=F.getAxesPermutation(p,l),h=o.data.get(r.dataId).values;if(c!=null){let v=new Array(l);for(let b=0;b<v.length;b++)v[b]=u[c[b]];h=wg(h,u,r.dtype,c,v),p=F.getInnerMostAxes(p.length,l),u=v}we(r,"max"),F.assertAxesAreInnerMostDims("max",p,l);let[m,f]=F.computeOutAndReduceShapes(u,p),g=k.sizeFromShape(f),y=E7(h,g,m,r.dtype),A=o.write(y,m,r.dtype),x=m;return i&&(x=F.expandShapeToKeepDim(m,d)),{dataId:A,shape:x,dtype:r.dtype}}var H_={kernelName:Ls,backendName:"cpu",kernelFunc:iv};function G_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u),p;if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))p=Ya({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=Tg(c,r.shape,r.dtype,h,d,"max");p=n.makeTensorInfo(d.outShape,r.dtype,m.values)}return p}var q_={kernelName:Bs,backendName:"cpu",kernelFunc:G_};function X_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=a;we(r,"maxPool3d");let d=F.computePool3DInfo(r.shape,s,i,1,o,u,l),p=n.data.get(r.dataId).values,c=nv(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var K_={kernelName:Vu,backendName:"cpu",kernelFunc:X_};function Z_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=a;we([r,s],"maxPool3DGrad");let d=F.computePool3DInfo(s.shape,i,o,1,u,l),p=n.bufferSync(s),c=BO(p,d),h=d.strideDepth,m=d.strideHeight,f=d.strideWidth,g=d.dilationDepth,y=d.dilationHeight,A=d.dilationWidth,x=d.effectiveFilterDepth,v=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=x-1-d.padInfo.front,N=b-1-d.padInfo.left,C=v-1-d.padInfo.top,E=Ve(s.shape,"float32"),_=n.bufferSync(r);for(let $=0;$<d.batchSize;++$)for(let S=0;S<d.inChannels;++S)for(let z=0;z<d.inDepth;++z)for(let O=0;O<d.inHeight;++O)for(let W=0;W<d.inWidth;++W){let G=z-w,H=O-C,J=W-N,K=0;for(let ne=0;ne<x;ne+=g){let Q=(G+ne)/h;if(!(Q<0||Q>=d.outDepth||Math.floor(Q)!==Q))for(let se=0;se<v;se+=y){let Z=(H+se)/m;if(!(Z<0||Z>=d.outHeight||Math.floor(Z)!==Z))for(let le=0;le<b;le+=A){let oe=(J+le)/f;if(oe<0||oe>=d.outWidth||Math.floor(oe)!==oe)continue;let xe=x*v*b-1-c.get($,Q,Z,oe,S),fe=ne*v*b+se*b+le,Ne=xe===fe?1:0;Ne!==0&&(K+=_.get($,Q,Z,oe,S)*Ne)}}}E.set(K,$,z,O,W,S)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var Y_={kernelName:mc,backendName:"cpu",kernelFunc:Z_};function J_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,u,l,1,d,p),h=n.data.get(o.dataId).values,m=Ve(c.outShape,o.dtype,tv(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,y=c.dilationHeight,A=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,b=v-1-c.padInfo.left,w=x-1-c.padInfo.top,N=Ve(o.shape,"float32"),C=n.data.get(r.dataId).values,E=Ve(r.shape,"float32",C);for(let _=0;_<c.batchSize;++_)for(let $=0;$<c.inChannels;++$)for(let S=0;S<c.inHeight;++S)for(let z=0;z<c.inWidth;++z){let O=S-w,W=z-b,G=0;for(let H=0;H<x;H+=y){let J=(O+H)/f;if(!(J<0||J>=c.outHeight||Math.floor(J)!==J))for(let K=0;K<v;K+=A){let ne=(W+K)/g;if(ne<0||ne>=c.outWidth||Math.floor(ne)!==ne)continue;let Q=x*v-1-m.get(_,J,ne,$),se=H*v+K,Z=Q===se?1:0;Z!==0&&(G+=E.get(_,J,ne,$)*Z)}}N.set(G,_,S,z,$)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var Q_={kernelName:fc,backendName:"cpu",kernelFunc:J_};function eP(e,t,n,a,r){let s=k.computeStrides(t),i=Tg(e,t,n,s,r,"max"),o=tv(e,t,n,r,!0,a);return[i.values,o.values]}var tP={kernelName:gc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,u=n;we(a,"MaxPoolWithArgmax");let l=u.data.get(a.dataId).values,d=F.computePool2DInfo(a.shape,r,s,[1,1],i),[p,c]=eP(l,a.shape,a.dtype,o,d),h=u.write(p,d.outShape,a.dtype),m=u.write(c,d.outShape,a.dtype);return[{dataId:h,shape:d.outShape,dtype:a.dtype},{dataId:m,shape:d.outShape,dtype:"int32"}]}};function nP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),u=F.computeOutAndReduceShapes(r.shape,o)[1],l=k.sizeFromShape(u),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([l]));d.push(p);let c=Zr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(c);let h=Cg({inputs:{a:c,b:p},backend:n});d.push(h);let m=Sd({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var aP={kernelName:Vs,backendName:"cpu",kernelFunc:nP};function rP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"min");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*h,x=f[A];for(let v=0;v<h;++v){let b=f[A+v];(Number.isNaN(b)||b<x)&&(x=b)}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),A=gt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var sP={kernelName:js,backendName:"cpu",kernelFunc:rP};function iP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;we(r,"mirrorPad");let o=s.map((A,x)=>A[0]+r.shape[x]+A[1]),u=s.map(A=>A[0]),l=s.map((A,x)=>A[0]+r.shape[x]),d=i==="reflect"?0:1,p=n.data.get(r.dataId).values,c=r.shape.length,h=k.computeStrides(r.shape),m=k.sizeFromShape(o),f=o.length,g=k.computeStrides(o),y=k.getTypedArrayFromDType(r.dtype,m);for(let A=0;A<m;A++){let x=k.indexToLoc(A,f,g);for(let b=0;b<f;b++)x[b]<u[b]?x[b]=u[b]*2-x[b]-d:x[b]>=l[b]&&(x[b]=(l[b]-1)*2-x[b]+d);x=x.map((b,w)=>b-u[w]);let v=k.locToIndex(x,c,h);y[A]=p[v]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var oP={kernelName:Hs,backendName:"cpu",kernelFunc:iP},lP=Ot((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),uP=Yt(Uo,lP),dP={kernelName:Uo,backendName:"cpu",kernelFunc:uP},pP=gs(S5());function ov(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let u=k.parseAxisParam([o],r.shape),l=iv({inputs:{x:r},backend:n,attrs:{reductionIndices:u,keepDims:!1}}),d=F.expandShapeToKeepDim(l.shape,u),p=gt({inputs:{x:l},backend:n,attrs:{shape:d}}),c=Sg({inputs:{a:r,b:p},backend:n}),h=A7({inputs:{x:c},backend:n}),m=Sd({inputs:{x:h},backend:n,attrs:{axis:u,keepDims:!1}}),f=gt({inputs:{x:m},backend:n,attrs:{shape:d}}),g=Cg({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var cP={kernelName:oi,backendName:"cpu",kernelFunc:ov};function hP(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;we(r,"multinomial");let u=o?r:ov({inputs:{logits:r},backend:n,attrs:{dim:-1}}),l=u.shape[0],d=u.shape[1],p=n.data.get(u.dataId).values,c=[l,s],h=k.makeZerosTypedArray(k.sizeFromShape(c),"int32");for(let m=0;m<l;++m){let f=m*d,g=new Float32Array(d-1);g[0]=p[f];for(let x=1;x<g.length;++x)g[x]=g[x-1]+p[f+x];let y=pP.alea(i.toString()),A=m*s;for(let x=0;x<s;++x){let v=y();h[A+x]=g.length;for(let b=0;b<g.length;b++)if(v<g[b]){h[A+x]=b;break}}}return o||n.disposeIntermediateTensorInfo(u),n.makeTensorInfo(c,"int32",h)}var fP={kernelName:yc,backendName:"cpu",kernelFunc:hP},mP=Za.nonMaxSuppressionV3Impl;function gP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=a;we(r,"NonMaxSuppression");let l=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:p}=mP(l,d,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var yP={kernelName:qo,backendName:"cpu",kernelFunc:gP},AP=Za.nonMaxSuppressionV4Impl;function xP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=a;we(r,"NonMaxSuppressionPadded");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=AP(d,p,i,o,u,l);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var bP={kernelName:Xo,backendName:"cpu",kernelFunc:xP},vP=Za.nonMaxSuppressionV5Impl;function wP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=a;we(r,"NonMaxSuppressionWithScore");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,c=i,h=o,m=u,f=l,{selectedIndices:g,selectedScores:y}=vP(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var kP={kernelName:Ko,backendName:"cpu",kernelFunc:wP};function IP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;we(r,"oneHot");let u=k.sizeFromShape(r.shape),l=new Float32Array(u*s);l.fill(o);let d=n.data.get(r.dataId).values;for(let p=0;p<u;++p)d[p]>=0&&d[p]<s&&(l[p*s+d[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",l)}var SP={kernelName:qs,backendName:"cpu",kernelFunc:IP};function Dh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=$i({inputs:{input:a},backend:n}),s=Dh({inputs:{x:r},backend:n}),i=Ll({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),u=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Mg({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var NP={kernelName:hl,backendName:"cpu",kernelFunc:Dh};function lv(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=$i({inputs:{input:a},backend:n}),s=lv({inputs:{x:r},backend:n}),i=Ll({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),u=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Mg({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var TP={kernelName:Zo,backendName:"cpu",kernelFunc:lv};function uv(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $h({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=$h({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Wl({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var CP={kernelName:Yo,backendName:"cpu",kernelFunc:uv};function EP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;we(r,"pad");let o=s.map((y,A)=>y[0]+r.shape[A]+y[1]),u=s.map(y=>y[0]),l=n.data.get(r.dataId).values,d=k.sizeFromShape(r.shape),p=r.shape.length,c=k.computeStrides(r.shape),h=k.sizeFromShape(o),m=o.length,f=k.computeStrides(o),g=k.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let y=0;y<d;y++){let A=k.indexToLoc(y,p,c).map((v,b)=>v+u[b]),x=k.locToIndex(A,m,f);g[x]=l[y]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var dv={kernelName:Xs,backendName:"cpu",kernelFunc:EP},RP=Ot((e,t)=>Math.pow(e,t)),MP=Yt(Ks,RP),FP={kernelName:Ks,backendName:"cpu",kernelFunc:MP};function $P(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=kg(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var DP={kernelName:ju,backendName:"cpu",kernelFunc:$P},OP=rt(Qo,e=>1/e),zP={kernelName:Qo,backendName:"cpu",kernelFunc:OP};function _P(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeBilinear");let u=k.computeStrides(r.shape),[l,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,l,d,m])),y=[s&&l>1?c-1:c,s&&d>1?h-1:h],A=[s&&l>1?l-1:l,s&&d>1?d-1:d],x=0,v=y[0]/A[0],b=y[1]/A[1];for(let w=0;w<p;w++)for(let N=0;N<l;N++){let C;i?C=v*(N+.5)-.5:C=v*N;let E=Math.max(0,Math.floor(C)),_=C-E,$=Math.min(c-1,Math.ceil(C)),S=w*u[0]+E*u[1],z=w*u[0]+$*u[1];for(let O=0;O<d;O++){let W;i?W=b*(O+.5)-.5:W=b*O;let G=Math.max(0,Math.floor(W)),H=W-G,J=Math.min(h-1,Math.ceil(W)),K=S+G*u[2],ne=z+G*u[2],Q=S+J*u[2],se=z+J*u[2];for(let Z=0;Z<m;Z++){let le=f[K+Z],oe=f[ne+Z],xe=f[Q+Z],fe=f[se+Z],Ne=le+(xe-le)*H,Te=oe+(fe-oe)*H,Oe=Ne+(Te-Ne)*_;g[x++]=Oe}}}return n.makeTensorInfo([p,l,d,m],"float32",g)}var PP={kernelName:Js,backendName:"cpu",kernelFunc:_P};function LP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[u,l,d,p]=r.shape,[,c,h]=s.shape,m=new Float32Array(u*l*d*p),f=[i&&c>1?l-1:l,i&&h>1?d-1:d],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/g[0],A=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let b=0;b<u;b++){let w=b*o[0];for(let N=0;N<c;N++){let C=N*y,E=Math.floor(C),_=Math.min(Math.ceil(C),l-1),$=w+E*o[1],S=w+_*o[1],z=C-E,O=1-z;for(let W=0;W<h;W++){let G=W*A,H=Math.floor(G),J=Math.min(Math.ceil(G),d-1),K=G-H,ne=1-K,Q=$+H*o[2],se=$+J*o[2],Z=S+H*o[2],le=S+J*o[2],oe=O*ne,xe=O*K,fe=z*ne,Ne=z*K;for(let Te=0;Te<p;Te++){let Oe=x[v++];m[Q+Te]+=Oe*oe,m[se+Te]+=Oe*xe,m[Z+Te]+=Oe*fe,m[le+Te]+=Oe*Ne}}}}return n.makeTensorInfo([u,d,l,p],"float32",m)}var WP={kernelName:bc,backendName:"cpu",kernelFunc:LP};function BP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeNearestNeighbor");let u=k.computeStrides(r.shape),[l,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(p*l*d*m),y=[s&&l>1?c-1:c,s&&d>1?h-1:h],A=[s&&l>1?l-1:l,s&&d>1?d-1:d],x=y[0]/A[0],v=y[1]/A[1],b=0;for(let w=0;w<p;w++){let N=w*u[0];for(let C=0;C<l;C++){let E=i?x*(C+.5):x*C,_=Math.min(c-1,s?Math.round(E):Math.floor(E));i&&(_=Math.max(0,_));let $=N+_*u[1];for(let S=0;S<d;S++){let z=i?v*(S+.5):v*S,O=Math.min(h-1,s?Math.round(z):Math.floor(z));i&&(O=Math.max(0,O));let W=$+O*u[2];for(let G=0;G<m;G++){let H=f[W+G];g[b++]=H}}}}return n.makeTensorInfo([p,l,d,m],r.dtype,g)}var VP={kernelName:Uu,backendName:"cpu",kernelFunc:BP};function jP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeNearestNeighborGrad");let o=k.computeStrides(r.shape),u=k.computeStrides(s.shape),[l,d,p,c]=r.shape,[,h,m]=s.shape,f=new Float32Array(l*d*p*c),g=n.data.get(s.dataId).values,y=[i&&h>1?d-1:d,i&&m>1?p-1:p],A=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=y[0]/A[0],v=y[1]/A[1],b=1/x,w=1/v,N=Math.ceil(b)*2+2,C=Math.ceil(w)*2+2;for(let E=0;E<l;E++){let _=E*o[0];for(let $=0;$<d;$++){let S=_+$*o[1],z=Math.floor($*b),O=Math.floor(z-N/2);for(let W=0;W<p;W++){let G=S+W*o[2],H=Math.floor(W*w),J=Math.floor(H-C/2);for(let K=0;K<c;K++){let ne=0;for(let Q=0;Q<N;Q++){let se=Q+O;if(se<0||se>=h)continue;let Z=_+se*u[1],le=se*x,oe=Math.min(d-1,i?Math.round(le):Math.floor(le));if($===oe)for(let xe=0;xe<C;xe++){let fe=xe+J;if(fe<0||fe>=m)continue;let Ne=Z+fe*u[2],Te=fe*v,Oe=Math.min(p-1,i?Math.round(Te):Math.floor(Te));W===Oe&&(ne+=g[Ne+K])}}f[G+K]=ne}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var UP={kernelName:xc,backendName:"cpu",kernelFunc:jP};function HP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;we(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Ya({inputs:{x:r},backend:n});let u=new Lt(r.shape,r.dtype),l=n.bufferSync(r);for(let d=0;d<u.size;d++){let p=u.indexToLoc(d),c=p.slice();o.forEach(h=>c[h]=r.shape[h]-1-c[h]),u.set(l.get(...c),...p)}return n.makeTensorInfo(u.shape,u.dtype,u.values)}var GP={kernelName:ei,backendName:"cpu",kernelFunc:HP},qP={kernelName:fl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,u=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[l,d,p,c]=a.shape,[h,m]=F.getImageCenter(i,d,p),f=255,g=Math.sin(r),y=Math.cos(r),A=o.data.get(a.dataId).values;for(let x=0;x<l;x++){let v=x*p*d*c;for(let b=0;b<d;b++){let w=b*(p*c);for(let N=0;N<p;N++){let C=N*c;for(let E=0;E<c;E++){let _=[l,b,N,E],$=_[2],S=_[1],z=($-h)*y-(S-m)*g,O=($-h)*g+(S-m)*y;z=Math.round(z+h),O=Math.round(O+m);let W=s;if(typeof s!="number"&&(E===3?W=f:W=s[E]),z>=0&&z<p&&O>=0&&O<d){let H=O*(p*c),J=z*c,K=v+H+J+E;W=A[K]}let G=v+w+C+E;u[G]=W}}}}return{dataId:o.write(u,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},XP=rt(ti,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}),KP={kernelName:ti,backendName:"cpu",kernelFunc:XP};function pv(e,t,n,a,r,s,i,o,u,l){let d=[a/r,r],p=e.values,c=t.values;if(a===0)return Ve(n,t.dtype);let h=Ve(d,t.dtype);h.values.fill(u);for(let m=0;m<s;m++){let f=[],g=0;for(let y=0;y<i;y++){let A=p[m*i+y];f.push(A),g+=A*o[y]}if(g<0||g>=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<r;y++)l?h.values[g*r+y]+=c[m*r+y]:h.values[g*r+y]=t.rank===0?c[0]:c[m*r+y]}return h}function ZP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:u,sliceSize:l,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=pv(h,m,i,p,l,u,o,d,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var YP={kernelName:tl,backendName:"cpu",kernelFunc:ZP};function JP(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;we([a,r,s],"select");let i=a.shape.length,o=n.data.get(a.dataId).values,u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=Aa(r.dtype,s.dtype),p=k.makeZerosTypedArray(k.sizeFromShape(r.shape),d),c=0,h=i===0||i>1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<h;f++)o[m]===1?p[c++]=u[m]:p[c++]=l[m];return n.makeTensorInfo(r.shape,d,p)}var QP={kernelName:nl,backendName:"cpu",kernelFunc:JP},eL=F.SELU_SCALEALPHA,tL=F.SELU_SCALE,nL=rt(al,e=>e>=0?tL*e:eL*(Math.exp(e)-1)),aL={kernelName:al,backendName:"cpu",kernelFunc:nL},rL=rt(il,e=>e<0?-1:e>0?1:0),sL={kernelName:il,backendName:"cpu",kernelFunc:rL},iL=rt(ai,e=>Math.sin(e)),oL={kernelName:ai,backendName:"cpu",kernelFunc:iL},lL=rt(sl,e=>Math.sinh(e)),uL={kernelName:sl,backendName:"cpu",kernelFunc:lL},dL=11920928955078125e-23,cv=Math.log(dL)+2,pL=rt(ol,e=>{let t=e>-cv,n=e<cv,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),cL={kernelName:ol,backendName:"cpu",kernelFunc:pL};function hL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;we([r],"spaceToBatchND");let o=k.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let g=1+s.length;g<r.shape.length;++g)u.push([0,0]);let l=dv.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),d=F.getReshaped(l.shape,s,o,!1),p=F.getPermuted(d.length,s.length,!1),c=F.getReshapedPermuted(l.shape,s,o,!1),h=gt({inputs:{x:l},backend:n,attrs:{shape:d}}),m=ua({inputs:{x:h},backend:n,attrs:{perm:p}}),f=gt({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var fL={kernelName:Hu,backendName:"cpu",kernelFunc:hL};function mL(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=n.data.get(a.dataId).values,u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values[0],[p,c,h,m,f]=z7(o,a.shape,a.dtype,u,r.dtype,l,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var gL={kernelName:vc,backendName:"cpu",kernelFunc:mL};function yL(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(r.dataId).values),o=n.data.get(a.dataId).values,u=Array.from(n.data.get(s.dataId).values),[l,d,p]=_7(o,a.shape,a.dtype,i,u);return[n.makeTensorInfo(d,a.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var AL={kernelName:wc,backendName:"cpu",kernelFunc:yL};function xL(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,[l,d]=Ig(i,a.shape,a.dtype,o,u,!0);return n.makeTensorInfo(d,a.dtype,l)}var bL={kernelName:kc,backendName:"cpu",kernelFunc:xL};function vL(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,[l,d]=Ig(i,a.shape,a.dtype,o,u);return n.makeTensorInfo(d,a.dtype,l)}var wL={kernelName:Ic,backendName:"cpu",kernelFunc:vL};function kL(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:u,numUpdates:l,sliceSize:d,strides:p,outputSize:c}=F.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=pv(m,f,o,c,d,l,u,p,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var IL={kernelName:Sc,backendName:"cpu",kernelFunc:kL};function SL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],u=F.prepareSplitSize(r,s,o),l=new Array(r.shape.length).fill(0),d=r.shape.slice();return u.map(p=>{let c=[...d];c[o]=p;let h=Di({inputs:{x:r},backend:n,attrs:{begin:l,size:c}});return l[o]+=p,h})}var NL={kernelName:ll,backendName:"cpu",kernelFunc:SL},TL=rt(si,e=>Math.sqrt(e)),CL={kernelName:si,backendName:"cpu",kernelFunc:TL},EL={kernelName:Gu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;we(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},RL=rt(Pr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),ML={kernelName:Pr,backendName:"cpu",kernelFunc:RL};function FL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a;we(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=fn.sliceInfo(r.shape,s,i,o,u,l,d,p,c),x=gt({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=Di({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=gt({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))v=n.makeTensorInfo(A,r.dtype,[]);else{let w=n.bufferSync(x),N=L7(A,w,f,m);v=n.makeTensorInfo(N.shape,N.dtype,N.values)}let b=gt({inputs:{x:v},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var $L={kernelName:ul,backendName:"cpu",kernelFunc:FL};function DL(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=a,{data:d,dataSplits:p}=t,c=n.data.get(d.dataId).values,h=n.data.get(p.dataId).values,[m,f]=W7(c,h,r,s,i,o,u,l);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var OL={kernelName:Nc,backendName:"cpu",kernelFunc:DL};function zL(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values[0],[l,d,p]=B7(o,u,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",l),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var _L={kernelName:Tc,backendName:"cpu",kernelFunc:zL};function PL(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.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 i=n.data.get(s.dataId).values,o=V7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var LL={kernelName:Cc,backendName:"cpu",kernelFunc:PL},WL=rt(di,e=>Math.tan(e)),BL={kernelName:di,backendName:"cpu",kernelFunc:WL},VL=rt(pi,e=>Math.tanh(e)),jL={kernelName:pi,backendName:"cpu",kernelFunc:VL};function UL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;we(r,"tile");let i=U7(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var HL={kernelName:_r,backendName:"cpu",kernelFunc:UL};function GL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;we(r,"topk");let o=n.data.get(r.dataId).values,[u,l]=H7(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}var qL={kernelName:dl,backendName:"cpu",kernelFunc:GL};function XL(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=n,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],g=[d,m,f,h],y=k.computeStrides(r.shape),A=y[0],x=y[1],v=y[2],b=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(g));b.fill(u);let w=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values;for(let C=0;C<d;++C){let E=s.shape[0]===1?N:N.subarray(C*8,C*8+8);for(let _=0;_<m;++_)for(let $=0;$<f;++$)for(let S=0;S<h;++S){let z,O=E[6]*$+E[7]*_+1;if(O===0)continue;let W=(E[0]*$+E[1]*_+E[2])/O,G=(E[3]*$+E[4]*_+E[5])/O,H=hv(W,c,o),J=hv(G,p,o);switch(i){case"nearest":z=eW(w,p,c,A,x,v,C,J,H,S,u);break;case"bilinear":z=tW(w,p,c,A,x,v,C,J,H,S,u);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let K=C*A+_*x+$*v+S;b[K]=z}return a.makeTensorInfo(g,r.dtype,b)}return{dataId:a.write(b,g,r.dtype),shape:r.shape,dtype:r.dtype}}var KL={kernelName:pl,backendName:"cpu",kernelFunc:XL};function hv(e,t,n){switch(n){case"reflect":return ZL(e,t);case"wrap":return YL(e,t);case"nearest":return QL(e,t);case"constant":default:return JL(e,t)}}function ZL(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let a=2*t;n<a&&(n=a*Math.trunc(-n/a)+n),n=n<-t?n+a:-n-1}else if(n>t-1)if(t<=1)n=0;else{let a=2*t;n-=a*Math.trunc(n/a),n>=t&&(n=a-n-1)}return k.clamp(0,n,t-1)}function YL(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let a=t-1;n+=t*(Math.trunc(-n/a)+1)}else if(n>t-1)if(t<=1)n=0;else{let a=t-1;n-=t*Math.trunc(n/a)}return k.clamp(0,n,t-1)}function JL(e,t){return e}function QL(e,t){return k.clamp(0,e,t-1)}function Nd(e,t,n,a,r,s,i,o,u,l,d){let p=i*a+o*r+u*s+l;return 0<=o&&o<t&&0<=u&&u<n?e[p]:d}function eW(e,t,n,a,r,s,i,o,u,l,d){let p=Math.round(o),c=Math.round(u);return Nd(e,t,n,a,r,s,i,p,c,l,d)}function tW(e,t,n,a,r,s,i,o,u,l,d){let p=Math.floor(o),c=Math.floor(u),h=p+1,m=c+1,f=(m-u)*Nd(e,t,n,a,r,s,i,p,c,l,d)+(u-c)*Nd(e,t,n,a,r,s,i,p,m,l,d),g=(m-u)*Nd(e,t,n,a,r,s,i,h,c,l,d)+(u-c)*Nd(e,t,n,a,r,s,i,h,m,l,d);return(h-o)*f+(o-p)*g}function nW(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;we(s,"unique");let i=a.data.get(s.dataId).values,{outputValues:o,outputShape:u,indices:l}=G7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(u,s.dtype,o),a.makeTensorInfo([l.length],"int32",l)]}var aW={kernelName:Ec,backendName:"cpu",kernelFunc:nW};function rW(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape.length,o=r.shape[s],u=new Array(i-1),l=0;for(let h=0;h<i;h++)h!==s&&(u[l++]=r.shape[h]);let d=new Array(i).fill(0),p=r.shape.slice();p[s]=1;let c=new Array(o);for(let h=0;h<c.length;h++){d[s]=h;let m=Di({inputs:{x:r},backend:n,attrs:{begin:d,size:p}});c[h]=gt({inputs:{x:m},backend:n,attrs:{shape:u}}),n.disposeIntermediateTensorInfo(m)}return c}var sW={kernelName:cl,backendName:"cpu",kernelFunc:rW};function iW(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a;we(r,"unsortedSegmentSum");let o=r.shape.length,u=s.shape.length,l=[],d=[],p=o-u,c=s;for(let m=0;m<p;++m){let f=$h({inputs:{input:c},backend:n,attrs:{dim:m+1}});c=f,d.push(f)}for(let m=0;m<i;++m){let f=k.createScalarValue(m,"int32"),g=n.makeTensorInfo([],"int32",f),y=g7({inputs:{a:g,b:c},backend:n}),A=Zr({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=Mh({inputs:{a:A,b:r},backend:n}),v=Sd({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});l.push(v),d.push(g),d.push(y),d.push(A),d.push(x),d.push(v)}let h=uv({inputs:l,backend:n,attrs:{axis:0}});return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var oW={kernelName:qu,backendName:"cpu",kernelFunc:iW},lW=[mO,cD,yO,xO,AD,vO,kO,SO,TO,EO,MO,$O,OO,PO,WO,jO,HO,qO,KO,hO,YO,QO,tz,gD,bD,az,hD,sz,oz,dz,cz,lz,gz,Az,fz,bz,wz,Iz,Nz,Cz,Rz,Mz,$z,Oz,_z,Pz,Wz,Lz,Eg,jz,sO,Hz,vD,Qz,wD,e_,ID,i_,o_,u_,ND,c_,f_,g_,A_,b_,CD,RD,fD,w_,iz,I_,N_,C_,iO,FD,DD,R_,zD,F_,O_,__,W_,V_,U_,PD,q_,K_,Y_,Q_,tP,H_,aP,sP,WD,oP,dP,fP,VD,UD,yP,bP,kP,GD,SP,TP,CP,dv,FP,lO,KD,DP,mD,zP,uO,dO,cO,PP,WP,VP,UP,GP,qP,KP,YD,YP,QP,aL,pO,sL,oL,uL,JD,cP,cL,fL,gL,AL,bL,wL,IL,NL,CL,EL,eO,ML,$L,OL,_L,LL,rO,Bz,BL,jL,HL,qL,qD,KL,aW,sW,oW,NP];for(let e of lW)gi(e);var fv={};Fe(fv,{assertNotComplex:()=>Vl,bindCanvasToFramebuffer:()=>bW,bindColorTextureToFramebuffer:()=>_h,bindTextureToProgramUniformSampler:()=>Ev,bindTextureUnit:()=>Nv,bindVertexBufferToProgramAttribute:()=>Dg,callAndCheck:()=>be,canBeRepresented:()=>mv,createFragmentShader:()=>Av,createFramebuffer:()=>Sv,createProgram:()=>xv,createStaticIndexBuffer:()=>wv,createStaticVertexBuffer:()=>vv,createTexture:()=>kv,createVertexShader:()=>yv,getBatchDim:()=>zi,getExtensionOrThrow:()=>Rd,getFramebufferErrorMessage:()=>Rv,getMaxTexturesInShader:()=>Dv,getNumChannels:()=>AW,getProgramUniformLocation:()=>Cv,getProgramUniformLocationOrThrow:()=>Tv,getRowsCols:()=>_i,getShapeAs3D:()=>Ph,getTextureShapeFromLogicalShape:()=>Fv,getWebGLDisjointQueryTimerVersion:()=>Ov,getWebGLErrorMessage:()=>gv,getWebGLMaxTextureSize:()=>$v,hasExtension:()=>pa,isCapableOfRenderingToFloatTexture:()=>zv,isDownloadFloatTextureEnabled:()=>_v,isReshapeFree:()=>Fd,isWebGLFenceEnabled:()=>Pv,isWebGLVersionEnabled:()=>zg,linkProgram:()=>bv,resetMaxTextureSize:()=>vW,resetMaxTexturesInShader:()=>wW,unbindColorTextureFromFramebuffer:()=>Og,unbindTextureUnit:()=>xW,validateFramebuffer:()=>Md,validateProgram:()=>zh,validateTextureSize:()=>Iv});var Oi={},Fg={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Oh(e,t){Oi[e]=t}function Ja(e){if(!(e in Oi)){let n=dW(e);if(n!==null)Oi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Oi[e];return t.isContextLost()?(delete Oi[e],Ja(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),Oi[e])}function uW(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 dW(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=uW(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Oi[e]},!1),e===1?t.getContext("webgl",Fg)||t.getContext("experimental-webgl",Fg):t.getContext("webgl2",Fg)}var Td;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(Td||(Td={}));var da;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(da||(da={}));var rn;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(rn||(rn={}));function Cd(e,t){return[t,e]}function pW(e,t){return e*t}function Ed(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Bl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function cW(e,t){let[n,a]=Bl(e,t);return n*a*4}function $g(e,t){let n=e,a,r,s,i,o,u,l,d,p,c;return te().getNumber("WEBGL_VERSION")===2?(a=n.R32F,r=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,l=4,d=1,p=n.HALF_FLOAT,c=n.FLOAT):(a=e.RGBA,r=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,l=4,d=4,p=t!=null?t.HALF_FLOAT_OES:null,c=e.FLOAT),u=e.RGBA,{internalFormatFloat:a,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:u,downloadUnpackNumChannels:l,defaultNumChannels:d,textureTypeHalfFloat:p,textureTypeFloat:c}}function be(e,t){let n=t();return te().getBool("DEBUG")&&hW(e),n}function hW(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+gv(e,t))}var fW=596e-10,mW=65504;function mv(e){return!!(te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||fW<Math.abs(e)&&Math.abs(e)<mW)}function gv(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 Rd(e,t){return br(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function yv(e,t){let n=br(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(be(e,()=>e.shaderSource(n,t)),be(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 Av(e,t){let n=br(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(be(e,()=>e.shaderSource(n,t)),be(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw yW(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var gW=/ERROR: [0-9]+:([0-9]+):/g;function yW(e,t){let n=gW.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let a=+n[1],r=e.split(`
|
|
`),s=r.length.toString().length+2,i=r.map((p,c)=>k.rightPad((c+1).toString(),s)+p),o=0;for(let p=0;p<i.length;p++)o=Math.max(i[p].length,o);let u=i.slice(0,a-1),l=i.slice(a-1,a),d=i.slice(a);console.log(u.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${k.rightPad(l[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(d.join(`
|
|
`))}function xv(e){return br(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function bv(e,t){if(be(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function zh(e,t){if(be(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function vv(e,t){let n=br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function wv(e,t){let n=br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function AW(){return te().getNumber("WEBGL_VERSION")===2?1:4}function kv(e){return br(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Iv(e,t){let n=te().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${e}x${t}]`;throw new Error("Requested texture size "+a+" is invalid.")}if(e>n||t>n){let a=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+a+" greater than WebGL maximum on this browser / GPU "+r+".")}}function Sv(e){return br(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Dg(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),be(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),be(e,()=>e.enableVertexAttribArray(o)),!0)}function Nv(e,t,n){Mv(e,n),be(e,()=>e.activeTexture(e.TEXTURE0+n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function xW(e,t){Mv(e,t),be(e,()=>e.activeTexture(e.TEXTURE0+t)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Tv(e,t,n){return br(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Cv(e,t,n){return e.getUniformLocation(t,n)}function Ev(e,t,n,a){be(e,()=>Nv(e,t,a)),be(e,()=>e.uniform1i(n,a))}function bW(e){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),be(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function _h(e,t,n){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),be(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function Og(e,t){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),be(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Md(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+Rv(e,t))}function Rv(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 ${t}`}}function br(e,t,n){let a=be(e,()=>t());if(a==null)throw new Error(n);return a}function Mv(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function zi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function _i(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 Ph(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[zi(e),..._i(e)]),t}function Fv(e,t=!1){let n=te().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let a=k.sizeFromShape(e);if(e.length<=1&&a<=n)return[1,a];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=zi(e),s=2,i=2;return e.length&&([s,i]=_i(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function Lh(e){return e%2==0}function Fd(e,t){if(e=e.slice(-2),t=t.slice(-2),k.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],a=t.slice(-1)[0];if(n===a||Lh(n)&&Lh(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Lh(e[0])&&Lh(t[0])}var Wh,Bh;function $v(e){if(Wh==null){let t=Ja(e);Wh=t.getParameter(t.MAX_TEXTURE_SIZE)}return Wh}function vW(){Wh=null}function wW(){Bh=null}function Dv(e){if(Bh==null){let t=Ja(e);Bh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Bh)}function Ov(e){if(e===0)return 0;let t,n=Ja(e);return pa(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:pa(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function pa(e,t){return e.getExtension(t)!=null}function zg(e){try{if(Ja(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function zv(e){if(e===0)return!1;let t=Ja(e);if(e===1){if(!pa(t,"OES_texture_float"))return!1}else if(!pa(t,"EXT_color_buffer_float"))return!1;return _g(t)}function _v(e){if(e===0)return!1;let t=Ja(e);if(e===1){if(!pa(t,"OES_texture_float")||!pa(t,"WEBGL_color_buffer_float"))return!1}else{if(pa(t,"EXT_color_buffer_float"))return _g(t);let n="EXT_color_buffer_half_float";if(pa(t,n)){let a=t.getExtension(n);return kW(t,a)}return!1}return _g(t)}function _g(e){let t=$g(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function kW(e,t){let n=$g(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function Pv(e){return e!==2?!1:Ja(e).fenceSync!=null}function Vl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Me=te();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>zg(2)?2:zg(1)?1:0);Me.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Me.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Me.get("WEBGL_VERSION")===2);Me.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Me.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Me.registerFlag("WEBGL_PACK",()=>Me.getBool("HAS_WEBGL"));Me.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_CLIP",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_REDUCE",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_LAZILY_UNPACK",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_CONV_IM2COL",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>$v(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Dv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Me.getNumber("WEBGL_VERSION");return e===0?0:Ov(e)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ad.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>zv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Me.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Me.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Me.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>_v(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Pv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Me.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Me.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}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>ad.isMobile()&&Me.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}.`)});Me.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);function An(){let e,t,n,a,r,s,i,o,u,l;return te().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,u="",l=`
|
|
#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",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,u=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,l=`
|
|
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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function Pi(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Pg(e){let t=k.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var Lv=`
|
|
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;
|
|
}
|
|
`,IW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Td.DENSE;let t=Ed(e),n=An();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},SW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Td.DENSE;let t=Ed(e),n=An();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},NW=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
|
|
${Lv}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},TW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
|
|
${Lv}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},CW=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=An(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${Pg(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / ${s};
|
|
int c = imod(flatIndex, ${s});
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
vec4 values = ${a.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];
|
|
}
|
|
|
|
${a.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}},EW=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=An(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let l=0;l<=1;l++){let d=u*2+l;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${l} < ${e[2]}) {
|
|
localCoords[2] += ${l};
|
|
if(localCoords[1] + ${u} < ${e[1]}) {
|
|
localCoords[1] += ${u};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${s};
|
|
c = imod(flatIndex, ${s});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
values = ${a.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${d}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${d}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${d}] = values[2];
|
|
} else {
|
|
result[${d}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${Pg(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${a.output} = ${o};
|
|
}
|
|
`}},Wv={};Fe(Wv,{bindVertexProgramAttributeStreams:()=>Kv,createBufferFromOutputTexture:()=>Jv,createFloat16MatrixTexture:()=>Hv,createFloat16PackedMatrixTexture:()=>Xv,createFloat32MatrixTexture:()=>Uv,createIndexBuffer:()=>jv,createPackedMatrixTexture:()=>qv,createUnsignedBytesMatrixTexture:()=>Gv,createVertexBuffer:()=>Vv,createVertexShader:()=>Bv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>ew,downloadFloat32MatrixFromBuffer:()=>Qv,downloadMatrixFromPackedOutputTexture:()=>nw,downloadPackedMatrixFromBuffer:()=>tw,getInternalFormatForFloat16MatrixTexture:()=>Wg,getInternalFormatForFloat16PackedMatrixTexture:()=>jg,getInternalFormatForFloat32MatrixTexture:()=>Lg,getInternalFormatForPackedMatrixTexture:()=>Vg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Bg,uploadDenseMatrixToTexture:()=>Zv,uploadPixelDataToTexture:()=>Yv});function Bv(e){let t=An(),n=`${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 yv(e,n)}function Vv(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 vv(e,t)}function jv(e){let t=new Uint16Array([0,1,2,2,1,3]);return wv(e,t)}function $d(e,t,n,a,r,s){Iv(t,n);let i=kv(e),o=e.TEXTURE_2D;return be(e,()=>e.bindTexture(o,i)),be(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),be(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),be(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Lg(e){return e.internalFormatFloat}function Uv(e,t,n,a){let[r,s]=Cd(t,n);return $d(e,r,s,Lg(a),a.textureFormatFloat,e.FLOAT)}function Wg(e){return e.internalFormatHalfFloat}function Hv(e,t,n,a){let[r,s]=Cd(t,n);return $d(e,r,s,Wg(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function Bg(e){return e.downloadTextureFormat}function Gv(e,t,n,a){let[r,s]=Cd(t,n);return $d(e,r,s,Bg(a),e.RGBA,e.UNSIGNED_BYTE)}function Vg(e){return e.internalFormatPackedFloat}function qv(e,t,n,a){let[r,s]=Bl(t,n);return $d(e,r,s,Vg(a),e.RGBA,e.FLOAT)}function jg(e){return e.internalFormatPackedHalfFloat}function Xv(e,t,n,a){let[r,s]=Bl(t,n);return $d(e,r,s,jg(a),e.RGBA,a.textureTypeHalfFloat)}function Kv(e,t,n){let a=0,r=3*4,s=3*4+2*4;return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Dg(e,t,"clipSpacePos",n,3,s,a)&&Dg(e,t,"uv",n,2,s,r)}function Zv(e,t,n,a,r,s){be(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,u=s.internalFormatPackedFloat),i.set(r),be(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,a,0,e.RGBA,o,i)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Yv(e,t,n){be(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Jv(e,t,n,a){let r=e.createBuffer();be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return be(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function Qv(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function ew(e,t,n,a){let[r,s]=Cd(t,n),i=4,o=new Uint8Array(pW(t*n,i));return be(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function tw(e,t,n,a,r,s,i,o){let u=e,l=new Float32Array(cW(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function nw(e,t,n){let a=new Float32Array(t*n*4);return be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Vh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=te().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Oh(t,e)):this.gl=Ja(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(te().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Rd(this.gl,r),pa(this.gl,s))this.textureHalfFloatExtension=Rd(this.gl,s);else if(te().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),pa(this.gl,a))this.colorBufferHalfFloatExtension=Rd(this.gl,a);else if(te().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",pa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(pa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Vv(this.gl),this.indexBuffer=jv(this.gl),this.framebuffer=Sv(this.gl),this.textureConfig=$g(this.gl,this.textureHalfFloatExtension)}get debug(){return te().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;be(e,()=>e.finish()),be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.deleteFramebuffer(this.framebuffer)),be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),be(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Uv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Hv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Gv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Yv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Zv(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Xv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),qv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Og(this.gl,this.framebuffer),this.outputTexture=null),be(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>ew(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return tw(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Qv(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=Jv(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(te().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>nw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Av(t,e);this.vertexShader==null&&(this.vertexShader=Bv(t));let a=xv(t);return be(t,()=>t.attachShader(a,this.vertexShader)),be(t,()=>t.attachShader(a,n)),bv(t,a),this.debug&&zh(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Kv(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&zh(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Tv(this.gl,e,t):Cv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(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(),Ev(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Bl(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&zh(this.gl,this.program),Md(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Rd(this.gl,te().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(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(te().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,te().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=RW(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),_h(this.gl,e,this.framebuffer),this.debug&&Md(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(_h(this.gl,this.outputTexture,this.framebuffer),this.debug&&Md(this.gl)):Og(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;_h(a,e,this.framebuffer),this.debug&&Md(a),this.outputTexture=e,be(a,()=>a.viewport(0,0,t,n)),be(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),be(this.gl,()=>this.gl.scissor(e,t,n,a))}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 RW(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:aw}=F;function MW(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
|
|
`),i=e.map(h=>FW(h,t,a)).join(`
|
|
`),o=t.texShape,u=An(),l=OW(u),d,p,c=PW(u);return t.isPacked?(d=$W(t.logicalShape,o),p=_W(u)):(d=DW(t.logicalShape,o),p=zW(u)),a&&(c+=VW),[c,l,p,s,d,i,n].join(`
|
|
`)}function jl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return eB(e);case 1:return nB(e);case 2:return rB(e);case 3:return iB(e);case 4:return lB(e);case 5:return uB(e);case 6:return dB(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function rw(e){switch(e.shapeInfo.logicalShape.length){case 0:return QW(e);case 1:return tB(e);case 2:return aB(e);case 3:return sB(e);default:return oB(e)}}function FW(e,t,n=!1){let a="";n?a+=rw(e):a+=jl(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=pB(e,t):a+=cB(e,t)),a}function $W(e,t){switch(e.length){case 0:return sw();case 1:return jW(e,t);case 2:return YW(e,t);case 3:return HW(e,t);default:return qW(e,t)}}function DW(e,t){switch(e.length){case 0:return sw();case 1:return UW(e,t);case 2:return JW(e,t);case 3:return GW(e,t);case 4:return XW(e,t);case 5:return KW(e,t);case 6:return ZW(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function OW(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function zW(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function _W(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function PW(e){return`${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);
|
|
}
|
|
|
|
${LW}
|
|
${WW}
|
|
${BW}
|
|
`}var LW=`
|
|
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);
|
|
}
|
|
`,WW=`
|
|
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);
|
|
}
|
|
`,BW=`
|
|
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);
|
|
}
|
|
`,VW=`
|
|
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 sw(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function jW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function UW(e,t){return t[0]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function HW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function GW(e,t){let n=Pi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function qW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let u=2;u<e.length-1;u++)s*=e[e.length-u-1],i=`
|
|
int b${u} = index / ${s};
|
|
index -= b${u} * ${s};
|
|
`+i,o=`b${u}, `+o;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec${e.length}(${o});
|
|
}
|
|
`}function XW(e,t){let n=Pi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function KW(e,t){let n=Pi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function ZW(e,t){let n=Pi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function YW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let a=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function JW(e,t){return k.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Li(e){return`offset${e}`}function QW(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=An();return`
|
|
vec4 ${n}() {
|
|
return ${a.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function eB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let[s,i]=e.shapeInfo.texShape,o=Li(t);return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function tB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=An();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${r[0]}, ${r[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function nB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Ul(e)}
|
|
}
|
|
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let i=Li(t);return s===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:r===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function aB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=An();if(r!=null&&k.arraysEqual(t,r))return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let u=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],l=Math.ceil(t[1]/2);return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${l}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function rB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],c=r[1];return`
|
|
float ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=Hl(e,o),c=["row","col"];return`
|
|
${jl(p)}
|
|
float ${a}(int row, int col) {
|
|
return ${a}(${Gl(c,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let u=r[0],l=r[1],d=Li(n);return l===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:u===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${u}, ${l}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function sB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),c=[1,2],h=Hl(e,p),m=["b","row","col"];return`
|
|
${rw(h)}
|
|
vec4 ${a}(int b, int row, int col) {
|
|
return ${a}(${Gl(m,c)});
|
|
}
|
|
`}let i=s[0],o=s[1],u=Math.ceil(t[2]/2),l=u*Math.ceil(t[1]/2),d=An();return`
|
|
vec4 ${a}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${l}, ${u}, b, row, col);
|
|
return ${d.texture2D}(${n}, uv);
|
|
}
|
|
`}function iB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),u=i;if(u.length<t.length){let m=Hl(e,u),f=["row","col","depth"];return`
|
|
${jl(m)}
|
|
float ${a}(int row, int col, int depth) {
|
|
return ${a}(${Gl(f,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${r}, ${s}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let l=e.shapeInfo.texShape,d=l[0],p=l[1],c=e.shapeInfo.flatOffset;if(p===r&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(p===s&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Li(n);return`
|
|
float ${a}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${r} + col * ${s} + depth + ${h};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function oB(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],u=i[1],l=Math.ceil(t[n-1]/2),d=l*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${d} + (row / 2) * ${l} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,d*=t[n-m-1],c=`b${m} * ${d} + `+c;let h=An();return`
|
|
vec4 ${r}(${p}) {
|
|
int index = ${c};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${o});
|
|
return ${h.texture2D}(${a}, uv);
|
|
}
|
|
`}function lB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:u}=k.squeezeShape(t);if(o.length<t.length){let m=Hl(e,o),f=["row","col","depth","depth2"];return`
|
|
${jl(m)}
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
return ${a}(${Gl(f,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${s}, ${r}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],c=d[1];if(c===i&&l==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(c===r&&l==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${t[1]*t[2]}, ${t[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Li(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${s} +
|
|
depth * ${r} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${c}, index + ${h});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function uB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:u,keptDims:l}=k.squeezeShape(t);if(u.length<t.length){let f=Hl(e,u),g=["row","col","depth","depth2","depth3"];return`
|
|
${jl(f)}
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${a}(${Gl(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${r})) +
|
|
depth3;
|
|
${Ul(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${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, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=Li(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${r} + depth3 + ${m};
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function dB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=Hl(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${jl(g)}
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${a}(${Gl(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,d=t[1]*l;if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${d}, ${l}, ${u}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===d&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${l}, ${u}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(m===i&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=Li(n);return`
|
|
float ${a}(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 * ${d} + col * ${l} + depth * ${u} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
|
|
vec2 uv = uvFromFlat(${h}, ${m}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Ul(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function pB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=aw(e.shapeInfo.logicalShape,t.logicalShape),u=ut(i),l=i-s,d,p=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${p[g+l]} = 0;`).join(`
|
|
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+l]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!f)i===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${u} coords = getOutputCoords();
|
|
${d}
|
|
vec4 outputValue = get${a}(${c});
|
|
${h}
|
|
}
|
|
`}function cB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let l=ut(u),d=aw(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,c,h=["x","y","z","w","u","v"];o===0?c="":u<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(`
|
|
`);let m="";return u<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
|
|
float ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
return get${a}(${m});
|
|
}
|
|
`}function ut(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 ${e} is not yet supported`)}function Hl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Gl(e,t){return t.map(n=>e[n]).join(", ")}function hB(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},u=MW(s,o,r,t.packedInputs),l=e.createProgram(u),d=null,p=e.getUniformLocation(l,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(l,"INFINITY",!1));let c={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;c[m]=e.getUniformLocation(l,m,f),c[`offset${m}`]=e.getUniformLocation(l,`offset${m}`,f)}return{program:t,source:u,webGLProgram:l,uniformLocations:c,inShapeInfos:i,outShapeInfo:o,infLoc:d,nanLoc:p}}function iw(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,u=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function fB(e,t,n,a,r){iw(t.inShapeInfos,n),iw([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,u)=>{let l=t.program.variableNames[u],d=t.uniformLocations[l],p=t.uniformLocations[`offset${l}`];if(d!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(d,o.uniformValues[0]);else{let c=o.uniformValues;c instanceof Float32Array||(c=new Float32Array(c)),e.gl.uniform1fv(d,c)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,d,u)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function mB(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,u=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${u}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:gB,bincountImpl:ow,bincountReduceImpl:yB,ceilImpl:AB,concatImpl:xB,equalImpl:bB,expImpl:vB,expm1Impl:wB,floorImpl:kB,gatherNdImpl:IB,gatherV2Impl:SB,greaterImpl:NB,greaterEqualImpl:TB,lessImpl:CB,lessEqualImpl:EB,linSpaceImpl:RB,logImpl:MB,maxImpl:FB,maximumImpl:$B,minimumImpl:DB,multiplyImpl:OB,negImpl:zB,notEqualImpl:_B,prodImpl:PB,rangeImpl:LB,rsqrtImpl:WB,simpleAbsImpl:lw,sliceImpl:BB,sparseFillEmptyRowsImpl:VB,sparseReshapeImpl:jB,sparseSegmentReductionImpl:uw,stridedSliceImpl:UB,stringNGramsImpl:HB,stringSplitImpl:GB,stringToHashBucketFastImpl:qB,subImpl:XB,tileImpl:KB,topKImpl:ZB,transposeImpl:Ug,uniqueImpl:YB}=yg;function dw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function xn(e,t){return t===1?[e]:dw(e,t)}function JB(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var QB=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=xn("rc",t),a=ut(t),r=tV(t,e,n),s=nV(t,e[e.length-1],e[e.length-2],n),i=aV(e,n);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function eV(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function tV(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function nV(e,t,n,a){if(e===1)return"";let r=a.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 aV(e,t){let n=e.length,a=eV(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${a[0]}),
|
|
cEdge ? 0. : getA(${a[1]}),
|
|
rEdge ? 0. : getA(${a[2]}),
|
|
rEdge || cEdge ? 0. : getA(${a[3]})`}var pw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${a}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${a>0?"}":""}
|
|
`}this.userCode=`
|
|
${rV(t)}
|
|
${Pg(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function rV(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var sV=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=hw(t,n),r=fw(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=cw(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=hw(n,a),s=fw(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=cw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=te().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[s],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 iV(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function cw(e,t,n,a,r){let s=oV(t,a),i;if(r){let[u,l]=Bl(e[0],e[1]);i=u*l}else{let[u,l]=Cd(e[0],e[1]);i=u*l}let o=iV(n,s);return i*o}function oV(e,t){switch(e){case rn.PACKED_2X2_FLOAT32:return Vg(t);case rn.PACKED_2X2_FLOAT16:return jg(t);case rn.UNPACKED_FLOAT32:return Lg(t);case rn.UNPACKED_FLOAT16:return Wg(t);case rn.PACKED_4X1_UNSIGNED_BYTE:return Bg(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function lV(e){return te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?rn.PACKED_2X2_FLOAT32:rn.UNPACKED_FLOAT32:e?rn.PACKED_2X2_FLOAT16:rn.UNPACKED_FLOAT16}function hw(e,t){if(e===da.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return lV(t);if(e===da.DOWNLOAD||e===da.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function fw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Yr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},Ca="if (isnan(x)) return x;",uV="return x;",mw="return abs(x);",dV="return (x >= 0.0) ? x : (exp(x) - 1.0);",pV=Ca+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,cV=Ca+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,jh="return x;",hV="return 1.0 / (1.0 + exp(-1.0 * x));",fV="return x;",mV=`
|
|
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;
|
|
`,gV=`
|
|
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;
|
|
`,yV=`
|
|
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;
|
|
`,AV="return 1.0 / (1.0 + exp(-1.0 * x));",ql=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},xV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=xn("rc",t),a=ut(t),r=JB(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},bV=Za.whereImpl,vV=1e-7,wV=1e-4,Hg={};function kV(e){return e in Hg||(Hg[e]={}),Hg[e]}var IV=te().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),SV=600;function NV(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*SV/1024/1024}var Xl=class extends Eu{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,!te().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ja(te().getNumber("WEBGL_VERSION"));this.binaryCache=kV(te().getNumber("WEBGL_VERSION")),this.gpgpu=new Vh(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 sV(this.gpgpu),this.numMBBeforeWarning=NV(),this.texData=new Up(this,fr())}nextDataId(){return Xl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((te().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||te().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:da.UPLOAD,refCount:1}),a}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,a,r){if(te().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new ql(i,jh):p=new Yr(i,jh);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let u=this.activeTimers!=null,l;u&&(l=k.now());let d;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=F.mergeRealAndImagArrays(p,c)}else d=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=k.now()-l),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new ql(a,jh):h=new Yr(a,jh);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&te().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(s!=="complex64"&&te().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture,...Ed(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=F.mergeRealAndImagArrays(m,f)}else if(u==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}l!=null&&this.disposeIntermediateTensorInfo(l);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&fr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!mv(n))throw te().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...Ed(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?Ph(t):t,o=s?new TW(i):new NW(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),d}timerAvailable(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:s[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(te().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:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=IV){return te().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){F.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return bV(e.shape,t)}packedUnaryOp(e,t,n){let a=new ql(e.shape,t),r=this.compileAndRun(a,[e],n);return fr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=lw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(te().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mw,e.dtype);let t=new Yr(e.shape,mw),n=this.compileAndRun(t,[e]);return fr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return fr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new xV(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new QB(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[zi(e.shape),..._i(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[zi(t),..._i(t)],s=new pw(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=Ph(a),i;n?i=new SW(s):i=new IW(s);let o=!0,u=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:u.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Td.DENSE){let f=Ed(e.outputShape);i.texShape=f.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],u=t.map(f=>{if(f.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(f.dataId);if(g.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=te().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=f.shape)}else if(!!g.isPacked!=!!e.packedInputs)f=g.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),g=this.texData.get(f.dataId);else if(g.isPacked&&!Fd(g.shape,f.shape)){let y=f,A=f.shape;f.shape=g.shape,f=this.packedReshape(f,A),o.push(f),g=this.texData.get(f.dataId),y.shape=A}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:g,isUniform:!1}});this.uploadToGPU(s.dataId);let l={shape:s.shape,texData:i,isUniform:!1},d=mB(e,u,l),p=this.getAndSaveBinary(d,()=>hB(this.gpgpu,e,u,l)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),fB(this.gpgpu,p,u,l,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=te().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!te().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(te().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!te().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=te().getBool("DEBUG");te().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(te().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?vV:wV}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let u=this.activeTimers!=null,l;u&&(l=k.now());let d=t.texShape;if(d==null&&(d=Fv(n,o),t.texShape=d),r!=null){let p=Ph(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Bl(d[0],d[1]),c=new EW(p,[m,h],f)):c=new CW(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=da.PIXELS:this.texData.get(g.dataId).usage=da.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,A=this.runWebGLProgram(c,[g],a,null,y),x=this.texData.get(A.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(A.dataId),t.values=null,u&&(this.uploadWaitMs+=k.now()-l)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=TV(t,a)),n.values}acquireTexture(e,t,n,a){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,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Xl.nextDataId=0;function TV(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var gw="3.7.0";function yw(){te().set("WEBGL_FORCE_F16_TEXTURES",!0)}ad.isBrowser()&&kl("webgl",()=>new Xl,2);var CV={forceHalfFloat:yw},Aw=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Kl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},Uh=`
|
|
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;
|
|
`,Dd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=F.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${ut(r)} coords = getOutputCoords();
|
|
`,r===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=xn("coords",r);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Xn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var EV={kernelName:zs,backendName:"webgl",kernelFunc:Xn};function Jr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Xn({inputs:{x:a},backend:n}),u=Xn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},s}var RV={kernelName:Yp,backendName:"webgl",kernelFunc:Jr},xw="return (a < 0.) ? b * a : a;",bw=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function MV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(bw,r.shape,i.shape):new Kl(xw,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),u}var FV={kernelName:_s,backendName:"webgl",kernelFunc:MV},vw="return (a < 0.) ? b * a : a;",ww=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function $V(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(ww,a.shape,r.shape):new Kl(vw,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var DV={kernelName:Zs,backendName:"webgl",kernelFunc:$V},kw="if (isnan(x)) return x;",OV=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,zV=`
|
|
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 Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,u=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,u);return o.makeTensorInfo(i.shape,u,c)}let l=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return l?d=new ql(i.shape,t):d=new Yr(i.shape,e),o.runWebGLProgram(d,[i],u)}}function sn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,d=o;if(a&&u.dtype==="complex64"){let m=d.texData.get(u.dataId),f=d.texData.get(l.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,b]=x,w={dataId:v.dataId,dtype:v.dtype,shape:u.shape},N={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C=new Kl(e,u.shape,l.shape);return d.runWebGLProgram(C,[w,N],Aa(v.dtype,b.dtype))}),A=Jr({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let p=s||Aa(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||d.shouldExecuteOnCPU([u,l]))&&r!=null){let m=d.texData.get(u.dataId).values,f=d.texData.get(l.dataId).values,g=u.dtype==="string"?F.fromUint8ToStringArray(m):m,y=u.dtype==="string"?F.fromUint8ToStringArray(f):f,[A,x]=r(u.shape,l.shape,g,y,p),v=d.makeTensorInfo(x,p),b=d.texData.get(v.dataId);return b.values=A,v}let c=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Dd(t,u.shape,l.shape,n):h=new Kl(e,u.shape,l.shape),d.runWebGLProgram(h,[u,l],p)}}function Hh(e,t=!1){if(e==="linear")return t?fV:uV;if(e==="relu")return t?gV:pV;if(e==="elu")return t?mV:dV;if(e==="relu6")return t?yV:cV;if(e==="prelu")return t?ww:vw;if(e==="leakyrelu")return t?bw:xw;if(e==="sigmoid")return t?AV:hV;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Iw=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let l=a?e[1]:e[2],d=Math.ceil(l/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:u?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:f=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${f}
|
|
|
|
const float sharedDimension = ${d}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${d}; i++) {
|
|
int batchA = ${A};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${p});
|
|
vec4 b = getMatrixB(batchB, ${c});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${m[0]});
|
|
result += (${h[1]} * ${m[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},Sw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Nw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},Tw="return a * b;";function Gg(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=F.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),l=new Nw(Sw.REAL,a.shape,r.shape),d=new Nw(Sw.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Jr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),[l,d]=OB(a.shape,r.shape,o.values,u.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=l,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Dd(Tw,a.shape,r.shape):i=new Kl(Tw,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var _V={kernelName:Gs,backendName:"webgl",kernelFunc:Gg};function PV(e,t,n){let a=[zi(e.shape),..._i(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[zi(t),..._i(t)],i=new pw(s,a),o=!0,u=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),u=k.inferFromImplicitShape(s,o),l=k.sizeFromShape(u);k.assert(o===l,()=>`The new shape (${u}) 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.`);let d=i.texData.get(r.dataId);return d.isPacked&&!Fd(r.shape,u)&&!(d.texture!==null&&Fd(d.shape,u))?PV(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var LV={kernelName:el,backendName:"webgl",kernelFunc:Ae},Cw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let d=1/t;u=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let l="";r%n>0&&(l=`
|
|
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) {
|
|
${l}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${u}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${u}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},WV=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,d=n%4,p=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,c="vec4";t==="all"?(i="1.0",p=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,c="bvec4"):t==="any"&&(i="0.0",p=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,c="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${l}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${l};
|
|
if (${d===1}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===2}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===3}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function BV(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=F.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Wi(e,t,n,a){let r=BV(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],d,p;n==="mean"?d=i===0?new Cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new Cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):d=new WV({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var VV=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=jV(t);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function jV(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var UV=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ut(this.rank),r=dw("rc",this.rank),s=new Array(this.rank);for(let l=0;l<t.length;l++)s[t[l]]=r[l];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${u};
|
|
if(${o}) {
|
|
result[1] = ${u};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${u};
|
|
if(${o}) {
|
|
result[3] = ${u};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Gh(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UV(e.shape,t):new VV(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function HV(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,u=F.getAxesPermutation(o,s),l=u!=null,d=e;l&&(d=Gh(e,u,a),o=F.getInnerMostAxes(o.length,s)),F.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=F.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=F.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),y=zc(e.dtype),A=Wi(g,y,"sum",a),x=Ae({inputs:{x:A},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),l&&a.disposeIntermediateTensorInfo(d),x}function qh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return HV(r,s,i,n)}var GV={kernelName:ii,backendName:"webgl",kernelFunc:qh};function bn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,u=new Array(o);for(let d=0;d<u.length;d++)u[d]=r.shape[s[d]];let l;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=Ug(d,r.shape,r.dtype,s,u);l=i.makeTensorInfo(u,r.dtype);let c=i.texData.get(l.dataId);c.values=p}else l=Gh(r,s,i);return l}var qV={kernelName:ci,backendName:"webgl",kernelFunc:bn},Ew=1e3;function Xh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,d=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[l-1]:e.shape[l-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),A=k.sizeFromShape(g),x=y===A||y===1||A===1;k.assert(l>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[y,p,h]:[y,h,p],w=a?[A,m,c]:[A,c,m],N=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ae({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[N,C],_=Math.max(y,A),$=n?N.shape[1]:N.shape[2],S=s!=null,z=i!=null,O=u==="leakyrelu",W=u!=null?Hh(u,!0):null,G=S||z||O||W!=null,H;if((h===1||m===1)&&$>Ew&&G===!1){let K=N,ne=C;n&&(K=bn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(ne=bn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let Q=m!==1,se=m===1,Z=K;Q&&(Z=Ae({inputs:{x:K},backend:r,attrs:{shape:[_,$,1]}}),E.push(Z));let le=m===1?2:1,oe=ne;se&&(oe=Ae({inputs:{x:ne},backend:r,attrs:{shape:[_,1,$]}}),E.push(oe));let xe=Gg({inputs:{a:Z,b:oe},backend:r});H=qh({inputs:{x:xe},backend:r,attrs:{axis:le,keepDims:!0}}),E.push(xe)}else{let K=Aa(e.dtype,t.dtype),ne=new Iw(b,w,[_,h,m],n,a,S,W,z,O),Q=[N,C];if(s!=null&&Q.push(s),z&&Q.push(i),O){let se=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(se),E.push(se)}H=r.runWebGLProgram(ne,Q,K)}let J=Ae({inputs:{x:H},backend:r,attrs:{shape:v}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return J}function XV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a;return Xh({a:r,b:s,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var KV={kernelName:hi,backendName:"webgl",kernelFunc:XV},Rw="return abs(x);";function ZV(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=lw(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ql(a.shape,Rw):r=new Yr(a.shape,Rw),n.runWebGLProgram(r,[a],a.dtype)}var YV={kernelName:fo,backendName:"webgl",kernelFunc:ZV},JV=Ca+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,QV=Ke({opSnippet:JV}),ej={kernelName:mo,backendName:"webgl",kernelFunc:QV},tj=Ca+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,nj=Ke({opSnippet:tj}),aj={kernelName:go,backendName:"webgl",kernelFunc:nj},Mw="return a + b;",rj=sn({opSnippet:Mw,packedOpSnippet:Mw,supportsComplex:!0,cpuKernelImpl:gB}),sj={kernelName:Or,backendName:"webgl",kernelFunc:rj},ij=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}},oj=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}};function Kh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Xn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),u=Kh({inputs:a.slice(0,o),backend:n}),l=Kh({inputs:a.slice(o),backend:n});return Kh({inputs:[u,l],backend:n})}let r=a.map(o=>o.dtype).reduce((o,u)=>Aa(o,u)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new oj(a[0].shape,s):new ij(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var lj={kernelName:xs,backendName:"webgl",kernelFunc:Kh};function uj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,o)),F.assertAxesAreInnerMostDims("all",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"all",n),y;if(i){let A=F.expandShapeToKeepDim(c,u);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var dj={kernelName:yo,backendName:"webgl",kernelFunc:uj};function pj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,o)),F.assertAxesAreInnerMostDims("any",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"any",n),y;if(i){let A=F.expandShapeToKeepDim(c,u);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var cj={kernelName:Ao,backendName:"webgl",kernelFunc:pj},hj=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${a};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
int inIdx = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},fj=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=ut(o),l=xn("coords",o),d,p;if(s===1){p=o+1;let N=ut(p);d=`
|
|
${N} sourceLocR = ${N}(${l.join()}, 0);
|
|
++${l[o-1]};
|
|
${N} sourceLocG = ${N}(${l.join()}, 0);
|
|
++${l[o-2]};
|
|
${N} sourceLocA = ${N}(${l.join()}, 0);
|
|
--${l[o-1]};
|
|
${N} sourceLocB = ${N}(${l.join()}, 0);
|
|
--${l[o-2]};`}else p=o,d=`
|
|
${u} sourceLocR = coords;
|
|
++${l[o-1]};
|
|
${u} sourceLocG = coords;
|
|
++${l[o-2]};
|
|
${u} sourceLocA = coords;
|
|
--${l[o-1]};
|
|
${u} sourceLocB = coords;
|
|
--${l[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=xn("sourceLocR",p-1).concat("inIdx.r"),g=xn("sourceLocG",p-1).concat("inIdx.g"),y=xn("sourceLocB",p-1).concat("inIdx.b"),A=xn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${A.join()})));`,b=`vec4(
|
|
getAChannel(${f.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,w=a?"":`
|
|
float getBestIndicesAChannel(${m.join()}) {
|
|
return getChannel(getBestIndicesA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${m.join()}) {
|
|
return getChannel(getA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}
|
|
${w}
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};
|
|
${d}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${b};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${v}
|
|
vec4 candidate = ${b};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${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 Fw(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=F.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},u=new hj(o,n,a==null),l=[t];a!=null&&l.push(a);let d=e.runWebGLProgram(u,l,"int32");if(d.shape[1]===1)return d;let p=Fw(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function $w(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=F.computeOptimalWindowSize(s),o=new fj(r,i,n,a==null),u=a==null?[t]:[t,a],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let d=$w(e,t,n,l);return e.disposeIntermediateTensorInfo(l),d}return l}function Dw(e,t,n,a){let r=[n];if(F.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=F.computeOutAndReduceShapes(t.shape,r),u=k.sizeFromShape(o),l=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,u]}});s.push(l);let d=Fw(e,l,a);s.push(d);let p=Ae({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return $w(e,t,a)}function mj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),F.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let d=Dw(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var gj={kernelName:bs,backendName:"webgl",kernelFunc:mj};function yj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),F.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let d=Dw(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var Aj={kernelName:Fu,backendName:"webgl",kernelFunc:yj},xj=Ca+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,bj=Ke({opSnippet:xj}),vj={kernelName:xo,backendName:"webgl",kernelFunc:bj},wj=Ca+"return log(x + sqrt(x * x + 1.0));",kj=Ke({opSnippet:wj}),Ij={kernelName:bo,backendName:"webgl",kernelFunc:kj},Sj=Ca+`
|
|
return atan(x);
|
|
`,Nj=Ke({opSnippet:Sj}),Tj={kernelName:vo,backendName:"webgl",kernelFunc:Nj},Cj=OV+`
|
|
return atan(a, b);
|
|
`,Ej=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+zV+`
|
|
return result;
|
|
`,Rj=sn({opSnippet:Cj,packedOpSnippet:Ej}),Mj={kernelName:ko,backendName:"webgl",kernelFunc:Rj},Fj=Ca+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$j=Ke({opSnippet:Fj}),Dj={kernelName:wo,backendName:"webgl",kernelFunc:$j},Od=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${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 < ${d};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${l}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,b=s%4,w=`
|
|
if (${m}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${A}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${v}; wC += 4) {
|
|
int xC = xCCorner + wC * ${l};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
getValue(batch, xR, xC + 2 * ${l}, d),
|
|
getValue(batch, xR, xC + 3 * ${l}, d)
|
|
);
|
|
|
|
${w}
|
|
}
|
|
|
|
int xC = xCCorner + ${v};
|
|
if (${b===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
} else if (${b===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
} else if (${b===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
getValue(batch, xR, xC + 2 * ${l}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},qg=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${u});
|
|
const ivec3 pads = ivec3(${f}, ${g}, ${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${l}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${d}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
wC += ${p}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${E} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?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} * ${m} +
|
|
wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let v="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let w=Math.floor(s/4)*4,N=s%4,C=`
|
|
if (${A}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${v}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${u});
|
|
const ivec3 pads = ivec3(${f}, ${g}, ${y});
|
|
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 < ${c};
|
|
wD += ${l}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${d}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${w}; wC += 4) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${w};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${b});
|
|
}
|
|
}
|
|
`}};function Oj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Vl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Xn({inputs:{x:r},backend:n});let p=new Od(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var zj={kernelName:vs,backendName:"webgl",kernelFunc:Oj};function _j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new qg(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Pj={kernelName:$u,backendName:"webgl",kernelFunc:_j},Lj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,d=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${l}, ${d});
|
|
const float avgMultiplier = float(${p});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${o};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.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+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Wj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${m}, ${f});
|
|
const float avgMultiplier = float(${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 < ${d};
|
|
wD += ${o}) {
|
|
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 < ${p};
|
|
wR += ${u}) {
|
|
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 < ${c};
|
|
wC += ${l}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Bj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,u,p,l,d),h=new Wj(c);return n.runWebGLProgram(h,[r],i.dtype)}var Vj={kernelName:Kp,backendName:"webgl",kernelFunc:Bj};function jj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Vl([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=F.computePool2DInfo(i.shape,o,u,1,l),p=new Lj(d);return n.runWebGLProgram(p,[r],i.dtype)}var Uj={kernelName:Xp,backendName:"webgl",kernelFunc:jj};function Hj(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Xh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Gj={kernelName:ws,backendName:"webgl",kernelFunc:Hj},qj=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},Xj=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},Kj=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[a,r,s],d=null;i!=null&&(d=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let c=te().getBool("WEBGL_PACK_NORMALIZATION")?new Xj(a.shape,r.shape,s.shape,d,p,u):new qj(a.shape,r.shape,s.shape,d,p,u);return t.runWebGLProgram(c,l,l[0].dtype)},Zj={kernelName:Ds,backendName:"webgl",kernelFunc:Kj},Yj=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=Jj(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Xg[o]} = start[${o}] + coords.${Xg[o]};`);r=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${n}
|
|
void main() {
|
|
${r}
|
|
setOutput(getSource(${a}));
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Xg=["x","y","z","w","u","v"];function Jj(e){if(e===1)return"sourceLoc";if(e<=6)return Xg.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Qj=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=xn("coords",this.rank),a=xn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.y = ${s};
|
|
--${a[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${a[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,u=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((l,d)=>`start[${d}]`).join()});`:e.map((l,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${u}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function eU(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=fn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,u+1),s}function zd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,u]=fn.parseSliceParams(r,s,i);if(fn.assertParamsValid(r,o,u),k.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=BB(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,c)}let{isPacked:l}=n.texData.get(r.dataId),d=fn.isSliceContinous(r.shape,o,u);if(l||!d){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Qj(u):new Yj(u),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),eU(r,o,u,n)}var tU={kernelName:rl,backendName:"webgl",kernelFunc:zd},nU=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),u=F.getReshaped(r.shape,s,o),l=F.getPermuted(u.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:u}}),f=bn({inputs:{x:m},backend:n,attrs:{perm:l}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:d}}),y=zd({inputs:{x:g},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},aU={kernelName:Du,backendName:"webgl",kernelFunc:nU};function rU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),u=n.readSync(s.dataId),l=ow(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var sU={kernelName:Zp,backendName:"webgl",kernelFunc:rU},iU="return float(a != b);",Ow=sn({opSnippet:iU,cpuKernelImpl:_B,dtype:"bool"}),oU={kernelName:Go,backendName:"webgl",kernelFunc:Ow};function _d(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Xn({inputs:{x:r.complexTensorInfos.real},backend:n})}var lU={kernelName:Ac,backendName:"webgl",kernelFunc:_d},uU="return float(int(x));";function dU(e,t){let n=new Yr(e.shape,uU),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Kg(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Xn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Kg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Jr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=_d({inputs:{input:r},backend:n}),o=Kg({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Xn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return dU(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=Ow({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var pU={kernelName:ks,backendName:"webgl",kernelFunc:Kg},zw="return ceil(x);",cU=Ke({opSnippet:zw,packedOpSnippet:zw,cpuKernelImpl:AB}),hU={kernelName:Is,backendName:"webgl",kernelFunc:cU},fU=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},mU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function gU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new mU(r.shape):o=new fU(r.shape);let u=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,u)}var yU={kernelName:zr,backendName:"webgl",kernelFunc:gU},AU=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 _w(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function xU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new AU(a.shape),i=[_w(a,r.complexTensorInfos.real),_w(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var bU={kernelName:Ou,backendName:"webgl",kernelFunc:xU},vU=class{constructor(e){this.outputShape=[],this.outputShape=F.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},wU=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=F.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=xn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let u=i[t],l=i.slice(-2),d=i.join(),p=`if (${u} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${d}), vec2(${l.join()}));
|
|
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
|
|
if (${u} < ${o[m]} && ${u} >= ${o[m-1]}) {
|
|
return getChannel(
|
|
getT${m}(${Zh(i,u,f)}),
|
|
vec2(${Zh(l,u,f)}));
|
|
}`}let c=o.length,h=o[o.length-1];p+=`
|
|
return getChannel(
|
|
getT${c}(${Zh(i,u,h)}),
|
|
vec2(${Zh(l,u,h)}));`,this.userCode=`
|
|
float getValue(${i.map(m=>"int "+m)}) {
|
|
${p}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[a-1]} = ${s[a-1]} + 1;
|
|
if (${s[a-1]} < ${n[a-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[a-2]} = ${s[a-2]} + 1;
|
|
if (${s[a-2]} < ${n[a-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[a-1]} = ${s[a-1]} - 1;
|
|
if (${s[a-2]} < ${n[a-2]} &&
|
|
${s[a-1]} < ${n[a-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Zh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Yh(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Xn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var kU={kernelName:pc,backendName:"webgl",kernelFunc:Yh};function Zl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>_d({inputs:{input:f},backend:n})),p=e.map(f=>Yh({inputs:{input:f},backend:n})),c=Zl(d,t,n),h=Zl(p,t,n),m=Jr({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(y=>{let A=k.sizeFromShape(y.shape.slice(t));return Ae({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),p=d.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=F.computeOutShape(d.map(y=>y.shape),1),h=d[0].shape[0]===1,m=xB(p,c,a,h),f=F.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(f,a,m);return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=Zl(e.slice(0,d),t,n),c=Zl(e.slice(d),t,n),h=Zl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new wU(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=IU(e,t,n),o=new vU(s.map(d=>d.shape)),u=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let l=Ae({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function IU(e,t,n){let a=F.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function Pw(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.computeOutShape(t.map(l=>l.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>k.sizeFromShape(l.shape)>0);if(o.length===1)return Xn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return F.assertParamsConsistent(u,s),Zl(o,s,n)}var SU={kernelName:Io,backendName:"webgl",kernelFunc:Pw},Lw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?3:1,x="",v="";n&&(a?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}
|
|
}
|
|
`,v="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${u});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${A}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${y}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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 (${m===1}) {
|
|
|
|
if (${f}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${m===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${m===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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;
|
|
${b}
|
|
${v}
|
|
setOutput(result);
|
|
}
|
|
`}},NU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${a});
|
|
|
|
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 < ${d}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${l};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${m===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${m===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, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${m===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);
|
|
}
|
|
`}},TU=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:u,dilationWidth:l,dilationHeight:d,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=An(),g=p==="channelsLast",y=g?0:1,A=g?1:2,x="";for(let v=0;v<=1;v++)for(let b=0;b<=1;b++)x+=`
|
|
blockIndex = rc.y + ${b};
|
|
pos = rc.x + ${v};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${u})) * ${i} - ${h};
|
|
d0 = offsetY + ${d} * (pos / ${m});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${u}.) * ${s}. - ${c}.);
|
|
d1 = offsetX + ${l} * (int(mod(float(pos), ${m}.) / ${r}.));
|
|
|
|
if(d1 < ${t[A]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${r}.));
|
|
|
|
if (${g}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${v*2+b}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${v*2+b}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${x}
|
|
|
|
${f.output} = result;
|
|
}
|
|
`}};function Ww({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=a.texData.get(e.dataId),d=n.inChannels,p=u[0]*u[1]*u[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],A=(p===1||c===1)&&d>Ew,x=u[2]%2!=0&&!!l.isPacked;if(A||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],b=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),w=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Xh({a:b,b:w,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=Ae({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(w),y.push(N)}else{let v=h?u[0]*u[1]*(u[2]+1):u[0]*u[2]*(u[3]+1),b={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,k.assert(Fd(l.shape,b.shape),()=>`packed reshape ${l.shape} to ${b.shape} isn't free`);let N=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let C=Xh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=a.texData.get(C.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=w,E.shape=n.outShape,g=Xn({inputs:{x:C},backend:a}),g.shape=n.outShape,y.push(C)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function Bw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=u*l*d,g=c*p,y=[f,g],A=!0,x=!1,v=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let N=new TU(y,b.shape,n),C=a.runWebGLProgram(N,[b],"float32"),E=Ae({inputs:{x:C},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(C),v.push(E);let _=r!=null,$=s!=null,S=o==="leakyrelu",z=o?Hh(o,!0):null,O=new Iw(E.shape,w.shape,[1,g,n.outChannels],A,x,_,z,$,S),W=[E,w];if(r&&W.push(r),$&&W.push(s),S){let K=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));W.push(K),v.push(K)}let G=a.runWebGLProgram(O,W,"float32"),H=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],J=Ae({inputs:{x:G},backend:a,attrs:{shape:H}});v.push(G);for(let K of v)a.disposeIntermediateTensorInfo(K);return J}function CU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,s.shape,i,l,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Ww({x:r,filter:s,convInfo:c,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=Bw({x:r,filter:s,convInfo:c,backend:n});else{let f=new Lw(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var EU={kernelName:Ss,backendName:"webgl",kernelFunc:CU},RU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int d2 = coords.w;
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${a};
|
|
|
|
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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${s}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},MU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=s?1:2,l=s?2:3,d=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${d}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - 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) / ${a}.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 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},FU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${a} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},$U=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=a-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${u}, ${l});
|
|
|
|
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) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${a} - 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 DU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:d}=a,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,d,i,1,o,l,!1,p),h=new RU(c);return n.runWebGLProgram(h,[r,s],"float32")}var OU={kernelName:Jp,backendName:"webgl",kernelFunc:DU};function zU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(i,s.shape,o,1,u,d,!1,p),h=new MU(c);return n.runWebGLProgram(h,[r,s],"float32")}var _U={kernelName:Ns,backendName:"webgl",kernelFunc:zU};function PU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=F.computeConv3DInfo(r.shape,s.shape,i,u,o),d=new NU(l);return n.runWebGLProgram(d,[r,s],"float32")}var LU={kernelName:zu,backendName:"webgl",kernelFunc:PU};function WU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:u}=a,l=F.computeConv3DInfo(r.shape,u,i,1,o),d=new FU(l);return n.runWebGLProgram(d,[r,s],"float32")}var BU={kernelName:Qp,backendName:"webgl",kernelFunc:WU};function VU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:u}=a,l=F.computeConv3DInfo(u,s.shape,o,1,i),d=new $U(l);return n.runWebGLProgram(d,[r,s],"float32")}var jU={kernelName:ec,backendName:"webgl",kernelFunc:VU},UU=kw+`
|
|
return cos(x);
|
|
`,HU=Ke({opSnippet:UU}),GU={kernelName:Ts,backendName:"webgl",kernelFunc:HU},qU=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,XU=Ke({opSnippet:qU}),KU={kernelName:So,backendName:"webgl",kernelFunc:XU},ZU=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,u]=e,[l]=t,[d,p]=n;this.outputShape=[l,d,p,u];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,v]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
|
|
const float height_ratio = float(${f});
|
|
const float width_ratio = float(${A});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${v};
|
|
if( in_x < 0.0 || in_x > ${m} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${c} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},YU=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=a,d=new ZU(r.shape,s.shape,o,u,l);return n.runWebGLProgram(d,[r,s,i],"float32")},JU={kernelName:No,backendName:"webgl",kernelFunc:YU},Vw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${jw(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${ut(a)} coords = getOutputCoords();
|
|
int end = ${Uw(a,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${Uw(a,"coords")} = idx;
|
|
val += getX(${jw(a,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function jw(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 ${e} is not yet supported`)}function Uw(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function QU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,u=r.shape.length,l=F.getAxesPermutation([s],u),d=r;l!=null&&(d=bn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=F.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Xn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new Vw(d.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new Vw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(l!=null){let m=F.getUndoAxesPermutation(l),f=bn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var eH={kernelName:Cs,backendName:"webgl",kernelFunc:QU};function tH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=ow(u,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(s),d=yB(u,l,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var nH={kernelName:tc,backendName:"webgl",kernelFunc:tH},aH=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 rH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=u*s,c=l*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new aH(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var sH={kernelName:To,backendName:"webgl",kernelFunc:rH},Hw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,u=e.padInfo.left,l=e.strideHeight,d=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?g=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:g=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,y="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${g}
|
|
|
|
const ivec2 strides = ivec2(${l}, ${d});
|
|
const ivec2 pads = ivec2(${o}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${f};
|
|
int q = d2 - d1 * ${f};
|
|
|
|
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 < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${p};
|
|
|
|
if (xR < 0 || xR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${A}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},Gw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,u=e.padInfo.top,l=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,g=f,y=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
|
|
vec4 xTexelC${b*2};
|
|
int xTexelC${b*2}Ready;
|
|
vec4 xC${b};`;for(let b=0;b<m;b++){for(let w=0;w<f;w++)y+=`
|
|
xTexelC${w*2} = vec4(0.0);
|
|
xTexelC${w*2}Ready = 0;
|
|
xC${w} = vec4(0.0);`;y+=`
|
|
xR = xRCorner + ${b*c};
|
|
if (xR >=0 && xR < ${i}) {
|
|
`;for(let w=0;w<(g+1)/2;w++){let N=w*2,C=N*h;if(y+=`
|
|
xC = xCCorner + ${C};
|
|
`,p===1){if(N<f&&(l%2==1?(y+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
`,h===1&&C>0?y+=`
|
|
xC${N} = vec4(xTexelC${C-2}.zw, xTexelC${C}.xy);
|
|
`:y+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${N} = vec4(previous.zw, xTexelC${C}.xy);
|
|
} else {
|
|
xC${N} = vec4(0.0, 0.0, xTexelC${C}.xy);
|
|
}
|
|
`):y+=`
|
|
if (xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
xC${N} = xTexelC${C};
|
|
`,C+1<f)){let E=l%2==0?k.nearestLargerEven(h):h;h%2==0&&l%2==1||h%2!=0&&l%2!=1?(y+=`
|
|
xCOffset = xC + ${l%2} + ${E};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
`,h>1&&(y+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
`),y+=`
|
|
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.xy);
|
|
`):E===1?y+=`
|
|
xC${N+1} = xTexelC${C};
|
|
`:y+=`
|
|
xCOffset = xC + ${E};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N+1} = xTexelC${C+2};
|
|
`}}else C<f&&(l%2==1?(y+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
|
|
`,C+1<f&&(y+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${N+1} = vec4(xTexelC${C+2}.xy, final.xy);
|
|
`)):(y+=`
|
|
if(xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(
|
|
xTexelC${C}.xy, xTexelC${C+2}.xy);
|
|
`,C+1<f&&(y+=`
|
|
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
|
|
`)));N<f&&(y+=`
|
|
wTexel = getW(${b}, ${C}, d1, q);
|
|
dotProd += xC${N} * vec4(wTexel.xz, wTexel.xz);
|
|
`,C+1<f&&(y+=`
|
|
wTexel = getW(${b}, ${C+1}, d1, q);
|
|
dotProd += xC${N+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}y+=`
|
|
}
|
|
`}let A="",x="";n&&(a?A=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?A=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:A=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${d}, ${p});
|
|
const ivec2 pads = ivec2(${u}, ${l});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${y}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${v}
|
|
${x}
|
|
setOutput(result);
|
|
}
|
|
`}};function iH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=a,d=u;d==null&&(d=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=F.computeConv2DInfo(r.shape,s.shape,i,d,o,l,!0),c;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new Gw(p):c=new Hw(p),n.runWebGLProgram(c,[r,s],"float32")}var oH={kernelName:Es,backendName:"webgl",kernelFunc:iH},lH=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int dm = coords.w;
|
|
int d2 = d1 * ${s} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${a};
|
|
|
|
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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},uH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.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 < ${o}; dm++) {
|
|
int d2 = d1 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function dH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:d}=a,p=F.computeConv2DInfo(r.shape,d,i,o,u,l,!0),c=new lH(p);return n.runWebGLProgram(c,[r,s],"float32")}var pH={kernelName:nc,backendName:"webgl",kernelFunc:dH};function cH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a,p=F.computeConv2DInfo(d,s.shape,i,o,u,l,!0),c=new uH(p);return n.runWebGLProgram(c,[r,s],"float32")}var hH={kernelName:ac,backendName:"webgl",kernelFunc:cH},fH=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function mH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new fH(s),u=n.runWebGLProgram(o,[i],i.dtype),l=Ae({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var gH={kernelName:rc,backendName:"webgl",kernelFunc:mH},yH=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:d,left:p}=a;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${s});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${i}; h++) {
|
|
int hIn = hBeg + h * ${u};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${l};
|
|
|
|
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 AH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=F.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",u),d,p=new yH(l);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(d),c}var xH={kernelName:_u,backendName:"webgl",kernelFunc:AH};function bH(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:u}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:d}=F.getEinsumComputePath(o,u),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=F.getEinsumPermutation(h,u[g]),x;F.isIdentityPermutation(y)?x=s[g]:(x=bn({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let b=0;b<A.length;++b)v.splice(A[b],0,1);k.arraysEqual(x.shape,v)||(x=Ae({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Gg({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(l[f]>=0&&(c=qh({inputs:{x:c},backend:n,attrs:{axis:l[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var vH={kernelName:oc,backendName:"webgl",kernelFunc:bH},wH="return (x >= 0.0) ? x : (exp(x) - 1.0);",kH=`
|
|
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;
|
|
`,IH=Ke({opSnippet:wH,packedOpSnippet:kH}),SH={kernelName:Co,backendName:"webgl",kernelFunc:IH},NH="return (b >= 1.0) ? a : a * (b + 1.0);",TH=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,CH=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(TH,a.shape,r.shape):new Kl(NH,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},EH={kernelName:lc,backendName:"webgl",kernelFunc:CH},RH=`
|
|
return vec4(equal(a, b));
|
|
`,MH="return float(a == b);",FH=sn({opSnippet:MH,packedOpSnippet:RH,dtype:"bool",cpuKernelImpl:bB}),$H={kernelName:Ro,backendName:"webgl",kernelFunc:FH},DH=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${F.ERF_P};
|
|
float a1 = ${F.ERF_A1};
|
|
float a2 = ${F.ERF_A2};
|
|
float a3 = ${F.ERF_A3};
|
|
float a4 = ${F.ERF_A4};
|
|
float a5 = ${F.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,OH=Ke({opSnippet:DH}),zH={kernelName:Eo,backendName:"webgl",kernelFunc:OH},qw="return exp(x);",Xw=Ke({opSnippet:qw,packedOpSnippet:qw,cpuKernelImpl:vB}),_H={kernelName:Ms,backendName:"webgl",kernelFunc:Xw};function Zg(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),u=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var PH={kernelName:Mo,backendName:"webgl",kernelFunc:Zg},Kw="return exp(x) - 1.0;",LH=Ke({opSnippet:Kw,packedOpSnippet:Kw,cpuKernelImpl:wB}),WH={kernelName:Fo,backendName:"webgl",kernelFunc:LH},Zw=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${a});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
// x = (-2|2 * PI / N) * index * i;
|
|
float x = exponentMultiplierTimesIndexRatio * float(i);
|
|
float expR = cos(x);
|
|
float expI = sin(x);
|
|
float real = getReal(batch, i);
|
|
float imag = getImag(batch, i);
|
|
|
|
result +=
|
|
unaryOpComplex(real, expR, imag, expI) / ${s};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function Yw(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),u=o.shape,l=new Zw("real",u,t),d=new Zw("imag",u,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:u},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:u}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Jr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function BH(e){let{inputs:t,backend:n}=e,{input:a}=t;return Yw(a,!1,n)}var VH={kernelName:uc,backendName:"webgl",kernelFunc:BH},jH=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
uniform float value;
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Yg(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new jH(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var UH={kernelName:Pu,backendName:"webgl",kernelFunc:Yg},HH=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},GH={kernelName:$o,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new HH(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Jw="return floor(x);",qH=Ke({opSnippet:Jw,packedOpSnippet:Jw,cpuKernelImpl:kB}),XH={kernelName:Fs,backendName:"webgl",kernelFunc:qH},KH=`
|
|
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;
|
|
}
|
|
`,ZH=`
|
|
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);
|
|
`,YH=sn({opSnippet:KH,packedOpSnippet:ZH,dtype:"int32"}),JH={kernelName:$s,backendName:"webgl",kernelFunc:YH},QH=class{constructor(e){this.variableNames=["A"];let t=An(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},eG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=An(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}.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;
|
|
}
|
|
`}},tG={kernelName:Rc,backendName:"webgl",kernelFunc:nG},Yl;function nG(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[l,u],p=[l,u,s];(o||i)&&(Yl==null&&(Yl=document.createElement("canvas").getContext("2d")),Yl.canvas.width=u,Yl.canvas.height=l,Yl.drawImage(r,0,0,u,l),r=Yl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=da.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=te().getBool("WEBGL_PACK")?new eG(p):new QH(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function aG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=F.convertConv2DDataFormat(d),g=F.computeConv2DInfo(r.shape,s.shape,u,p,l,c,!1,f),y,A=[];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"))y=Ww({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=Bw({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,w=h==="leakyrelu",N=h?Hh(h,!1):null,C=new Lw(g,v,N,b,w),E=[r,s];if(i&&E.push(i),o&&E.push(o),w){let _=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));E.push(_),A.push(_)}y=n.runWebGLProgram(C,E,"float32")}let x=Ae({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var rG={kernelName:fi,backendName:"webgl",kernelFunc:aG};function sG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let g=F.computeConv2DInfo(r.shape,s.shape,u,f,l,p,!0),y=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=c?Hh(c,y):null,x=[r,s],v=i!=null,b=o!=null,w=c==="leakyrelu";if(v&&x.push(i),b&&x.push(o),w){let E=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(E),m.push(E)}let N;y?N=new Gw(g,v,A,b,w):N=new Hw(g,v,A,b,w);let C=n.runWebGLProgram(N,x,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var iG={kernelName:mi,backendName:"webgl",kernelFunc:sG},oG=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${a} strides = ${a}(${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 * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function lG(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=k.sizeFromShape(a.shape),[u,l,d,p]=F.prepareAndValidate(a,r),c=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/d,d]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),A=n.bufferSync(a),x=IB(y,A,a.dtype,l,i,d,p,a.shape,o);return n.makeTensorInfo(u,a.dtype,x.values)}let m=new oG(i,p,[l,d]),f=n.runWebGLProgram(m,[h,c],h.dtype),g=Ae({inputs:{x:f},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var uG={kernelName:Oo,backendName:"webgl",kernelFunc:lG},dG=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=pG(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function pG(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function cG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,u=k.parseAxisParam(i,r.shape)[0],l=F.segment_util.collectGatherOpShapeInfo(r,s,u,o),d=k.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,d/l.batchSize]}});p.push(c),p.push(h);let m=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let A=n.bufferSync(h),x=n.bufferSync(c),v=SB(x,A,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(l.outputShape,v.dtype,v.values)}let f=new dG(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);p.push(g);let y=Ae({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var hG={kernelName:Do,backendName:"webgl",kernelFunc:cG},fG="return float(a > b);",mG=`
|
|
return vec4(greaterThan(a, b));
|
|
`,gG=sn({opSnippet:fG,packedOpSnippet:mG,cpuKernelImpl:NB,dtype:"bool"}),yG={kernelName:zo,backendName:"webgl",kernelFunc:gG},AG="return float(a >= b);",xG=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,bG=sn({opSnippet:AG,packedOpSnippet:xG,dtype:"bool",cpuKernelImpl:TB}),vG={kernelName:Os,backendName:"webgl",kernelFunc:bG};function wG(e){let{inputs:t,backend:n}=e,{input:a}=t;return Yw(a,!0,n)}var kG={kernelName:dc,backendName:"webgl",kernelFunc:wG},IG="return float(!isnan(x) && !isinf(x));",SG=Ke({opSnippet:IG,dtype:"bool"}),NG={kernelName:_o,backendName:"webgl",kernelFunc:SG},TG="return float(isinf(x));",CG=Ke({opSnippet:TG,dtype:"bool"}),EG={kernelName:Po,backendName:"webgl",kernelFunc:CG},RG="return float(isnan(x));",MG=Ke({opSnippet:RG,dtype:"bool"}),FG={kernelName:Lo,backendName:"webgl",kernelFunc:MG},$G="return float(a < b);",DG=`
|
|
return vec4(lessThan(a, b));
|
|
`,OG=sn({opSnippet:$G,packedOpSnippet:DG,cpuKernelImpl:CB,dtype:"bool"}),zG={kernelName:Wo,backendName:"webgl",kernelFunc:OG},_G="return float(a <= b);",PG=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,LG=sn({opSnippet:_G,packedOpSnippet:PG,cpuKernelImpl:EB,dtype:"bool"}),WG={kernelName:Bo,backendName:"webgl",kernelFunc:LG};function BG(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=RB(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var VG={kernelName:cc,backendName:"webgl",kernelFunc:BG},jG=`if (x < 0.0) return NAN;
|
|
return log(x);`,UG=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,HG=Ke({opSnippet:jG,packedOpSnippet:UG,cpuKernelImpl:MB}),GG={kernelName:Ps,backendName:"webgl",kernelFunc:HG},qG="return log(1.0 + x);",XG=Ke({opSnippet:qG}),KG={kernelName:Vo,backendName:"webgl",kernelFunc:XG},ZG="return float(a >= 1.0 && b >= 1.0);",YG=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,JG=sn({opSnippet:ZG,packedOpSnippet:YG,dtype:"bool"}),QG={kernelName:jo,backendName:"webgl",kernelFunc:JG},eq="return float(!(x >= 1.0));",tq=Ke({opSnippet:eq}),nq={kernelName:Lu,backendName:"webgl",kernelFunc:tq},aq="return float(a >= 1.0 || b >= 1.0);",rq=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,sq=sn({opSnippet:aq,packedOpSnippet:rq,dtype:"bool"}),iq={kernelName:Wu,backendName:"webgl",kernelFunc:sq},oq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${s}; j <= ${s}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},lq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * 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 - ${s};
|
|
vec2 cache = vec2(0.);
|
|
if(firstChannel >= 0){
|
|
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
|
|
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
|
|
if(hasNextRow){
|
|
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
|
|
}
|
|
}
|
|
|
|
ivec2 depth = ivec2(d, d + 1);
|
|
for (int j = - ${s}; j <= ${s}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},uq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:u}=a,l=te().getBool("WEBGL_PACK_NORMALIZATION")?new lq(r.shape,s,i,o,u):new oq(r.shape,s,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},dq={kernelName:Bu,backendName:"webgl",kernelFunc:uq},pq=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,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 = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${a}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${a})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},cq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:d}=a,p=new pq(r.shape,o,u,l,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},hq={kernelName:hc,backendName:"webgl",kernelFunc:cq};function fq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,e.dtype,"max",a),u=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}function Qw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let A=n.texData.get(h.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[d[w]];let v=Ug(A,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=v}else h=Gh(r,d,n);l=F.getInnerMostAxes(l.length,o)}F.assertAxesAreInnerMostDims("max",l,o);let[m,f]=F.computeOutAndReduceShapes(h.shape,l),g=m;i&&(g=F.expandShapeToKeepDim(m,u));let y;if(c){let A=n.texData.get(h.dataId).values,x=FB(A,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=fq(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var mq={kernelName:Ls,backendName:"webgl",kernelFunc:Qw},gq=Aw+`
|
|
return max(a, b);
|
|
`,yq=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Uh+`
|
|
return result;
|
|
`,Aq=sn({opSnippet:gq,packedOpSnippet:yq,cpuKernelImpl:$B}),xq={kernelName:Ws,backendName:"webgl",kernelFunc:Aq};function bq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Vl(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Xn({inputs:{x:r},backend:n});let p=new Od(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var vq={kernelName:Bs,backendName:"webgl",kernelFunc:bq};function wq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new qg(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var kq={kernelName:Vu,backendName:"webgl",kernelFunc:wq},Iq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,u=r*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Sq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=u-1-e.padInfo.top,c=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${d}, ${p}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${o};
|
|
wD += ${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 < ${u};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(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 * ${u} * ${l} +
|
|
wR * ${l} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Nq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,u,p,l,d),h=new qg(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Sq(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Tq={kernelName:mc,backendName:"webgl",kernelFunc:Nq};function Cq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Vl([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,u,l,1,d,p),h=!0,m=new Od(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Iq(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Eq={kernelName:fc,backendName:"webgl",kernelFunc:Cq};function Rq(e,t,n,a){let r=new Od(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Od(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Mq={kernelName:gc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,u=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let l=[1,1];k.assert(F.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let d=F.computePool2DInfo(a.shape,r,s,l,i),[p,c]=Rq(a,o,d,u);return[p,c]}};function Fq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,"float32","mean",a),u=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}var $q={kernelName:Vs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,u=k.parseAxisParam(s,a.shape),l=u,d=F.getAxesPermutation(l,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let N=0;N<v.length;N++)v[N]=a.shape[d[N]];let b=Ug(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=Gh(a,d,i);h.push(m),l=F.getInnerMostAxes(l.length,o)}F.assertAxesAreInnerMostDims("sum",l,o);let[f,g]=F.computeOutAndReduceShapes(m.shape,l),y=f;r&&(y=F.expandShapeToKeepDim(f,u));let A=Fq(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return A}};function Dq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"min",n),y;if(i){let A=F.expandShapeToKeepDim(c,u);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var Oq={kernelName:js,backendName:"webgl",kernelFunc:Dq},zq=Aw+`
|
|
return min(a, b);
|
|
`,_q=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Uh+`
|
|
return result;
|
|
`,Pq=sn({opSnippet:zq,packedOpSnippet:_q,cpuKernelImpl:DB}),Lq={kernelName:Us,backendName:"webgl",kernelFunc:Pq},Wq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),u=n==="reflect"?0:1;if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${u};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${u};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${a}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${u};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},Bq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=xn("rc",a),u=xn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${p};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${p};
|
|
}
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
`}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 - ${p}) +
|
|
gte * ((end - 1) * 2 - source + ${p});
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},Vq=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Bq(a.shape,r,s):new Wq(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},jq={kernelName:Hs,backendName:"webgl",kernelFunc:Vq},Uq=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Hq=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Uh+`
|
|
return result;
|
|
`,Gq=sn({opSnippet:Uq,packedOpSnippet:Hq}),qq={kernelName:Uo,backendName:"webgl",kernelFunc:Gq},Xq=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
|
|
uniform float seed;
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},Kq=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Zq=`
|
|
// 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;
|
|
`,e6=sn({opSnippet:Kq,packedOpSnippet:Zq,checkOutOfBounds:!0}),Yq={kernelName:Rs,backendName:"webgl",kernelFunc:e6},t6="return a - b;",n6=sn({opSnippet:t6,packedOpSnippet:t6,supportsComplex:!0,cpuKernelImpl:XB}),Jq={kernelName:ui,backendName:"webgl",kernelFunc:n6};function a6(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=Qw({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=F.expandShapeToKeepDim(o.shape,i),l=Ae({inputs:{x:o},backend:n,attrs:{shape:u}}),d=n6({inputs:{a:r,b:l},backend:n}),p=Xw({inputs:{x:d},backend:n}),c=qh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:u}}),m=e6({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Qq={kernelName:oi,backendName:"webgl",kernelFunc:a6};function eX(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,u=o?r:a6({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],d=u.shape[1],p=new Xq(l,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[u],"int32",c);return o||n.disposeIntermediateTensorInfo(u),h}var tX={kernelName:yc,backendName:"webgl",kernelFunc:eX},r6="return -x;";function nX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=zB(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ql(a.shape,r6):r=new Yr(a.shape,r6),n.runWebGLProgram(r,[a],a.dtype)}var aX={kernelName:Ho,backendName:"webgl",kernelFunc:nX},rX=Za.nonMaxSuppressionV3Impl;function sX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=a,l=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=rX(l,d,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var iX={kernelName:qo,backendName:"webgl",kernelFunc:sX},oX=Za.nonMaxSuppressionV4Impl;function lX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=oX(d,p,i,o,u,l);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var uX={kernelName:Xo,backendName:"webgl",kernelFunc:lX},dX=Za.nonMaxSuppressionV5Impl;function pX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=u,f=l,{selectedIndices:g,selectedScores:y}=dX(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var cX={kernelName:Ko,backendName:"webgl",kernelFunc:pX},hX=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${a}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},fX=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,u=k.sizeFromShape(r.shape),l=new hX(u,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},mX={kernelName:qs,backendName:"webgl",kernelFunc:fX};function Jh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=_d({inputs:{input:a},backend:n}),s=Jh({inputs:{x:r},backend:n}),i=Yh({inputs:{input:a},backend:n}),o=Jh({inputs:{x:i},backend:n}),u=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var gX={kernelName:hl,backendName:"webgl",kernelFunc:Jh};function s6(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=_d({inputs:{input:a},backend:n}),s=s6({inputs:{x:r},backend:n}),i=Yh({inputs:{input:a},backend:n}),o=Jh({inputs:{x:i},backend:n}),u=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var yX={kernelName:Zo,backendName:"webgl",kernelFunc:s6};function AX(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Zg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=Zg({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Pw({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var xX={kernelName:Yo,backendName:"webgl",kernelFunc:AX},bX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},vX=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=xn("rc",a),u=xn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
|
|
if(${l}) {
|
|
`,a===1?"":`}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
|
|
if(${l}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
|
|
${p[m]}
|
|
if (${c}) {
|
|
result[${m}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${m}] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
`;h+=a===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},i6=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vX(r.shape,s,i):new bX(r.shape,s,i),u=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,u)},wX={kernelName:Xs,backendName:"webgl",kernelFunc:i6},kX=`
|
|
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);
|
|
`,IX=`
|
|
// 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));
|
|
`+Uh+`
|
|
return result;
|
|
`,SX=sn({opSnippet:kX,packedOpSnippet:IX}),NX={kernelName:Ks,backendName:"webgl",kernelFunc:SX};function TX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=[],l=k.parseAxisParam(s,r.shape),d=l,p=F.getAxesPermutation(d,o),c=r;p!=null&&(c=bn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=F.getInnerMostAxes(d.length,o),u.push(c)),F.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=PB(c.shape,c.dtype,m,d);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=F.computeOutAndReduceShapes(c.shape,d),g=k.sizeFromShape(f),y=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),A=zc(r.dtype),x=Wi(y,A,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),u.push(y),u.push(x)}if(i){u.push(h);let m=F.expandShapeToKeepDim(h.shape,l);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var CX={kernelName:Jo,backendName:"webgl",kernelFunc:TX},o6=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=LB(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},EX={kernelName:ju,backendName:"webgl",kernelFunc:o6},RX="return 1.0 / x;",MX=Ke({opSnippet:RX}),FX={kernelName:Qo,backendName:"webgl",kernelFunc:MX},$X=Ca+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,DX=`
|
|
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;
|
|
`,OX=Ke({opSnippet:$X,packedOpSnippet:DX}),zX={kernelName:Ys,backendName:"webgl",kernelFunc:OX},_X=Ca+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,PX=`
|
|
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;
|
|
`,LX=Ke({opSnippet:_X,packedOpSnippet:PX}),WX={kernelName:Qs,backendName:"webgl",kernelFunc:LX},BX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// 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);
|
|
}
|
|
`}},VX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]},
|
|
${l[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// 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 < ${u-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 jX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new VX(r.shape,u,l,s,i):new BX(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],"float32")}var UX={kernelName:Js,backendName:"webgl",kernelFunc:jX},HX=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${l});
|
|
const float widthScale = float(${d});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${a-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 GX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new HX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var qX={kernelName:bc,backendName:"webgl",kernelFunc:GX},XX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},KX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]},
|
|
${l[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${u-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function ZX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new KX(r.shape,u,l,s,i):new XX(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var YX={kernelName:Uu,backendName:"webgl",kernelFunc:ZX},JX=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${l});
|
|
const float widthScale = float(${d});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${u[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${u[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function QX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new JX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var eK={kernelName:xc,backendName:"webgl",kernelFunc:QX},tK=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},nK=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-${n} tensor is not yet supported`);this.outputShape=e;let a=xn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(a.slice())};
|
|
if(${r}){
|
|
result.g = ${u(a.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${l(a.slice())};
|
|
if(${r}) {
|
|
result.a = ${d(a.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,A)=>c(A,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function aK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Xn({inputs:{x:r},backend:n});let u=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nK(r.shape,o):new tK(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var rK={kernelName:ei,backendName:"webgl",kernelFunc:aK},sK=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=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=`
|
|
uniform vec4 params;
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},iK={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,u=new sK(a.shape,s),[l,d]=F.getImageCenter(i,a.shape[1],a.shape[2]),p=u.getCustomSetupFunc(l,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(u,[a],a.dtype,p)}},oK=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,lK=Ke({opSnippet:oK}),uK={kernelName:ti,backendName:"webgl",kernelFunc:lK},dK="return inversesqrt(x);",pK=Ke({opSnippet:dK,cpuKernelImpl:WB}),cK={kernelName:ni,backendName:"webgl",kernelFunc:pK},l6=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),u=ut(s.length),l="";n===1?l="i":n===2&&(l="i, j");let d=`getIndices(${l})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${r});
|
|
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${d});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${c};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function hK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:u,sliceSize:l,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[u,l]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new l6(u,o,h.shape.length,m.shape.length,d,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),A=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),A}var fK={kernelName:tl,backendName:"webgl",kernelFunc:hK},mK=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);a=o.join(),r=u.join()}let s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${a});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function gK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new mK(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Aa(r.dtype,s.dtype))}var yK={kernelName:nl,backendName:"webgl",kernelFunc:gK},AK=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${F.SELU_SCALEALPHA};
|
|
float scale = ${F.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,xK=Ke({opSnippet:AK}),bK={kernelName:al,backendName:"webgl",kernelFunc:xK},vK="return 1.0 / (1.0 + exp(-1.0 * x));",wK=Ke({opSnippet:vK}),kK={kernelName:ri,backendName:"webgl",kernelFunc:wK},IK=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,SK=Ke({opSnippet:IK}),NK={kernelName:il,backendName:"webgl",kernelFunc:SK},TK=kw+`
|
|
return sin(x);
|
|
`,CK=Ke({opSnippet:TK}),EK={kernelName:ai,backendName:"webgl",kernelFunc:CK},RK=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,MK=Ke({opSnippet:RK}),FK={kernelName:sl,backendName:"webgl",kernelFunc:MK},$K=`
|
|
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;
|
|
`,DK=Ke({opSnippet:$K}),OK={kernelName:ol,backendName:"webgl",kernelFunc:DK},zK=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),u=[[0,0]];u.push(...i);for(let y=1+s.length;y<r.shape.length;++y)u.push([0,0]);let l=[],d=i6({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=F.getReshaped(d.shape,s,o,!1),c=F.getPermuted(p.length,s.length,!1),h=F.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:n,attrs:{shape:p}}),f=bn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:h}});return l.push(d),l.push(m),l.push(f),l.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},_K={kernelName:Hu,backendName:"webgl",kernelFunc:zK};function PK(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=VB(o,a.shape,a.dtype,u,r.dtype,l,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var LK={kernelName:vc,backendName:"webgl",kernelFunc:PK};function WK(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),u=Array.from(n.readSync(s.dataId)),[l,d,p]=jB(o,a.shape,a.dtype,i,u);return[n.makeTensorInfo(d,a.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var BK={kernelName:wc,backendName:"webgl",kernelFunc:WK};function VK(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),u=n.readSync(s.dataId),[l,d]=uw(i,a.shape,a.dtype,o,u,!0);return n.makeTensorInfo(d,a.dtype,l)}var jK={kernelName:kc,backendName:"webgl",kernelFunc:VK};function UK(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),u=n.readSync(s.dataId),[l,d]=uw(i,a.shape,a.dtype,o,u);return n.makeTensorInfo(d,a.dtype,l)}var HK={kernelName:Ic,backendName:"webgl",kernelFunc:UK};function GK(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:u,numUpdates:l,strides:d,outputSize:p}=F.calculateShapes(s,r,o),c=!1,h=new l6(l,u,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var qK={kernelName:Sc,backendName:"webgl",kernelFunc:GK};function XK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],u=F.prepareSplitSize(r,s,o),l=r.shape.length,d=new Array(l).fill(0),p=r.shape.slice();return u.map(c=>{let h=[...p];h[o]=c;let m=zd({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var KK={kernelName:ll,backendName:"webgl",kernelFunc:XK},ZK="return sqrt(x);",YK=Ke({opSnippet:ZK}),JK={kernelName:si,backendName:"webgl",kernelFunc:YK},QK="return x * x;",eZ=Ke({opSnippet:QK}),tZ={kernelName:Gu,backendName:"webgl",kernelFunc:eZ},u6="return (a - b) * (a - b);",nZ=sn({opSnippet:u6,packedOpSnippet:u6}),aZ={kernelName:li,backendName:"webgl",kernelFunc:nZ};function rZ({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ca+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Yr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var sZ={kernelName:Pr,backendName:"webgl",kernelFunc:rZ},iZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function oZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=fn.sliceInfo(r.shape,s,i,o,u,l,d,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=zd({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=Ae({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))v=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let w=n.texData.get(x.dataId).values,N=Ve(x.shape,x.dtype,w),C=UB(A,N,f,m);v=n.makeTensorInfo(A,x.dtype,C.values)}else{let w=new iZ(m,f,A);v=n.runWebGLProgram(w,[x],x.dtype)}let b=Ae({inputs:{x:v},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var lZ={kernelName:ul,backendName:"webgl",kernelFunc:oZ};function uZ(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=a,{data:d,dataSplits:p}=t,c=n.readSync(d.dataId),h=n.readSync(p.dataId),[m,f]=HB(c,h,r,s,i,o,u,l);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var dZ={kernelName:Nc,backendName:"webgl",kernelFunc:uZ};function pZ(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(i.dataId)[0],[l,d,p]=GB(o,u,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",l),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var cZ={kernelName:Tc,backendName:"webgl",kernelFunc:pZ};function hZ(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.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 i=n.readSync(s.dataId),o=qB(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var fZ={kernelName:Cc,backendName:"webgl",kernelFunc:hZ},mZ="return tan(x);",gZ=Ke({opSnippet:mZ}),yZ={kernelName:di,backendName:"webgl",kernelFunc:gZ},AZ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,xZ=Ke({opSnippet:AZ}),bZ={kernelName:pi,backendName:"webgl",kernelFunc:xZ},vZ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=wZ(e);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function wZ(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function d6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),u=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,l=Ve(r.shape,r.dtype,u),d=KB(l,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new vZ(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var kZ={kernelName:_r,backendName:"webgl",kernelFunc:d6};function IZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[u,l]=ZB(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}var SZ={kernelName:dl,backendName:"webgl",kernelFunc:IZ},NZ=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${o} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${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(${e}));
|
|
|
|
if (${i} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function TZ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=a,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],g=[d,m,f,h],y=new NZ(p,c,i,o,u,g);return n.runWebGLProgram(y,[r,s],"float32")}var CZ={kernelName:pl,backendName:"webgl",kernelFunc:TZ};function EZ(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Vl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:u,indices:l}=YB(i,r,s.shape,s.dtype);return[a.makeTensorInfo(u,s.dtype,o),a.makeTensorInfo([l.length],"int32",l)]}var RZ={kernelName:Ec,backendName:"webgl",kernelFunc:EZ};function MZ(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[s],l=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(l[d++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(u);for(let f=0;f<m.length;f++){c[s]=f;let g=zd({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=Ae({inputs:{x:g},backend:n,attrs:{shape:l}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var FZ={kernelName:cl,backendName:"webgl",kernelFunc:MZ},$Z=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,d=n%4,p=`
|
|
sumValue += dot(values, segFilter);
|
|
`,c="";r%n>0&&(c=`
|
|
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 = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
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(
|
|
${s})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${l}; 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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${l};
|
|
if (${d===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function DZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,u=[],l=0,d=F.getAxesPermutation([l],o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u.push(p),l=F.getInnerMostAxes(1,o)[0]);let c=F.segment_util.computeOutShape(p.shape,l,i),h=k.sizeFromShape([p.shape[l]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(m);let f=zc(r.dtype),g=(v,b,w,N,C)=>{let E=v.shape[0],_=v.shape[1],$=F.segment_util.segOpComputeOptimalWindowSize(_,C),S={windowSize:$,inSize:_,batchSize:E,numSegments:C},z=new $Z(S,b),O=n.compileAndRun(z,[v,w],N);if(u.push(O),O.shape[1]===C)return O;let W=o6({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),G=d6({inputs:{x:W},backend:n,attrs:{reps:[_/$]}});return u.push(W),u.push(G),g(O,b,G,N,C)},y=g(m,"unsortedSegmentSum",s,f,i),A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}}),x=A;if(d!=null){u.push(A);let v=F.getUndoAxesPermutation(d);x=bn({inputs:{x},backend:n,attrs:{perm:v}})}return u.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var OZ={kernelName:qu,backendName:"webgl",kernelFunc:DZ},zZ=[dq,hq,KV,YV,ej,aj,sj,lj,dj,cj,gj,Aj,vj,Ij,Mj,Tj,Dj,Pj,zj,Vj,Uj,Gj,Zj,aU,sU,pU,hU,yU,bU,RV,SU,OU,_U,EU,BU,jU,LU,GU,KU,JU,eH,nH,sH,pH,hH,oH,gH,xH,vH,SH,EH,$H,zH,_H,PH,WH,VH,UH,GH,XH,JH,tG,rG,iG,uG,hG,yG,vG,EV,kG,kU,NG,EG,FG,FV,zG,WG,VG,KG,GG,QG,nq,iq,mq,kq,vq,Tq,Eq,Mq,xq,$q,Oq,Lq,jq,qq,tX,_V,aX,iX,uX,cX,oU,mX,yX,xX,wX,NX,DV,CX,EX,lU,Yq,FX,WX,zX,LV,UX,qX,YX,eK,rK,iK,uK,cK,fK,yK,bK,kK,NK,EK,FK,tU,Qq,OK,_K,LK,BK,jK,HK,qK,KK,JK,tZ,aZ,sZ,lZ,dZ,cZ,fZ,Jq,GV,yZ,bZ,kZ,SZ,CZ,qV,RZ,FZ,OZ,gX];for(let e of zZ)gi(e);var Fn;(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"})(Fn||(Fn={}));var Pd;(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"})(Pd||(Pd={}));var p6;function _Z(e){p6=e.wasm.cwrap(hi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function PZ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let C=n.dataIdMap.get(i.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);m=C.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Pd[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=u?r.shape[2]:r.shape[1],A=l?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,A],r.dtype),b=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return p6(c,w,r.shape.length,h,N,s.shape.length,u,l,g,m,f,p||0,b),v}var LZ={kernelName:hi,backendName:"wasm",setupFunc:_Z,kernelFunc:PZ};function vn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,u=s.makeOutput(i.shape,i.dtype),l=s.dataIdMap.get(u.dataId).id;return k.sizeFromShape(u.shape)===0||t(o,l),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var WZ=vn(fo);function wn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:u}=i,{a:l,b:d}=u,p=o.dataIdMap.get(l.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:l.dtype,m=F.assertAndGetBroadcastShape(l.shape,d.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(l.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,l.shape.length,c,y,d.shape.length,Fn[l.dtype],A);if(t&&l.dtype==="float32")return x(),f;let v=F.getBroadcastDims(l.shape,m),b=F.getBroadcastDims(d.shape,m),w=v.every((C,E)=>C===E),N=b.every((C,E)=>C===E);if(w&&N)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${l.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var BZ=!0,VZ=wn(Or,BZ),c6;function jZ(e){c6=e.wasm.cwrap(xs,null,["array","number","number","number"])}function UZ(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return c6(s,r.length,Fn[a.dtype],i),a}var HZ={kernelName:xs,backendName:"wasm",setupFunc:jZ,kernelFunc:UZ};function Qh(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var GZ={kernelName:zs,backendName:"wasm",kernelFunc:Qh},h6;function qZ(e){h6=e.wasm.cwrap(ci,null,["number","array","number","number","number","array","number"])}function e0(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=KZ(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=XZ(t.x.shape,a.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Qh({inputs:t,backend:n});return m.shape=o,m}let l=n.makeOutput(o,u.dtype),d=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return h6(d,h,u.shape.length,Fn[u.dtype],p,c,s.length),l}function XZ(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function KZ(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var ZZ={kernelName:ci,backendName:"wasm",kernelFunc:e0,setupFunc:qZ};function Qr(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=F.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let d=new Array(r);for(let c=0;c<d.length;c++)d[c]=a[o[c]];i=F.getInnerMostAxes(i.length,r),u=e0({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var f6;function YZ(e){f6=e.wasm.cwrap(yo,null,["number, number, number"])}function JZ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(l.dataId).id;u=l,o=A}let h=u.shape.length;F.assertAxesAreInnerMostDims("all",d,h);let[m,f]=F.computeOutAndReduceShapes(u.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;f6(o,g,A)}if(c&&t.disposeData(l.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var QZ={kernelName:yo,backendName:"wasm",setupFunc:YZ,kernelFunc:JZ},m6;function eY(e){m6=e.wasm.cwrap(Ao,null,["number, number, number"])}function tY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(l.dataId).id;u=l,o=A}let h=u.shape.length;F.assertAxesAreInnerMostDims("any",d,h);let[m,f]=F.computeOutAndReduceShapes(u.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;m6(o,g,A)}if(c&&t.disposeData(l.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var nY={kernelName:Ao,backendName:"wasm",setupFunc:eY,kernelFunc:tY},g6;function aY(e){g6=e.wasm.cwrap(bs,null,["number","number","number","number","number"])}function rY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,u=s,{transposed:l,axes:d,inputWasTransposed:p}=Qr(s,r,t);if(p){let y=t.dataIdMap.get(l.dataId).id;y!==i&&(u=l,o=y)}let c=u.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=u.shape[d[0]];return g6(o,Fn[u.dtype],f,g,m),p&&t.disposeData(l.dataId),h}var sY={kernelName:bs,backendName:"wasm",kernelFunc:rY,setupFunc:aY},y6;function iY(e){y6=e.wasm.cwrap(vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oY(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,d=F.computePool2DInfo(r.shape,i,o,1,u,l),p=d.filterHeight,c=d.filterWidth,h=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let v=a.makeOutput(d.outShape,"float32"),b=a.dataIdMap.get(v.dataId).id;return y6(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,g,y,A,x,b),v}var lY={kernelName:vs,backendName:"wasm",setupFunc:iY,kernelFunc:oY};function Ea(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var uY={kernelName:el,backendName:"wasm",kernelFunc:Ea},A6;function dY(e){A6=e.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number"])}function pY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=s.shape.length,d=i?r.shape[u-2]:r.shape[u-1],p=o?s.shape[l-1]:s.shape[l-2],c=i?r.shape[u-1]:r.shape[u-2],h=o?s.shape[l-2]:s.shape[l-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),A=g===y||g===1||y===1;k.assert(u>=2&&l>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,d,c]:[g,c,d],b=o?[y,h,p]:[y,p,h],w=Ea({inputs:{x:r},backend:n,attrs:{shape:v}}),N=Ea({inputs:{x:s},backend:n,attrs:{shape:b}}),C=n.dataIdMap.get(w.dataId).id,E=n.dataIdMap.get(N.dataId).id,_=i?w.shape[2]:w.shape[1],$=o?N.shape[1]:N.shape[2],S=Math.max(g,y),z=n.makeOutput([S,_,$],w.dtype),O=n.dataIdMap.get(z.dataId).id,W=new Uint8Array(new Int32Array(w.shape).buffer),G=new Uint8Array(new Int32Array(N.shape).buffer);return A6(C,W,w.shape.length,E,G,N.shape.length,i,o,O),n.disposeData(w.dataId),n.disposeData(N.dataId),z.shape=x,z}var cY={kernelName:ws,backendName:"wasm",setupFunc:dY,kernelFunc:pY};function t0(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var hY={kernelName:ks,backendName:"wasm",kernelFunc:t0},fY=vn(Is),x6;function mY(e){x6=e.wasm.cwrap(zr,null,["number","number","number","number"])}function gY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return x6(o,s,i,l),u}var yY={kernelName:zr,backendName:"wasm",setupFunc:mY,kernelFunc:gY};function b6(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=F.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Qh({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(F.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=k.sizeFromShape(x.shape.slice(a));return Ea({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=F.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=bg(m,r,t[0].dtype,f),y=F.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let A=n.dataIdMap.get(i.dataId);return A.stringBytes=F.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let u=k.sizeFromShape(s[0].shape.slice(0,a)),l=0,d=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return l+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let m=h*l;for(let f=0;f<p.length;f++){let g=d[f],y=h*g,A=p[f].subarray(y,y+g);c.set(A,m),m+=g}}return i}var AY={kernelName:Io,backendName:"wasm",kernelFunc:b6},v6;function xY(e){v6=e.wasm.cwrap(Ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bY(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:d,dimRoundingMode:p,dataFormat:c}=n,h=F.convertConv2DDataFormat(c),m=F.computeConv2DInfo(r.shape,s.shape,u,l,d,p,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,A=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,b=m.dilationHeight,w=m.dilationWidth,N=m.strideHeight,C=m.strideWidth,E=m.inChannels,_=m.outChannels,$=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(m.outShape,"float32"),z=a.dataIdMap.get(S.dataId).id;return v6(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,A,x,v,$,b,w,N,C,E,_,z),S}var vY={kernelName:Ss,backendName:"wasm",setupFunc:xY,kernelFunc:bY},w6;function wY(e){w6=e.wasm.cwrap(Ns,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 kY(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:d}=a,p=1,c=F.convertConv2DDataFormat(u),h=F.computeConv2DInfo(d,s.shape,i,p,o,l,!1,c),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:v,outHeight:b,outWidth:w,strideHeight:N,strideWidth:C}=h,E=f-1-h.padInfo.top,_=g-1-h.padInfo.left,$=h.dataFormat==="channelsLast",S=k.computeStrides(h.inShape),z=k.computeStrides(r.shape),[O,W,G]=k.computeStrides(s.shape),H=S[0],J=$?S[1]:S[2],K=$?S[2]:1,ne=$?1:S[1],Q=z[0],se=$?z[1]:z[2],Z=$?z[2]:1,le=$?1:z[1],oe=t.makeOutput(h.inShape,"float32"),xe=t.dataIdMap.get(oe.dataId).id,fe=t.dataIdMap.get(r.dataId).id,Ne=t.dataIdMap.get(s.dataId).id;return w6(fe,Ne,m,f,g,A,x,y,b,w,v,N,C,E,_,O,W,G,H,J,K,ne,Q,se,Z,le,xe),oe}var IY={kernelName:Ns,backendName:"wasm",setupFunc:wY,kernelFunc:kY},SY=vn(Ts),Jg;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Jg||(Jg={}));var k6;function NY(e){k6=e.wasm.cwrap(No,null,["number","number","number","number","array","number","number","number","number","number"])}function TY(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:u,boxInd:l}=n,d=u.shape[0],[p,c]=i,h=[d,p,c,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=t0({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(u.dataId).id,A=t.dataIdMap.get(l.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(o.shape).buffer);return k6(g,y,A,d,b,p,c,Jg[r],s,v),f!=null&&t.disposeData(f.dataId),x}var CY={kernelName:No,backendName:"wasm",setupFunc:NY,kernelFunc:TY},I6;function EY(e){I6=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number"])}function RY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,u=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=F.getAxesPermutation([s],u),d=r;l!==null&&(d=e0({inputs:{x:r},attrs:{perm:l},backend:n}));let p=F.getInnerMostAxes(1,u)[0];F.assertAxesAreInnerMostDims("cumsum",[p],u);let c=n.makeOutput(d.shape,d.dtype),h=d.shape[p],m=n.dataIdMap.get(d.dataId).id,f=n.dataIdMap.get(c.dataId).id;I6(m,i?1:0,o?1:0,h,f,Fn[r.dtype]);let g=c;if(l!==null){let y=F.getUndoAxesPermutation(l);g=e0({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(d.dataId),n.disposeData(c.dataId)}return g}var MY={kernelName:Cs,backendName:"wasm",setupFunc:EY,kernelFunc:RY},S6;function FY(e){S6=e.wasm.cwrap(To,null,["number","number","number","array","number","array","array","number","number"])}function $Y(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=u*s,c=l*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return S6(g,s,i==="NHWC"?1:0,y,r.shape.length-1,A,x,m.length,v),f}var DY={kernelName:To,backendName:"wasm",setupFunc:FY,kernelFunc:$Y},N6;function OY(e){N6=e.wasm.cwrap(Es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zY(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:d,dimRoundingMode:p}=n,c=l==null?[1,1]:l,h=F.computeConv2DInfo(r.shape,s.shape,u,c,d,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,A=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,b=h.dilationWidth,w=h.strideHeight,N=h.strideWidth,C=h.inChannels,E=h.outChannels,_=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 $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return N6(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,A,x,_,v,b,w,N,C,E,S),$}var _Y={kernelName:Es,backendName:"wasm",setupFunc:OY,kernelFunc:zY},PY=!1,LY=wn(Ro,PY,"bool"),WY=vn(Ms);function Qg(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),u=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),o.splice(u,0,1),Ea({inputs:{x:r},backend:a,attrs:{shape:o}})}var BY={kernelName:Mo,backendName:"wasm",kernelFunc:Qg};function VY(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var jY={kernelName:Pu,backendName:"wasm",kernelFunc:VY},T6;function UY(e){T6=e.wasm.cwrap($o,null,["number","number","number","number","number","number"])}function HY(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,d]=a.shape;return T6(s,o,u,l,d,i),r}var GY={kernelName:$o,backendName:"wasm",kernelFunc:HY,setupFunc:UY},qY=vn(Fs),XY=!1,KY=wn($s,XY),C6;function ZY(e){C6=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number"])}function YY(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:u,scale:l}=n,d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,m=l!=null?t.dataIdMap.get(l.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return C6(d,p,c,h,m,r,g),f}var JY={kernelName:Ds,backendName:"wasm",setupFunc:ZY,kernelFunc:YY},E6;function QY(e){E6=e.wasm.cwrap(fi,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 eJ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=F.computeConv2DInfo(r.shape,s.shape,u,d,l,c),g=Pd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let b=f.filterHeight,w=f.filterWidth,N=f.padInfo.top,C=f.padInfo.right,E=f.padInfo.bottom,_=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,z=f.strideHeight,O=f.strideWidth,W=f.inChannels,G=f.padInfo.type==="SAME"?1:0,H=f.batchSize,J=f.inHeight,K=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(ne.dataId).id,se=o==null?0:a.dataIdMap.get(o.dataId).id;return E6(y,H,J,K,A,b,w,v,N,C,E,_,G,$,S,z,O,W,x,g,se,m||0,Q),ne}var tJ={kernelName:fi,backendName:"wasm",setupFunc:QY,kernelFunc:eJ},R6;function nJ(e){R6=e.wasm.cwrap(mi,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 aJ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=F.computeConv2DInfo(r.shape,s.shape,u,d,l,c,!0),g=Pd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let b=f.filterHeight,w=f.filterWidth,N=f.padInfo.top,C=f.padInfo.right,E=f.padInfo.bottom,_=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,z=f.strideHeight,O=f.strideWidth,W=f.inChannels,G=f.padInfo.type==="SAME"?1:0,H=f.batchSize,J=f.inHeight,K=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(ne.dataId).id,se=o==null?0:a.dataIdMap.get(o.dataId).id;return R6(y,H,J,K,A,b,w,v,N,C,E,_,G,$,S,z,O,W,x,g,se,m||0,Q),ne}var rJ={kernelName:mi,backendName:"wasm",setupFunc:nJ,kernelFunc:aJ},M6;function sJ(e){M6=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","array","number"])}function iJ(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,u]=g1.prepareAndValidate(a,r),l=t.makeOutput(s,a.dtype);if(i===0)return l;let d=r.shape,p=d[d.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(u).buffer),f=t.dataIdMap.get(l.dataId).id;return M6(c,Fn[a.dtype],h,i,p,o,m,f),l}var oJ={kernelName:Oo,backendName:"wasm",setupFunc:sJ,kernelFunc:iJ},F6;function lJ(e){F6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function uJ(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,u=k.parseAxisParam(i,r.shape)[0],l=F.segment_util.collectGatherOpShapeInfo(r,s,u,o),d=Ea({inputs:{x:r},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),c=Ea({inputs:{x:s},attrs:{shape:[l.batchSize,p/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,p/l.batchSize,l.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=d.shape.length-1,g=t.dataIdMap.get(d.dataId).id,y=t.dataIdMap.get(c.dataId).id,A=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(k.computeStrides(d.shape)).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return F6(g,Fn[r.dtype],x,f,y,l.batchSize,v,A),t.disposeData(d.dataId),t.disposeData(c.dataId),m.shape=l.outputShape,m}var dJ={kernelName:Do,backendName:"wasm",setupFunc:lJ,kernelFunc:uJ},pJ=!1,cJ=wn(zo,pJ,"bool"),hJ=!1,fJ=wn(Os,hJ,"bool"),$6;function mJ(e){$6=e.wasm.cwrap(_s,null,["number","number","number"])}function gJ(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;$6(r,n,i)}return s}var yJ={kernelName:_s,backendName:"wasm",setupFunc:mJ,kernelFunc:gJ},AJ=!1,xJ=wn(Wo,AJ,"bool"),bJ=!1,vJ=wn(Bo,bJ,"bool"),wJ=vn(Ps),kJ=!1,IJ=wn(jo,kJ,"bool"),D6;function SJ(e){D6=e.wasm.cwrap(Ls,null,["number, number, number"])}function NJ(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(l.dataId).id;u=l,o=A}let h=u.shape.length;F.assertAxesAreInnerMostDims("max",d,h);let[m,f]=F.computeOutAndReduceShapes(u.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;D6(o,g,A)}if(c&&t.disposeData(l.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var TJ={kernelName:Ls,backendName:"wasm",setupFunc:SJ,kernelFunc:NJ},CJ=!1,EJ=wn(Ws,CJ),O6;function RJ(e){O6=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function MJ(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,d=F.computePool2DInfo(r.shape,i,o,1,u,l),p=d.filterHeight,c=d.filterWidth,h=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,v=d.strideWidth,b=d.inChannels,w=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let N=a.makeOutput(d.outShape,"float32"),C=a.dataIdMap.get(N.dataId).id;return O6(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,g,y,A,x,v,b,w,C),N}var FJ={kernelName:Bs,backendName:"wasm",setupFunc:RJ,kernelFunc:MJ},z6;function $J(e){z6=e.wasm.cwrap(Vs,null,["number, number, number"])}function DJ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Qr(i,r,t),m=p;if(h){let v=t.dataIdMap.get(d.dataId).id;v!==o&&(l=d,u=v,m=F.getInnerMostAxes(m.length,l.shape.length))}F.assertAxesAreInnerMostDims("mean",m,l.shape.length);let[f,g]=F.computeOutAndReduceShapes(l.shape,m),y=k.sizeFromShape(g),A=l;l.dtype!=="float32"&&(A=t0({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;z6(u,y,v)}if(h&&t.disposeData(d.dataId),s){let v=F.expandShapeToKeepDim(x.shape,c);x.shape=v}return l.dtype!=="float32"&&t.disposeData(A.dataId),x}var OJ={kernelName:Vs,backendName:"wasm",setupFunc:$J,kernelFunc:DJ},_6;function zJ(e){_6=e.wasm.cwrap(js,null,["number, number, number"])}function _J(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Qr(i,r,t);if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(l=d,u=x)}let m=l.shape.length;F.assertAxesAreInnerMostDims("min",p,m);let[f,g]=F.computeOutAndReduceShapes(l.shape,p),y=k.sizeFromShape(g),A=t.makeOutput(f,l.dtype);if(k.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;_6(u,y,x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var PJ={kernelName:js,backendName:"wasm",setupFunc:zJ,kernelFunc:_J},LJ=!1,WJ=wn(Us,LJ),ey;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(ey||(ey={}));var P6;function BJ(e){P6=e.wasm.cwrap(Hs,null,["number","array","number","number","array","array","number","number"])}function VJ(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return P6(i,l,t.shape.length,Fn[t.dtype],c,h,ey[r],u),o}var jJ={kernelName:Hs,backendName:"wasm",kernelFunc:VJ,setupFunc:BJ},UJ=!0,HJ=wn(Gs,UJ),GJ=vn(Ho);function ty(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var L6;function qJ(e){L6=e.wasm.cwrap(qo,"number",["number","number","number","number","number"])}function XJ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=L6(l,d,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=ty(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var KJ={kernelName:qo,backendName:"wasm",setupFunc:qJ,kernelFunc:XJ},W6;function ZJ(e){W6=e.wasm.cwrap(Xo,"number",["number","number","number","number","number","bool"])}function YJ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:u,scores:l}=n,d=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=W6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=ty(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),A=t.makeOutput([],"int32",g);return[y,A]}var JJ={kernelName:Xo,backendName:"wasm",setupFunc:ZJ,kernelFunc:YJ},B6;function QJ(e){B6=e.wasm.cwrap(Ko,"number",["number","number","number","number","number","number"])}function eQ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:u,scores:l}=n,d=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=B6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=ty(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),A=t.makeOutput([m],"float32",f);return[y,A]}var tQ={kernelName:Ko,backendName:"wasm",setupFunc:QJ,kernelFunc:eQ},nQ=!1,aQ=wn(Go,nQ,"bool"),V6;function rQ(e){V6=e.wasm.cwrap(qs,null,["number","number","number","number","number"])}function sQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,u=n.makeOutput([...r.shape,s],"int32"),l=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return V6(d,s,i,o,l),u}var iQ={kernelName:qs,backendName:"wasm",setupFunc:rQ,kernelFunc:sQ};function oQ(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var lQ={kernelName:Zo,backendName:"wasm",kernelFunc:oQ};function uQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Qg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=Qg({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b6({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeData(d.dataId)),l}var dQ={kernelName:Yo,backendName:"wasm",kernelFunc:uQ},j6;function pQ(e){j6=e.wasm.cwrap(Xs,null,["number","array","number","number","array","array","number","number"])}function cQ(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return j6(i,l,t.shape.length,Fn[t.dtype],c,h,r,u),o}var hQ={kernelName:Xs,backendName:"wasm",kernelFunc:cQ,setupFunc:pQ},fQ=!1,mQ=wn(Ks,fQ),U6;function gQ(e){U6=e.wasm.cwrap(Zs,null,["number","number","number"])}function yQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),u=n.dataIdMap.get(o.dataId).id;return U6(s,i,u),o}var AQ={kernelName:Zs,backendName:"wasm",setupFunc:gQ,kernelFunc:yQ},H6;function xQ(e){H6=e.wasm.cwrap(Jo,null,["number","number","number","number"])}function bQ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Qr(i,r,t),m=p;if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(l=d,u=x,m=F.getInnerMostAxes(m.length,l.shape.length))}F.assertAxesAreInnerMostDims("prod",m,l.shape.length);let[f,g]=F.computeOutAndReduceShapes(l.shape,m),y=k.sizeFromShape(g),A=t.makeOutput(f,l.dtype);if(k.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;H6(u,y,Fn[A.dtype],x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var vQ={kernelName:Jo,backendName:"wasm",setupFunc:xQ,kernelFunc:bQ},wQ=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=kg(a,r,s,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},kQ={kernelName:ju,backendName:"wasm",kernelFunc:wQ},IQ=!0,SQ=wn(Rs,IQ),NQ=vn(Ys),TQ=vn(Qs),G6;function CQ(e){G6=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number"])}function EQ(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,[d,p,c,h]=r.shape,m=[d,u,l,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=t0({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,A=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return G6(y,d,p,c,h,u,l,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var RQ={kernelName:Js,backendName:"wasm",setupFunc:CQ,kernelFunc:EQ},q6;function MQ(e){q6=e.wasm.cwrap(ei,null,["number","array","number","array","number","number"])}function FQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Qh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);q6(u,d,i.length,p,r.shape.length,l);let c=Ea({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var $Q={kernelName:ei,backendName:"wasm",kernelFunc:FQ,setupFunc:MQ},X6;function DQ(e){X6=e.wasm.cwrap(fl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,d=n.dataIdMap.get(u.dataId).id,[p,c,h,m]=r.shape,[f,g]=F.getImageCenter(o,c,h),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],v=new Uint8Array(new Int32Array(x).buffer);return X6(l,p,c,h,m,s,f,g,v,x.length,d),u}var zQ={kernelName:fl,backendName:"wasm",kernelFunc:OQ,setupFunc:DQ},_Q=vn(ti),PQ=vn(ni),K6;function LQ(e){K6=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number","number"])}function WQ(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:d,strides:p,outputSize:c}=y1.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return K6(h,m,Fn[s.dtype],u,l,d,f,c,g),o}var BQ={kernelName:tl,backendName:"wasm",setupFunc:LQ,kernelFunc:WQ},Z6;function VQ(e){Z6=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function jQ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(s.dataId).id,l=n.makeOutput(r.shape,r.dtype),d=n.dataIdMap.get(l.dataId).id,p=a.shape.length,c=r.shape.length,h=p===0||p>1||c===1?1:k.sizeFromShape(r.shape.slice(1));return Z6(i,o,u,h,d),l}var UQ={kernelName:nl,backendName:"wasm",kernelFunc:jQ,setupFunc:VQ},Y6;function HQ(e){Y6=e.wasm.cwrap(ri,null,["number","number"])}function GQ(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||Y6(a,s),r}var qQ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:HQ,kernelFunc:GQ},XQ=vn(ai);function n0(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=fn.parseSliceParams(t,n,a),o=fn.isSliceContinous(t.shape,s,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),d=k.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let m=fn.computeFlatOffset(s,d);return t.dtype==="string"?p.stringBytes=u.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(m,m+k.sizeFromShape(i))),l}if(t.dtype==="string"){let m=Fh(u,s,i,t.shape,t.dtype);return p.stringBytes=m,l}let c=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)KQ(u,d[0],c,s,i);else if(h===3)ZQ(u,d[0],d[1],c,s,i);else if(h===4)YQ(u,d[0],d[1],d[2],c,s,i);else{let m=Fh(u,s,i,t.shape,t.dtype);c.set(m)}return l}function KQ(e,t,n,a,r){let s=0,i=a[0],o=a[1],u=i+r[0];for(let l=i;l<u;l++){let d=l*t+o;n.set(e.subarray(d,d+r[1]),s),s+=r[1]}}function ZQ(e,t,n,a,r,s){let i=0,o=r[0],u=r[1],l=r[2],d=o+s[0],p=u+s[1];for(let c=o;c<d;c++)for(let h=u;h<p;h++){let m=c*t+h*n+l;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function YQ(e,t,n,a,r,s,i){let o=0,u=s[0],l=s[1],d=s[2],p=u+i[0],c=l+i[1],h=d+i[2],m=s[3];for(let f=u;f<p;f++)for(let g=l;g<c;g++)for(let y=d;y<h;y++){let A=f*t+g*n+y*a+m;r.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var JQ={kernelName:rl,backendName:"wasm",kernelFunc:n0},J6;function QQ(e){J6=e.wasm.cwrap(oi,null,["number","number","number","number"])}function eee(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],u=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||J6(r,i,o,u),s}var tee={kernelName:oi,backendName:"wasm",setupFunc:QQ,kernelFunc:eee};function nee(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],u=F.prepareSplitSize(r,s,o),l=new Array(r.shape.length).fill(0),d=r.shape.slice();return u.map(p=>{let c=[...d];c[o]=p;let h=n0({inputs:{x:r},attrs:{begin:l,size:c},backend:a});return l[o]+=p,h})}var aee={kernelName:ll,backendName:"wasm",kernelFunc:nee},ree=vn(si),see=vn(Gu),iee=!0,oee=wn(li,iee),Q6;function lee(e){Q6=e.wasm.cwrap(Pr,null,["number","number","number"])}function uee(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),u=t.dataIdMap.get(o.dataId).id;return Q6(i,r,u),o}var dee={kernelName:Pr,backendName:"wasm",setupFunc:lee,kernelFunc:uee},e4;function pee(e){e4=e.wasm.cwrap(ul,null,["number","array","number","array","array","array","array","array","number","number"])}function cee(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,h=F.slice_util.maskToAxes(d);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(d!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(d!==0&&c!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=F.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(_=>{s[_]=0,i[_]=1,g.splice(_,0,1)});let y=Ea({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:A,end:x,strides:v}=F.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,u,l,d);s=A,i=x,o=v;let b=F.slice_util.maskToAxes(c);b.forEach(_=>{i[_]=s[_]+1,o[_]=1});let w=F.slice_util.computeOutShape(s,i,o),N=w.filter((_,$)=>b.indexOf($)===-1);if(o.every(_=>_===1)){let _=n0({inputs:{x:y},attrs:{begin:s,size:w},backend:t});t.disposeData(y.dataId);let $=Ea({inputs:{x:_},attrs:{shape:N},backend:t});return t.disposeData(_.dataId),$}let C=t.makeOutput(N,"float32");if(!N.some(_=>_===0)){let _=t.dataIdMap.get(y.dataId).id,$=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),S=new Uint8Array(new Int32Array(s).buffer),z=new Uint8Array(new Int32Array(i).buffer),O=new Uint8Array(new Int32Array(o).buffer),W=new Uint8Array(new Int32Array(N).buffer),G=new Uint8Array(new Int32Array(k.computeStrides(N)).buffer),H=t.dataIdMap.get(C.dataId).id;e4(_,$,y.shape.length,S,z,O,W,G,N.length,H)}t.disposeData(y.dataId);let E=Ea({inputs:{x:C},attrs:{shape:N},backend:t});return t.disposeData(C.dataId),E}var hee={kernelName:ul,backendName:"wasm",setupFunc:pee,kernelFunc:cee},fee=!0,mee=wn(ui,fee),t4;function gee(e){t4=e.wasm.cwrap(ii,null,["number, number, number"])}function yee(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Qr(i,r,t),m=p;if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(l=d,u=x,m=F.getInnerMostAxes(m.length,l.shape.length))}F.assertAxesAreInnerMostDims("sum",m,l.shape.length);let[f,g]=F.computeOutAndReduceShapes(l.shape,m),y=k.sizeFromShape(g),A=t.makeOutput(f,l.dtype);if(k.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;t4(u,y,x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var Aee={kernelName:ii,backendName:"wasm",setupFunc:gee,kernelFunc:yee},xee=vn(di),bee=vn(pi),n4;function vee(e){n4=e.wasm.cwrap(_r,null,["number","array","number","array","number","number"])}function wee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let c=0;c<o.length;c++)o[c]=r.shape[c]*i[c];let u=new Uint8Array(new Int32Array(r.shape).buffer),l=new Uint8Array(new Int32Array(o).buffer),d=n.makeOutput(o,r.dtype),p=n.dataIdMap.get(d.dataId).id;return n4(s,u,r.shape.length,l,o.length,Fn[d.dtype],p),d}var kee={kernelName:_r,backendName:"wasm",setupFunc:vee,kernelFunc:wee},a4;function Iee(e){a4=e.wasm.cwrap(dl,null,["number","array","number","number","number","bool","number","number"])}var See=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),u=a.shape.slice();u[u.length-1]=r;let l=t.makeOutput(u,a.dtype),d=t.dataIdMap.get(l.dataId).id,p=t.makeOutput(u,"int32"),c=t.dataIdMap.get(p.dataId).id;return a4(i,o,a.shape.length,Fn[a.dtype],r,s,d,c),[l,p]},Nee={kernelName:dl,backendName:"wasm",setupFunc:Iee,kernelFunc:See},r4;function Tee(e){r4=e.wasm.cwrap(pl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Cee(e){let{backend:t,inputs:n,attrs:a}=e,{image:r,transforms:s}=n,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=a,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],g=[d,m,f,h],y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),A=t.makeOutput(g,r.dtype),x=t.dataIdMap.get(A.dataId).id,v=t.dataIdMap.get(r.dataId).id,b=t.dataIdMap.get(s.dataId).id,w=i==="nearest"?1:2,N;switch(o){case"constant":N=1;break;case"reflect":N=2;break;case"wrap":N=3;break;case"nearest":N=4;break;default:N=1;break}return r4(v,b,s.shape[0]>1,d,m,f,h,c,p,y,r.shape.length-1,w,N,u,x),A}var Eee={kernelName:pl,backendName:"wasm",setupFunc:Tee,kernelFunc:Cee};function Ree(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,u=new Array(o-1),l=0;for(let h=0;h<o;h++)h!==s&&(u[l++]=r.shape[h]);let d=new Array(i),p=new Array(o).fill(0),c=r.shape.slice();c[s]=1;for(let h=0;h<d.length;h++)p[s]=h,d[h]=n0({inputs:{x:r},attrs:{begin:p,size:c},backend:n});return d.map(({dataId:h,dtype:m})=>({dataId:h,dtype:m,shape:u}))}var Mee={kernelName:cl,backendName:"wasm",kernelFunc:Ree};function Fee(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var $ee={kernelName:hl,backendName:"wasm",kernelFunc:Fee},Dee=[WZ,VZ,HZ,QZ,nY,sY,lY,cY,hY,fY,yY,AY,vY,IY,SY,CY,MY,DY,_Y,LY,WY,BY,jY,GY,qY,KY,LZ,JY,tJ,rJ,oJ,dJ,cJ,fJ,GZ,yJ,xJ,vJ,wJ,IJ,TJ,EJ,FJ,OJ,PJ,WJ,jJ,HJ,GJ,KJ,JJ,tQ,aQ,iQ,lQ,dQ,hQ,mQ,AQ,vQ,kQ,SQ,NQ,TQ,uY,RQ,$Q,zQ,PQ,_Q,BQ,UQ,qQ,XQ,JQ,tee,aee,ree,see,oee,dee,hee,mee,Aee,xee,bee,kee,Nee,Eee,ZZ,Mee,$ee];for(let e of Dee)gi(e);var ny=te();ny.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])));ny.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ny.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 s4=gs(iS()),Oee='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()}}}}',zee=gs(oS()),i4=class extends Eu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Up(this,fr())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let l=t;this.dataIdMap.set(e,{id:s,stringBytes:l,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=k.sizeFromShape(n),o=i*k.bytesPerElement(a),u=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:u,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,u),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),u)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+k.sizeFromShape(a)*k.bytesPerElement(n));return Lee(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=k.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=k.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function _ee(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function o4(e,t,n){if(a0!=null)return a0;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Wd!=null&&Wd[a]!=null?Wd[a]:n+a}async function Pee(){let[e,t]=await Promise.all([te().getAsync("WASM_HAS_SIMD_SUPPORT"),te().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,u)=>{if(o.endsWith(".worker.js")){let l=Oee,d=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(d)}return o.endsWith(".wasm")?o4(e,t,Ld!=null?Ld:u):u+o},ay&&(r.instantiateWasm=_ee(o4(e,t,Ld!=null?Ld:"")));let s=!1;r.onAbort=()=>{s||Bd||(Bd=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&a0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+s4.default.toString()],{type:"text/javascript"}),i=(0,s4.default)(r)):i=(0,zee.default)(r),i.then(o=>{s=!0,Bd=!1;let u=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",u,["number"]),dispose:o.cwrap("dispose",u,[])},n({wasm:o})})})}function Lee(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 Wee=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],a0=null,Ld=null,Wd={},Bd=!1,ay=!1;function Bee(e,t=!1){if(k1("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Bd)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");a0=e,ay=t}function Vee(e,t=!1){if(Bd)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")Ld=e;else{Wd=e;let n=Wee.filter(a=>Wd[a]==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.`)}ay=t}var l4="3.7.0",jee=2;kl("wasm",async()=>{let{wasm:e}=await Pee();return new i4(e)},jee);ee().prototype.abs=function(){return this.throwIfDisposed(),Wt(this)};ee().prototype.acos=function(){return this.throwIfDisposed(),S1(this)};ee().prototype.acosh=function(){return this.throwIfDisposed(),N1(this)};ee().prototype.add=function(e){return this.throwIfDisposed(),ie(this,e)};ee().prototype.all=function(e,t){return this.throwIfDisposed(),Uc(this,e,t)};ee().prototype.any=function(e,t){return this.throwIfDisposed(),id(this,e,t)};ee().prototype.argMax=function(e){return this.throwIfDisposed(),ki(this,e)};ee().prototype.argMin=function(e){return this.throwIfDisposed(),T1(this,e)};ee().prototype.asScalar=function(){return this.throwIfDisposed(),D(this.size===1,()=>"The array must have only 1 element."),q(this,[])};ee().prototype.asType=function(e){return this.throwIfDisposed(),ge(this,e)};ee().prototype.as1D=function(){return this.throwIfDisposed(),q(this,[this.size])};ee().prototype.as2D=function(e,t){return this.throwIfDisposed(),q(this,[e,t])};ee().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),q(this,[e,t,n])};ee().prototype.as4D=function(e,t,n,a){return this.throwIfDisposed(),q(this,[e,t,n,a])};ee().prototype.as5D=function(e,t,n,a,r){return this.throwIfDisposed(),q(this,[e,t,n,a,r])};ee().prototype.asin=function(){return this.throwIfDisposed(),C1(this)};ee().prototype.asinh=function(){return this.throwIfDisposed(),E1(this)};ee().prototype.atan=function(){return this.throwIfDisposed(),R1(this)};ee().prototype.atan2=function(e){return this.throwIfDisposed(),M1(this,e)};ee().prototype.atanh=function(){return this.throwIfDisposed(),F1(this)};ee().prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),ld(this,e,t,n,a)};ee().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),ud(this,e,t)};ee().prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),Ni(this,e,t,n,a,r)};ee().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Sl(this,e)};ee().prototype.cast=function(e){return this.throwIfDisposed(),ge(this,e)};ee().prototype.ceil=function(){return this.throwIfDisposed(),_1(this)};ee().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Mn(this,e,t)};ee().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Be&&(e=[e]),lt([this,...e],t)};ee().prototype.conv1d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Gc(this,e,t,n,a,r,s)};ee().prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),qc(this,e,t,n,a,r)};ee().prototype.conv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),mr(this,e,t,n,a,r,s)};ee().prototype.cos=function(){return this.throwIfDisposed(),dd(this)};ee().prototype.cosh=function(){return this.throwIfDisposed(),Xc(this)};ee().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Kc(this,e,t,n)};ee().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),W1(this,e,t)};ee().prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Tl(this,e,t,n,a,r,s)};ee().prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),B1(this,e,t,n,a,r)};ee().prototype.divNoNan=function(e){return this.throwIfDisposed(),V1(this,e)};ee().prototype.div=function(e){return this.throwIfDisposed(),me(this,e)};ee().prototype.dot=function(e){return this.throwIfDisposed(),S3(this,e)};ee().prototype.elu=function(){return this.throwIfDisposed(),Cl(this)};ee().prototype.equal=function(e){return this.throwIfDisposed(),Hr(this,e)};ee().prototype.erf=function(){return this.throwIfDisposed(),j1(this)};ee().prototype.exp=function(){return this.throwIfDisposed(),la(this)};ee().prototype.expandDims=function(e){return this.throwIfDisposed(),mn(this,e)};ee().prototype.expm1=function(){return this.throwIfDisposed(),U1(this)};ee().prototype.fft=function(){return this.throwIfDisposed(),bd(this)};ee().prototype.flatten=function(){return this.throwIfDisposed(),q(this,[this.size])};ee().prototype.floor=function(){return this.throwIfDisposed(),Rl(this)};ee().prototype.floorDiv=function(e){return this.throwIfDisposed(),Vc(this,e)};ee().prototype.gather=function(e,t){return this.throwIfDisposed(),Ti(this,e,t)};ee().prototype.greaterEqual=function(e){return this.throwIfDisposed(),qr(this,e)};ee().prototype.greater=function(e){return this.throwIfDisposed(),Wn(this,e)};ee().prototype.ifft=function(){return this.throwIfDisposed(),Dl(this)};ee().prototype.irfft=function(){return this.throwIfDisposed(),ch(this)};ee().prototype.isFinite=function(){return this.throwIfDisposed(),T3(this)};ee().prototype.isInf=function(){return this.throwIfDisposed(),C3(this)};ee().prototype.isNaN=function(){return this.throwIfDisposed(),G1(this)};ee().prototype.leakyRelu=function(e){return this.throwIfDisposed(),pd(this,e)};ee().prototype.lessEqual=function(e){return this.throwIfDisposed(),Xr(this,e)};ee().prototype.less=function(e){return this.throwIfDisposed(),Yc(this,e)};ee().prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),q1(this,e,t,n,a)};ee().prototype.logSigmoid=function(){return this.throwIfDisposed(),M3(this)};ee().prototype.logSoftmax=function(e){return this.throwIfDisposed(),eh(this,e)};ee().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),Z1(this,e,t)};ee().prototype.log=function(){return this.throwIfDisposed(),Bn(this)};ee().prototype.log1p=function(){return this.throwIfDisposed(),Jc(this)};ee().prototype.logicalAnd=function(e){return this.throwIfDisposed(),xa(this,e)};ee().prototype.logicalNot=function(){return this.throwIfDisposed(),cd(this)};ee().prototype.logicalOr=function(e){return this.throwIfDisposed(),th(this,e)};ee().prototype.logicalXor=function(e){return this.throwIfDisposed(),O3(this,e)};ee().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),je(this,e,t,n)};ee().prototype.maxPool=function(e,t,n,a){return this.throwIfDisposed(),hd(this,e,t,n,a)};ee().prototype.max=function(e,t){return this.throwIfDisposed(),Vn(this,e,t)};ee().prototype.maximum=function(e){return this.throwIfDisposed(),Xa(this,e)};ee().prototype.mean=function(e,t){return this.throwIfDisposed(),Nt(this,e,t)};ee().prototype.min=function(e,t){return this.throwIfDisposed(),fd(this,e,t)};ee().prototype.minimum=function(e){return this.throwIfDisposed(),Ml(this,e)};ee().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),J1(this,e,t)};ee().prototype.mod=function(e){return this.throwIfDisposed(),Q1(this,e)};ee().prototype.mul=function(e){return this.throwIfDisposed(),B(this,e)};ee().prototype.neg=function(){return this.throwIfDisposed(),St(this)};ee().prototype.norm=function(e,t,n){return this.throwIfDisposed(),gh(this,e,t,n)};ee().prototype.notEqual=function(e){return this.throwIfDisposed(),Ri(this,e)};ee().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),vl(this,e,t,n)};ee().prototype.onesLike=function(){return this.throwIfDisposed(),Un(this)};ee().prototype.pad=function(e,t){return this.throwIfDisposed(),gr(this,e,t)};ee().prototype.pool=function(e,t,n,a,r){return this.throwIfDisposed(),P3(this,e,t,n,a,r)};ee().prototype.pow=function(e){return this.throwIfDisposed(),yr(this,e)};ee().prototype.prelu=function(e){return this.throwIfDisposed(),gd(this,e)};ee().prototype.prod=function(e,t){return this.throwIfDisposed(),ah(this,e,t)};ee().prototype.reciprocal=function(){return this.throwIfDisposed(),ng(this)};ee().prototype.relu=function(){return this.throwIfDisposed(),Ka(this)};ee().prototype.relu6=function(){return this.throwIfDisposed(),rh(this)};ee().prototype.reshapeAs=function(e){return this.throwIfDisposed(),q(this,e.shape)};ee().prototype.reshape=function(e){return this.throwIfDisposed(),q(this,e)};ee().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),n7(this,e,t,n)};ee().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),a7(this,e,t,n)};ee().prototype.reverse=function(e){return this.throwIfDisposed(),Hn(this,e)};ee().prototype.rfft=function(){return this.throwIfDisposed(),vd(this)};ee().prototype.round=function(){return this.throwIfDisposed(),sh(this)};ee().prototype.rsqrt=function(){return this.throwIfDisposed(),ih(this)};ee().prototype.selu=function(){return this.throwIfDisposed(),oh(this)};ee().prototype.separableConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),ag(this,e,t,n,a,r,s)};ee().prototype.sigmoid=function(){return this.throwIfDisposed(),Rn(this)};ee().prototype.sign=function(){return this.throwIfDisposed(),rg(this)};ee().prototype.sin=function(){return this.throwIfDisposed(),lh(this)};ee().prototype.sinh=function(){return this.throwIfDisposed(),uh(this)};ee().prototype.slice=function(e,t){return this.throwIfDisposed(),Re(this,e,t)};ee().prototype.softmax=function(e){return this.throwIfDisposed(),xd(this,e)};ee().prototype.softplus=function(){return this.throwIfDisposed(),Ci(this)};ee().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),md(this,e,t)};ee().prototype.split=function(e,t){return this.throwIfDisposed(),Zt(this,e,t)};ee().prototype.sqrt=function(){return this.throwIfDisposed(),an(this)};ee().prototype.square=function(){return this.throwIfDisposed(),ot(this)};ee().prototype.squaredDifference=function(e){return this.throwIfDisposed(),hh(this,e)};ee().prototype.squeeze=function(e){return this.throwIfDisposed(),Vt(this,e)};ee().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Be?[this,e]:[this,...e];return gn(n,t)};ee().prototype.step=function(e){return this.throwIfDisposed(),Ol(this,e)};ee().prototype.stridedSlice=function(e,t,n,a,r,s,i,o){return this.throwIfDisposed(),ig(this,e,t,n,a,r,s,i,o)};ee().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};ee().prototype.sum=function(e,t){return this.throwIfDisposed(),Se(this,e,t)};ee().prototype.tan=function(){return this.throwIfDisposed(),og(this)};ee().prototype.tanh=function(){return this.throwIfDisposed(),Si(this)};ee().prototype.tile=function(e){return this.throwIfDisposed(),Gr(this,e)};ee().prototype.toBool=function(){return this.throwIfDisposed(),ge(this,"bool")};ee().prototype.toFloat=function(){return this.throwIfDisposed(),ge(this,"float32")};ee().prototype.toInt=function(){return this.throwIfDisposed(),ge(this,"int32")};ee().prototype.topk=function(e,t){return this.throwIfDisposed(),lg(this,e,t)};ee().prototype.transpose=function(e){return this.throwIfDisposed(),Qe(this,e)};ee().prototype.unique=function(e){return this.throwIfDisposed(),mh(this,e)};ee().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),ug(this,e,t)};ee().prototype.unstack=function(e){return this.throwIfDisposed(),Gn(this,e)};ee().prototype.where=function(e,t){return this.throwIfDisposed(),un(e,this,t)};ee().prototype.zerosLike=function(){return this.throwIfDisposed(),Ge(this)};var u4={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,Ol(ge(n,"float32"),-1))}}},Uee={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ot(ge(n,"float32")),r=an(ye(ke(1),a));return St(me(e,r))}}}},Hee={kernelName:go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(ye(ot(ge(n,"float32")),1));return me(e,a)}}}},Gee={kernelName:Or,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=e,i=Bt(n.shape,r);return i.length>0&&(s=Se(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Bt(a.shape,r);return i.length>0&&(s=Se(s,i)),q(s,a.shape)}}}},qee={kernelName:xs,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},Xee={kernelName:bs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},Kee={kernelName:Fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},Zee={kernelName:xo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,an(ye(ke(1),ot(ge(n,"float32")))))}}},Yee={kernelName:bo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(ie(ke(1),ot(ge(n,"float32"))));return me(e,a)}}}},Jee={kernelName:ko,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=ie(ot(n),ot(a)),i=B(e,me(a,s)),o=Bt(n.shape,r);return o.length>0&&(i=Se(i,o)),q(i,n.shape)},b:()=>{let s=ie(ot(n),ot(a)),i=St(B(e,me(n,s))),o=Bt(a.shape,r);return o.length>0&&(i=Se(i,o)),q(i,a.shape)}}}},Qee={kernelName:vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ie(ot(ge(n,"float32")),1))}}},ete={kernelName:wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ye(ke(1),ot(ge(n,"float32"))))}}};function tte(e,t,n,a,r,s){let i=M(e,"dy","avgPool3dGrad"),o=M(t,"input","avgPool3dGrad"),u=i,l=o,d=!1;o.rank===4&&(d=!0,u=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),l=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),D(u.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),D(l.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`),s!=null&&D(qt(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let p={dy:u,input:l},c={filterSize:n,strides:a,pad:r,dimRoundingMode:s},h=P.runKernel(Kp,p,c);return d?q(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var nte=L({avgPool3dGrad_:tte}),ate={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>nte(e,a,r,s,i,o)}}};function rte(e,t,n,a,r){let s=M(e,"dy","avgPoolGrad"),i=M(t,"input","avgPoolGrad");D(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,u=s,l=!1;i.rank===3&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),D(u.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`),D(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let d={dy:u,input:o},p={filterSize:n,strides:a,pad:r},c=P.runKernel(Xp,d,p);return l?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var ste=L({avgPoolGrad_:rte}),ite={kernelName:vs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>ste(e,a,r,s,i)}}},ote={kernelName:ws,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>je(e,r,!1,!0),b:()=>je(a,e,!0,!1)}:!s&&i?{a:()=>je(e,r,!1,!1),b:()=>je(e,a,!0,!1)}:s&&!i?{a:()=>je(r,e,!1,!0),b:()=>je(a,e,!1,!1)}:{a:()=>je(r,e,!0,!0),b:()=>je(e,a,!0,!0)}}},lte={kernelName:Du,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>md(e,a,r)}}},ute={kernelName:mb,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let u=r.length-1;u>=0;u--)if(r[u]===s[u])i[u]=1;else if(r[u]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let u=0;u<i.length;u++)i[u]>1&&o.push(u);return{x:()=>Se(e,o,!0)}}},dte={kernelName:ks,gradFunc:e=>({x:()=>e.clone()})},pte={kernelName:Is,gradFunc:e=>({x:()=>Ge(e)})},cte={kernelName:zr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>un(xa(qr(a,r),Xr(a,s)),e,Ge(e))}}},hte={kernelName:Ou,inputsToSave:["x"],gradFunc:u4.gradFunc},fte={kernelName:Io,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=ya(r,t[0].shape)[0],i=a.map(o=>o[s]);return Zt(e,i,s).map(o=>()=>o)}},mte={kernelName:Ss,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:u}=n;return D(Ur(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>P1(a.shape,e,r,i,o,u),filter:()=>hg(a,e,r.shape,i,o,u)}}},gte={kernelName:Ns,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:u}=n;return{dy:()=>mr(e,r,s,i,o,1,u),filter:()=>hg(e,a,r.shape,s,i,o,u)}}};function yte(e,t,n,a,r){let s=e;e.rank===4&&(s=q(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),D(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),D(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),D(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),D(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),D(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},u={strides:a,pad:r,filterShape:n};return P.runKernel(Qp,o,u)}var Ate=L({conv3DBackpropFilter_:yte}),xte={kernelName:zu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s}=n;D(Ur(a),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let[i,o]=t;return{x:()=>w3(i.shape,e,o,r,s),filter:()=>Ate(i,e,o.shape,r,s)}}},bte={kernelName:Ts,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(St(lh(ge(n,"float32"))),e)}}},vte={kernelName:So,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(uh(ge(n,"float32")),e)}}},wte={kernelName:Cs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=D3([r],a.rank),u=Kc(e,r,s,!i);return o!=null&&(u=Qe(u,o)),u}}}},kte={kernelName:Es,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;D(Ur(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[u,l]=t;return D(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`),D(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`),D(u.shape[3]===l.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),D(Ga(r,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${o}'.`),i!=null&&D(qt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>K3(u.shape,e,l,r,s,a,i),filter:()=>X3(u,e,l.shape,r,s,a,i)}}},Ite={kernelName:_u,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,s={x:a,filter:r,dy:e},i={x:a,filter:r,dy:e};return{x:()=>P.runKernel(sc,s,n),filter:()=>P.runKernel(ic,i,n)}}},Ste={kernelName:Co,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>P.runKernel(lc,a)}}},Nte={kernelName:Eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(la(St(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>B(e,a)}}},Tte={kernelName:Ms,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,n)}}},Cte={kernelName:Mo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>q(e,n.shape)}}},Ete={kernelName:Fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,la(n))}}},Rte={kernelName:Fs,gradFunc:e=>({x:()=>Ge(e)})},Mte={kernelName:$s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=me(e,ge(a,"float32")),i=Bt(n.shape,r);return i.length>0?q(Se(s,i),n.shape):s},b:()=>{let s=B(e,ge(n,"float32")),i=Bt(a.shape,r);i.length>0&&(s=q(Se(s,i),a.shape));let o=ot(a);return St(me(s,ge(o,"float32")))}}}},Fte={kernelName:Ds,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,u=o==null?ke(1):o,l=Bt(s.shape,r.shape),d=[];if(s.rank===1){for(let f=0;f<r.shape.length-1;++f)d.push(r.shape[f]);d.push(1)}let p=ye(r,s),c=B(e,u),h=ih(ie(i,ke(a))),m=B(B(B(h,h),h),ke(-.5));return{x:()=>s.rank===1?q(B(B(e,Gr(q(h,[1,1,1,s.shape[0]]),d)),u),r.shape):q(B(B(e,h),u),r.shape),mean:()=>{let f=B(B(h,ke(-1)),c);return s.rank===1&&(f=Se(f,l)),q(f,s.shape)},variance:()=>{let f=B(B(m,p),c);return s.rank===1&&(f=Se(f,l)),q(f,s.shape)},scale:()=>{let f=B(p,h),g=B(e,f);return s.rank===1&&(g=Se(g,l)),q(g,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Se(f,l)),q(f,s.shape)}}}},$te={kernelName:Do,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=ya(s,a.shape)[0];return{x:()=>{let o=a.shape,u=r.size,l=o.slice(0,i),d=l.length,p=o.slice(s,o.length).slice(1),c=p.length,h=d4(0,d),m=d4(d+1,d+1+c),f=p4([l,[u],p]),g=q(e,f),y=q(r,[u]),A=p4([[d],h,m]),x=Qe(g,A),v=ug(x,y,a.shape[i]),b=K1(A);return v=Qe(v,b),v},indices:()=>r}}};function d4(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function p4(e){let t=[];for(let n=0;n<e.length;++n)for(let a=0;a<e[n].length;++a)t.push(e[n][a]);return t}var Dte={kernelName:Os,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>Ge(n),b:()=>Ge(a)}}},Ote={kernelName:zs,gradFunc:e=>({x:()=>ge(e,"float32")})},zte={kernelName:_o,gradFunc:e=>({x:()=>Ge(e)})},_te={kernelName:Po,gradFunc:e=>({x:()=>Ge(e)})},Pte={kernelName:Lo,gradFunc:e=>({x:()=>Ge(e)})},Lte={kernelName:_s,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=Wn(a,0);return{x:()=>un(s,e,B(e,r))}}},Wte={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ie(n,1))}}},Bte={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ge(n,"float32"))}}},Vte={kernelName:gb,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=!0,i=la(a);return ye(e,B(Se(e,r,s),i))}}}};function jte(e,t,n,a=5,r=1,s=1,i=.5){let o={x:e,y:t,dy:n},u={depthRadius:a,bias:r,alpha:s,beta:i};return P.runKernel(hc,o,u)}var Ute=L({localResponseNormalizationBackprop_:jte}),Hte={kernelName:Bu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:u}=n;return{x:()=>Ute(a,r,e,s,i,o,u)}}};function c4(e,t,n,a){return t.rank<n.rank&&(t=q(t,Ei(t.shape,a))),e.rank<n.rank&&(e=q(e,Ei(e.shape,a))),{x:()=>B(e,ge(Hr(n,t),e.dtype))}}var h4={kernelName:Ls,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=ya(r,s.shape),u=c4(e,i,s,o);return{x:()=>u.x()}}},Gte={kernelName:Ws,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>B(e,ge(qr(n,a),"float32")),b:()=>B(e,ge(Yc(n,a),"float32"))}}};function qte(e,t,n,a,r,s,i){let o=M(e,"dy","maxPool3dGrad"),u=M(t,"input","maxPool3dGrad"),l=M(n,"output","maxPool3dGrad"),d=o,p=u,c=l,h=!1;u.rank===4&&(h=!0,d=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),p=q(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]]),c=q(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]])),D(d.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${d.rank}.`),D(p.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`),D(c.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${c.rank}.`),i!=null&&D(qt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let m={dy:d,input:p,output:c},f={filterSize:a,strides:r,pad:s,dimRoundingMode:i},g=P.runKernel(mc,m,f);return h?q(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var Xte=L({maxPool3dGrad_:qte}),Kte={kernelName:Vu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=n;return{x:()=>Xte(e,a,r,s,i,o,u)}}};function Zte(e,t,n,a,r,s,i){let o=M(e,"dy","maxPoolGrad"),u=M(t,"input","maxPoolGrad"),l=M(n,"output","maxPoolGrad");D(u.rank===o.rank,()=>`Rank of input (${u.rank}) does not match rank of dy (${o.rank})`),D(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),D(u.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`),i!=null&&D(qt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let d={dy:o,input:u,output:l},p={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return P.runKernel(fc,d,p)}var Yte=L({maxPoolGrad_:Zte}),Jte={kernelName:Bs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>Yte(e,a,r,s,i,o)}}},Qte={kernelName:Vs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=ya(r,a.shape),i=$3(a.shape,s)[1],o=Mt(i);return{x:()=>{let u=a.shape.slice();s.forEach(d=>{u[d]=1});let l=q(e,u);return me(B(l,jn(a.shape,"float32")),o)}}}},ene={kernelName:js,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=ya(r,s.shape),u=c4(e,i,s,o);return{x:()=>u.x()}}},tne={kernelName:Us,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>B(e,ge(Xr(n,a),"float32")),b:()=>B(e,ge(Wn(n,a),"float32"))}}},nne={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Re(e,s,a.shape)}}},ane={kernelName:Uo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=Bt(n.shape,r);return s.length>0?q(Se(e,s),n.shape):e},b:()=>{let s=B(e,St(Rl(me(n,a)))),i=Bt(a.shape,r);return i.length>0?q(Se(s,i),a.shape):s}}}},rne={kernelName:Gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=B(e,ge(a,"float32")),i=Bt(n.shape,r);return i.length>0?q(Se(s,i),n.shape):s},b:()=>{let s=B(e,ge(n,"float32")),i=Bt(a.shape,r);return i.length>0?q(Se(s,i),a.shape):s}}}},sne={kernelName:Ho,gradFunc:e=>({x:()=>St(e)})},ine={kernelName:qs,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>$t(n.shape,"float32")}}},one={kernelName:Zo,gradFunc:e=>({x:()=>Ge(e)})},lne={kernelName:Yo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return Gn(e,a).map(r=>()=>r)}},f4={kernelName:Xs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Re(e,s,a.shape)}}},une={kernelName:Ks,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=mt(s.shape,i.shape);return{a:()=>{let u=ge(i,"float32"),l=B(e,B(u,yr(s,ye(u,ke(1))))),d=Bt(s.shape,o);return d.length>0&&(l=Se(l,d)),q(l,s.shape)},b:()=>{let u=Wn(s,0),l=un(u,Bn(s),Ge(s)),d=B(e,B(r,l)),p=Bt(i.shape,o);return p.length>0&&(d=Se(d,p)),q(d,i.shape)}}}},dne={kernelName:Zs,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=Wn(n,0);return{x:()=>un(r,e,B(e,a)),alpha:()=>{let s=un(r,Ge(e),B(e,n)),i=Bt(a.shape,e.shape);return i.length>0&&(s=Se(s,i)),q(s,a.shape)}}}},pne={kernelName:Rs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=me(e,ge(a,"float32")),i=Bt(n.shape,r);return i.length>0?q(Se(s,i),n.shape):s},b:()=>{let s=B(e,ge(n,"float32")),i=Bt(a.shape,r);i.length>0&&(s=q(Se(s,i),a.shape));let o=ot(a);return St(me(s,ge(o,"float32")))}}}},cne={kernelName:Qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,St(ot(n)))}}},hne={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(Xr(n,6),Ol(n));return{x:()=>B(e,ge(a,"float32"))}}},fne={kernelName:Ys,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,ge(Ol(n),"float32"))}}},mne={kernelName:el,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>q(e,n.shape)}}},gne={kernelName:Js,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(bc,r,n)}}},yne={kernelName:Uu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(xc,r,n)}}},Ane={kernelName:ei,gradFunc:(e,t,n)=>{let{dims:a}=n,r=ya(a,e.shape);return{x:()=>Hn(e,r)}}},xne={kernelName:ti,gradFunc:e=>({x:()=>Ge(e)})},bne={kernelName:ni,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(me(e,B(yr(n,1.5),2)))}}},vne={kernelName:nl,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>ge(Ge(n),"float32"),t:()=>B(e,ge(n,e.dtype)),e:()=>B(e,ge(cd(n),e.dtype))}}},wne={kernelName:al,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Wn(n,ke(0)),r=ke(i7),s=ke(o7),i=B(e,s),o=B(B(e,r),la(ge(n,"float32")));return un(a,i,o)}}}},kne={kernelName:ri,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(n,ye(ke(1),n)))}}},Ine={kernelName:il,gradFunc:e=>({x:()=>Ge(e)})},Sne={kernelName:ai,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(dd(ge(n,"float32")),e)}}},Nne={kernelName:sl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(Xc(ge(n,"float32")),e)}}},Tne={kernelName:rl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,u]=o3(a,r,s),l=[];for(let d=0;d<e.rank;d++)l.push([o[d],i[d]-o[d]-u[d]]);return{x:()=>gr(e,l)}}},Cne={kernelName:oi,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=B(e,a);return{logits:()=>ye(i,B(Se(i,[r],s),a))}}},Ene={kernelName:ol,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,Rn(n))}}},m4={kernelName:Hu,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>ud(e,a,r)}}},g4={kernelName:ll,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>lt(e,a)}}},Rne={kernelName:si,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,B(an(ge(n,"float32")),2))}}},Mne={kernelName:Gu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(ge(n,"float32"),2))}}},Fne={kernelName:li,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ke(2);return{a:()=>B(e,B(r,ye(n,a))),b:()=>B(e,B(r,ye(a,n)))}}},$ne={kernelName:Pr,gradFunc:e=>({x:()=>Ge(e)})},Dne={kernelName:ui,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=e,i=Bt(n.shape,r);return i.length>0&&(s=Se(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Bt(a.shape,r);return i.length>0&&(s=Se(s,i)),q(St(s),a.shape)}}}},One={kernelName:ii,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;ya(s,a.shape).forEach(u=>{r[u]=1});let i=q(e,r),o=B(i,jn(a.shape,"float32"));return{x:()=>o}}},zne={kernelName:di,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ot(dd(n)))}}},_ne={kernelName:pi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(ye(ke(1),ot(n)),e)}}},Pne={kernelName:_r,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=Ge(a);if(a.rank===1)for(let i=0;i<r[0];++i)s=ie(s,Re(e,[i*a.shape[0]],[a.shape[0]]));else if(a.rank===2)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1]],[a.shape[0],a.shape[1]]));else if(a.rank===3)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let u=0;u<r[2];++u)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1],u*a.shape[2]],[a.shape[0],a.shape[1],a.shape[2]]));else if(a.rank===4)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let u=0;u<r[2];++u)for(let l=0;l<r[3];++l)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1],u*a.shape[2],l*a.shape[3]],[a.shape[0],a.shape[1],a.shape[2],a.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${a.rank} tensors yet.`);return s}}}},Lne={kernelName:ci,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=K1(r);return{x:()=>Qe(e,s)}}},Wne={kernelName:cl,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>gn(e,r)}}},Bne={kernelName:qu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Vne(e,n)}}};function Vne(e,t){let n=Xa(t,Ge(t)),a=Ti(e,n),r=qr(t,ke(0,"int32")),s=a.rank-r.rank;for(let o=0;o<s;++o)r=mn(r,o+1);r=xa(r,jn(a.shape,"bool"));let i=Ge(a);return un(r,a,i)}var jne={kernelName:hl,gradFunc:e=>({x:()=>Ge(e)})},Une=[u4,Uee,Hee,Gee,qee,Xee,Kee,Zee,Yee,Jee,Qee,ete,ate,ite,ote,lte,ute,dte,pte,cte,hte,fte,gte,mte,xte,bte,vte,wte,kte,Ite,pne,Ste,Nte,Tte,Cte,Ete,Mte,Rte,Fte,$te,Dte,Ote,zte,_te,Pte,Lte,Wte,Bte,Vte,Hte,h4,h4,Gte,Kte,Jte,Qte,ene,tne,nne,ane,rne,sne,ine,one,lne,f4,f4,une,dne,cne,hne,fne,mne,gne,yne,Ane,xne,bne,vne,wne,kne,Ine,Sne,Nne,Tne,Cne,Ene,m4,m4,g4,g4,Rne,Fne,Mne,$ne,Dne,One,zne,_ne,Pne,Lne,Wne,Bne,jne];for(let e of Une)yb(e);var y4={};Fe(y4,{maxNorm:()=>Xne,minMaxNorm:()=>Yne,nonNeg:()=>Zne,unitNorm:()=>Kne});var ry;function jt(){return ry==null&&(ry=c3().epsilon()),ry}function Ra(){return"channelsLast"}var vr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,vr.prototype)}},Ma=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ma.prototype)}},U=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,U.prototype)}},_e=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,_e.prototype)}},A4=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,A4.prototype)}};function Bi(e,t){if(Array.isArray(e)){let n=[];for(let a=0;a<t;a++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Qa(e,t){if(!e)throw new A4(t)}function x4(e,t){let n=0;for(let a of e)a===t&&n++;return n}function $n(e){return e.length===1?e[0]:e}function yt(e){return Array.isArray(e)?e:[e]}function wr(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Vi(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var ba={};function sy(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function iy(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>iy(t));else{let t=Object.keys(e);for(let n of t){let a=e[n];a!=null&&typeof a=="object"&&(!Array.isArray(a)&&a.type==="ndarray"&&typeof a.value=="number"?e[n]=a.value:iy(a))}}}function Vd(e,t={},n={},a="object",r=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in ba)i=ba[s];else if(i=t[s],i==null)throw new U(`Unknown ${a}: ${e}. This may be due to one of the following reasons:
|
|
1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new U(`${a}: Improper config format: ${JSON.stringify(s)}.
|
|
'className' and 'config' must set.`);let i=s.className,o,u;if(i in n?[o,u]=n[i]:i in ba?[o,u]=ba.className:i in t&&([o,u]=t[i]),o==null)throw new U(`Unknown ${a}: ${i}. This may be due to one of the following reasons:
|
|
1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(u!=null){let l={};for(let h of Object.keys(ba))l[h]=ba[h];for(let h of Object.keys(n))l[h]=n[h];let d=s.config;d.customObjects=l;let p=Object.assign({},ba);for(let h of Object.keys(n))ba[h]=n[h];iy(s.config);let c=u(o,s.config,n,r);return ba=Object.assign({},p),c}else{let l=Object.assign({},ba);for(let p of Object.keys(n))ba[p]=n[p];let d=new o(s.config);return ba=Object.assign({},l),d}}}function Hne(e,t){return e<t?-1:e>t?1:0}function r0(e,t){return-1*Hne(e,t)}function es(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function Gne(e){if(e==null)throw new U(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function ji(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new U(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function oy(e,t,n=0,a=Infinity){return Qa(n>=0),Qa(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function Jt(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,a)=>Jt(n,`element ${a+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${b4(e)}.`)}function b4(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>b4(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function qne(e,t){let n=k.now(),a;return(...r)=>{let s=k.now();return s-n<t||(n=s,a=e(...r)),a}}function v4(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function ly(e,t){return V(()=>an(Se(B(e,e),t,!0)))}var jd=class extends re.Serializable{getConfig(){return{}}},uy=class extends jd{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 V(()=>{let t=ly(e,this.axis),n=Mn(t,0,this.maxValue);return B(e,me(n,ie(jt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};uy.className="MaxNorm";re.registerClass(uy);var dy=class extends jd{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return V(()=>me(e,ie(jt(),ly(e,this.axis))))}getConfig(){return{axis:this.axis}}};dy.className="UnitNorm";re.registerClass(dy);var py=class extends jd{apply(e){return Ka(e)}};py.className="NonNeg";re.registerClass(py);var cy=class extends jd{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 V(()=>{let t=ly(e,this.axis),n=ie(B(this.rate,Mn(t,this.minValue,this.maxValue)),B(1-this.rate,t));return B(e,me(n,ie(jt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};cy.className="MinMaxNorm";re.registerClass(cy);var w4={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ut(e){return sy(e)}function k4(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Ht(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in w4?w4[e]:e,config:{}};return k4(t)}else return e instanceof jd?e:k4(e)}function Xne(e){return new uy(e)}function Kne(e){return new dy(e)}function Zne(){return new py}function Yne(e){return new cy(e)}var I4={};Fe(I4,{constant:()=>bae,glorotNormal:()=>Tae,glorotUniform:()=>Nae,heNormal:()=>Cae,heUniform:()=>Eae,identity:()=>Iae,leCunNormal:()=>Rae,leCunUniform:()=>Mae,ones:()=>xae,orthogonal:()=>Fae,randomNormal:()=>wae,randomUniform:()=>vae,truncatedNormal:()=>kae,varianceScaling:()=>Sae,zeros:()=>Aae});var Jne=["channelsFirst","channelsLast"],Qne=["nearest","bilinear"],eae=["valid","same","causal"],tae=["max","avg"],nae=["sum","mul","concat","ave"],Jl=new Map;function Ft(e){ji(Jne,"DataFormat",e)}function aae(e){ji(Qne,"InterpolationFormat",e)}function ca(e){ji(eae,"PaddingMode",e)}function S4(e){ji(tae,"PoolMode",e)}var Ud=[],N4="/";function Ui(e,t){Ud.push(e);try{let n=t();return Ud.pop(),n}catch(n){throw Ud.pop(),n}}function rae(){return Ud.length===0?"":Ud.join(N4)+N4}function T4(e){if(!E4(e))throw new Error("Not a valid tensor name: '"+e+"'");return rae()+e}function C4(e){if(!E4(e))throw new Error("Not a valid tensor name: '"+e+"'");Jl.has(e)||Jl.set(e,0);let t=Jl.get(e);if(Jl.set(e,Jl.get(e)+1),t>0){let n=`${e}_${t}`;return Jl.set(n,1),n}else return e}var sae=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function E4(e){return!!e.match(sae)}function iae(e){return e===parseInt(e.toString(),10)}function ts(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let a=1;for(let r=t;r<n;++r)a*=e[r];return a}function Ql(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a<t&&(t=a)}return t}function ns(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a>t&&(t=a)}return t}function Fa(e,t){if(t<e)throw new U(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let a=e;a<t;++a)n.push(a);return n}function Hd(e,t){return e.asType(t)}function Gd(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function oae(e,t){return V(()=>{if(e.shape.length!==2)throw new U(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Gd(e,1);return my(n,[1,t,1])})}function lae(e){let t=[ts(e.shape)];return e.reshape(t)}function uae(e){if(e.rank<=1)throw new U(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],ts(e.shape,1)];return e.reshape(t)}function Hi(e,t,n){return V(()=>{switch(e.rank){case 1:return dh(e,t,n);case 2:return sg(e,[t,0],[n,e.shape[1]]);case 3:return ph(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Ad(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(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 U(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function hy(e,t,n){return V(()=>{switch(e.rank){case 1:return dh(e,t,n);case 2:return sg(e,[0,t],[e.shape[0],n]);case 3:return ph(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Ad(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new U(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function s0(e,t,n,a){return V(()=>{switch(e.rank){case 1:return dh(e,t,n);case 2:switch(a){case 1:return Hi(e,t,n);case 2:return hy(e,t,n);default:throw new U(`The axis is not within the rank of the tensor ${a}`)}case 3:switch(a){case 1:return Hi(e,t,n);case 2:return ph(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return hy(e,t,n);default:throw new U(`The axis is not within the rank of the tensor ${a}`)}case 4:switch(a){case 1:return Hi(e,t,n);case 2:return Ad(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Ad(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return hy(e,t,n);default:throw new U(`The axis is not within the rank of the tensor ${a}`)}default:throw new U(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function fy(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),lt(e,t)}function R4(e,t){switch(e.rank){case 1:return x3([e,t]);case 2:return Nl([e,t],0);case 3:return b3([e,t],0);case 4:return v3([e,t],0);default:throw new U(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function my(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new U(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Gr(e,t)}function i0(e,t=0,n=1,a,r){return L3(e,t,n,a,r)}function er(e,t,n,a){if(e.rank<2||t.rank<2)throw new _e(`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],s=t.shape.slice(-2)[0];if(r!==s)throw new _e(`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,s=!1;return Kr.matMul({a:e,b:t,transposeA:r,transposeB:s,bias:a?gy(e.rank,a,Ra()):null,activation:n})}else{let r=e.shape.slice(),s=r.pop();e=e.reshape([-1,s]);let i=t.shape.slice(),o=i.pop(),u=i.pop(),l=[...i,o],d=Array.from({length:t.rank},(m,f)=>f===0?t.rank-2:f<=t.rank-2?f-1:f);t=t.transpose(d).reshape([u,-1]);let p=[...r,...l],c=!1,h=!1;return Kr.matMul({a:e,b:t,transposeA:c,transposeB:h,bias:a?gy(e.rank,a,Ra()):null,activation:n}).reshape(p)}}function M4(e,t,n){return V(()=>(Array.isArray(t)?t=Dt(t,"int32"):t=t.toInt(),Ti(e,t,n)))}function qd(e){return B(e,e)}function gy(e,t,n){let a=t.shape;if(t.rank!==1&&t.rank!==e)throw new U(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1,1,1]):t.reshape([1,a[3],a[0],a[1],a[2]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,1,1,a[0]]):t.reshape([1].concat(a))}else if(e===4){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1,1]):t.reshape([1,a[2],a[0],a[1]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,1,a[0]]):t.reshape([1].concat(a))}else if(e===3){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1]):t.reshape([1,a[1],a[0]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,a[0]]):t.reshape([1].concat(a))}else if(e<3)return t;throw new U(`Unsupported input rank by biasAdd: ${t.rank}`)}function $a(e,t,n){return V(()=>(n==null&&(n=Ra()),Ft(n),e.add(gy(e.rank,t,n))))}function dae(e,t=1){if(t!==1)throw new _e(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Cl(e)}function pae(e){return V(()=>me(e,Wt(e).add(1)))}function F4(e,t,n,a){return V(()=>G3(e,t,n,a))}function cae(e){return V(()=>{let t=ie(.5,B(.2,e));return Mn(t,0,1)})}function Xd(e,t,n=!1){return n?e():t()}var hae=["fanIn","fanOut","fanAvg"],fae=["normal","uniform","truncatedNormal"];function mae(e){ji(hae,"FanMode",e)}function gae(e){ji(fae,"Distribution",e)}var va=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},yy=class extends va{apply(e,t){return $t(e,t)}};yy.className="Zeros";re.registerClass(yy);var o0=class extends va{apply(e,t){return jn(e,t)}};o0.className="Ones";re.registerClass(o0);var Ay=class extends va{constructor(e){super();if(typeof e!="object")throw new U(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new U(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return V(()=>B(ke(this.value),jn(e,t)))}getConfig(){return{value:this.value}}};Ay.className="Constant";re.registerClass(Ay);var xy=class extends va{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 Fl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};xy.className="RandomUniform";re.registerClass(xy);var by=class extends va{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 _e(`randomNormal does not support dType ${t}.`);return i0(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};by.className="RandomNormal";re.registerClass(by);var vy=class extends va{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 _e(`truncatedNormal does not support dType ${t}.`);return fh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};vy.className="TruncatedNormal";re.registerClass(vy);var wy=class extends va{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return V(()=>{if(e.length!==2||e[0]!==e[1])throw new U("Identity matrix initializer can only be used for 2D square matrices.");return B(this.gain,H1(e[0]))})}getConfig(){return{gain:this.gain}}};wy.className="Identity";re.registerClass(wy);function yae(e,t="channelsLast"){let n,a;if(Ft(t),e.length===2)n=e[0],a=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=ts(e,2);n=e[1]*r,a=e[0]*r}else if(t==="channelsLast"){let r=ts(e,0,e.length-2);n=e[e.length-2]*r,a=e[e.length-1]*r}}else{let r=ts(e);n=Math.sqrt(r),a=Math.sqrt(r)}return[n,a]}var Dn=class extends va{constructor(e){super();if(e.scale<0)throw new U(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,mae(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,gae(this.distribution),this.seed=e.seed}apply(e,t){let n=yae(e),a=n[0],r=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,a):this.mode==="fanOut"?s/=Math.max(1,r):s/=Math.max(1,(a+r)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new _e(`${this.getClassName()} does not support dType ${t}.`);return fh(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Fl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Dn.className="VarianceScaling";re.registerClass(Dn);var l0=class extends Dn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};l0.className="GlorotUniform";re.registerClass(l0);var u0=class extends Dn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};u0.className="GlorotNormal";re.registerClass(u0);var d0=class extends Dn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};d0.className="HeNormal";re.registerClass(d0);var p0=class extends Dn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};p0.className="HeUniform";re.registerClass(p0);var c0=class extends Dn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};c0.className="LeCunNormal";re.registerClass(c0);var h0=class extends Dn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};h0.className="LeCunNormal";re.registerClass(h0);var ky=class extends va{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new _e("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return V(()=>{if(e.length<2)throw new _e("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,a=i0(n,0,1,"float32"),r=s7.gramSchmidt(a);return e[0]>e[1]&&(r=r.transpose()),B(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};ky.className="Orthogonal";re.registerClass(ky);var $4={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 D4(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function Tt(e){return sy(e)}function bt(e){if(typeof e=="string"){let t=e in $4?$4[e]:e;if(t==="GlorotNormal")return new u0;if(t==="GlorotUniform")return new l0;if(t==="HeNormal")return new d0;if(t==="HeUniform")return new p0;if(t==="LeCunNormal")return new c0;if(t==="LeCunUniform")return new h0;{let n={};return n.className=t,n.config={},D4(n)}}else return e instanceof va?e:D4(e)}function Aae(){return new yy}function xae(){return new o0}function bae(e){return new Ay(e)}function vae(e){return new xy(e)}function wae(e){return new by(e)}function kae(e){return new vy(e)}function Iae(e){return new wy(e)}function Sae(e){return new Dn(e)}function Nae(e){return new l0(e)}function Tae(e){return new u0(e)}function Cae(e){return new d0(e)}function Eae(e){return new p0(e)}function Rae(e){return new c0(e)}function Mae(e){return new h0(e)}function Fae(e){return new ky(e)}var O4={};Fe(O4,{Layer:()=>Xe,RNN:()=>ar,RNNCell:()=>ap,activation:()=>fse,add:()=>kse,alphaDropout:()=>iie,average:()=>Ise,averagePooling1d:()=>BA,averagePooling2d:()=>VA,averagePooling3d:()=>jA,avgPool1d:()=>$se,avgPool2d:()=>Ose,avgPool3d:()=>_se,avgPooling1d:()=>Dse,avgPooling2d:()=>zse,avgPooling3d:()=>Pse,batchNormalization:()=>Rse,bidirectional:()=>Jse,concatenate:()=>Sse,conv1d:()=>sse,conv2d:()=>ise,conv2dTranspose:()=>ose,conv3d:()=>lse,conv3dTranspose:()=>use,convLstm2d:()=>Xse,convLstm2dCell:()=>Kse,cropping2D:()=>pse,dense:()=>mse,depthwiseConv2d:()=>hse,dot:()=>Ese,dropout:()=>gse,elu:()=>Qre,embedding:()=>wse,flatten:()=>Ase,gaussianDropout:()=>sie,gaussianNoise:()=>rie,globalAveragePooling1d:()=>Lse,globalAveragePooling2d:()=>Wse,globalMaxPool1d:()=>eie,globalMaxPool2d:()=>tie,globalMaxPooling1d:()=>U8,globalMaxPooling2d:()=>H8,gru:()=>Vse,gruCell:()=>jse,input:()=>f8,inputLayer:()=>Jre,layerNormalization:()=>Mse,leakyReLU:()=>tse,lstm:()=>Use,lstmCell:()=>Hse,masking:()=>oie,maxPool1d:()=>nie,maxPool2d:()=>aie,maxPooling1d:()=>G8,maxPooling2d:()=>q8,maxPooling3d:()=>Bse,maximum:()=>Nse,minimum:()=>Tse,multiply:()=>Cse,permute:()=>vse,prelu:()=>nse,reLU:()=>ese,repeatVector:()=>xse,reshape:()=>bse,rnn:()=>Zse,separableConv2d:()=>dse,simpleRNN:()=>Gse,simpleRNNCell:()=>qse,softmax:()=>ase,spatialDropout1d:()=>yse,stackedRNNCells:()=>Yse,thresholdedReLU:()=>rse,timeDistributed:()=>Qse,upSampling2d:()=>cse,zeroPadding2d:()=>Fse});var $ae=0;function z4(){return $ae++}var f0={};function m0(e=""){return e in f0||(f0[e]=0),f0[e]+=1,e+f0[e].toString()}function Iy(e){return Array.isArray(e)&&Array.isArray(e[0])}function g0(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Le(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new U(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function st(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new U(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function y0(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((a,r)=>a*r);return t}var _4="Variable",P4=class{constructor(e,t="float32",n=_4,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=z4(),n=n==null?_4:n,this.originalName=T4(n),this.name=C4(this.originalName),this.trainable_=a,this.constraint=r,this.val=B3(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Dae(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 Dae(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Sy(e){return e.map(t=>t.read())}function Ny(e){e.forEach(t=>{t[0].write(t[1])})}var zt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Da=class{constructor(e,t,n,a,r,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=a,this.callArgs=r,this.outputTensorIndex=i,this.id=z4(),s!=null&&(this.originalName=T4(s),this.name=C4(this.originalName)),this.rank=t.length}},Oae=0,A0=class{constructor(e,t){this.callArgs=t,this.id=Oae++,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}}},zae=0,Xe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=zae++,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=wr(n)+"_"+m0(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 a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}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 Ma(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return $n(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return $n(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new vr(`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 vr(`Layer ${this.name} is not connected, no input to return.`);return $n(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new vr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new vr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return $n(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=yt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=yt(this.inputSpec);if(e.length!==t.length)throw new U(`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 a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],d=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(d)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=a.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=yt(e),a=!0;for(let s of n)if(!(s instanceof Da)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Da){r=!1;break}if(a===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ui(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of yt(e))s.push(i.shape);this.build($n(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=yt(s),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(s=$n(o),this.activityRegularizer!=null)throw new _e("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=_ae(e),i=this.computeOutputShape(s),o,u=Pae(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,d)=>new Da(u,l,this,yt(e),t,this.name,d)):o=new Da(u,i,this,yt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new _e("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 vr(`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 vr(`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 Ma(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return y0(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Sy(e?this.trainableWeights:this.weights)}setWeights(e){V(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${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=[],a=Sy(t);for(let r=0;r<a.length;++r){let s=a[r],i=t[r],o=e[r];if(!k.arraysEqual(s.shape,o.shape))throw new U(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Ny(n)})}addWeight(e,t,n,a,r,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(a=bt("zeros"));let o=a.apply(t,n),u=new P4(o,n,e,s,i);return o.dispose(),r!=null&&this.addLoss(()=>r.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=yt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=yt(e);t=yt(t),n=yt(n),a=yt(a),r=g0(r),s=g0(s);let u=[],l=[],d=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),d.push(p.tensorIndex);new A0({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 _ae(e){e=yt(e);let t=[];for(let n of e)t.push(n.shape);return $n(t)}function Pae(e){return"float32"}function L4(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s<a.inboundLayers.length;s++){let i=a.inputTensors[s],o=a.inboundLayers[s],u=a.nodeIndices[s],l=L4(i,o,u);for(let d of l)r.indexOf(d)===-1&&r.push(d)}return r}}}var eu=class extends Xe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:m0("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 U("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 U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("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 a=new Da(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new A0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`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}}};eu.className="InputLayer";re.registerClass(eu);function W4(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 U("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 eu({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function as(e){if(e==null)return;let t=[],n=[],a=[];for(let r in e){let s=e[r];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(r),a.push(i)}}if(t.length>0){let r=await Promise.all(t);for(let s=0;s<r.length;++s)e[n[s]]=r[s][0];he(a)}}function B4(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var V4;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(V4||(V4={}));var Lae=125,tu=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){}},j4=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)}},Wae=class extends tu{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 a in t){let r=t[a];if(typeof r=="number")this.totals.hasOwnProperty(a)||(this.totals[a]=0),this.totals[a]=this.totals[a]+r*n;else{let s;a in this.totals?s=this.totals[a]:this.totals[a]=0;let i=V(()=>ie(this.totals[a],B(r,n)));this.totals[a]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:V(()=>{let a=B(me(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),Kt(t[n])}))}},U4=class extends tu{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 s=this.history[r];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(r),n.push(i)}}let a=await Promise.all(e);for(let r=0;r<a.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=a[r][0]}},H4=class extends tu{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Lae),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");k.isNumber(this.yieldEvery)&&(this.maybeWait=qne(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 a=[];this.yield!=null&&(await as(n),a.push(this.yield(e,t,n))),a.push(Ch()),await Promise.all(a)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await as(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await as(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Ch()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await as(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await as(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Ch()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await as(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await as(e),await this.trainEnd(e))}};function G4(e,t){return e==null&&(e={}),e instanceof tu?[e]:Array.isArray(e)&&e[0]instanceof tu?e:yt(e).map(n=>new H4(n,t))}var wa=class{constructor(){}static registerCallbackConstructor(e,t){k.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),wa.checkForDuplicate(t),wa.constructors[e]==null&&(wa.constructors[e]=[]),wa.constructors[e].push(t)}static checkForDuplicate(e){for(let t in wa.constructors)wa.constructors[+t].forEach(n=>{if(n===e)throw new U("Duplicate callback constructor.")})}static clear(){wa.constructors={}}static createCallbacks(e){let t=[];for(let n in wa.constructors){let a=+n;e>=a&&t.push(...wa.constructors[a])}return t.map(n=>new n)}};wa.constructors={};function q4(e,t,n,a,r,s,i,o,u){let l=new U4,d=[new Wae,...wa.createCallbacks(t)];e!=null&&d.push(...e),d.push(l);let p=new j4(d);return p.setParams({epochs:n,initialEpoch:a,samples:r,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:u}),{callbackList:p,history:l}}function Oa(e,t={},n=!1){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function x0(e,t){return V(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Se(qd(e),t,!0),a=El(n.shape,jt()),r=an(Xa(n,a));return me(e,r)})}function Gi(e,t){return V(()=>Nt(qd(ye(t,e)),-1))}function b0(e,t){return V(()=>Nt(Wt(ye(t,e)),-1))}function nu(e,t){return V(()=>{let n=ye(e,t),a=Mn(Wt(e),jt(),Number.MAX_VALUE),r=Wt(me(n,a));return B(100,Nt(r,-1))})}function Bae(e,t){return V(()=>{let n=Mn(t,jt(),Number.MAX_VALUE),a=Bn(ie(1,n)),r=Mn(e,jt(),Number.MAX_VALUE),s=Bn(ie(1,r));return Nt(qd(ye(a,s)),-1)})}function Vae(e,t){return V(()=>{let n=Xa(0,ye(1,B(e,t)));return Nt(qd(n),-1)})}function jae(e,t){return V(()=>{let n=Xa(0,ye(1,B(e,t)));return Nt(n,-1)})}function Uae(e,t){return V(()=>{let n=Se(B(e,t),-1),a=Vn(B(ye(1,e),t),-1);return Xa(0,ie(1,ye(a,n)))})}function Hae(e,t){return V(()=>{let n=Math.log(2),a=ye(t,e),r=ye(ie(a,Ci(B(-2,a))),n);return Nt(r,-1)})}function Kd(e,t,n=!1){return V(()=>{if(n)t=xd(t);else{let a=Se(t,t.shape.length-1,!0);t=me(t,a)}return t=Mn(t,jt(),1-jt()),St(Se(B(e.toFloat(),Bn(t)),t.shape.length-1))})}function v0(e,t,n=!1){return V(()=>{let a=Rl(lae(e)).toInt();t=Mn(t,jt(),1-jt());let r=t.shape,s=vl(a,r[r.length-1]).reshape(r);return Kd(s,t,n)})}function Gae(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new U(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return V(()=>{let n=t.relu(),a=t.abs().neg();return n.sub(t.mul(e)).add(a.exp().log1p())})}function w0(e,t){return V(()=>{let n;return n=Mn(t,jt(),1-jt()),n=Bn(me(n,ye(1,n))),Nt(Gae(e,n),-1)})}function qae(e,t){return V(()=>{let n=Mn(e,jt(),1),a=Mn(t,jt(),1);return Se(B(e,Bn(me(n,a))),-1)})}function Xae(e,t){return V(()=>{let n=Bn(ie(jt(),t));return Nt(ye(t,B(e,n)),-1)})}function Ty(e,t){return V(()=>{let n=x0(e,-1),a=x0(t,-1),r=B(n,a);return St(Se(r,-1))})}var k0={meanSquaredError:Gi,meanAbsoluteError:b0,meanAbsolutePercentageError:nu,meanSquaredLogarithmicError:Bae,squaredHinge:Vae,hinge:jae,categoricalHinge:Uae,logcosh:Hae,categoricalCrossentropy:Kd,sparseCategoricalCrossentropy:v0,binaryCrossentropy:w0,kullbackLeiblerDivergence:qae,poisson:Xae,cosineProximity:Ty};function Cy(e){if(typeof e=="string"){if(e in k0)return k0[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 U(t)}else return e}function Ey(e,t){return V(()=>{let n=B(.5,Un(t)),a=Hd(Wn(t,n),e.dtype);return Nt(Hr(e,a),-1)})}function Ry(e,t){return V(()=>Hd(Hr(ki(e,-1),ki(t,-1)),"float32"))}function X4(e,t){return V(()=>xa(e.equal(1),t.equal(1)).sum().cast("float32"))}function Kae(e,t){return V(()=>xa(e.equal(1),t.equal(0)).sum().cast("float32"))}function Zae(e,t){return V(()=>xa(e.equal(0),t.equal(1)).sum().cast("float32"))}function K4(e,t){return V(()=>{let n=X4(e,t),a=Zae(e,t),r=n.add(a);return un(Wn(r,0),n.div(r),0).cast("float32")})}function Yae(e,t){return V(()=>{let n=X4(e,t),a=Kae(e,t),r=n.add(a);return un(Wn(r,0),n.div(r),0).cast("float32")})}function Z4(e,t){return w0(e,t)}function Y4(e,t){return e.rank===t.rank&&(e=e.squeeze([e.rank-1])),t=t.argMax(-1),t.dtype!==e.dtype&&(t=t.asType(e.dtype)),Hr(e,t).asType("float32")}var Jae=Gi,Qae=Gi,ere=b0,tre=b0,nre=nu,are=nu,My=Kd,rre=Ty,J4=v0,I0={binaryAccuracy:Ey,categoricalAccuracy:Ry,precision:K4,categoricalCrossentropy:My,sparseCategoricalCrossentropy:J4,mse:Jae,MSE:Qae,mae:ere,MAE:tre,mape:nre,MAPE:are,cosine:rre};function sre(e){if(typeof e=="string"&&e in I0)return I0[e];if(typeof e!="string"&&e!=null)return e;throw new U(`Unknown metric ${e}`)}function S0(e){if(Qa(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(k0))if(k0[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(I0))if(I0[n]===e){t=n;break}return t!==void 0?t:e.name}}function ire(e){let t={Adagrad:()=>Fi.adagrad(.01),Adadelta:()=>Fi.adadelta(1,.95,jt()),Adam:()=>Fi.adam(.001,.9,.999,jt()),Adamax:()=>Fi.adamax(.002,.9,.999,jt(),0),RMSProp:()=>Fi.rmsprop(.001,.9,0,jt()),SGD:()=>Fi.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 U(`Unknown Optimizer ${e}`)}var Q4=1*1024*1024;function e8(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!Fy(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>Q4&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${a.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Q4}.`)}}function Fy(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"||!Fy(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!Fy(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function ore(e,t,n,a=console.log){let r=ure(e),s=["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(d=>Math.floor(t*d)));let i;if(!r){s.push("Receives inputs"),i=[];for(let d in e.nodesByDepth)i.push(...e.nodesByDepth[d])}a("_".repeat(t)),N0(s,n,a),a("=".repeat(t));let o=e.layers;for(let d=0;d<o.length;++d)r?dre(o[d],n,a):pre(o[d],n,i,a),a((d===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let u=lre(e),l=y0(e.nonTrainableWeights);a(`Total params: ${u+l}`),a(`Trainable params: ${u}`),a(`Non-trainable params: ${l}`),a("_".repeat(t))}function lre(e){let t;return e.collectedTrainableWeights!=null?t=y0(e.collectedTrainableWeights):t=y0(e.trainableWeights),t}function ure(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function N0(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function dre(e,t,n){let a;try{a=JSON.stringify(e.outputShape)}catch(o){a="multiple"}let r=e.name,s=e.getClassName(),i=[`${r} (${s})`,a,e.countParams().toString()];N0(i,t,n)}function pre(e,t,n,a){let r;try{r=JSON.stringify(e.outputShape)}catch(d){r="multiple"}let s=[];for(let d of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(d)===-1))for(let p=0;p<d.inboundLayers.length;++p){let c=d.inboundLayers[p].name,h=d.nodeIndices[p],m=d.tensorIndices[p];s.push(`${c}[${h}][${m}]`)}let i=e.name,o=e.getClassName(),u=s.length===0?"":s[0],l=[`${i} (${o})`,r,e.countParams().toString(),u];N0(l,t,a);for(let d=1;d<s.length;++d)N0(["","","",s[d]],t,a)}function t8(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Zd(e,t){if(e===null)return null;if(typeof e=="string")return Vi(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];t8(t,r,s)?n.push(s):n.push(Zd(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a];if(a==="name"&&typeof r=="string")n[a]=r;else{let s=Vi(a);n[s]=Zd(r,s)}}return n}}function $y(e,t){if(e==null)return null;if(typeof e=="string")return wr(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];t8(t,r,s)?n.push(s):n.push($y(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a],s=wr(a);(a==="name"||a==="className")&&typeof r=="string"?n[s]=r:n[s]=$y(r,a)}return n}}var Dy="3.7.0";function cre(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ge(t,e.dtype)}catch(n){throw new U(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var qi=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof qi)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]=cre(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new U(`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 Da){if(this.id2Value[e.id]==null)throw new U(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new U(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Da){if(this.id2Value[e.id]==null)throw new U(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new U(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&he(this.id2Mask)}},Oy={},n8={};function Yd(e,t,n,a){let r=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(m=>m.name),u=[],l=t.names();for(let m of o)l.indexOf(m)!==-1?u.push(t.getValue(m)):u.push(null);a!=null&&(a.maxNumTensors=-Infinity,a.minNumTensors=Infinity);let d=o.join(",")+"|"+t.names().join(","),p,c;if(Oy[d]==null){let m=hre(i,t);p=m.sorted,c=m.recipientCounts,Oy[d]=p,n8[d]=c}p=Oy[d],c={},r||Object.assign(c,n8[d]);let h=new qi(t);for(let m=0;m<p.length;++m){if(a!=null){let E=Bc().numTensors;E>a.maxNumTensors&&(a.maxNumTensors=E),E<a.minNumTensors&&(a.minNumTensors=E)}let f=p[m],g=f.sourceLayer;if(g instanceof eu)continue;let y=[],A=[],x=[],v=!1;for(let E of f.inputs){let _=h.getValue(E),$=h.getMask(E);y.push(_),A.push($),$!=null&&(v=!0),r||(c[E.name]--,c[E.name]===0&&!t.hasKey(E)&&o.indexOf(E.name)===-1&&!_.isDisposed&&E.sourceLayer.stateful!==!0&&x.push(_))}v&&(n=n||{},n.mask=A[0]);let b=yt(g.apply(y,n)),w=null;g.supportsMasking&&(w=g.computeMask(y,A));let N=mre(f),C=Array.isArray(N)?N:[N];for(let E=0;E<C.length;++E){h.hasKey(C[E])||h.add(C[E],b[E],Array.isArray(w)?w[0]:w);let _=o.indexOf(C[E].name);_!==-1&&(u[_]=b[E])}r||he(x)}return h.disposeMasks(),s?u:u[0]}function hre(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],a={};if(e.length===1){let r=a8(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=a8(s,t);for(let u of i)r.has(u.name)||(n.push(u),r.add(u.name));for(let u in o)a[u]==null&&(a[u]=new Set),o[u].forEach(l=>a[u].add(l))}}return{sorted:n,recipientCounts:fre(a)}}function fre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function a8(e,t){let n=new Set,a=[],r={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let u=i[i.length-1]===s.length-1;if(o.inputs.length===0||u)s.pop(),a.push(o),n.add(o.name),u&&i.pop();else{i.push(s.length-1);for(let l of o.inputs)r[l.name]==null&&(r[l.name]=new Set),r[l.name].add(o.name),!n.has(l.name)&&s.push(l)}}return{sorted:a,recipientMap:r}}function mre(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let a=0;a<e.sourceLayer.inboundNodes.length;++a)for(let r of e.sourceLayer.inboundNodes[a].outputTensors)if(r.id===e.id){n=a;break}t=e.sourceLayer.getOutputAt(n)}return t}var tr=class extends Xe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=m0(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],es(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);es(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Qa(x===0,"input layer has >1 nodes"),Qa(v===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof eu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,A,x,v,b,w)=>{(v==null||b==null||w==null)&&(v=y.sourceLayer,b=y.nodeIndex,w=y.tensorIndex);let N=v.inboundNodes[b];if(x.indexOf(N)!==-1)throw new Ma(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(A.indexOf(N)!==-1)return;this.containerNodes.add(tr.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let C=N.inboundLayers.length;for(let E=0;E<C;E++){let _=N.inputTensors[E],$=N.inboundLayers[E],S=N.nodeIndices[E],z=N.tensorIndices[E];o(_,A,x,$,S,z)}for(A.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},u=[],l=[];for(let y of this.outputs)o(y,u,l);let d=i.slice().reverse();for(let y of d){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];A=Math.max(A,x),a[y.outboundLayer.id]=A,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let v=0;v<y.inboundLayers.length;v++){let b=y.inboundLayers[v],w=y.nodeIndices[v],N=b.inboundNodes[w],C=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(A+1,C),n[N.id]=N}}let p={};for(let y in t){let A=t[y];A in p||(p[A]=[]),p[A].push(n[y])}let c={};for(let y in a){let A=a[y];A in c||(c[A]=[]),c[A].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(r0);this.layers=[];for(let y of h){let A=c[y];A.sort((x,v)=>{let b=s[x.id],w=s[v.id];return b<w?-1:b>w?1:0});for(let x of A)x instanceof tr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(y=>parseInt(y,10)).sort(r0);let m=this.inputs.slice(),f=[];for(let y of h)for(let A of p[y]){let x=A.outboundLayer;if(x!=null){for(let v of A.inputTensors)if(m.indexOf(v)===-1)throw new Ma(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of A.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new Ma(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new A0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("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={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new U(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new U(`${s.length} of ${a} weights are not set: ${s}`)}Ny(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=$y(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return V(()=>{e=yt(e);let n=new qi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Yd(this.outputs,n,t)})}computeMask(e,t){return V(()=>{e=yt(e);let n;return t==null?n=Bi(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=g0(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(r0);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(l.id)!==-1)continue;let d=[];for(let m=0;m<u.inboundLayers.length;m++){let f=u.inboundLayers[m],g=u.nodeIndices[m],y=u.tensorIndices[m],A=`${f.name}_${g}_${y}`,x=n[A];d.push(x)}let p=l.computeOutputShape($n(d)),c=g0(p),h=l.inboundNodes.indexOf(u);for(let m=0;m<c.length;m++){let f=`${l.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],d=`${o.name}_${u}_${l}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Qa(o in n),r.push(n[o])}return $n(r)}runInternalGraph(e,t){t==null&&(t=Bi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],d=t[o];n[u.id]=[l,d]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(r0);for(let o of a){let u=this.nodesByDepth[o];for(let l of u){let d=l.outboundLayer,p=l.inputTensors,c=l.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,A;if(l.callArgs!=null&&(m=l.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),y=yt(d.call(x,m)),A=yt(d.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=yt(d.call(f,m)),A=yt(d.computeMask(f,g));if(d.activityRegularizer)throw new _e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],b=y[x],w=A[x];n[v.id]=[b,w]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Qa(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),s.push(l)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof tr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=tr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`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 U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=tr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),u=[];for(let d=0;d<s.inboundNodes.length;d++){let p=s.inboundNodes[d],c=tr.nodeKey(s,d),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],A=p.tensorIndices[f],x=tr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,A,h])}u.push(m)}}}let l={};l.name=s.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],u=tr.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let d=this.inputLayersTensorIndices[s];a.push([i.name,l,d])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],u=tr.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let d=this.outputLayersTensorIndices[s];r.push([i.name,l,d])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],A;for(let x of g){let v=x[0],b=x[1],w=x[2];if(A=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let N=r[v];if(N.inboundNodes.length<=b){i(f,g);return}let C=N.inboundNodes[b];y.push(C.outputTensors[w])}y.length>0&&f.apply($n(y),A)}function u(f){let g=f.name,y=Oa(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let l=t.name,d=t.layers;for(let f of d)u(f);for(;!Gne(s);)for(let f of d){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],A=f[2];Qa(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[A])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],A=f[2];Qa(g in r);let x=r[g].inboundNodes[y].outputTensors;c.push(x[A])}return new e({inputs:p,outputs:c,name:l})}get stateful(){if(this._stateful)throw new U("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(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gre(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===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!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function r8(e,t){return gre(e,t,"classWeight")}async function s8(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=V(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());he(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Dt(i,"float32")}else return null}function yre(e,t){return B(e,t)}var Are=32;function i8(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=o8("input",e.inputNames,n),i=o8("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<s.length;u++)k.assert(s[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${s[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)k.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function o8(e,t,n){if(n instanceof Be)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function xre(e){if(e.length===3)throw new _e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function bre(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.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,s,i;if(r)if(l8(n.validationData))k.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=xre(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let d=G4(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=q4(d,p,n.epochs,null,null,vre(t,n),null,r,l);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await c.onEpochBegin(m);let y=0,A=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:b}=i8(e,x.value),w={};w.batch=A,w.size=v[0].shape[0],await c.onBatchBegin(A,w);let N=[];if(n.classWeight!=null){let _=r8(n.classWeight,e.outputNames);for(let $=0;$<_.length;++$)N.push(await s8(b[$],null,_[$]))}let C=v.concat(b).concat(N),E=o(C);he(C);for(let _=0;_<u.length;++_){let $=u[_],S=E[_];w[$]=S,Kt(S)}await c.onBatchEnd(A,w),B4(w),A++,y++}if(a?y>=n.batchesPerEpoch:x.done){if(r){let v;l8(n.validationData)?v=yt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=yt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Are:n.validationBatchSize,verbose:0}));for(let b=0;b<e.metricsNames.length;++b)g[`val_${e.metricsNames[b]}`]=v[b]}break}if(e.stopTraining_)break}if(await c.onEpochEnd(m,g),m++,e.stopTraining_)break}return await c.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function vre(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function l8(e){return typeof e.iterator=="function"}function wre(e){return typeof e.next=="function"}async function kre(e,t,n){n=n||{};let a=n.batches!=null,r=e.testFunction,s=[];if(n.verbose>0)throw new _e("Verbose mode is not implemented yet.");k.assert(!a||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=wre(t)?t:await t.iterator(),o=0,u=0;for(;a?u<n.batches:!0;){let l=await i.next();if(s=V(()=>{if(l.value){let{xs:d,ys:p}=i8(e,l.value),c=d.concat(p),h=V(()=>r(c));if(he(c),u===0)for(let f=0;f<h.length;++f)s.push(ke(0));let m=c[0].shape[0];for(let f=0;f<h.length;++f){let g=h[f],y=s[f];s[f]=V(()=>ie(s[f],B(m,g))),u>0&&he(y)}he(h),o+=m,++u}return s}),l.done){a&&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 l=0;l<s.length;++l){let d=s[l];s[l]=me(s[l],o),he(d)}return $n(s)}function zy(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Jd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Hi(a,t,n-t)):Hi(e,t,n-t)}function _y(e,t){return V(()=>e==null?null:Array.isArray(e)?e.map(n=>_y(n,t)):M4(e,t.dtype==="int32"?t:t.toInt()))}function Py(e,t){let n=[],a=0,r=null;for(;a<e;)r=a+t,r>=e&&(r=e),n.push([a,r]),a=r;return n}async function Ire(e,t,n,a,r,s,i,o,u,l,d,p,c,h,m){r==null&&(r=32),s==null&&(s=1),d==null&&(d=!0),c==null&&(c=0);let f=!1;if(u!=null&&l!=null&&(f=!0),m!=null&&(f=!0,h==null))throw new U("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),y;g!=null&&(y=Fa(0,g)),i==null&&(i=1);let{callbackList:A,history:x}=q4(o,i,s,c,g,h,r,f,p);A.setModel(e),e.history=x,await A.onTrainBegin(),e.stopTraining_=!1;for(let v=c;v<s;++v){await A.onEpochBegin(v);let b={};if(h!=null)throw new _e("stepsPerEpoch mode is not implemented yet.");{if(d==="batch")throw new _e("batch shuffling is not implemneted yet");d&&k.shuffle(y);let w=Dt(y),N=Py(g,r);for(let C=0;C<N.length;++C){let E={};if(await A.onBatchBegin(C,E),V(()=>{let _=N[C][0],$=N[C][1],S=Hi(w,_,$-_);E.batch=C,E.size=$-_;let z=_y(n,S),O=t(z);for(let W=0;W<a.length;++W){let G=a[W],H=O[W];E[G]=H,Kt(H)}if(C===N.length-1&&f){let W=e.testLoop(u,l,r);for(let G=0;G<a.length;++G){let H=a[G],J=W[G];Kt(J),b["val_"+H]=J}}}),await A.onBatchEnd(C,E),B4(E),e.stopTraining_)break}w.dispose()}if(await A.onEpochEnd(v,b),e.stopTraining_)break}return await A.onTrainEnd(),await e.history.syncData(),e.history}async function Sre(e,t,n,a={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,s,i,o,u,l,d;try{let p=a.batchSize==null?32:a.batchSize;zy(p);let c=!1,h=await e.standardizeUserData(t,n,a.sampleWeight,a.classWeight,c,p);r=h[0],s=h[1],d=h[2];let m=!1,f;if(a.validationData!=null&&a.validationData.length>0){if(m=!0,a.validationData.length===2)i=a.validationData[0],o=a.validationData[1];else throw a.validationData.length===3?new _e("validationData including sample weights is not supported yet."):new U(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${a.validationData} is invalid.`);let w=!0,N=await e.standardizeUserData(i,o,null,null,w,p);u=N[0],l=N[1],f=u.concat(l)}else if(a.validationSplit!=null&&a.validationSplit>0&&a.validationSplit<1){m=!0;let w=Math.floor(r[0].shape[0]*(1-a.validationSplit)),N=r[0].shape[0];u=Jd(r,w,N),r=Jd(r,0,w),l=Jd(s,w,N),s=Jd(s,0,w),f=u.concat(l)}else a.validationSteps!=null&&(m=!0);let g=r.concat(s).concat(d);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),A=e.getDedupedMetricsNames(),x,v;m?(e.makeTestFunction(),x=e.testFunction,v=A.slice().concat(A.map(w=>"val_"+w))):(x=null,f=[],v=A.slice());let b=G4(a.callbacks,a.yieldEvery);return await Ire(e,y,g,A,p,a.epochs,a.verbose,b,x,f,a.shuffle,v,a.initialEpoch,null,null)}finally{e.isTraining=!1,Xi(r,t),Xi(s,n),Xi(u,i),Xi(l,o),d!=null&&he(d)}}function u8(e){let t=[];e instanceof Be&&(e=[e]);for(let n=0;n<e.length;++n){let a=e[n];if(a.rank===1)t.push(Gd(a,1));else{if(a.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(a)}}return t}function Xi(e,t){if(e==null)return;let n=[];if(t instanceof Be)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 s=t[r];n.push(s.id)}let a=[];if(e instanceof Be)n.indexOf(e.id)===-1&&a.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&a.push(r)});else if(e!=null)for(let r in e){let s=e[r];n.indexOf(s.id)===-1&&a.push(s)}a.forEach(r=>{r.isDisposed||r.dispose()})}function Nre(e){return e instanceof Be}function Ly(e){return Array.isArray(e)}function d8(e){return!Nre(e)&&!Ly(e)}function p8(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(Ly(e)&&e.length>0)i=!0;else if(d8(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new U(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(d8(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new U(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(Ly(e)){if(e=e,e.length!==t.length)throw new U(`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}`);s=e}else{if(e=e,t.length>1)throw new U(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=u8(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new U(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let u=0;u<n[i].length;++u){if(u===0&&!a)continue;let l=o.shape[u],d=n[i][u];if(d!=null&&d>=0&&l!==d)throw new U(`Error when checking ${r}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Tre(e,t,n){let a=es(e.map(s=>s.shape[0]));a.sort();let r=es(t.map(s=>s.shape[0]));if(r.sort(),a.length>1)throw new U(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(r.length>1)throw new U(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(a.length>0&&r.length>0&&!k.arraysEqual(a,r))throw new U(`Input Tensors should have the same number of samples as target Tensors. Found ${a[0]} input sample(s) and ${r[0]} target sample(s).`)}function Cre(e,t,n){let a=[Gi,w0,Kd];for(let r=0;r<e.length;++r){let s=e[r],i=t[r],o=n[r];if(i!=null){if(i===Kd&&s.shape[s.shape.length-1]===1)throw new U(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(a.indexOf(i)!==-1){let u=s.shape.slice(1),l=o.slice(1);for(let d=0;d<u.length;++d){let p=u[d],c=l[d];if(c!=null&&p!==c)throw new U(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function c8(e,t,n,a=!0,r=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new U(`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).`);s=e}else{if(t.length>1)throw new U(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new U(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let u=0;u<n[i].length;++u){if(u===0&&!a)continue;let l=o.shape[u],d=n[i][u];if(d!=null&&d!==l)throw new U(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Ere(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>[]);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: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var Rre="layers-model",kr=class extends tr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new U("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");ore(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=ire(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof xr))throw new U("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new U(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(Cy(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new U(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(s=>Cy(s))}else{let s=Cy(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s<this.outputs.length;++s){let i=this.internalOutputShapes[s],o=this.outputNames[s];this.feedOutputNames.push(o),this.feedOutputShapes.push(i),this.feedLossFns.push(this.lossFunctions[s])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Ui("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=this.lossFunctions[s];this.outputs.length>1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let a=Ere(e.metrics,this.outputNames),r=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};Ui("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=a[s];(o=>{let u="",l,d,p;for(let c of o){if(typeof c=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(c)!==-1){let m=this.internalOutputShapes[s];m[m.length-1]===1||this.lossFunctions[s]===w0?["accuracy","acc"].indexOf(c)!==-1?d=Ey:["crossentropy","ce"].indexOf(c)!==-1&&(d=Z4):this.lossFunctions[s]===v0?["accuracy","acc"].indexOf(c)!==-1?d=Y4:["crossentropy","ce"].indexOf(c)!==-1&&(d=J4):["accuracy","acc"].indexOf(c)!==-1?d=Ry:["crossentropy","ce"].indexOf(c)!==-1&&(d=My);let f;["accuracy","acc"].indexOf(c)!==-1?f="acc":["crossentropy","ce"].indexOf(c)!==-1&&(f="ce"),p=d,l=u+f}else p=sre(c),l=u+S0(c);let h;Ui(l,()=>{h=p}),r(s,l,h)}})(i)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let a=n.batchSize==null?32:n.batchSize;zy(a);let r=!0,s=this.standardizeUserDataXY(e,t,r,a);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,u=this.testLoop(o,i,a,n.verbose,n.steps);return $n(u)}finally{Xi(s[0],e),Xi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),kre(this,e,t)}checkNumSamples(e,t,n,a="steps"){let r;if(n!=null){if(r=null,t!=null)throw new U(`If ${a} 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 U(`Either the input data should have a defined shape, or ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new U("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new qi;if(e instanceof Be&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new U(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let u=e[o.name];if(u==null)throw new U(`No value is provided for the model's input ${o.name}`);s.add(o,u)}let i=Yd(r,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Bi(null,e.length),n=e.length;for(let a of this.layers){let r=Array.isArray(a.output)?a.output:[a.output],s=r.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=r[o],n--),n===0)break}if(n===0)break}if(n>0){let a=[];throw t.forEach((r,s)=>{r==null&&a.push(e[s])}),new U(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return V(()=>{let a=this.checkNumSamples(e);if(n)throw new _e("Verbose predictLoop() is not implemented yet.");let r=Py(a,t),s=this.outputs.map(i=>[]);for(let i=0;i<r.length;++i)V(()=>{let o=r[i][0],u=r[i][1],l=Jd(e,o,u),d=[];if(Array.isArray(l))for(let c=0;c<l.length;++c)d.push({key:this.inputs[c],value:l[c]});else d.push({key:this.inputs[0],value:l});let p=new qi(d);return Yd(this.outputs,p)}).forEach((o,u)=>s[u].push(o));return $n(s.map(i=>lt(i,0)))})}predict(e,t={}){let n=u8(e);c8(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return zy(a),this.predictLoop(n,a)}finally{Xi(n,e)}}predictOnBatch(e){c8(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,a){if(this.optimizer_==null)throw new Ma("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===v0?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=p8(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=p8(t,this.feedOutputNames,r,!1,"target"),Tre(e,t,null),Cre(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&a!=null&&a>0&&e[0].shape[0]%a!=0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${a}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,a,r=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,r,s);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(a!=null){let l=r8(a,this.outputNames);u=[];for(let d=0;d<l.length;++d)u.push(await s8(o[d],null,l[d]))}return[i,o,u]}testLoop(e,t,n,a=0,r){return V(()=>{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new _e("Verbose mode is not implemented yet.");if(r!=null)throw new _e("steps mode in testLoop() is not implemented yet");{let o=Py(s,n),u=Dt(Fa(0,s));for(let l=0;l<o.length;++l){let d=o[l][0],p=o[l][1],c=Hi(u,d,p-d),h=_y(t,c),m=e(h);if(l===0)for(let f=0;f<m.length;++f)i.push(ke(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=ie(i[f],B(p-d,g))}}for(let l=0;l<i.length;++l)i[l]=me(i[l],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;x4(e,a)>1&&(r+=`_${x4(e.slice(0,n),a)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=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),s=[],i=()=>{let l=[];for(let h=0;h<this.inputs.length;++h)l.push({key:this.inputs[h],value:n[h]});let d=new qi(l),p=Yd(this.outputs,d,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=yre(m,r[h]));let f=Nt(m);t.push(f),h===0?c=m:c=ie(c,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Nt(f(a[g],p[g]))}Kt(m),s.push(m)}return c=Nt(c),this.calculateLosses().forEach(h=>{c=ie(c,h)}),c},o=this.collectedTrainableWeights.map(l=>l.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>V(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let u=0;u<this.inputs.length;++u)s.push({key:this.inputs[u],value:a[u]});let i=new qi(s),o=Yd(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],d=Nt(l(r[u],o[u]));u===0?n=d:n=ie(n,d),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],d=this.metricsTensors[u][1],p=Nt(l(r[d],o[d]));t.push(p)}return t})}async fit(e,t,n={}){return Sre(this,e,t,n)}async fitDataset(e,t){return bre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let u=await o.data();i.push(u[0])}return he(s),$n(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Bc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=wr(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=>wr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=wr(n[a]);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[wr(S0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>wr(S0(e)));{let e={};for(let t in this.metrics)e[t]=wr(S0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Zd(e.optimizer_config),n=Oa(t),a;if(typeof e.loss=="string")a=Vi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Vi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Vi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Vi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Vi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=En.getSaveHandlers(e);if(i.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new U(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await En.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:Rre,generatedBy:`TensorFlow.js tfjs-layers v${Dy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:u}=await En.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...u),n.data=En.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;e8(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){e8(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};kr.className="Model";re.registerClass(kr);var h8=class extends kr{};h8.className="Functional";re.registerClass(h8);async function Mre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Zd(n),r=Oa(a,t);if(e.weightsManifest!=null){let s=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),he(s)}return r}async function Fre(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return $re(e,void 0,t)}async function $re(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=Oa(Zd(r),t,i),u=a.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:d}=Dre(a.weightData,a.weightSpecs);o.loadWeights(l,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),he(l),he(d.map(p=>p.tensor))}return o}function Dre(e,t){let n=En.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var au=class extends kr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:m0("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof au||e instanceof kr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("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 U("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 U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=W4({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`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 U("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=L4(this.outputs[0])}this.inboundNodes=[],new A0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))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=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new kr({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 Ma("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 Ma("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 Ma("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 Ma("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={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else k.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,s=t;let i=new e(s);if(!(i instanceof au))throw new _e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let u=Oa(o,void 0,a);a&&u.setFastWeightInitDuringBuild(!0),i.add(u)}return i}set stopTraining(e){if(this.model==null)throw new U("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 U("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}}};au.className="Sequential";re.registerClass(au);function Ore(e){return new kr(e)}function zre(e){return new au(e)}function _re(e,t){return t==null&&(t={}),Fre(e,t)}function f8(e){return W4(e)}function Pre(e,t){wa.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},m8=class extends On{apply(e,t=1){return dae(e,t)}};m8.className="elu";re.registerClass(m8);var g8=class extends On{apply(e){return oh(e)}};g8.className="selu";re.registerClass(g8);var y8=class extends On{apply(e){return Ka(e)}};y8.className="relu";re.registerClass(y8);var A8=class extends On{apply(e){return V(()=>Ml(6,Ka(e)))}};A8.className="relu6";re.registerClass(A8);var x8=class extends On{apply(e){return e}};x8.className="linear";re.registerClass(x8);var b8=class extends On{apply(e){return Rn(e)}};b8.className="sigmoid";re.registerClass(b8);var v8=class extends On{apply(e){return cae(e)}};v8.className="hardSigmoid";re.registerClass(v8);var w8=class extends On{apply(e){return Ci(e)}};w8.className="softplus";re.registerClass(w8);var k8=class extends On{apply(e){return pae(e)}};k8.className="softsign";re.registerClass(k8);var I8=class extends On{apply(e){return Si(e)}};I8.className="tanh";re.registerClass(I8);var Wy=class extends On{apply(e,t=-1){return xd(e,t)}};Wy.className="softmax";re.registerClass(Wy);var S8=class extends On{apply(e,t=-1){return eh(e,t)}};S8.className="logSoftmax";re.registerClass(S8);var N8=class extends On{apply(e,t=1){return V(()=>Rn(e.mul(t)).mul(e))}};N8.className="swish";re.registerClass(N8);var T8=class extends On{apply(e){return V(()=>B(e,Si(Ci(e))))}};T8.className="mish";re.registerClass(T8);function rs(e){return e.getClassName()}function By(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ss(e){if(e==null){let t={};return t.className="linear",t.config={},By(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},By(t)}else return e instanceof On?e:By(e)}function Vy(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var C8=class extends re.Serializable{},Qd=class extends C8{constructor(e){super();Vy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,Se(B(this.l1,Wt(e))))),this.hasL2&&(t=ie(t,Se(B(this.l2,qd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Qd.className="L1L2";re.registerClass(Qd);function Lre(e){return Vy(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function Wre(e){return Vy(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var E8={l1l2:"L1L2"};function dt(e){return sy(e)}function R8(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function vt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in E8?E8[e]:e,config:{}};return R8(t)}else return e instanceof C8?e:R8(e)}var jy=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Le(e);let n=Ka(e);return this.maxValue!=null&&(n=Mn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};jy.className="ReLU";re.registerClass(jy);var Uy=class extends Xe{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=Le(e);return pd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="LeakyReLU";re.registerClass(Uy);var Hy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=bt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=vt(e.alphaRegularizer),this.alphaConstraint=Ht(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 U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Le(e),gd(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Tt(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Ut(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="PReLU";re.registerClass(Hy);var Gy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new _e(`Non-default alpha value (${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=Le(e);return Cl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="ELU";re.registerClass(Gy);var qy=class extends Xe{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=Le(e);return n.mul(Hd(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qy.className="ThresholdedReLU";re.registerClass(qy);var Xy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Le(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}};Xy.className="Softmax";re.registerClass(Xy);function ru(e,t,n){if(typeof e=="number")return Bi(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!iae(r))throw new U(`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 za(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function nr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ns([n-t,0]);else if(a==="same")e=e*t;else throw new U(`Unsupport padding mode: ${a}.`);return e}function Ky(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function M8(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function Bre(e,t,n,a=1,r="valid",s,i=1){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Qe(e,[0,2,1])),r==="causal")throw new _e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Gc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=$a(o,n)),o})}function F8(e,t,n,a=[1,1],r="valid",s,i,o=null){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Ky(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Kr.conv2d({x:u,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(u=Qe(u,[0,3,1,2])),u})}function Vre(e,t,n,a=[1,1,1],r="valid",s,i){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=M8(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=L1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=$a(o,n)),s==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var Zy=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zy.verifyArgs(t),this.rank=e,Jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ru(t.kernelSize,e,"kernelSize"),this.strides=ru(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ca(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=ss(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=bt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ht(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=ru(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,3))throw new U(`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:rs(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ep=class extends Zy{constructor(e,t){super(e,t);this.kernel=null,ep.verifyArgs(t),this.filters=t.filters,Jt(this.filters,"filters"),this.kernelInitializer=bt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ht(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 V(()=>{e=Le(e);let n,a=this.bias==null?null:this.bias.read(),r=v4(this.activation.getClassName());if(r!=null&&this.rank===2)n=F8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Bre(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=F8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Vre(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=za(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},tp=class extends ep{constructor(e){super(2,e);tp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};tp.className="Conv2D";re.registerClass(tp);var np=class extends ep{constructor(e){super(3,e);np.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 U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};np.className="Conv3D";re.registerClass(np);var Yy=class extends tp{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],u=a[i],l=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=nr(o,p,l,this.padding),m=nr(u,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,1]));let g=qc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Qe(g,[0,3,1,2])),this.bias!=null&&(g=$a(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[a]=nr(t[a],o,s,this.padding),t[r]=nr(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Yy.className="Conv2DTranspose";re.registerClass(Yy);var Jy=class extends np{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let u=a[o],l=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=nr(u,m,p,this.padding),A=nr(l,f,c,this.padding),x=nr(d,g,h,this.padding),v=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,4,1]));let b=k3(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Qe(b,[0,4,1,2,3])),this.bias!==null&&(b=$a(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=nr(t[a],l,i,this.padding),t[r]=nr(t[r],d,o,this.padding),t[s]=nr(t[s],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jy.className="Conv3DTranspose";re.registerClass(Jy);var $8=class extends ep{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Ht(t.depthwiseConstraint),this.pointwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Ht(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${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 U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Le(e);let n;if(this.rank===1)throw new _e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Qe(e,[0,2,3,1])),n=ag(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=$a(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};$8.className="SeparableConv";var Qy=class extends $8{constructor(e){super(2,e)}};Qy.className="SeparableConv2D";re.registerClass(Qy);var T0=class extends ep{constructor(e){super(1,e);T0.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"&&!oy(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};T0.className="Conv1D";re.registerClass(T0);var eA=class extends Xe{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 V(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=s0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return s0(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=s0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return s0(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}};eA.className="Cropping2D";re.registerClass(eA);var tA=class extends Xe{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,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,aae(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 V(()=>{let n=Le(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Qe(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Qe(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};tA.className="UpSampling2D";re.registerClass(tA);function jre(e,t,n=[1,1],a="valid",r,s){return V(()=>{r==null&&(r=Ra()),Ft(r);let i=Ky(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Tl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}var nA=class extends Zy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=bt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ht(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 V(()=>{e=Le(e);let n=jre(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=$a(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=za(t,this.kernelSize[0],this.padding,this.strides[0]),s=za(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};nA.className="DepthwiseConv2D";re.registerClass(nA);function D8(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function O8(e,t,n,a=!1,r,s,i=!1,o=!1){return V(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Fa(2,u));if(t=Qe(t,l),s!=null)throw new _e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===u-1&&(r=mn(r,-1)),r=Qe(r,l)),a&&(t=Hn(t,0),r!=null&&(r=Hn(r,0)));let d=[],p,c=n,h=t.shape[0],m=Gn(t),f;r!=null&&(f=Gn(r));for(let y=0;y<h;++y){let A=m[y],x=V(()=>e(A,c));if(r==null)p=x[0],c=x[1];else{let v=V(()=>{let b=f[y],w=Un(b).sub(b),N=x[0].mul(b).add(c[0].mul(w)),C=c.map((E,_)=>x[1][_].mul(b).add(E.mul(w)));return{output:N,newStates:C}});p=v.output,c=v.newStates}o&&d.push(p)}let g;return o&&(g=gn(d,1)),[p,g,c]})}var ar=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new R0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Fa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Iy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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 _e("Constants support is not implemented in RNN yet.");Iy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new zt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new _e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)he(this.states_),this.keptStates!=null&&(he(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):he(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new U(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Kt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=D8(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Da){let o=[e].concat(s),u=this.inputSpec.concat(i),l=this.inputSpec;this.inputSpec=u;let d=super.apply(o,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Le(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new U(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=O8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?l:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return V(()=>{let t=$t(e.shape);return t=Se(t,[1,2]),t=Gd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?my(t,[1,n]):t):this.cell.stateSize>1?[my(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()===ar.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Oa(a,n);return new e(Object.assign(t,{cell:r}))}};ar.className="RNN";re.registerClass(ar);var ap=class extends Xe{},C0=class extends ap{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,Jt(this.units,"units"),this.activation=ss(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Ql([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=er(B(e,s),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=$a(r,this.bias.read())),i!=null&&(n=B(n,i));let o=ie(r,er(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};C0.className="SimpleRNNCell";re.registerClass(C0);var aA=class extends ar{constructor(e){e.cell=new C0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};aA.className="SimpleRNN";re.registerClass(aA);var E0=class extends ap{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 U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Jt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Ql([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let l=er(e,this.kernel.read());this.useBias&&(l=$a(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let d=this.recurrentKernel.read(),[p,c]=Zt(d,[2*this.units,this.units],d.rank-1),h=er(a,p),[m,f,g]=Zt(l,3,l.rank-1),[y,A]=Zt(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(m,y)),o=this.recurrentActivation.apply(ie(f,A));let x=er(B(o,a),c);u=this.activation.apply(ie(g,x));let v=ie(B(i,a),B(ie(1,St(i)),u));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};E0.className="GRUCell";re.registerClass(E0);var rA=class extends ar{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 E0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};rA.className="GRU";re.registerClass(rA);var rp=class extends ap{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,Jt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Ql([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends va{apply(i,o){let u=r.apply([s]),l=new o0().apply([s]),d=r.apply([s*2]);return R4(R4(u,l),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,d;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),p=ie(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=$a(p,this.bias.read()));let[c,h,m,f]=Zt(p,4,p.rank-1);o=this.recurrentActivation.apply(c),u=this.recurrentActivation.apply(h),l=ie(B(u,r),B(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=B(d,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};rp.className="LSTMCell";re.registerClass(rp);var sA=class extends ar{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 rp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sA.className="LSTM";re.registerClass(sA);var R0=class extends ap{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 V(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Iy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Oa(r,n));return new e({cells:a})}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 Sy(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Ny(t)}};R0.className="StackedRNNCells";re.registerClass(R0);function is(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>F4(t(),n),i=()=>Xd(s,t,a);return!r||r<=1?Kt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Kt(o.clone()))}var Ure=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},z8=class extends ar{constructor(e){if(e.unroll)throw new _e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new _e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new zt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,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 V(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new U("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(()=>$t(r)):this.states_=[$t(r)];else if(e==null)he(this.states_),this.keptStates!=null&&(he(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):he(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Kt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],d=za(u,a[0],r,s[0],i[0]),p=za(l,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};z8.className="ConvRNN2D";var M0=class extends rp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Jt(this.filters,"filters"),this.kernelSize=ru(n,2,"kernelSize"),this.kernelSize.forEach(o=>Jt(o,"kernelSize")),this.strides=ru(a||1,2,"strides"),this.strides.forEach(o=>Jt(o,"strides")),this.padding=r||"valid",ca(this.padding),this.dataFormat=s||"channelsLast",Ft(this.dataFormat),this.dilationRate=ru(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Jt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends va{apply(d,p){let c=u.apply([l]),h=jn([l]),m=u.apply([l*2]);return fy([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,u=(K,ne,Q)=>!ne||!ne[Q]?K:B(ne[Q],K),l=u(a,o,0),d=u(a,o,1),p=u(a,o,2),c=u(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=u(r,h,0),f=u(r,h,1),g=u(r,h,2),y=u(r,h,3),A=3,[x,v,b,w]=Zt(this.kernel.read(),i,A),[N,C,E,_]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,x,N,this.padding),d=this.inputConv(d,v,C,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,w,_,this.padding);let[$,S,z,O]=Zt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let W=this.recurrentActivation.apply(ie(l,m)),G=this.recurrentActivation.apply(ie(d,f)),H=ie(B(G,s),B(W,this.activation.apply(ie(p,g)))),J=B(this.recurrentActivation.apply(ie(c,y)),this.activation.apply(H));return[J,J,H]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Ure(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=mr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$a(r,n,this.dataFormat):r}recurrentConv(e,t){return mr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};M0.className="ConvLSTM2DCell";re.registerClass(M0);var iA=class extends z8{constructor(e){let t=new M0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};iA.className="ConvLSTM2D";re.registerClass(iA);var F0=class extends Xe{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Xd(()=>F4(n,this.rate,r,this.seed),()=>n,a)}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()}};F0.className="Dropout";re.registerClass(F0);var oA=class extends F0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};oA.className="SpatialDropout1D";re.registerClass(oA);var lA=class extends Xe{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,Jt(this.units,"units"),this.activation=ss(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ht(e.kernelConstraint),this.biasConstraint=Ht(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),a=v4(this.activation.getClassName()),r;return a!=null?r=er(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=$a(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};lA.className="Dense";re.registerClass(lA);var uA=class extends Xe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new U(`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],ts(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return uae(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};uA.className="Flatten";re.registerClass(uA);var dA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ss(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:rs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};dA.className="Activation";re.registerClass(dA);var pA=class extends Xe{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 V(()=>(e=Le(e),oae(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};pA.className="RepeatVector";re.registerClass(pA);var cA=class extends Xe{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let u=a[o];if(this.isUnknown(u))if(s===null)s=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=ts(e);if(s!==null){if(r===0||i%r!=0)throw new U(n);a[s]=i/r}else if(i!==r)throw new U(n);return a}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 V(()=>{this.invokeCallHook(e,t);let n=Le(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};cA.className="Reshape";re.registerClass(cA);var hA=class extends Xe{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=Fa(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Qe(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};hA.className="Permute";re.registerClass(hA);var fA=class extends Xe{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=Le(e),a=-1;return id(Ri(n,this.maskValue),a)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),a=-1,r=!0,s=id(Ri(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};fA.className="Masking";re.registerClass(fA);var mA=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Jt(this.outputDim,"outputDim"),this.embeddingsInitializer=bt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Ht(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 V(()=>this.maskZero?(e=Le(e),Ri(e,Ge(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return n.dtype!=="int32"&&(n=Hd(n,"int32")),M4(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};mA.className="Embedding";re.registerClass(mA);var Ki=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new _e}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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new U("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=[st(e)]),e=e,e.length<2)throw new U(`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=es(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&es(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ns(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Gd(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,d=l[0],p=l.slice(1).concat([d]),c=o.reshape([d].concat(ts(l.slice(1))));c=Qe(c,[1,0]),c=c.reshape(p),n.push(c),r=!0}else if(u>1){let l=Fa(1,u).concat([0]);n.push(Qe(o,l)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,u=o.length,l=o[u-1],d=[l].concat(o.slice(0,o.length-1));s=Qe(s.reshape([-1,l]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Fa(0,i-1));s=Qe(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=es(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:mn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=xa(n,t[a]);return n})}},gA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};gA.className="Add";re.registerClass(gA);var yA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};yA.className="Multiply";re.registerClass(yA);var AA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return B(1/e.length,t)})}};AA.className="Average";re.registerClass(AA);var xA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xa(t,e[n]);return t})}};xA.className="Maximum";re.registerClass(xA);var bA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ml(t,e[n]);return t})}};bA.className="Minimum";re.registerClass(bA);var vA=class extends Ki{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 U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>fy(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Un(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(mn(t[s],-1)):a.push(t[s]);let r=lt(a,this.axis);return Uc(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};vA.className="Concatenate";re.registerClass(vA);function sp(e,t){for(;e<0;)e+=t;return e}function Hre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new _e("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 _e("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return V(()=>{let i;if(a>r){i=a-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=t.reshape(t.shape.concat(u))}else if(r>a){i=r-a;let u=[];for(let l=0;l<i;++l)u.push(1);e=e.reshape(e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let u=s[0]!==e.shape.length-1,l=s[1]===t.shape.length-1;o=e.matMul(t,u,l)}if(i>0){let u;a>r?u=a+r-3:u=a-1;let l=[];for(let d=u;d<u+i;++d)l.push(d);o=o.squeeze(l)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var wA=class extends Ki{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 _e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new U(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>sp(r,e[s].shape.length)):a=[sp(this.axes,t.shape.length),sp(this.axes,n.shape.length)],this.normalize&&(t=x0(t,a[0]),n=x0(n,a[1])),Hre(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[sp(this.axes,e.length),sp(this.axes,t.length)],n}computeOutputShape(e){k.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 _e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[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}};wA.className="Dot";re.registerClass(wA);var kA=class extends Xe{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 V(()=>{this.invokeCallHook(e,t);let n=Le(e);return Xd(()=>i0(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};kA.className="GaussianNoise";re.registerClass(kA);var IA=class extends Xe{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 V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?Xd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(i0(n.shape,1,a))},()=>n,t.training||!1):n})}};IA.className="GaussianDropout";re.registerClass(IA);var SA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(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 V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Xd(()=>{let a=Le(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=qr(Fl(n),this.rate);o=Hd(o,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,l=-u*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(u).add(l)},()=>Le(e),t.training||!1)}return e})}};SA.className="AlphaDropout";re.registerClass(SA);function ip(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=g3(e,t,n,a,r,s);else if(e.rank===3)i=y3(e,t,n,a,r,s);else if(e.rank===4)i=A3(e,t,n,a,r,s);else throw new _e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Gre(e,t,n,a,r=.001){return V(()=>{let s=nh(e,a),i=s.mean,o=s.variance;return[ip(e,i,o,n,t,r),i,o]})}function qre(e,t,n,a,r=.001){return V(()=>{let s=nh(e,a),i=s.mean,o=s.variance,u=[];for(let h of Fa(0,e.rank))a.indexOf(h)!==-1?u.push(1):u.push(e.shape[h]);let l=i.reshape(u),d=o.reshape(u),p=t==null?null:t.reshape(u),c=n==null?null:n.reshape(u);return[ip(e,l,d,c,p,r),i,o]})}function Xre(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Fa(0,e.rank-1))?Gre(e,t,n,a,r):qre(e,t,n,a,r)}var NA=class extends Xe{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=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.movingMeanInitializer=bt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=bt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ht(e.betaConstraint),this.gammaConstraint=Ht(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,a=Le(e),r=a.shape,s=r.length,i=Fa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let u=Bi(1,s);u[o]=r[o];let l=i.slice();l.sort();let d=!k.arraysEqual(l,Fa(0,s).slice(0,s-1)),p=()=>{if(d){let g=this.movingMean.read().reshape(u),y=this.movingVariance.read().reshape(u),A=this.center?this.beta.read().reshape(u):null,x=this.scale?this.gamma.read().reshape(u):null;return ip(a,g,y,A,x,this.epsilon)}else return ip(a,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 p();let[c,h,m]=Xre(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,A)=>{V(()=>{let x=1-A,v=g.read(),b=v.sub(y).mul(x);g.write(v.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),movingMeanInitializer:Tt(this.movingMeanInitializer),movingVarianceInitializer:Tt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};NA.className="BatchNormalization";re.registerClass(NA);var TA=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(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!==es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Le(e),a=n.shape,r=a.length;return V(()=>{let s=!0,{mean:i,variance:o}=nh(n,this.axis,s),u=Bi(1,r);for(let m of this.axis)u[m]=a[m];let l=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(u):m,d=l(this.gamma.read()),p=l(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=i.tile(c),o=o.tile(c),d=d.tile(h),p=p.tile(h),ip(n,i,o,p,d,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};TA.className="LayerNormalization";re.registerClass(TA);function Kre(e,t,n){return V(()=>{if(e.rank!==4)throw new U(`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 U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ra()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],gr(e,a)})}var CA=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ra():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>Kre(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ZeroPadding2D";re.registerClass(CA);function $0(e,t,n,a,r,s){return V(()=>{Ft(r),S4(s),ca(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ra()),s==null&&(s="max"),e=Ky(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=hd(e,t,n,o):i=ld(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}function _8(e,t,n,a,r,s){return V(()=>{Ft(r),S4(s),ca(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ra()),s==null&&(s="max"),e=M8(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Y1(e,t,n,o):i=O1(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var P8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Jt(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 U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ca(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=st(e);let t=za(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=Gd(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Vt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},EA=class extends P8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"max")}};EA.className="MaxPooling1D";re.registerClass(EA);var RA=class extends P8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"avg")}};RA.className="AveragePooling1D";re.registerClass(RA);var L8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`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];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ca(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=za(t,this.poolSize[0],this.padding,this.strides[0]),n=za(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 V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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}},MA=class extends L8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"max")}};MA.className="MaxPooling2D";re.registerClass(MA);var FA=class extends L8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"avg")}};FA.className="AveragePooling2D";re.registerClass(FA);var W8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ca(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=za(t,this.poolSize[0],this.padding,this.strides[0]),n=za(n,this.poolSize[1],this.padding,this.strides[1]),a=za(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},$A=class extends W8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),_8(e,t,n,a,r,"max")}};$A.className="MaxPooling3D";re.registerClass($A);var DA=class extends W8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),_8(e,t,n,a,r,"avg")}};DA.className="AveragePooling3D";re.registerClass(DA);var B8=class extends Xe{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new _e}},OA=class extends B8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Le(e);return Nt(n,1)})}};OA.className="GlobalAveragePooling1D";re.registerClass(OA);var zA=class extends B8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Le(e);return Vn(n,1)})}};zA.className="GlobalMaxPooling1D";re.registerClass(zA);var V8=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new _e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_A=class extends V8{call(e,t){return V(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};_A.className="GlobalAveragePooling2D";re.registerClass(_A);var PA=class extends V8{call(e,t){return V(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Vn(n,[1,2]):Vn(n,[2,3])})}};PA.className="GlobalMaxPooling2D";re.registerClass(PA);var j8=class extends Xe{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 a=t.layer,r=Oa(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},LA=class extends j8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return V(()=>(e=Le(e),O8((n,a)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};LA.className="TimeDistributed";re.registerClass(LA);function Zre(e){ji(nae,"BidirectionalMergeMode",e)}var Yre="concat",WA=class extends j8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Oa(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Oa(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Yre:e.mergeMode,Zre(this.mergeMode),e.weights)throw new _e("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):$n(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=D8(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let l=n.map(d=>new zt({shape:d.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(a!=null)throw new _e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Da;for(let u of s)if(u instanceof Da!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Hn(r,1));let i;return this.mergeMode==="concat"?i=fy([a,r]):this.mergeMode==="sum"?i=ie(a,r):this.mergeMode==="ave"?i=B(.5,ie(a,r)):this.mergeMode==="mul"?i=B(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ui(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ui(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 a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}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=Oa(t.layer);if(delete t.layer,t.numConstants!=null)throw new _e("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};WA.className="Bidirectional";re.registerClass(WA);function Jre(e){return new eu(e)}function Qre(e){return new Gy(e)}function ese(e){return new jy(e)}function tse(e){return new Uy(e)}function nse(e){return new Hy(e)}function ase(e){return new Xy(e)}function rse(e){return new qy(e)}function sse(e){return new T0(e)}function ise(e){return new tp(e)}function ose(e){return new Yy(e)}function lse(e){return new np(e)}function use(e){return new Jy(e)}function dse(e){return new Qy(e)}function pse(e){return new eA(e)}function cse(e){return new tA(e)}function hse(e){return new nA(e)}function fse(e){return new dA(e)}function mse(e){return new lA(e)}function gse(e){return new F0(e)}function yse(e){return new oA(e)}function Ase(e){return new uA(e)}function xse(e){return new pA(e)}function bse(e){return new cA(e)}function vse(e){return new hA(e)}function wse(e){return new mA(e)}function kse(e){return new gA(e)}function Ise(e){return new AA(e)}function Sse(e){return new vA(e)}function Nse(e){return new xA(e)}function Tse(e){return new bA(e)}function Cse(e){return new yA(e)}function Ese(e){return new wA(e)}function Rse(e){return new NA(e)}function Mse(e){return new TA(e)}function Fse(e){return new CA(e)}function BA(e){return new RA(e)}function $se(e){return BA(e)}function Dse(e){return BA(e)}function VA(e){return new FA(e)}function Ose(e){return VA(e)}function zse(e){return VA(e)}function jA(e){return new DA(e)}function _se(e){return jA(e)}function Pse(e){return jA(e)}function Lse(e){return new OA(e)}function Wse(e){return new _A(e)}function U8(e){return new zA(e)}function H8(e){return new PA(e)}function G8(e){return new EA(e)}function q8(e){return new MA(e)}function Bse(e){return new $A(e)}function Vse(e){return new rA(e)}function jse(e){return new E0(e)}function Use(e){return new sA(e)}function Hse(e){return new rp(e)}function Gse(e){return new aA(e)}function qse(e){return new C0(e)}function Xse(e){return new iA(e)}function Kse(e){return new M0(e)}function Zse(e){return new ar(e)}function Yse(e){return new R0(e)}function Jse(e){return new WA(e)}function Qse(e){return new LA(e)}var eie=U8,tie=H8,nie=G8,aie=q8;function rie(e){return new kA(e)}function sie(e){return new IA(e)}function iie(e){return new SA(e)}function oie(e){return new fA(e)}var X8={};Fe(X8,{MAPE:()=>Aie,MSE:()=>vie,binaryAccuracy:()=>lie,binaryCrossentropy:()=>uie,categoricalAccuracy:()=>pie,categoricalCrossentropy:()=>cie,cosineProximity:()=>mie,mape:()=>xie,meanAbsoluteError:()=>gie,meanAbsolutePercentageError:()=>yie,meanSquaredError:()=>bie,mse:()=>wie,precision:()=>hie,recall:()=>fie,sparseCategoricalAccuracy:()=>die});function lie(e,t){return Ey(e,t)}function uie(e,t){return Z4(e,t)}function die(e,t){return Y4(e,t)}function pie(e,t){return Ry(e,t)}function cie(e,t){return My(e,t)}function hie(e,t){return K4(e,t)}function fie(e,t){return Yae(e,t)}function mie(e,t){return Ty(e,t)}function gie(e,t){return b0(e,t)}function yie(e,t){return nu(e,t)}function Aie(e,t){return nu(e,t)}function xie(e,t){return nu(e,t)}function bie(e,t){return Gi(e,t)}function vie(e,t){return Gi(e,t)}function wie(e,t){return Gi(e,t)}var K8={};Fe(K8,{modelFromJSON:()=>Mre});var Z8={};Fe(Z8,{l1:()=>Iie,l1l2:()=>kie,l2:()=>Sie});function kie(e){return new Qd(e)}function Iie(e){return Lre(e)}function Sie(e){return Wre(e)}var Y8=class extends tu{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof kr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function D0(e,t){return e<t}function J8(e,t){return e>t}var Q8=class extends Y8{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new _e("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=D0:this.mode==="max"?this.monitorFunc=J8:this.monitor.indexOf("acc")!==-1?this.monitorFunc=J8:this.monitorFunc=D0,this.monitorFunc===D0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===D0?Infinity:-Infinity}async onEpochEnd(e,t){await as(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 Nie(e){return new Q8(e)}var Tie={earlyStopping:Nie},_a;(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"})(_a||(_a={}));var ek;(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={}))})(ek||(ek={}));var UA={};function Cie(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};UA[e]=n}function tk(e){return UA[e]}function Eie(e){delete UA[e]}function I(e,t,n,a,r){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,u=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return kn(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,u).map(p=>kn(p,n,a,r));let l=kn(t.inputNames.slice(o)[0],n,a,r),d=l.dataSync();return s.type==="number"?d[0]:k.toNestedArray(l.shape,d)}let i=t.attrParams[e];return i&&i.value}function kn(e,t,n,a){let[r,s]=Kn(e);if(a!=null){let o=a.getHashTableHandleByName(r);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[O0(r,o)]);return i!==void 0?t[O0(r,i)][s]:void 0}function Rie(e,t,n){return t[O0(e,n.currentContextId)]}function Ir(e,t){let[n,a,r]=Kn(e);return[O0(n,t&&t.currentContextId),a,r]}function O0(e,t){return t?`${e}-${t}`:e}function Kn(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],a=t.length===3?t[1]:void 0,r=Number(t[t.length-1]);return[n,r,a]}function z0(e,t,n){let a=I("pad",e,t,n);if(a==="explicit"){a=I("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)r[s][0]=a[s*2],r[s][1]=a[s*2+1];return r}return a}function Sr(e){return e.kept?e:Ha(e)}var nk={};Fe(nk,{json:()=>Mie});var Mie=[{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}]}],ak={};Fe(ak,{json:()=>Fie});var Fie=[{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}]}],rk={};Fe(rk,{json:()=>$ie});var $ie=[{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"}]}],sk={};Fe(sk,{json:()=>Die});var Die=[{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"}]}],ik={};Fe(ik,{json:()=>Oie});var Oie=[{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"}]}],ok={};Fe(ok,{json:()=>zie});var zie=[{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}]}],lk={};Fe(lk,{json:()=>_ie});var _ie=[{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"}]}],uk={};Fe(uk,{json:()=>Pie});var Pie=[{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"}]}],dk={};Fe(dk,{json:()=>Lie});var Lie=[{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"}]}],pk={};Fe(pk,{json:()=>Wie});var Wie=[{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"}]}],ck={};Fe(ck,{json:()=>Bie});var Bie=[{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}]}],hk={};Fe(hk,{json:()=>Vie});var Vie=[{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"}]}],fk={};Fe(fk,{json:()=>jie});var jie=[{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}]}],mk={};Fe(mk,{json:()=>Uie});var Uie=[{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"}]}],gk={};Fe(gk,{json:()=>Hie});var Hie=[{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}]}],yk={};Fe(yk,{json:()=>Gie});var Gie=[{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"}]}],Ak={};Fe(Ak,{json:()=>qie});var qie=[{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}]}],xk={};Fe(xk,{json:()=>Xie});var Xie=[{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"}]}],bk={};Fe(bk,{json:()=>Kie});var Kie=[{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:[]}],vk=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[nk,ak,rk,sk,ik,ok,lk,uk,dk,pk,ck,hk,fk,mk,gk,yk,Ak,xk,bk],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,a)=>(n[a.tfOpName]=a,n),{})}transformGraph(e,t={}){let n=e.node,a=[],r=[],s=[],i=n.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op.startsWith("Placeholder")?a.push(m[f.name]):f.op==="Const"?r.push(m[f.name]):(f.input==null||f.input.length===0)&&s.push(m[f.name]),m),{}),o=[],u=[],l={},d={};t!=null&&(l=this.mapSignatureEntries(t.inputs),d=this.mapSignatureEntries(t.outputs));let p=Object.keys(i);p.forEach(m=>{let f=i[m];f.inputNames.forEach((g,y)=>{let[A,,x]=Ir(g),v=i[A];if(v.outputs!=null){let b=v.outputs.indexOf(x);if(b!==-1){let w=`${A}:${b}`;f.inputNames[y]=w}}f.inputs.push(v),v.children.push(f)})}),Object.keys(d).length===0?p.forEach(m=>{let f=i[m];f.children.length===0&&u.push(f)}):Object.keys(d).forEach(m=>{let[f]=Ir(m),g=i[f];g!=null&&(g.signatureKey=d[m],u.push(g))}),Object.keys(l).length>0?Object.keys(l).forEach(m=>{let[f]=Ir(m),g=i[f];g&&(g.signatureKey=l[m],o.push(g))}):o=a;let c={};e.library!=null&&e.library.function!=null&&(c=e.library.function.reduce((m,f)=>(m[f.signature.name]=this.mapFunction(f),m),{}));let h={nodes:i,inputs:o,outputs:u,weights:r,placeholders:a,signature:t,functions:c};return s.length>0&&(h.initNodes=s),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=tk(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(a=>a.startsWith("^")?a.substr(1):a),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((a,r)=>(a[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},a),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((a,r)=>{let s=r.type,i;switch(r.type){case"string":i=HA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=HA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=QA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=QA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=qA(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=qA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=JA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=JA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=GA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=GA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=t2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=t2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":i=YA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=YA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":i=e2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=e2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=KA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=KA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":i=ZA(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=ZA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":i=kk(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=kk(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return a[r.name]={value:i,type:s},a},{})),n}mapFunction(e){let t=e.nodeDef,n=[],a=[],r={};t!=null&&(r=t.reduce((l,d)=>(l[d.name]=this.mapNode(d),d.op==="Const"&&a.push(l[d.name]),l),{}));let s=[],i=[];e.signature.inputArg.forEach(l=>{let[d]=Ir(l.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:XA(l.type),type:"dtype"}},children:[]};p.signatureKey=l.name,s.push(p),r[d]=p}),Object.keys(r).forEach(l=>{let d=r[l];d.inputNames.forEach((p,c)=>{let[h,,m]=Ir(p),f=r[h];if(f.outputs!=null){let g=f.outputs.indexOf(m);if(g!==-1){let y=`${h}:${g}`;d.inputNames[c]=y}}d.inputs.push(f),f.children.push(d)})});let o=e.ret;e.signature.outputArg.forEach(l=>{let[d,p]=Ir(o[l.name]),c=r[d];c!=null&&(c.defaultOutput=p,i.push(c))});let u=this.mapArgsToSignature(e);return{nodes:r,inputs:s,outputs:i,weights:a,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 Zie(e){let t=te().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 wk(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Zie(e);return t?n:n.toLowerCase()}function HA(e,t,n,a=!1){let r=e[t];return r!=null?wk(r.s,a):n}function GA(e,t,n){let a=e[t];return a?a.b:n}function qA(e,t,n){let a=e[t]||{},r=a.i!=null?a.i:a.f!=null?a.f:n;return typeof r=="number"?r:parseInt(r,10)}function XA(e){switch(typeof e=="string"&&(e=_a[e]),e){case _a.DT_FLOAT:return"float32";case _a.DT_INT32:case _a.DT_INT64:case _a.DT_INT8:case _a.DT_UINT8:return"int32";case _a.DT_BOOL:return"bool";case _a.DT_DOUBLE:return"float32";case _a.DT_STRING:return"string";default:return null}}function kk(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function KA(e,t,n){let a=e[t];return a&&a.type?XA(a.type):n}function ZA(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>XA(r)):n}function Ik(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function YA(e,t,n){let a=e[t];return a&&a.shape?Ik(a.shape):n}function JA(e,t,n){let a=e[t];return a?((a.list.f&&a.list.f.length?a.list.f:a.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function QA(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>wk(s,a)):n}function e2(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>Ik(r)):n}function t2(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var Yie=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(a=>this.getInput(a)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((a,r)=>(a[r]=this.getAttr(r),a),{}))}getInput(e){return kn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return kn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return qA(this.node.rawAttrs,e,t);if(n.s!=null)return HA(this.node.rawAttrs,e,t);if(n.b!=null)return GA(this.node.rawAttrs,e,t);if(n.shape!=null)return YA(this.node.rawAttrs,e,t);if(n.type!=null)return KA(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return JA(this.node.rawAttrs,e,t);if(n.list.s!=null)return QA(this.node.rawAttrs,e,t);if(n.list.shape!=null)return e2(this.node.rawAttrs,e,t);if(n.list.b!=null)return t2(this.node.rawAttrs,e,t);if(n.list.type!=null)return ZA(this.node.rawAttrs,e,t)}return t}},Jie=(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[jc(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[Q1(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[B(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[me(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[V1(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Vc(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[Ml(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Xa(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[yr(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[hh(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qie=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Wt(I("x",e,t,n))];case"Acos":return[S1(I("x",e,t,n))];case"Acosh":return[N1(I("x",e,t,n))];case"Asin":return[C1(I("x",e,t,n))];case"Asinh":return[E1(I("x",e,t,n))];case"Atan":return[R1(I("x",e,t,n))];case"Atan2":return[M1(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[F1(I("x",e,t,n))];case"Ceil":return[_1(I("x",e,t,n))];case"Complex":return[Wr(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[dd(I("x",e,t,n))];case"Cosh":return[Xc(I("x",e,t,n))];case"Elu":return[Cl(I("x",e,t,n))];case"Erf":return[j1(I("x",e,t,n))];case"Exp":return[la(I("x",e,t,n))];case"Expm1":return[U1(I("x",e,t,n))];case"Floor":return[Rl(I("x",e,t,n))];case"Log":return[Bn(I("x",e,t,n))];case"Log1p":return[Jc(I("x",e,t,n))];case"Imag":return[Zc(I("x",e,t,n))];case"Neg":return[St(I("x",e,t,n))];case"Reciprocal":return[ng(I("x",e,t,n))];case"Real":return[yd(I("x",e,t,n))];case"Relu":return[Ka(I("x",e,t,n))];case"Round":return[sh(I("x",e,t,n))];case"Selu":return[oh(I("x",e,t,n))];case"Sigmoid":return[Rn(I("x",e,t,n))];case"Sin":return[lh(I("x",e,t,n))];case"Sign":return[rg(I("x",e,t,n))];case"Sinh":return[uh(I("x",e,t,n))];case"Softplus":return[Ci(I("x",e,t,n))];case"Sqrt":return[an(I("x",e,t,n))];case"Square":return[ot(I("x",e,t,n))];case"Tanh":return[Si(I("x",e,t,n))];case"Tan":return[og(I("x",e,t,n))];case"ClipByValue":return[Mn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[rh(I("x",e,t,n))];case"Rsqrt":return[ih(kn(e.inputNames[0],t,n))];case"Prod":return[ah(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[pd(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[gd(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[G1(kn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ka(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){k.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let a=0;a<e.length;a++){let r=e[a],s=t[a];k.assert(r<0||s<0||r===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Sk(e){return!(typeof e=="number"||e.some(t=>t<0))}function op(e,t,n){let a=n2(e,n),r=!Sk(a);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${a}`);if(r&&t.forEach(s=>{a=n2(s.shape,a)}),!Sk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function n2(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 a=0;a<e.length;++a){let r=e[a],s=t[a];if(r>=0&&s>=0&&r!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[a]=r>=0?r:s}return n}var eoe=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Kt(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),ka(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,Kt(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,a)=>this.write(n,t[a]))}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 a=0;a<this.size();a++)e.push(a)}if(e.length===0)return ln([],[0].concat(this.elementShape));let n=this.readMany(e);return ka(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),gn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return ln([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return ka(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),lt(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,Gn(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,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];V(()=>{t=q(t,[1,n,r]);for(let o=0;o<e.length;++o){let u=o===0?0:a[o-1],l=[0,u,0],d=[1,e[o],r];s[o]=q(Re(t,l,d),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},lp=class{constructor(e,t,n,a=-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}`);ka(t,r.shape,"TensorList shape mismatch: "),Kt(r)}),this.idTensor=ke(0),this.maxNumElements=a,Kt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new lp([...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.`);ka(e,this.elementShape,"TensorList shape mismatch: ");let a=op(this.elementShape,this.tensors,e);return V(()=>{let r=this.tensors.map(s=>q(s,a));return gn(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=op(this.elementShape,this.tensors,e),a=this.tensors.pop();return ka(a.shape,e,"TensorList shape mismatch: "),q(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ka(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Kt(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.`);ka(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=op(this.elementShape,this.tensors,t);return q(this.tensors[e],a)}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.`);ka(this.elementShape,t.shape,"TensorList shape mismatch: "),Kt(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}`);ka(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=op(this.elementShape,this.tensors,n);return e.length===0?ln([],[0].concat(a)):V(()=>{let r=e.map(s=>q(this.tensors[s],a));return gn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ka(this.elementShape,t,"TensorList shape mismatch: ");let n=op(this.elementShape,this.tensors,t);return this.size()===0?ln([],[0].concat(n)):V(()=>{let a=this.tensors.map(r=>q(r,n));return lt(a,0)})}};function toe(e,t,n){let a=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);ka(r,t,"TensorList shape mismatch: ");let s=Gn(e);return new lp(s,t,a)}function noe(e,t,n){return new lp([],e,t,n)}function aoe(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new lp([],n,e.dtype,a),i=Gn(e,0);return t.forEach((o,u)=>{s.setItem(o,i[u])}),s}function roe(e,t,n){let a=0,r=t.map(d=>(a+=d,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=n2(s,n),o=a===0?0:e.size/a,u=V(()=>{let d=[];e=q(e,[1,a,o]);for(let p=0;p<t.length;++p){let c=p===0?0:r[p-1],h=[0,c,0],m=[1,t[p],o];d[p]=q(Re(e,h,m),i)}return e.dispose(),d}),l=new lp([],n,e.dtype,t.length);for(let d=0;d<u.length;d++)l.setItem(d,u[d]);return l}var soe=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(d=>d.id),u=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let l=s;for(;u[0];){let d=l;l=await n.functionMap[a].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case"LoopCond":{let a=I("pred",e,t,n);return[Sr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Sr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>kn(r,t,n)!==void 0);if(a){let r=kn(a,t,n);return[Sr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Sr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Sr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Sr(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),u=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),d=new eoe(l,r,a,s,u,i,o);return n.addTensorArray(d),[d.idTensor,ke(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[ke(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=aoe(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=noe(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=toe(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=roe(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Nk(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",u=a==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(o&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let d=I("strides",e,t,n),p=z0(e,t,n),c=I("dataFormat",e,t,n).toUpperCase(),h=I("dilations",e,t,n),[m,f]=I("args",e,t,n);i&&(f=m,m=void 0);let g=I("leakyreluAlpha",e,t,n);return{stride:d,pad:p,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var ioe=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Gc(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=z0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[mr(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:d}=Nk(e,t,n);return[Kr.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:d}=Nk(e,t,n);return[Kr.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=I("outputShape",e,t,n),r=I("strides",e,t,n),s=z0(e,t,n);return[qc(I("x",e,t,n),I("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=I("strides",e,t,n),r=z0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[Tl(I("input",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[L1(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[ld(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[hd(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:u}=z3(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,u]}case"AvgPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[O1(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"MaxPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Y1(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"Dilation2D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dilations",e,t,n),i=a[1],o=a[2],u=s[1],l=s[2];return[B1(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[u,l],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ooe=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[El(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[E3(a,r,s)]}case"Multinomial":{let a=I("logits",e,t,n),r=I("numSamples",e,t,n),s=I("seed",e,t,n);return[_3(a,r,s)]}case"OneHot":{let a=I("indices",e,t,n),r=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[vl(a,r,s,i)]}case"Ones":return[jn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Un(I("x",e,t,n))];case"RandomUniform":return[Fl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("step",e,t,n);return[$l(a,r,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let a=I("shape",e,t,n),r=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[fh(a,r,s,I("dtype",e,t,n),i)]}case"Zeros":return[$t(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ge(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function a2(e,t,n){let a=I("boxes",e,t,n),r=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),u=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}}var loe=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}=a2(e,t,n),l=await De.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,u);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=a2(e,t,n),u=I("padToMaxOutputSize",e,t,n),l=await De.nonMaxSuppressionPaddedAsync(a,r,s,i,o,u);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=a2(e,t,n);return[await De.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=ge(I("condition",e,t,n),"bool"),r=[await dg(a)];return a.dispose(),r}case"ListDiff":return W3(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},uoe=(e,t,n)=>{switch(e.op){case"TopKV2":{let a=I("x",e,t,n),r=I("k",e,t,n),s=I("sorted",e,t,n),i=lg(a,r,s);return[i.values,i.indices]}case"Unique":{let a=I("x",e,t,n),r=mh(a);return[r.values,r.indices]}case"UniqueV2":{let a=I("x",e,t,n),r=I("axis",e,t,n),s=mh(a,r);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},doe=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[kn(e.name,t,n)||a];case"Placeholder":return[kn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let l=I("x",e,t,n);return[Sr(l)]}case"IdentityN":return I("x",e,t,n).map(l=>Sr(l));case"Snapshot":let r=I("x",e,t,n);return[Sr(r)];case"Shape":return[Dt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(l=>Dt(l.shape));case"Size":return[ke(I("x",e,t,n).size,"int32")];case"Rank":return[ke(I("x",e,t,n).rank,"int32")];case"NoOp":return[ke(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),u=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let l=0;l<i.length;l++)console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0,u));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},poe=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ke(0),this.tensorMap=new Map,Kt(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 ke(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),V(()=>{let a=Gn(t),r=n.length,s=a.length;k.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i<r;i++){let o=n[i],u=a[i];Kt(u),this.tensorMap.set(o,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return V(()=>{let a=[];for(let r=0;r<n.length;r++){let s=n[r],i=this.findWithDefault(s,t);a.push(i)}return gn(a)})}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}`)}},coe=async(e,t,n,a)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new poe(r,s);return a.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=I("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hoe=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[De.resizeBilinear(a,[r[0],r[1]],s,i)]}case"ResizeNearestNeighbor":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[De.resizeNearestNeighbor(a,[r[0],r[1]],s,i)]}case"CropAndResize":{let a=I("image",e,t,n),r=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),u=I("extrapolationValue",e,t,n);return[De.cropAndResize(a,r,s,i,o,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},foe=(e,t,n)=>{switch(e.op){case"Equal":return[Hr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[Ri(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Wn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[qr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Yc(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[Xr(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[xa(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[cd(I("a",e,t,n))];case"LogicalOr":return[th(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[un(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`)}},moe=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[je(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[N3(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Qe(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=I("numArgs",e,t,n),u=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[l,d]=I("args",e,t,n);return[Kr.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:l,activation:r,preluActivationWeights:d,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},goe=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ni(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[Ni(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[q1(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[xd(I("x",e,t,n))];case"LogSoftmax":return[eh(I("x",e,t,n))];case"SparseToDense":return[pg(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`)}},yoe=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Vn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Nt(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[fd(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Se(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Uc(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[id(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[ki(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[T1(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ah(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[Kc(I("x",e,t,n),i,o,u)]}case"Bincount":let a=I("x",e,t,n),r=I("weights",e,t,n),s=I("size",e,t,n);return[z1(a,r,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),u=I("size",e,t,n),l=I("binaryOutput",e,t,n);return[I3(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Aoe=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let a=I("n",e,t,n),r=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,a),[lt(s,r)]}case"Gather":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[Ti(a,ge(r,"int32"),0)]}case"GatherV2":{let a=I("axis",e,t,n),r=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[Ti(s,ge(i,"int32"),a,r)]}case"Reverse":{let a=I("dims",e,t,n),r=[];for(let i=0;i<a.length;i++)a[i]&&r.push(i);let s=I("x",e,t,n);return[Hn(s,r)]}case"ReverseV2":{let a=I("axis",e,t,n),r=I("x",e,t,n);return[Hn(r,a)]}case"Slice":{let a=I("begin",e,t,n),r=I("size",e,t,n);return[Re(I("x",e,t,n),a,r)]}case"StridedSlice":{let a=I("begin",e,t,n),r=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),u=I("ellipsisMask",e,t,n),l=I("newAxisMask",e,t,n),d=I("shrinkAxisMask",e,t,n),p=I("x",e,t,n);return[ig(p,a,r,s,i,o,u,l,d)]}case"Pack":return V(()=>{let a=I("axis",e,t,n),r=I("tensors",e,t,n),s=r[0].shape,i=Vt(r[0]).shape,o=r.map(u=>{let l=k.arraysEqual(u.shape,s);if(!l&&!k.arraysEqual(Vt(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:q(u,s)});return[gn(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return Gn(r,a)}case"Tile":{let a=I("reps",e,t,n);return[Gr(I("x",e,t,n),a)]}case"Split":case"SplitV":{let a=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return Zt(s,r,a)}case"ScatterNd":{let a=I("indices",e,t,n),r=I("values",e,t,n),s=I("shape",e,t,n);return[U3(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[H3(a,r)]}case"SparseToDense":{let a=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[pg(a,s,r,s.dtype===i.dtype?i:ge(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xoe=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:r,emptyRowIndicator:s,reverseIndexMap:i}=wd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[a,r,s,i]}case"SparseReshape":{let{outputIndices:a,outputShape:r}=wd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}case"SparseSegmentMean":return[wd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[wd.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`)}},boe=(e,t,n)=>{switch(e.op){case"FFT":return[bd(I("x",e,t,n))];case"IFFT":return[Dl(I("x",e,t,n))];case"RFFT":return[vd(I("x",e,t,n))];case"IRFFT":return[ch(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},voe=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:r}=vh.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[a,r]}case"StringSplit":{let{indices:a,values:r,shape:s}=vh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[a,r,s]}case"StringToHashBucketFast":return[vh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},woe=(e,t,n)=>{switch(e.op){case"Cast":return[ge(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[mn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[Vt(I("x",e,t,n),a)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[J1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[gr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let a=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[md(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[ud(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[W1(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Sl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Tk(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return V(()=>Jie(s,i,o));case"basic_math":return V(()=>Qie(s,i,o));case"control":return soe(s,i,o);case"convolution":return V(()=>ioe(s,i,o));case"creation":return V(()=>ooe(s,i,o));case"dynamic":return loe(s,i,o);case"evaluation":return V(()=>uoe(s,i,o));case"image":return V(()=>hoe(s,i,o));case"graph":return V(()=>doe(s,i,o));case"logical":return V(()=>foe(s,i,o));case"matrices":return V(()=>moe(s,i,o));case"normalization":return V(()=>goe(s,i,o));case"reduction":return V(()=>yoe(s,i,o));case"slice_join":return V(()=>Aoe(s,i,o));case"sparse":return V(()=>xoe(s,i,o));case"spectral":return V(()=>boe(s,i,o));case"string":return V(()=>voe(s,i,o));case"transformation":return V(()=>woe(s,i,o));case"hash_table":return coe(s,i,o,a);case"custom":let u=tk(s.op);if(u&&u.customExecutor)return u.customExecutor(new Yie(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var Ck=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,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 Ek(e,t,n,a){let r=new Set,s=[],i=null,o=null,u=new Set,l=Object.keys(e).map(c=>Kn(c)[0]),d=[];a!=null&&(d=a.map(c=>Kn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((Rk(c)||Toe(c)||Coe(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&l.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function koe(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>Kn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let u=new Set,l=[];for(;s.length>0;){let d=s.pop();u.add(d.name),t[d.name]||l.push(d),d.children.forEach(p=>{!u.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>u.has(c.name))&&s.push(p)})}return l}var Ioe=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Soe=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Noe=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Rk(e){return Ioe.indexOf(e.op)>=0}function Toe(e){return Soe.indexOf(e.op)>=0}function Coe(e){return Noe.indexOf(e.op)>=0}var r2=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 r2(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(a=>a.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(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Ek(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=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 [${s}]`);if(a.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return koe(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 a=n.map(d=>this.graph.nodes[Kn(d)[0]]),r=t.map(d=>Kn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let u={},l={};return V(()=>{let d=new Ck(this.weightMap,u,l,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Kn(m),y=[];y[g]=e[m],p[f]=y});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=Tk(f,p,d,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=g,this.checkTensorForDisposal(f.name,f,p,d,c,r,h)}}return this.parent==null&&d.dispose(c),t.map(m=>kn(m,p,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let u=Rie(o.name,n,a);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let d=i[l.id];d===1?(l.dispose(),delete i[l.id]):d!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new Ck(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>kn(p,i,s)),u=o.map(p=>p.id),l=Object.keys(e).map(p=>e[p].id),d=new Set([...u,...l,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!d.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[Kn(A)[0]]),i=n.map(A=>Kn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:d,syncInputs:p}=Ek(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,v]=Kn(A),b=[];b[v]=e[A],h[x]=b});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let A=this.processStack(s,c,t,h,g,f,i,m,u);await Promise.all(A)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!Rk(A)&&!kn(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${A}`)}return h}processStack(e,t,n,a,r,s,i,o,u){let l=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&I("isConstant",d.node,a,n)&&([p]=Ir(d.node.name,n)),a[d.node.name]==null){let c=Tk(d.node,a,n,this._resourceManager);p||([p]=Ir(d.node.name,n));let h=n.currentContext;k.isPromise(c)?l.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,u),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,u))}else this.processChildNodes(d.node,t,n,a,r,u)}return l}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!kn(u,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!kn(u,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Kn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,u)=>s[u]===-1||s[u]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Kn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Kn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Eoe=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Roe="?tfjs-format=file",Moe="model.json",Mk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Eoe}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=En.browserHTTPRequest(e,this.loadOptions);else{let t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new r2(vk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=vk.Instance.transformGraph(e.modelInitializer);this.initializer=new r2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=En.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Be)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Moe}${Roe}`);let n=new Mk(e,t);return await n.load(),n}var Foe="3.7.0",Fk={};Fe(Fk,{CSVDataset:()=>Uk,Dataset:()=>iu,FileDataSource:()=>Yk,TextLineDataset:()=>Bk,URLDataSource:()=>Jk,array:()=>tle,csv:()=>cle,func:()=>hle,generator:()=>fle,microphone:()=>gle,version_data:()=>yle,webcam:()=>mle,zip:()=>nle});var $oe=gs(N5()),Doe=gs(N5());function Ooe(e,t){return _0(e,t)}function _0(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.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(su(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],u=_0(o,t,n,a);s[i]=u}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function zoe(e,t=Dk){return $k(e,t)}function $k(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(su(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(l=>l[i]),u=$k(o,t,n);s[i]=u}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function Dk(e){return e===null?null:su(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Ok(e,t){let n=new Map;_0(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return _0(e,t,n)}function su(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Be))}function _oe(e){return e==null||Poe(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Be||k.isTypedArray(e)}function Poe(e){return e===null||typeof e!="object"&&typeof e!="function"}function Loe(e){return Ooe(e,Woe)}function Woe(e){return e instanceof Be?{value:e.clone(),recurse:!1}:su(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var zk=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}},s2=class extends zk{constructor(){super(s2.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 a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};s2.INITIAL_CAPACITY=32;function _k(e){return new joe(e)}function i2(e){return new Uoe(e)}function Boe(e,t){return new Lk(e,t)}function Voe(e,t=os.FAIL){return new Qoe(e,t)}var Qt=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 Yoe(this,e)}filter(e){return new Koe(this,e)}map(e){return new Zoe(this,e)}mapAsync(e){return new Pk(this,e)}serialMapAsync(e){return new Pk(this,e).serial()}flatmap(e){return new Joe(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 Xoe(this,e,t)}columnMajorBatch(e,t=!0,n=Dk){return this.rowMajorBatch(e,t).map(a=>zoe(a,n))}concatenate(e,t){return new Lk(_k([this,e]),t)}take(e){return e<0||e==null?this:new qoe(this,e)}skip(e){return e<0||e==null?this:new Goe(this,e)}prefetch(e){return new Wk(this,e)}shuffle(e,t){return new ele(this,e,t)}serial(){return new Hoe(this)}},joe=class extends Qt{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:Loe(e),done:!1}}},Uoe=class extends Qt{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: ${e.message}`,e}}},Hoe=class extends Qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Goe=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;he(e.value)}return this.upstream.next()}},qoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Xoe=class extends Qt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Koe=class extends Qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;he(e.value)}}},Zoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Yoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Pk=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},o2=class extends Qt{constructor(){super();this.outputQueue=new s2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Joe=class extends o2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Sa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Sa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return!0}},Lk=class extends Qt{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}},os;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(os||(os={}));var Qoe=class extends Qt{constructor(e,t=os.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 a(s){return s instanceof Qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await Ok(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case os.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case os.SHORTEST:return{value:null,done:!0};case os.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Wk=class extends Qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zk(t)}summary(){return`${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()}},ele=class extends Wk{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Doe.alea(n||k.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}}},iu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Zn(async()=>(await n.iterator()).columnMajorBatch(e,t,ale),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Zn(async()=>(await t.iterator()).filter(a=>V(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Zn(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return Zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Zn(async()=>{let a=i2(async()=>({value:await t.iterator(),done:!1}));return Boe(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=$oe.alea(t||k.now().toString());return Zn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Zn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};iu.MAX_BUFFER_SIZE=1e4;function Zn(e,t=null){return new class extends iu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function tle(e){return Zn(async()=>_k(e),e.length)}function nle(e){if(!su(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 Zn(async()=>{let n=await Ok(e,a=>{if(a instanceof iu)return{value:a.iterator(),recurse:!1};if(su(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Voe(n,os.SHORTEST)},t)}function ale(e){if(e===null)return null;let t=e[0];return _oe(t)?{value:rle(e),recurse:!1}:{value:null,recurse:!0}}function rle(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Be?gn(e):ln(e)}var Bk=class extends iu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
|
|
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},P0='"',up=Symbol("out"),Vk=Symbol("field"),L0=Symbol("quote"),l2=Symbol("quoteafterquote"),jk=Symbol("quoteinquote"),Uk=class extends iu{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 Bk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.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&&k.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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?a[s]=u:n[s]=u}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=up;for(let i=0;i<r;i++)switch(s){case up:switch(e.charAt(i)){case P0:a=i+1,s=L0;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=up;break;default:s=Vk,a=i;break}break;case Vk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=up,a=i+1;break;default:}break;case L0:switch(e.charAt(i)){case P0:s=l2;break;default:}break;case l2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=up,a=i+1;break;case P0:s=L0;break;default:s=jk;break}break;case jk:switch(e.charAt(i)){case P0:s=L0;break;default:}break;default:}if(s===l2?n.push(e.substring(a,r-1)):n.push(e.substring(a)),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}},Hk=class extends Qt{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(te().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Hk(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),ln(n,t)}},Gk=class extends Qt{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=Dt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ta([s,r,o,i],[1,4])}else this.cropBox=Ta([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(te().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 Gk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=oa.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=mn(ge(e,"float32"),0),n;n=De.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return q(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},qk=class{},Xk=class extends Qt{split(e){return new sle(this,e)}},sle=class extends Xk{constructor(e,t){super();this.upstream=e,this.impl=new ile(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ile=class extends o2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},ole=class extends Qt{decodeUTF8(){return new lle(this)}},lle=class extends Xk{constructor(e){super();this.upstream=e,this.impl=new ule(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ule=class extends o2{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=mS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return te().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Kk=class extends ole{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(te().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function dle(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=ple(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new Kk(s,t)}else throw new Error(r.statusText)}var ple=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 Zk(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Yk=class extends qk{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Zk(this.input)&&te().get("IS_NODE")){let e=po("fs");this.input=e.readFileSync(this.input.substr(7))}return new Kk(this.input,this.options)}},Jk=class extends qk{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Zk(this.url)?new Yk(this.url,this.fileOptions).iterator():dle(this.url,this.fileOptions)}};function cle(e,t={}){return new Uk(new Jk(e),t)}function hle(e){let t=i2(e);return Zn(async()=>t)}function fle(e){return Zn(async()=>{let t=await e();return i2(()=>t.next())})}async function mle(e,t){return Gk.create(e,t)}async function gle(e){return Hk.create(e)}var yle="3.7.0",Ale={tfjs:(_m==null?void 0:_m.version)||void 0,"tfjs-core":(Pm==null?void 0:Pm.version)||void 0,"tfjs-data":(Lm==null?void 0:Lm.version)||void 0,"tfjs-layers":(Wm==null?void 0:Wm.version)||void 0,"tfjs-converter":(Bm==null?void 0:Bm.version)||void 0,"tfjs-backend-cpu":q7||void 0,"tfjs-backend-webgl":gw||void 0,"tfjs-backend-wasm":l4||void 0};var Yn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Qk(){if(!I1(Yn.name)){de("backend registration:",Yn.name);try{Yn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Yn.width,Yn.height):document.createElement("canvas")}catch(e){de("error: cannot create canvas:",e);return}try{Yn.gl=Yn.canvas.getContext("webgl2",Yn.webGLattr)}catch(e){de("error: cannot get WebGL2 context:",e);return}try{Oh(2,Yn.gl)}catch(e){de("error: cannot set WebGL2 context:",e);return}try{let e=new Vh(Yn.gl);kl(Yn.name,()=>new Xl(e),Yn.priority)}catch(e){de("error: cannot register WebGL backend:",e);return}try{gl("webgl").forEach(t=>{let n={...t,backendName:Yn.name};gi(n)})}catch(e){de("error: cannot update WebGL backend registration:",e);return}try{sa.set("WEBGL_VERSION",2)}catch(e){de("error: cannot set WebGL backend flags:",e);return}de("backend registered:",Yn.name)}}function e9(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:a}}function pp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function ou(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function lu(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return De.cropAndResize(t,s,[0],n)}function W0(e,t=1.5){let n=ou(e),a=pp(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function B0(e){let t=ou(e),n=pp(e),r=Math.max(...n)/2,s=[Math.round(t[0]-r),Math.round(t[1]-r)],i=[Math.round(t[0]+r),Math.round(t[1]+r)];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function u2(e){let t=e.map(s=>s[0]),n=e.map(s=>s[1]),a=[Math.min(...t),Math.min(...n)],r=[Math.max(...t),Math.max(...n)];return{startPoint:a,endPoint:r,landmarks:e}}var t9=e=>({startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});var V0=[[1,0,0],[0,1,0],[0,0,1]];function xle(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function d2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return xle(n)}function n9(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function ls(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function ble(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function a9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(ls(e[r],ble(t,s)))}return n}function j0(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=n9(t[0],t[1]),i=a9(s,r),o=n9(-t[0],-t[1]);return a9(i,o)}function r9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-ls(t[0],n),-ls(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function s9(e,t){return[ls(e,t[0]),ls(e,t[1])]}function i9(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let a=0;a<t.strides.length;a++){let r=t.strides[a],s=Math.floor((e+r-1)/r),i=Math.floor((e+r-1)/r),o=t.anchors[a];for(let u=0;u<s;u++){let l=r*(u+.5);for(let d=0;d<i;d++){let p=r*(d+.5);for(let c=0;c<o;c++)n.push([p,l])}}}return n}var o9=6;function vle(e,t,n){let a=Re(e,[0,1],[-1,2]),r=ie(a,t),s=Re(e,[0,3],[-1,2]),i=me(s,n),o=me(r,n),u=me(i,2),l=ye(o,u),d=ie(o,u),p=B(l,n),c=B(d,n);return Nl([p,c],1)}var l9=class{constructor(t,n){this.model=t,this.anchorsData=i9(t.inputs[0].shape[1]),this.anchors=Ta(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,a,r]=V(()=>{let l=De.resizeBilinear(t,[this.inputSize,this.inputSize]).div(127.5).sub(.5),d=this.model.execute(l),p;if(Array.isArray(d)){let f=d.sort((x,v)=>x.size-v.size),g=lt([f[0],f[2]],2),y=lt([f[1],f[3]],2);p=lt([y,g],1).squeeze(0)}else p=Vt(d);let c=vle(p,this.anchors,[this.inputSize,this.inputSize]),h=Re(p,[0,0],[-1,1]),m=Rn(h).squeeze().dataSync();return[p,c,m]}),s=await De.nonMaxSuppressionAsync(a,r,this.config.face.detector.maxDetected,this.config.face.detector.iouThreshold,this.config.face.detector.minConfidence),i=s.arraySync();s.dispose();let o=[];for(let u=0;u<i.length;u++){let l=r[i[u]];if(l>this.config.face.detector.minConfidence){let d=Re(a,[i[u],0],[1,-1]),p=t9(d);d.dispose();let c=this.anchorsData[i[u]],h=V(()=>Re(n,[i[u],o9-1],[1,-1]).squeeze().reshape([o9,-1]));o.push({box:p,landmarks:h,anchor:c,confidence:l})}}return n.dispose(),a.dispose(),{boxes:o,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function u9(e){let t=await ct(ft(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new l9(t,e);return!t||!t.modelUrl?de("load model failed:",e.face.detector.modelPath):e.debug&&de("load model:",t.modelUrl),n}var rr={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]},p2=[{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]}],cp=[[.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]],Zi=[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 wle=[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],kle=[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],Ile=[33,133,362,263,1,78,308],fue=wle.map(e=>cp[e]),mue=kle.map(e=>cp[e]),gue=Ile.map(e=>cp[e]);var c2=rr.leftEyeLower0,h2=rr.rightEyeLower0,uu={leftBounds:[c2[0],c2[c2.length-1]],rightBounds:[h2[0],h2[h2.length-1]]},U0={count:468,mouth:13,symmetryLine:[13,rr.midwayBetweenEyes[0]]},d9={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},du={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function H0(e,t,n,a){for(let r=0;r<p2.length;r++){let{key:s,indices:i}=p2[r],o=rr[`${n}${s}`];if(!a||a.includes(s))for(let u=0;u<i.length;u++){let l=i[u];e[o[u]]=[t[l][0],t[l][1],(t[l][2]+e[o[u]][2])/2]}}}var f2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=pp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?j0(a,[0,0]):V0,u=a!==0?i.map(p=>[...s9(p,o),p[2]]):i,l=a!==0?r9(r):V0,d=[...ou({startPoint:n.startPoint,endPoint:n.endPoint}),1];return u.map(p=>[Math.round(p[0]+ls(d,l[0])),Math.round(p[1]+ls(d,l[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[uu.leftBounds[0]][2],a=t[uu.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=B0(W0(u2([t[a],t[r]]),this.irisEnlarge)),o=pp(i),u=De.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&sa.flags.IS_BROWSER&&(u=De.flipLeftRight(u)),{box:i,boxSize:o,crop:u}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<du.numCoordinates;i++){let o=t[i*3],u=t[i*3+1],l=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],u/this.irisSize*a[1]+n.startPoint[1],l])}return{rawCoords:s,iris:s.slice(du.index)}}getAdjustedIrisCoords(t,n,a){let r=t[rr[`${a}EyeUpper0`][du.upperCenter]][2],s=t[rr[`${a}EyeLower0`][du.lowerCenter]][2],i=(r+s)/2;return n.map((o,u)=>{let l=i;return u===2?l=r:u===4&&(l=s),[o[0],o[1],l]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),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)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){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 o=e9({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=W0(o),l=B0(u),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...l,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=V(()=>this.storedBoxes.map((i,o)=>{let u,l=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&sa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=U0.count?U0.symmetryLine:d9.symmetryLine;l=d2(i.landmarks[x],i.landmarks[v]);let b=ou({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t,l,0,w);d=j0(-l,b),n.face.mesh.enabled?u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=V0;let x=t.clone();n.face.mesh.enabled?u=lu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):u=lu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,p,c]=this.meshDetector.execute(u),h=p.dataSync()[0];if(h<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=h,null;let f=q(c,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:v,crop:b}=this.getEyeBox(f,u,uu.leftBounds[0],uu.leftBounds[1],!0),{box:w,boxSize:N,crop:C}=this.getEyeBox(f,u,uu.rightBounds[0],uu.rightBounds[1]),_=this.irisModel.predict(lt([b,C])).dataSync(),$=_.slice(0,du.numCoordinates*3),{rawCoords:S,iris:z}=this.getEyeCoords($,x,v,!0),O=_.slice(du.numCoordinates*3),{rawCoords:W,iris:G}=this.getEyeCoords(O,w,N),H=this.getLeftToRightEyeDepthDifference(f);Math.abs(H)<30?(H0(f,S,"left",null),H0(f,W,"right",null)):H<1?H0(f,S,"left",["EyeUpper0","EyeLower0"]):H0(f,W,"right",["EyeUpper0","EyeLower0"]);let J=this.getAdjustedIrisCoords(f,z,"left"),K=this.getAdjustedIrisCoords(f,G,"right");f=f.concat(J).concat(K)}let g=this.transformRawCoords(f,i,l,d),y=i.confidence;if(i=W0(u2(g),1.5),i.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&sa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=U0.count?U0.symmetryLine:d9.symmetryLine;l=d2(i.landmarks[x],i.landmarks[v]);let b=ou({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t.toFloat(),l,0,w);d=j0(-l,b),u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255)}let A={mesh:g,box:i,faceConfidence:h,boxConfidence:i.confidence,image:u};return this.storedBoxes[o]={...B0(i),confidence:i.confidence,faceConfidence:h},A}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Rt=[null,null,null],m2;async function p9(e,t){let n=await m2.predict(e,t),a=[],r=0;for(let s of n||[]){if(!s||s.isDisposedInternal)continue;let i=s.mesh.map(d=>[d[0]/(e.shape[2]||0),d[1]/(e.shape[1]||0),d[2]/m2.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(rr))o[d]=rr[d].map(p=>s.mesh[p]);let u=s.box?[Math.trunc(Math.max(0,s.box.startPoint[0])),Math.trunc(Math.max(0,s.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,s.box.endPoint[0])-Math.max(0,s.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,s.box.endPoint[1])-Math.max(0,s.box.startPoint[1]))]:[0,0,0,0],l=s.box?[s.box.startPoint[0]/(e.shape[2]||0),s.box.startPoint[1]/(e.shape[1]||0),(s.box.endPoint[0]-s.box.startPoint[0])/(e.shape[2]||0),(s.box.endPoint[1]-s.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];a.push({id:r++,score:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxScore:Math.round(100*s.boxConfidence)/100,faceScore:Math.round(100*s.faceConfidence)/100,box:u,boxRaw:l,mesh:s.mesh,meshRaw:i,annotations:o,image:s.image,tensor:s.image}),s.coords&&s.coords.dispose()}return a}async function g2(e){return!Rt[0]&&e.face.enabled||!Rt[1]&&e.face.mesh.enabled||!Rt[2]&&e.face.iris.enabled?(Rt=await Promise.all([!Rt[0]&&e.face.enabled?u9(e):null,!Rt[1]&&e.face.mesh.enabled?ct(ft(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Rt[2]&&e.face.iris.enabled?ct(ft(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Rt[1]||!Rt[1].modelUrl?de("load model failed:",e.face.mesh.modelPath):e.debug&&de("load model:",Rt[1].modelUrl)),e.face.iris.enabled&&(!Rt[2]||!Rt[2].modelUrl?de("load model failed:",e.face.iris.modelPath):e.debug&&de("load model:",Rt[2].modelUrl))):e.debug&&(Rt[0]&&de("cached model:",Rt[0].model.modelUrl),Rt[1]&&de("cached model:",Rt[1].modelUrl),Rt[2]&&de("cached model:",Rt[2].modelUrl)),m2=new f2(Rt[0],Rt[1],Rt[2]),Rt}var c9=Zi,h9=cp;var Sle=["angry","disgust","fear","happy","sad","surprise","neutral"],Pa,G0=[],f9=0,y2=Number.MAX_SAFE_INTEGER,A2=[.2989,.587,.114];async function x2(e){return Pa?e.debug&&de("cached model:",Pa.modelUrl):(Pa=await ct(ft(e.modelBasePath,e.face.emotion.modelPath)),!Pa||!Pa.modelUrl?de("load model failed:",e.face.emotion.modelPath):e.debug&&de("load model:",Pa.modelUrl)),Pa}async function b2(e,t,n,a){return Pa?y2<t.face.emotion.skipFrames&&t.skipFrame&&f9===a&&G0[n]&&G0[n].length>0?(y2++,G0[n]):(y2=0,new Promise(async r=>{let s=De.resizeBilinear(e,[Pa.inputs[0].shape[2],Pa.inputs[0].shape[1]],!1),[i,o,u]=Zt(s,3,3);s.dispose();let l=B(i,A2[0]),d=B(o,A2[1]),p=B(u,A2[2]);i.dispose(),o.dispose(),u.dispose();let c=jc([l,d,p]);l.dispose(),d.dispose(),p.dispose();let h=V(()=>c.sub(.5).mul(2));c.dispose();let m=[];if(t.face.emotion.enabled){let f=await Pa.predict(h),g=f.dataSync();he(f);for(let y=0;y<g.length;y++)g[y]>t.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*g[y])/100),emotion:Sle[y]});m.sort((y,A)=>A.score-y.score)}h.dispose(),G0[n]=m,f9=a,r(m)})):null}var La,q0=[],m9=0,v2=Number.MAX_SAFE_INTEGER;async function w2(e){let t=ft(e.modelBasePath,e.face.description.modelPath);return La?e.debug&&de("cached model:",t):(La=await ct(t),La?e.debug&&de("load model:",t):de("load model failed:",e.face.description.modelPath)),La}function k2(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 a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function g9(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=k2(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function I2(e){return V(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Be))return null;let a=[[.05,.15,.85,.85]];return La.inputs[0].shape?(n.shape.length===3?De.cropAndResize(mn(n,0),a,[0],[La.inputs[0].shape[2],La.inputs[0].shape[1]]):De.cropAndResize(n,a,[0],[La.inputs[0].shape[2],La.inputs[0].shape[1]])).mul(255):null})}async function S2(e,t,n,a){var r,s;return La?v2<t.face.description.skipFrames&&t.skipFrame&&m9===a&&((r=q0[n])==null?void 0:r.age)&&((s=q0[n])==null?void 0:s.age)>0?(v2++,q0[n]):(v2=0,new Promise(async i=>{let o=I2(e),u,l={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(u=await La.predict(o)),he(o),u&&(V(()=>{let d=u.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(l.gender=d[0]<=.5?"female":"male",l.genderScore=Math.min(.99,p));let c=u.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=u.find(f=>f.shape[1]===100).dataSync();l.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=u.find(f=>f.shape[1]===1024);l.descriptor=[...m.dataSync()]}),u.forEach(d=>he(d))),q0[n]=l,m9=a,i(l)})):null}var Nle=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],a=1,r=e.mesh[33][2]>e.mesh[263][2],s=r?e.mesh[473]:e.mesh[468],i=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],o=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]],u=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],l=Math.sqrt(u[0]**2+u[1]**2);return l=Math.min(l,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],u)+Math.PI/2)%Math.PI,strength:l}},Tle=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},a=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],v=g[2]-y[2];return[A,x,v]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],v=g[0]*y[1]-g[1]*y[0];return[A,x,v]},s=g=>{let[y,A,x,v,b,w,N,C,E]=g,_,$,S;return v<1?v>-1?(S=Math.asin(v),$=Math.atan2(-N,y),_=Math.atan2(-w,b)):(S=-Math.PI/2,$=-Math.atan2(C,E),_=0):(S=Math.PI/2,$=Math.atan2(C,E),_=0),{pitch:2*-_,yaw:2*-$,roll:2*-S}},i=g=>{let y=(x,v,b,w)=>Math.atan2(w-v,b-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},o=e.meshRaw;if(!o||o.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 u=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(g=>[g[0]*t[0]/u,g[1]*t[1]/u,g[2]]),d=n(a(l[1],l[0])),p=n(a(l[3],l[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?Nle(e):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}},N2=async(e,t)=>{var d,p,c,h,m,f;let n,a,r,s,i,o,u=[];e.state="run:face",n=Je();let l=await p9(t,e.config);if(e.performance.face=Math.trunc(Je()-n),!t.shape||t.shape.length!==4)return[];if(!l)return[];for(let g=0;g<l.length;g++){if(e.analyze("Get Face"),!l[g].image||l[g].image.isDisposedInternal){de("Face object is disposed:",l[g].image);continue}let y=Tle(l[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?b2(l[g].image||ln([]),e.config,g,l.length):{}:(e.state="run:emotion",n=Je(),s=e.config.face.emotion.enabled?await b2(l[g].image||ln([]),e.config,g,l.length):{},e.performance.emotion=Math.trunc(Je()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?S2(l[g].image||ln([]),e.config,g,l.length):[]:(e.state="run:description",n=Je(),o=e.config.face.description.enabled?await S2(l[g].image||ln([]),e.config,g,l.length):[],e.performance.embedding=Math.trunc(Je()-n)),e.analyze("End Description:"),e.config.async&&([a,r,s,i,o]=await Promise.all([a,r,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=l[g])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((h=(c=l[g])==null?void 0:c.annotations)==null?void 0:h.rightEyeIris)&&(delete l[g].annotations.leftEyeIris,delete l[g].annotations.rightEyeIris);let A=((m=l[g].annotations)==null?void 0:m.leftEyeIris)&&((f=l[g].annotations)==null?void 0:f.rightEyeIris)?Math.max(Math.abs(l[g].annotations.leftEyeIris[3][0]-l[g].annotations.leftEyeIris[1][0]),Math.abs(l[g].annotations.rightEyeIris[4][1]-l[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0;u.push({...l[g],id:g,age:o.age,gender:o.gender,genderScore:o.genderScore,embedding:o.descriptor,emotion:s,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:y,tensor:e.config.face.detector.return?Vt(l[g].image):null}),he(l[g].image),l[g].image&&delete l[g].image,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 hp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],y9=hp.length,fp=hp.reduce((e,t,n)=>(e[t]=n,e),{}),Cle=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Ele=Cle.map(([e,t])=>[fp[e],fp[t]]),A9=[["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 x9(e){let t=e.reduce(({maxX:n,maxY:a,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(a,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{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 b9(e,[t,n],[a,r]){let s=t/a,i=n/r,o=(l,d)=>({id:d,score:l.score,boxRaw:[l.box[0]/r,l.box[1]/a,l.box[2]/r,l.box[3]/a],box:[Math.trunc(l.box[0]*i),Math.trunc(l.box[1]*s),Math.trunc(l.box[2]*i),Math.trunc(l.box[3]*s)],keypoints:l.keypoints.map(({score:p,part:c,position:h})=>({score:p,part:c,position:[Math.trunc(h.x*i),Math.trunc(h.y*s)],positionRaw:[h.x/a,h.y/a]}))});return e.map((l,d)=>o(l,d))}var T2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(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 a=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=a}};function C2(e,t,n,a){return{y:a.get(e,t,n),x:a.get(e,t,n+y9)}}function E2(e,t,n){let{heatmapY:a,heatmapX:r,id:s}=e,{y:i,x:o}=C2(a,r,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function R2(e,t,n){return e<t?t:e>n?n:e}function v9(e,t,n,a){let r=n-e,s=a-t;return r*r+s*s}function M2(e,t){return{x:e.x+t.x,y:e.y+t.y}}var X0=1,pu=16,Rle=50**2;function w9(e,t,n,a,r,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),u=(y,A,x)=>({y:R2(Math.round(y.y/pu),0,A-1),x:R2(Math.round(y.x/pu),0,x-1)}),[l,d]=a.shape,p=u(t.position,l,d),c=o(p),m=M2(t.position,c);for(let y=0;y<i;y++){let A=u(m,l,d),x=C2(A.y,A.x,n,r);m=M2({x:A.x*pu,y:A.y*pu},{x:x.x,y:x.y})}let f=u(m,l,d),g=a.get(f.y,f.x,n);return{position:m,part:hp[n],score:g}}function Mle(e,t,n,a,r){let s=A9.map(([c,h])=>[fp[c],fp[h]]),i=s.map(([,c])=>c),o=s.map(([c])=>c),u=t.shape[2],l=i.length,d=new Array(u),p=E2(e.part,pu,n);d[e.part.id]={score:e.score,part:hp[e.part.id],position:p};for(let c=l-1;c>=0;--c){let h=i[c],m=o[c];d[h]&&!d[m]&&(d[m]=w9(c,d[h],m,t,n,r))}for(let c=0;c<l;++c){let h=o[c],m=i[c];d[h]&&!d[m]&&(d[m]=w9(c,d[h],m,t,n,a))}return d}function Fle(e,t,n,a,r){let[s,i]=r.shape,o=!0,u=Math.max(n-X0,0),l=Math.min(n+X0+1,s);for(let d=u;d<l;++d){let p=Math.max(a-X0,0),c=Math.min(a+X0+1,i);for(let h=p;h<c;++h)if(r.get(d,h,e)>t){o=!1;break}if(!o)break}return o}function $le(e,t){let[n,a,r]=t.shape,s=new T2(n*a*r,({score:i})=>i);for(let i=0;i<n;++i)for(let o=0;o<a;++o)for(let u=0;u<r;++u){let l=t.get(i,o,u);l<e||Fle(u,l,i,o,t)&&s.enqueue({score:l,part:{heatmapY:i,heatmapX:o,id:u}})}return s}function k9(e,{x:t,y:n},a){return e.some(({keypoints:r})=>{var i;let s=(i=r[a])==null?void 0:i.position;return s?v9(n,t,s.y,s.x)<=Rle:!1})}function Dle(e,t){return t.reduce((a,{position:r,score:s},i)=>(k9(e,r,i)||(a+=s),a),0)/t.length}function I9(e,t,n,a,r,s){let i=[],o=$le(s,t);for(;i.length<r&&!o.empty();){let u=o.dequeue(),l=E2(u.part,pu,e);if(k9(i,l,u.part.id))continue;let d=Mle(u,t,e,n,a);d=d.filter(h=>h.score>s);let p=Dle(i,d),c=x9(d);p>s&&i.push({keypoints:d,box:c,score:Math.round(100*p)/100})}return i}var Jn,Ole=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function F2(e,t){let n=V(()=>{if(!Jn.inputs[0].shape)return[];let o=De.resizeBilinear(e,[Jn.inputs[0].shape[2],Jn.inputs[0].shape[1]]).toFloat().div(127.5).sub(1),l=Jn.execute(o,Ole).map(d=>Vt(d,[0]));return l[1]=l[1].sigmoid(),l}),a=await Promise.all(n.map(i=>i.buffer()));for(let i of n)i.dispose();let r=await I9(a[0],a[1],a[2],a[3],t.body.maxDetected,t.body.minConfidence);return Jn.inputs[0].shape?b9(r,[e.shape[1],e.shape[2]],[Jn.inputs[0].shape[2],Jn.inputs[0].shape[1]]):[]}async function $2(e){return Jn?e.debug&&de("cached model:",Jn.modelUrl):(Jn=await ct(ft(e.modelBasePath,e.body.modelPath)),!Jn||!Jn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Jn.modelUrl)),Jn}function K0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function mp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function S9(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return De.cropAndResize(t,s,[0],n)}function N9(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:a,palmLandmarks:r,confidence:e.confidence}}function Z0(e,t=1.5){let n=mp(e),a=K0(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function Y0(e){let t=mp(e),n=K0(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var T9=[{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 D2=class{constructor(t){var n;this.model=t,this.anchors=T9.map(a=>[a.x,a.y]),this.anchorsTensor=Ta(this.anchors),this.inputSize=(n=this.model)==null?void 0:n.inputs[0].shape[2],this.inputSizeTensor=Dt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Dt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return V(()=>{let n=Re(t,[0,0],[-1,2]),a=Re(t,[0,2],[-1,2]),r=ie(me(n,this.inputSizeTensor),this.anchorsTensor),s=me(a,this.doubleInputSizeTensor),i=B(ye(r,s),this.inputSizeTensor),o=B(ie(r,s),this.inputSizeTensor);return Nl([i,o],1)})}normalizeLandmarks(t,n){return V(()=>{let a=ie(me(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return B(a,this.inputSizeTensor)})}async getBoxes(t,n){let a=this.model.predict(t),r=Vt(a);a.dispose();let s=V(()=>Rn(Re(r,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(r,[0,1],[-1,4]),u=this.normalizeBoxes(o);o.dispose();let l=await De.nonMaxSuppressionAsync(u,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=l.arraySync();s.dispose(),l.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(u,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=V(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),u.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=V(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let u of i){let l=u.box.dataSync(),d=l.slice(0,2),p=l.slice(2,4),c=u.palmLandmarks.arraySync();u.box.dispose(),u.palmLandmarks.dispose(),o.push(N9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:u.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function zle(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function C9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return zle(n)}var E9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function us(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function _le(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function R9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(us(e[r],_le(t,s)))}return n}function O2(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=E9(t[0],t[1]),i=R9(s,r),o=E9(-t[0],-t[1]);return R9(i,o)}function M9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-us(t[0],n),-us(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function z2(e,t){return[us(e,t[0]),us(e,t[1])]}var Ple=5,F9=1.65,$9=[0,5,9,13,17,1,2],Lle=0,Wle=2,_2=class{constructor(t,n){var a;this.handDetector=t,this.handPoseModel=n,this.inputSize=(a=this.handPoseModel)==null?void 0:a.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>z2([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return Z0(Y0(r),Ple)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=Z0(Y0(n),F9);a.palmLandmarks=[];for(let r=0;r<$9.length;r++)a.palmLandmarks.push(t[$9[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=K0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),u=O2(a,[0,0]),l=o.map(h=>[...z2(h,u),h[2]]),d=M9(r),p=[...mp(n),1],c=[us(p,d[0]),us(p,d[1])];return l.map(h=>[Math.trunc(h[0]+c[0]),Math.trunc(h[1]+c[1]),Math.trunc(h[2])])}async estimateHands(t,n){let a=!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&&(a=!0));let s=[];for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let u=n.hand.rotation?C9(o.palmLandmarks[Lle],o.palmLandmarks[Wle]):0,l=mp(o),d=[l[0]/t.shape[2],l[1]/t.shape[1]],p=n.hand.rotation&&sa.flags.IS_BROWSER?De.rotateWithOffset(t,u,0,d):t.clone(),c=O2(-u,l),h=a?this.getBoxForPalmLandmarks(o.palmLandmarks,c):o,m=S9(h,p,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),p.dispose();let[g,y]=await this.handPoseModel.predict(f);f.dispose();let A=g.dataSync()[0];if(g.dispose(),A>=n.hand.minConfidence){let x=q(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let b=this.transformRawCoords(v,h,u,c),w=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...w,confidence:A};let N={landmarks:b,confidence:A,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let u=Z0(Y0(o),F9),l={confidence:o.confidence,box:{topLeft:u.startPoint,bottomRight:u.endPoint}};s.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var D9={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},ds,ps,O9;async function P2(e,t){let n=await O9.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let l of Object.keys(D9))s[l]=D9[l].map(d=>n[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],u=[0,0,0,0];if(i&&i.length>0){for(let l of i)l[0]<o[0]&&(o[0]=l[0]),l[1]<o[1]&&(o[1]=l[1]),l[0]>o[2]&&(o[2]=l[0]),l[1]>o[3]&&(o[3]=l[1]);o[2]-=o[0],o[3]-=o[1],u=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=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],u=[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)];a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:u,keypoints:i,annotations:s})}return a}async function L2(e){!ds||!ps?([ds,ps]=await Promise.all([e.hand.enabled?ct(ft(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?ct(ft(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ds||!ds.modelUrl?de("load model failed:",e.hand.detector.modelPath):e.debug&&de("load model:",ds.modelUrl),!ps||!ps.modelUrl?de("load model failed:",e.hand.skeleton.modelPath):e.debug&&de("load model:",ps.modelUrl))):(e.debug&&de("cached model:",ds.modelUrl),e.debug&&de("cached model:",ps.modelUrl));let t=new D2(ds);return O9=new _2(t,ps),[ds,ps]}var z9=["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"],_9=["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 zn;async function J0(e){return zn?e.debug&&de("cached model:",zn.modelUrl):(zn=await ct(ft(e.modelBasePath,e.body.modelPath)),zn.width=parseInt(zn.signature.inputs["input_1:0"].tensorShape.dim[2].size),zn.height=parseInt(zn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!zn||!zn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",zn.modelUrl)),zn}async function W2(e,t){var f;if(!zn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},a=De.resizeBilinear(e,[zn.width,zn.height],!1),r=me(a,[255]);a.dispose();let s=await zn.predict(r),i=((f=s.find(g=>g.size===195||g.size===155))==null?void 0:f.dataSync())||[];s.forEach(g=>g.dispose()),r.dispose();let o=[],u=(i==null?void 0:i.length)===195?z9:_9,l=5;for(let g=0;g<i.length/l;g++)o.push({id:g,part:u[g],position:[Math.trunc(n.width*i[l*g+0]/255),Math.trunc(n.height*i[l*g+1]/255),Math.trunc(i[l*g+2])+0],positionRaw:[i[l*g+0]/255,i[l*g+1]/255,i[l*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[l*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[l*g+4]))))/100});let d=o.map(g=>g.position[0]),p=o.map(g=>g.position[1]),c=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],h=[0,0,0,0],m=o.reduce((g,y)=>y.score>g?y.score:g,0);return[{id:0,score:m,box:c,boxRaw:h,keypoints:o}]}var _n,sr=[],B2=[0,0,0,0],V2=[0,0,0,0],Q0=0,j2=Number.MAX_SAFE_INTEGER,Ble=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function P9(e){return _n?e.debug&&de("cached model:",_n.modelUrl):(_n=await ct(ft(e.modelBasePath,e.body.modelPath)),!_n||!_n.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",_n.modelUrl)),_n}function Vle(e,t){let[n,a]=e.shape;return V(()=>{let r=(o,u)=>ye(o,B(me(o,ke(u,"int32")),ke(u,"int32"))),s=q(e,[a*n]),i=Vn(s,0).dataSync()[0];if(i>t){let o=ki(s,0),u=r(o,n).dataSync()[0],l=me(o,ke(n,"int32")).dataSync()[0];return[u,l,i]}return[0,0,i]})}async function U2(e,t){return j2<t.body.skipFrames&&t.skipFrame&&Object.keys(sr).length>0?(j2++,[{id:0,score:Q0,box:B2,boxRaw:V2,keypoints:sr}]):(j2=0,new Promise(async n=>{let a=V(()=>{if(!_n.inputs[0].shape)return null;let l=De.resizeBilinear(e,[_n.inputs[0].shape[2],_n.inputs[0].shape[1]],!1);return B(l,2).sub(1)}),r;if(t.body.enabled&&(r=await _n.predict(a)),a.dispose(),r){sr.length=0;let l=r.squeeze();he(r);let d=l.unstack(2);he(l);for(let p=0;p<d.length;p++){let[c,h,m]=Vle(d[p],t.body.minConfidence);Q0>t.body.minConfidence&&sr.push({score:Math.round(100*m)/100,part:Ble[p],positionRaw:[c/_n.inputs[0].shape[2],h/_n.inputs[0].shape[1]],position:[Math.round(e.shape[2]*c/_n.inputs[0].shape[2]),Math.round(e.shape[1]*h/_n.inputs[0].shape[1])]})}d.forEach(p=>he(p))}Q0=sr.reduce((l,d)=>d.score>l?d.score:l,0);let s=sr.map(l=>l.position[0]),i=sr.map(l=>l.position[1]);B2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=sr.map(l=>l.positionRaw[0]),u=sr.map(l=>l.positionRaw[1]);V2=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:Q0,box:B2,boxRaw:V2,keypoints:sr}])}))}var Wa,ir=[],H2=[0,0,0,0],G2=[0,0,0,0],cu=0,q2=Number.MAX_SAFE_INTEGER,jle=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function X2(e){return Wa?e.debug&&de("cached model:",Wa.modelUrl):(Wa=await ct(ft(e.modelBasePath,e.body.modelPath)),!Wa||!Wa.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Wa.modelUrl)),Wa}async function K2(e,t){return q2<t.body.skipFrames&&t.skipFrame&&Object.keys(ir).length>0?(q2++,[{id:0,score:cu,box:H2,boxRaw:G2,keypoints:ir}]):(q2=0,new Promise(async n=>{let a=V(()=>{if(!Wa.inputs[0].shape)return null;let l=De.resizeBilinear(e,[Wa.inputs[0].shape[2],Wa.inputs[0].shape[1]],!1);return ge(l,"int32")}),r;if(t.body.enabled&&(r=await Wa.predict(a)),a.dispose(),r){ir.length=0;let l=r.arraySync();he(r);let d=l[0][0];for(let p=0;p<d.length;p++)cu=d[p][2],cu>t.body.minConfidence&&ir.push({score:Math.round(100*cu)/100,part:jle[p],positionRaw:[d[p][1],d[p][0]],position:[Math.round((e.shape[2]||0)*d[p][1]),Math.round((e.shape[1]||0)*d[p][0])]})}cu=ir.reduce((l,d)=>d.score>l?d.score:l,0);let s=ir.map(l=>l.position[0]),i=ir.map(l=>l.position[1]);H2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=ir.map(l=>l.positionRaw[0]),u=ir.map(l=>l.positionRaw[1]);G2=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:cu,box:H2,boxRaw:G2,keypoints:ir}])}))}var hu=[{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 Qn,Z2=[],Y2=Number.MAX_SAFE_INTEGER,ef=2.5;async function J2(e){if(Qn)e.debug&&de("cached model:",Qn.modelUrl);else{Qn=await ct(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(Qn.modelSignature.inputs);if(Qn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Qn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Qn||!Qn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",Qn.modelUrl)}return Qn}async function Ule(e,t,n,a){let r=0,s=[];for(let l of[1,2,4])V(()=>{var g,y;let d=l*13,p=(g=e.find(A=>A.shape[1]===d**2&&A.shape[2]===hu.length))==null?void 0:g.squeeze(),c=(y=e.find(A=>A.shape[1]===d**2&&A.shape[2]<hu.length))==null?void 0:y.squeeze(),m=c.reshape([-1,4,c.shape[1]/4]).argMax(2).arraySync(),f=p.arraySync();for(let A=0;A<p.shape[0];A++)for(let x=0;x<p.shape[1];x++){let v=f[A][x];if(v>a.object.minConfidence&&x!==61){let b=(.5+Math.trunc(A%d))/d,w=(.5+Math.trunc(A/d))/d,N=m[A].map(W=>W*(d/l/t)),[C,E]=[b-ef/l*N[0],w-ef/l*N[1]],[_,$]=[b+ef/l*N[2]-C,w+ef/l*N[3]-E],S=[C,E,_,$];S=S.map(W=>Math.max(0,Math.min(W,1)));let z=[S[0]*n[0],S[1]*n[1],S[2]*n[0],S[3]*n[1]],O={id:r++,score:Math.round(100*v)/100,class:x+1,label:hu[x].label,box:z.map(W=>Math.trunc(W)),boxRaw:S};s.push(O)}}});e.forEach(l=>he(l));let i=s.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),o=s.map(l=>l.score),u=[];if(i&&i.length>0){let l=await De.nonMaxSuppressionAsync(i,o,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);u=l.dataSync(),he(l)}return s=s.filter((l,d)=>u.includes(d)).sort((l,d)=>d.score-l.score),s}async function Q2(e,t){return Y2<t.object.skipFrames&&t.skipFrame&&Z2.length>0?(Y2++,Z2):(Y2=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[Qn.inputSize,Qn.inputSize],!1),s=r.div(255),i=s.transpose([0,3,1,2]);s.dispose(),r.dispose();let o;t.object.enabled&&(o=await Qn.predict(i)),i.dispose();let u=await Ule(o,Qn.inputSize,a,t);Z2=u,n(u)}))}var ea,e5=[],t5=Number.MAX_SAFE_INTEGER;async function n5(e){if(ea)e.debug&&de("cached model:",ea.modelUrl);else{ea=await ct(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(ea.modelSignature.inputs);if(ea.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!ea.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!ea||!ea.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",ea.modelUrl)}return ea}async function Hle(e,t,n,a){if(!e)return[];let r=[],s=e.arraySync(),i=Vt(e);e.dispose();let o=Zt(i,6,1);i.dispose();let l=gn([o[1],o[0],o[3],o[2]],1).squeeze(),d=o[4].squeeze(),p=o[5].squeeze();o.forEach(f=>f.dispose());let c=await De.nonMaxSuppressionAsync(l,d,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);l.dispose(),d.dispose(),p.dispose();let h=c.dataSync();c.dispose();let m=0;for(let f of h){let g=Math.trunc(100*s[0][f][4])/100,y=s[0][f][5],A=hu[y].label,x=[s[0][f][0]/t,s[0][f][1]/t,s[0][f][2]/t,s[0][f][3]/t],v=[Math.trunc(x[0]*n[0]),Math.trunc(x[1]*n[1]),Math.trunc(x[2]*n[0]),Math.trunc(x[3]*n[1])];r.push({id:m++,score:g,class:y,label:A,box:v,boxRaw:x})}return r}async function a5(e,t){return t5<t.object.skipFrames&&t.skipFrame&&e5.length>0?(t5++,e5):(t5=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[ea.inputSize,ea.inputSize]),s=t.object.enabled?ea.execute(r,["tower_0/detections"]):null;r.dispose();let i=await Hle(s,ea.inputSize,a,t);e5=i,n(i)}))}var L9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=e[n].keypoints.find(u=>u.part==="leftWrist"),r=e[n].keypoints.find(u=>u.part==="rightWrist"),s=e[n].keypoints.find(u=>u.part==="nose");s&&a&&r&&a.position.y<s.position.y&&r.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&a&&a.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&r&&r.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(u=>u.part==="leftShoulder"),o=e[n].keypoints.find(u=>u.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},W9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let a=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(a)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${a<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},B9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let a=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(a*r),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],u=Math.abs(i*o),l=!1;Math.abs(s-u)/Math.max(s,u)<.25&&(l=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],c=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(c>.06||p>.06)&&(l=!1),c>.06&&t.push({iris:n,gesture:"looking right"}),p>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],m=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(m<.01||h<.01||m>.022||h>.022)&&(l=!1),(m<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(m>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),l&&t.push({iris:n,gesture:"looking center"})}return t},V9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=[];for(let[r,s]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(s)&&a.push({name:r.toLowerCase(),position:s[0]});if(a&&a.length>0){let r=a.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=a.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${r.name} forward ${s.name} up`})}}return t};function Gle(e,t,n){let a=function(o,u,l){let d=new RegExp("\\b"+u+" \\w+ (\\w+)","ig");o.replace(d,(p,c)=>(l[c]=0,p))},r=function(o,u){let l=e.createShader(u);if(e.shaderSource(l,o),e.compileShader(l),!e.getShaderParameter(l,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(l));return l};this.uniform={},this.attribute={};let s=r(t,e.VERTEX_SHADER),i=r(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),a(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);a(t,"uniform",this.uniform),a(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function j9(e){e||(e={});let t=0,n=null,a=!1,r=-1,s=[null,null],i=[],o=-1,u=-1,l=null,d=null,p={},c=e.canvas||document.createElement("canvas"),h={},m={INTERMEDIATE:1},f=c.getContext("webgl");if(!f)throw new Error("Filter: getContext() failed");this.addFilter=function(b){let w=Array.prototype.slice.call(arguments,1),N=p[b];i.push({func:N,args:w})},this.reset=function(){i=[]};let g=function(b,w){if(!(b===o&&w===u)){if(c.width=b,o=b,c.height=w,u=w,!l){let N=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);l=f.createBuffer(),f.bindBuffer(f.ARRAY_BUFFER,l),f.bufferData(f.ARRAY_BUFFER,N,f.STATIC_DRAW),f.pixelStorei(f.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}f.viewport(0,0,o,u),s=[null,null]}},y=function(b,w){let N=f.createFramebuffer();f.bindFramebuffer(f.FRAMEBUFFER,N);let C=f.createRenderbuffer();f.bindRenderbuffer(f.RENDERBUFFER,C);let E=f.createTexture();return f.bindTexture(f.TEXTURE_2D,E),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,b,w,0,f.RGBA,f.UNSIGNED_BYTE,null),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.framebufferTexture2D(f.FRAMEBUFFER,f.COLOR_ATTACHMENT0,f.TEXTURE_2D,E,0),f.bindTexture(f.TEXTURE_2D,null),f.bindFramebuffer(f.FRAMEBUFFER,null),{fbo:N,texture:E}},A=function(b){return s[b]=s[b]||y(o,u),s[b]},x=function(b=null){var E,_;let w=null,N=null,C=!1;t===0?w=n:w=(E=A(r))==null?void 0:E.texture,t++,a&&!(b&m.INTERMEDIATE)?(N=null,C=t%2==0):(r=(r+1)%2,N=(_=A(r))==null?void 0:_.fbo),f.bindTexture(f.TEXTURE_2D,w),f.bindFramebuffer(f.FRAMEBUFFER,N),f.uniform1f(d.uniform.flipY,C?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(b){if(g(b.width,b.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,b),i.length===0)return x(),c;for(let w=0;w<i.length;w++){a=w===i.length-1;let N=i[w];N.func.apply(this,N.args||[])}return c};let v=function(b){if(h[b])return d=h[b],f.useProgram(d.id),d;let w={};w.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(`
|
|
`),w.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
|
|
`),d=new Gle(f,w.VERTEX_IDENTITY,b);let N=Float32Array.BYTES_PER_ELEMENT,C=4*N;return f.enableVertexAttribArray(d.attribute.pos),f.vertexAttribPointer(d.attribute.pos,2,f.FLOAT,!1,C,0*N),f.enableVertexAttribArray(d.attribute.uv),f.vertexAttribPointer(d.attribute.uv,2,f.FLOAT,!1,C,2*N),h[b]=d,d};p.colorMatrix=function(b){let w=new Float32Array(b);w[4]/=255,w[9]/=255,w[14]/=255,w[19]/=255;let N=w[18]===1&&w[3]===0&&w[8]===0&&w[13]===0&&w[15]===0&&w[16]===0&&w[17]===0&&w[19]===0?p.colorMatrix.SHADER.WITHOUT_ALPHA:p.colorMatrix.SHADER.WITH_ALPHA,C=v(N);f.uniform1fv(C.uniform.m,w),x()},p.colorMatrix.SHADER={},p.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(`
|
|
`),p.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(`
|
|
`),p.brightness=function(b){let w=(b||0)+1;p.colorMatrix([w,0,0,0,0,0,w,0,0,0,0,0,w,0,0,0,0,0,1,0])},p.saturation=function(b){let w=(b||0)*2/3+1,N=(w-1)*-.5;p.colorMatrix([w,N,N,0,0,N,w,N,0,0,N,N,w,0,0,0,0,0,1,0])},p.desaturate=function(){p.saturation(-1)},p.contrast=function(b){let w=(b||0)+1,N=-128*(w-1);p.colorMatrix([w,0,0,0,N,0,w,0,0,N,0,0,w,0,N,0,0,0,1,0])},p.negative=function(){p.contrast(-2)},p.hue=function(b){b=(b||0)/180*Math.PI;let w=Math.cos(b),N=Math.sin(b),C=.213,E=.715,_=.072;p.colorMatrix([C+w*(1-C)+N*-C,E+w*-E+N*-E,_+w*-_+N*(1-_),0,0,C+w*-C+N*.143,E+w*(1-E)+N*.14,_+w*-_+N*-.283,0,0,C+w*-C+N*-(1-C),E+w*-E+N*E,_+w*(1-_)+N*_,0,0,0,0,0,1,0])},p.desaturateLuminance=function(){p.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},p.sepia=function(){p.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},p.brownie=function(){p.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},p.vintagePinhole=function(){p.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},p.kodachrome=function(){p.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])},p.technicolor=function(){p.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])},p.polaroid=function(){p.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},p.shiftToBGR=function(){p.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},p.convolution=function(b){let w=new Float32Array(b),N=1/o,C=1/u,E=v(p.convolution.SHADER);f.uniform1fv(E.uniform.m,w),f.uniform2f(E.uniform.px,N,C),x()},p.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(`
|
|
`),p.detectEdges=function(){p.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},p.sobelX=function(){p.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},p.sobelY=function(){p.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},p.sharpen=function(b){let w=b||1;p.convolution.call(this,[0,-1*w,0,-1*w,1+4*w,-1*w,0,-1*w,0])},p.emboss=function(b){let w=b||1;p.convolution.call(this,[-2*w,-1*w,0,-1*w,1,1*w,0,1*w,2*w])},p.blur=function(b){let w=b/7/o,N=b/7/u,C=v(p.blur.SHADER);f.uniform2f(C.uniform.px,0,N),x(m.INTERMEDIATE),f.uniform2f(C.uniform.px,w,0),x()},p.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(`
|
|
`),p.pixelate=function(b){let w=b/o,N=b/u,C=v(p.pixelate.SHADER);f.uniform2f(C.uniform.size,w,N),x()},p.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 tf=2048,Ee,wt,_t;function fu(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Be)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Be)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Ha(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!s)return{tensor:null,canvas:Ee};let i=r,o=s;if(i>tf&&(i=tf,o=i*s/r),o>tf&&(o=tf,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ee||(Ee==null?void 0:Ee.width)!==i||(Ee==null?void 0:Ee.height)!==o)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==i&&(Ee.width=i),(Ee==null?void 0:Ee.height)!==o&&(Ee.height=o));let u=Ee.getContext("2d");if(e instanceof ImageData?u.putImageData(e,0,0):t.filter.flip&&typeof u.translate!="undefined"?(u.translate(r,0),u.scale(-1,1),u.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),u.setTransform(1,0,0,1,0,0)):u.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!_t||!wt||Ee.width!==wt.width||(Ee==null?void 0:Ee.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Ee==null?void 0:Ee.width)&&(wt.width=Ee==null?void 0:Ee.width),(wt==null?void 0:wt.height)!==(Ee==null?void 0:Ee.height)&&(wt.height=Ee==null?void 0:Ee.height),_t=sa.flags.IS_BROWSER?new j9({canvas:wt}):null),!_t)return{tensor:null,canvas:Ee};_t.reset(),_t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&_t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&_t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&_t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&_t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&_t.addFilter("hue",t.filter.hue),t.filter.negative&&_t.addFilter("negative"),t.filter.sepia&&_t.addFilter("sepia"),t.filter.vintage&&_t.addFilter("brownie"),t.filter.sepia&&_t.addFilter("sepia"),t.filter.kodachrome&&_t.addFilter("kodachrome"),t.filter.technicolor&&_t.addFilter("technicolor"),t.filter.polaroid&&_t.addFilter("polaroid"),t.filter.pixelate!==0&&_t.addFilter("pixelate",t.filter.pixelate),_t.apply(Ee)}else wt=Ee,_t&&(_t=null);let l;if(wt.data){let d=[wt.height,wt.width,3];l=Lc(wt.data,d,"int32")}else if(wt instanceof ImageData)l=oa?oa.fromPixels(wt):null;else if(t.backend==="webgl"||t.backend==="humangl"){let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let p=d.getContext("2d");p==null||p.drawImage(wt,0,0),l=oa?oa.fromPixels(d):null}else{let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let p=d.getContext("2d");p==null||p.drawImage(wt,0,0);let c=p==null?void 0:p.getImageData(0,0,i,o);l=oa?oa.fromPixels(c):null}if(l){let d=l.toFloat();n=d.expandDims(0),l.dispose(),d.dispose()}}let a=t.filter.return?wt:null;return{tensor:n,canvas:a}}var i5={};b5(i5,{all:()=>Kle,body:()=>G9,canvas:()=>Xle,face:()=>H9,gesture:()=>U9,hand:()=>q9,object:()=>X9,options:()=>cs,person:()=>qle});var cs={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},nf=e=>Math.round(e*180/Math.PI);function r5(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function gp(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function s5(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function yp(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){s5(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function U9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],u=[];if([o,u]=Object.entries(t[i]),u.length>1&&u[1].length>0){let l=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${l}: ${u[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function H9(e,t,n){var s,i,o,u;let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let l of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&gp(r,l.box[0],l.box[1],l.box[2],l.box[3],a);let d=[];if(d.push(`face: ${Math.trunc(100*l.score)}%`),l.genderScore&&d.push(`${l.gender||""} ${Math.trunc(100*l.genderScore)}%`),l.age&&d.push(`age: ${l.age||""}`),l.iris&&d.push(`distance: ${l.iris}`),l.emotion&&l.emotion.length>0){let p=l.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}l.rotation&&l.rotation.angle&&l.rotation.gaze&&(l.rotation.angle.roll&&d.push(`roll: ${nf(l.rotation.angle.roll)}\xB0 yaw:${nf(l.rotation.angle.yaw)}\xB0 pitch:${nf(l.rotation.angle.pitch)}\xB0`),l.rotation.gaze.bearing&&d.push(`gaze: ${nf(l.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=a.color;for(let p=d.length-1;p>=0;p--){let c=Math.max(l.box[0],0),h=p*a.lineHeight+l.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d[p],c+5,h+16)),r.fillStyle=a.labelColor,r.fillText(d[p],c+4,h+15)}if(r.lineWidth=1,l.mesh&&l.mesh.length>0){if(a.drawPoints)for(let p of l.mesh)r5(r,p[0],p[1],p[2],a);if(a.drawPolygons){r.lineWidth=1;for(let p=0;p<Zi.length/3;p++){let c=[Zi[p*3+0],Zi[p*3+1],Zi[p*3+2]].map(h=>l.mesh[h]);s5(r,c,a)}if(l.annotations&&l.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(l.annotations.leftEyeIris[3][0]-l.annotations.leftEyeIris[1][0])/2,c=Math.abs(l.annotations.leftEyeIris[4][1]-l.annotations.leftEyeIris[2][1])/2;r.ellipse(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(l.annotations&&l.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(l.annotations.rightEyeIris[3][0]-l.annotations.rightEyeIris[1][0])/2,c=Math.abs(l.annotations.rightEyeIris[4][1]-l.annotations.rightEyeIris[2][1])/2;r.ellipse(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=l.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((u=(o=l.rotation)==null?void 0:o.gaze)==null?void 0:u.bearing)&&l.annotations.leftEyeIris&&l.annotations.rightEyeIris&&l.annotations.leftEyeIris[0]&&l.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let p=[l.annotations.leftEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.leftEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1]),r.lineTo(p[0],p[1]);let c=[l.annotations.rightEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.rightEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1]),r.lineTo(c[0],c[1]),r.stroke()}}}}}async function G9(e,t,n){var s;let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(gp(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,r5(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4);if(a.drawPolygons&&t[i].keypoints){let o,u=[];u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),yp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),u.length===4&&s5(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftFoot"),o&&u.push([o.position[0],o.position[1]]),yp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightFoot"),o&&u.push([o.position[0],o.position[1]]),yp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftPalm"),o&&u.push([o.position[0],o.position[1]]),yp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightPalm"),o&&u.push([o.position[0],o.position[1]]),yp(r,u,a)}}}}async function q9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,r5(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,u)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(u,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let u=0;u<o.length;u++)r.beginPath(),r.strokeStyle=a.useDepth?`rgba(${127.5+2*o[u][2]}, ${127.5-2*o[u][2]}, 255, 0.5)`:a.color,r.moveTo(o[u>0?u-1:0][0],o[u>0?u-1:0][1]),r.lineTo(o[u][0],o[u][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function X9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function qle(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${s}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),r.fillStyle=a.labelColor,r.fillText(i,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2])}r.stroke()}}}async function Xle(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Kle(e,t,n){let a=Je(),r=Ln(cs,n);!t||!e||e instanceof HTMLCanvasElement&&(H9(e,t.face,r),G9(e,t.body,r),q9(e,t.hand,r),X9(e,t.object,r),U9(e,t.gesture,r),t.performance.draw=Math.trunc(Je()-a))}function K9(e,t,n,a,r){var o,u,l,d,p,c,h,m,f,g,y,A,x,v,b,w;let s=0,i=[];for(let N of e){let C={id:s++,face:N,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)N.box[0]>O.box[0]&&N.box[0]<O.box[0]+O.box[2]&&N.box[1]+N.box[3]>O.box[1]&&N.box[1]+N.box[3]<O.box[1]+O.box[3]&&(C.body=O);if(C.body)for(let O of n)O.box[0]+O.box[2]>C.body.box[0]&&O.box[0]+O.box[2]<C.body.box[0]+C.body.box[2]&&O.box[1]+O.box[3]>C.body.box[1]&&O.box[1]+O.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=O),O.box[0]<C.body.box[0]+C.body.box[2]&&O.box[0]>C.body.box[0]&&O.box[1]+O.box[3]>C.body.box[1]&&O.box[1]+O.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=O);for(let O of a)O.face!==void 0&&O.face===N.id?(o=C.gestures)==null||o.push(O):O.iris!==void 0&&O.iris===N.id?(u=C.gestures)==null||u.push(O):O.body!==void 0&&O.body===((l=C.body)==null?void 0:l.id)?(d=C.gestures)==null||d.push(O):O.hand!==void 0&&O.hand===((c=(p=C.hands)==null?void 0:p.left)==null?void 0:c.id)?(h=C.gestures)==null||h.push(O):O.hand!==void 0&&O.hand===((f=(m=C.hands)==null?void 0:m.right)==null?void 0:f.id)&&((g=C.gestures)==null||g.push(O));let E=[],_=[],$=O=>{O&&O.length===4&&(E.push(O[0],O[0]+O[2]),_.push(O[1],O[1]+O[3]))};$((y=C.face)==null?void 0:y.box),$((A=C.body)==null?void 0:A.box),$((v=(x=C.hands)==null?void 0:x.left)==null?void 0:v.box),$((w=(b=C.hands)==null?void 0:b.right)==null?void 0:w.box);let S=Math.min(...E),z=Math.min(..._);C.box=[S,z,Math.max(...E)-S,Math.max(..._)-z],r&&r.length===4&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),i.push(C)}return i}var $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Z9(e){var r,s,i,o,u,l,d,p,c,h,m,f,g,y,A,x,v,b,w,N,C;let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if($e.canvas=e.canvas,!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let E=0;E<e.body.length;E++){let _=e.body[E].box.map((z,O)=>((n-1)*$e.body[E].box[O]+z)/n),$=e.body[E].boxRaw.map((z,O)=>((n-1)*$e.body[E].boxRaw[O]+z)/n),S=e.body[E].keypoints.map((z,O)=>({score:z.score,part:z.part,position:[$e.body[E].keypoints[O]?((n-1)*$e.body[E].keypoints[O].position[0]+z.position[0])/n:z.position[0],$e.body[E].keypoints[O]?((n-1)*$e.body[E].keypoints[O].position[1]+z.position[1])/n:z.position[1]],positionRaw:[$e.body[E].keypoints[O]?((n-1)*$e.body[E].keypoints[O].positionRaw[0]+z.positionRaw[0])/n:z.position[0],$e.body[E].keypoints[O]?((n-1)*$e.body[E].keypoints[O].positionRaw[1]+z.positionRaw[1])/n:z.position[1]]}));$e.body[E]={...e.body[E],box:_,boxRaw:$,keypoints:S}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let E=0;E<e.hand.length;E++){let _=e.hand[E].box.map((W,G)=>((n-1)*$e.hand[E].box[G]+W)/n),$=e.hand[E].boxRaw.map((W,G)=>((n-1)*$e.hand[E].boxRaw[G]+W)/n),S=e.hand[E].keypoints.map((W,G)=>W.map((H,J)=>((n-1)*$e.hand[E].keypoints[G][J]+H)/n)),z=Object.keys(e.hand[E].annotations),O={};for(let W of z)O[W]=e.hand[E].annotations[W].map((G,H)=>G.map((J,K)=>((n-1)*$e.hand[E].annotations[W][H][K]+J)/n));$e.hand[E]={...e.hand[E],box:_,boxRaw:$,keypoints:S,annotations:O}}if(!$e.face||e.face.length!==$e.face.length)$e.face=JSON.parse(JSON.stringify(e.face));else for(let E=0;E<e.face.length;E++){let _=e.face[E].box.map((z,O)=>((n-1)*$e.face[E].box[O]+z)/n),$=e.face[E].boxRaw.map((z,O)=>((n-1)*$e.face[E].boxRaw[O]+z)/n),S={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};S.matrix=(r=e.face[E].rotation)==null?void 0:r.matrix,S.angle={roll:((n-1)*(((i=(s=$e.face[E].rotation)==null?void 0:s.angle)==null?void 0:i.roll)||0)+(((u=(o=e.face[E].rotation)==null?void 0:o.angle)==null?void 0:u.roll)||0))/n,yaw:((n-1)*(((d=(l=$e.face[E].rotation)==null?void 0:l.angle)==null?void 0:d.yaw)||0)+(((c=(p=e.face[E].rotation)==null?void 0:p.angle)==null?void 0:c.yaw)||0))/n,pitch:((n-1)*(((m=(h=$e.face[E].rotation)==null?void 0:h.angle)==null?void 0:m.pitch)||0)+(((g=(f=e.face[E].rotation)==null?void 0:f.angle)==null?void 0:g.pitch)||0))/n},S.gaze={bearing:((n-1)*(((A=(y=$e.face[E].rotation)==null?void 0:y.gaze)==null?void 0:A.bearing)||0)+(((v=(x=e.face[E].rotation)==null?void 0:x.gaze)==null?void 0:v.bearing)||0))/n,strength:((n-1)*(((w=(b=$e.face[E].rotation)==null?void 0:b.gaze)==null?void 0:w.strength)||0)+(((C=(N=e.face[E].rotation)==null?void 0:N.gaze)==null?void 0:C.strength)||0))/n},$e.face[E]={...e.face[E],rotation:S,box:_,boxRaw:$}}if(!$e.object||e.object.length!==$e.object.length)$e.object=JSON.parse(JSON.stringify(e.object));else for(let E=0;E<e.object.length;E++){let _=e.object[E].box.map((S,z)=>((n-1)*$e.object[E].box[z]+S)/n),$=e.object[E].boxRaw.map((S,z)=>((n-1)*$e.object[E].boxRaw[z]+S)/n);$e.object[E]={...e.object[E],box:_,boxRaw:$}}let a=e.persons;if(!$e.persons||a.length!==$e.persons.length)$e.persons=JSON.parse(JSON.stringify(a));else for(let E=0;E<a.length;E++)$e.persons[E].box=a[E].box.map((_,$)=>((n-1)*$e.persons[E].box[$]+_)/n);return $e.gesture=e.gesture,$e.performance=e.performance,$e}var ha,o5=!1;async function af(e){return ha?e.debug&&de("cached model:",ha.modelUrl):(ha=await ct(ft(e.modelBasePath,e.segmentation.modelPath)),!ha||!ha.modelUrl?de("load model failed:",e.segmentation.modelPath):e.debug&&de("load model:",ha.modelUrl)),ha}async function Zle(e,t){var h,m,f,g;if(!t.segmentation.enabled||!e.tensor||!e.canvas||!ha||!ha.inputs[0].shape)return null;let n=De.resizeBilinear(e.tensor,[ha.inputs[0].shape[1],ha.inputs[0].shape[2]],!1),a=n.div(255),r=ha.predict(a);he(n),he(a);let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(e.canvas.width,e.canvas.height):document.createElement("canvas");s.width=e.canvas.width,s.height=e.canvas.height;let i=Vt(r,0),o;if(i.shape[2]===2){let y=i.softmax(),[A,x]=Gn(y,2),v=x.expandDims(2),b=v.expandDims(0);he(y),he(A),he(x);let w=De.cropAndResize(b,[[0,0,.5,.5]],[0],[(h=e.tensor)==null?void 0:h.shape[1],(m=e.tensor)==null?void 0:m.shape[2]]);o=w.squeeze(0),he(w),he(v),he(b)}else o=De.resizeBilinear(i,[(f=e.tensor)==null?void 0:f.shape[1],(g=e.tensor)==null?void 0:g.shape[2]]);oa&&await oa.toPixels(o,s),he(o),he(i),he(r);let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(e.canvas.width,e.canvas.height):document.createElement("canvas");u.width=e.canvas.width,u.height=e.canvas.height;let l=u.getContext("2d");l.filter="blur(8px",await l.drawImage(s,0,0);let d=l.getImageData(0,0,e.canvas.width,e.canvas.height).data,p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(e.canvas.width,e.canvas.height):document.createElement("canvas");p.width=e.canvas.width,p.height=e.canvas.height;let c=p.getContext("2d");return await c.drawImage(e.canvas,0,0),c.globalCompositeOperation="darken",c.filter="blur(8px)",await c.drawImage(s,0,0),c.globalCompositeOperation="source-over",c.filter="none",e.canvas=p,d}async function Y9(e,t,n){var s;if(o5)return null;o5=!0,n.segmentation.enabled||(n.segmentation.enabled=!0),ha||await af(n);let a=fu(e,n),r=await Zle(a,n);if(he(a.tensor),t&&r){let i=fu(t,n),o=i.canvas;he(i.tensor);let u=a.canvas,l=(s=u.getContext("2d"))==null?void 0:s.getImageData(0,0,u.width,u.height).data,d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(u.width,u.height):document.createElement("canvas");d.width=u.width,d.height=u.height;let p=d.getContext("2d");p.globalCompositeOperation="copy",p.drawImage(o,0,0,d.width,d.height);let c=p.getImageData(0,0,d.width,d.height);for(let h=0;h<d.width*d.height;h++)c.data[4*h+0]=(255-r[4*h+0])/255*c.data[4*h+0]+r[4*h+0]/255*l[4*h+0],c.data[4*h+1]=(255-r[4*h+1])/255*c.data[4*h+1]+r[4*h+1]/255*l[4*h+1],c.data[4*h+2]=(255-r[4*h+2])/255*c.data[4*h+2]+r[4*h+2]/255*l[4*h+2],c.data[4*h+3]=(255-r[4*h+3])/255*c.data[4*h+3]+r[4*h+3]/255*l[4*h+3];p.putImageData(c,0,0),a.canvas=d}return o5=!1,a.canvas}var rf=`
|
|
/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==`,sf=`
|
|
/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
|
|
JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
|
|
RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
|
|
AhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAEDAgQFBgf/xABDEAEAAgECBAMECQIDBgUFAQAA
|
|
AQIDBBEFEiExE0FRBiJhcRQjMkJSgZGhsWLBJDNyFSVTY3OSNEPR4fAHFjWCokT/xAAYAQEAAwEA
|
|
AAAAAAAAAAAAAAAAAQIDBP/EACARAQEBAQADAQEBAQEBAAAAAAABAhEDITFBEjJRIhP/2gAMAwEA
|
|
AhEDEQA/APqYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAKNTq8OkxzfNkisQC8eb1XtRNbzXT4q7eU2nu0MntRq/D8StMccvW29ZmdvgjsTyvZjxOLj
|
|
+s8WLxn8TFPXs6Oj9oct7c14rkxz22nrB2I49KOdTjelmszfmpMeUxv/AA28OqwZ4icWWtt/SUi4
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAmdo3nsPNe0Pt
|
|
Fh09Z0+DNWL7+9O/7A3eJcZppsV5raI27esvH6jX5ddM25p79Ilo59VbUZOe2Tm/PeGvfPfT2iKR
|
|
PLv1+DO678XmW/a97U6TtOyzTbTF538/T9WjTNecm9a7126tqk3rSYxY5ta1plRZqZNXGjyZcPXl
|
|
mZmsx+qjBrsuO16xM7eXRt04JrdTltk5OWJnfaWf0a2lty5MdZnfzSn+WOHiOutFpjHa9e8bQ2fp
|
|
+alYy462pk7zXbuxjPesbRS0f6ZZV1ET1tErzXFLHo+A+1ddZf6NrI8PJHa1vN6iJi0bxMTHwfOa
|
|
zhzd61v1846utwniM6DUdb3nBaNrVmd9vjC/ZVePYirBqMWppz4rxaPgtEAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAItaK1m09ojcHnvarjM8P0vh49+a/eY8ng9D
|
|
h1fGM1rxjtGPfvbzdbjuTJxHX48cTPNltM/KsS9Dw7S49Jp6UpHaGe2vjz1y9J7LYK13vHWe7bj2
|
|
ex1tvM80ekuxW3RnW3Vm6P5jRx8H0+OYmMcb+bapo8GKPdpC6bQwtdHU8JpWkdJ/JweL6e23iU67
|
|
d4dubSqyVi9Zi0bwIs68XGp36TtEq7ZJmZmevzdbifCKWtbJinkt6eTgZPFw32t+sRurbWVzxs1y
|
|
Rv6T8V1NZNPtfq0seTm+Kevr+SZuxXjvaPiV8N4viycto9HseG6+uu08W6Rkj7UPmFck1tE1nlmP
|
|
Ld3eA8V8HVVi1pjq6Ma/pnqce/ERMTETHaUrKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAADW19+TQ5p/p2bLS4v04Zmt5VjeQeJ4bjnLqsupv+Ka1+ERLv4reTmcNxcuC
|
|
vy3l0qdI2hlr66sT02ot0ZV7qqrInruzrVZLGSZ37JjqgYTG0K5lbaFVhDT1Ub456RPweY4hixWi
|
|
eSdpjvD1eWejz3FNHWYtkpvFo9EIseb3tS3SerOms22rfpPqZKzvvHSYUz70TExG6Gdbs2rljeJ/
|
|
Mx5L0vEzPaelnOi98c9J2bFNTFpit47+a+PVUvx9T9nOIfT+GV5p3yY/ds67wvsXqpxau+G09Lx+
|
|
r3TqrEAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADV4ljnLw3U0jvO
|
|
O0fs2lWqyUw6XLkyfYrWZkHldBEV09eveG3Fq1mI3jd4vPrOIaid8G9MP3Y38k6fNrt/rMk9Ou8s
|
|
tfXXn49rGWInuy8SO/k5Gl1E3rG/fzbOe94wTy99mbRvTrMOOvNfJWsesywniukrG/jU6fF43WYN
|
|
TmtEeJtEQ06aSmK2+bNtEd+qfSO17unF9Hmvy1y13XWyVmN4tExLxVK8PmNq5NrT58zawam+m/yc
|
|
0Xj8NpRYSvQZ7xEOdqI3rPozxayNRXe0ct/ON03jmrKB5nV4q1yTO20Obmv4c+cx8HoeI6WZpNoj
|
|
q83niYmYscU0r8aJ6T1n49zeJ+Meqm1drb9J+Kd5p136StGVem9l9TbHxLDFp7W7+sS+q1nesT6w
|
|
+PcAzVjiGHftzQ+v4f8AJpv6On8jH9ZgIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAABp8VrW/C9TW0ztOO3b5Nxp8VmI4bn37TWYB8f1HFtTfUfR9FWJmsdZ9I7MtJxDX5s
|
|
d8ta1y0xzteaR2277rcuhycP12SceLxMeWNpjttHwlu8I0mfQ1y+D7k5YmJmY36T36Ka43z/AF1t
|
|
cI1ds+qxVj7/AEej19PCw9HJ4NoK4OIU5Y35YmZdzVTGebVZabx5jJS+Tmns81rNLm1Wrzc9rVw4
|
|
Yibbem72mXTTS0w0M3BvEta1bWrM95ie5EanY87wXgNOL6XPfxraXLhra/W28bR/dzYzarBqJxRe
|
|
bzE7Rt5vWU9n8mPHOGmS0Ypnea1naJb+k9ncNLR7u2y/WcxXO4TOoyUrN6zD0FaW5Y3hu49FiwUi
|
|
KxCvLMR0hlW0jn6ukWw3iXjOJzbDlneOj3GaN6zDzfFOH+LE7SRGo83XNSZ2lbG2/WfdlvaT2cy6
|
|
rNFInlrv1mfJ37cK4PwTTxOoidRm2+/2/KFuyMp47XB4LivXiunrH2b2iH2qn2K/J8x4fGDNxTSZ
|
|
9Nh8OviRvTyfT6xtWI+DeXs9MNZubypASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAOZx6/LoOWPvWiHTcf2hiZ0e8fc2mf1E5+vP/AEeuSd7RC2uKtI6QjHfeINTfwtPf
|
|
Jvty9WPfbt/lucP03gxfJf7d/wBoReYpm97zaNeLb4Ims9Nt94auDjem1Wo5PFi1onylS+1o7l8V
|
|
bxvtupjDMdNkYtXS1+Stt+m63xImEJ4xjHER2ZxMUjeUTO3VRmydBbjLJqPi08mbeVOXJPq1sl5Q
|
|
Vbkz9+rRy35rxHqzmZlVEe/Ez5LRlW5iyfR6zffaIjq1OSNZps2a21rZInafSPJhxGMl9LStLRWM
|
|
lorM/A4dkrWbYfLZC2W/7K6eubX6b4RzT+W76K8b7G6X62cu3Sten59nsm3j+OXz3/0ANGIAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/
|
|
/OwPFYbz2ls3jx8VqW6xMdWPEdP9D4lkx/dt79flLLHbkxTPwY6nt2512ORTRzE2x4/dpE7cvkme
|
|
E4IrW3hRMxO8THRtU1FKWtvtvK2upx22rzRCtXkqzh2jtF7ZbT122b01ndnpuWuP3Z3+Ky20qDVv
|
|
fauzVy3mejZzNK8dVjqi87KLRLYtXruqvXzkQp7Qoid88R6rcl+WGlW0/Sa22mfhCZOq2x082ix6
|
|
jkm822pO8VrPdr4dNObVeDo8XW3uzMbzK+mvxT7szE27cvnu9j7PcNjSaXx8mOIzZevbrEeic5tN
|
|
+SZnpt8J4fHD9HXHO3PPW0x/DeBtJxx29vaAJQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAKNRim9Z5e89Nl4DzXtVh5babURHrSf7f3ec1+qnDorWrvvt5Pccb0n0zhmWk
|
|
Rvevv1+cPE2rGTFNZU26PFfxwa5dVkjelI2772nZnX6bbrEUq3o0d678u8wmuDL2ittvVjXdneeK
|
|
cGv4jpJ6U56+kS7+j118+GLXpakzHaWlp9NNY3tv+bbiYiNoQy1y30uyZJlrWmZnuym6q1iIJnop
|
|
yW2Te8bdWnnypQqzZOadokiIpSZntWN5lrxki19vNRxrUeBwnNNd+fJEY6/OejXLn3Xe/wDp9wyn
|
|
E8uo4lqqxblv7lJ26T6vpD5X7G8QycKzeBMbzMRM1/FH/wA/h9QwZ6ajDXLitvWzRgsAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeL45w+dDrZvWv1OWd4+E+j2jX
|
|
12jx67TWw5Y6T2nzifU+rZ1y9eHwzDYxxEy18+DJodXfT5o96vafWPVbjyxDn1OOzHudbM0rt2UW
|
|
iI69mVtRXZq5tREb9VUoy2iIlRbJ0UX1VZ6btTLrI7V6yk62M2oisT1c7JmtkttVMUyZp6x0beDS
|
|
RWOvdKijDimvWd3G9pNRMfRcNfvZOb9Hpb0itJeP47k/3hgjaZnbaP1XxWW3T0movbNS0W645nbf
|
|
0nrMPpXs3xamoxdJiLbe/X1n8Uf3fKsOTw4jbaXo+EarJhtGTHMxeJ6xH7Sti9Zaj6x3HM4NxXFx
|
|
DS1mtoi8dJrv2l011QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AGjxLhODieOIye7kr9m8d4eM4to9RwjPXFa0ZIvG9bR0fQXmPbDFvTTZPOJmEWS/V8bs9R43NxLL
|
|
G8eFbePg1bajU5/s0l1ceKLx1hbjwRE9mOpx0y2uRTSZsm3PMw2aaKtIjo6kYo9EXpET0hVLXxYK
|
|
xC6MZvyx1lFs0RHfaPiCnU12pLyHGNDbUajBekWma2npWN3p8+opa20e9LSyZLxExTlpM+vdOdcZ
|
|
a9tPS8MyUvFrzWlI6727u1pYxYrbVmb7x+TQx6au3Nqcl7/0rcmW9axGnwZJj1novmxnZXV0fFp4
|
|
ZxLBPgTGK8xzXr5fOH0bFlpmxVyY7Rato3iYfNuG2x56Wrqa8s2jz+7Lu8O12bS6jkwzN6THNNI6
|
|
tvrN68Y4rxlx1vHa0bskAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAA4XtTTm0OKfTJ/aXdcL2pyRGjwU362yb7fkJz9eTxxyZJjyltRXzUZK7TFtl9Lbwy06YzrHwa+
|
|
fJFd/wCVt8m0bQ0eS2qzcm+1K/an+zNZFL5M1pjFXeI72ky48eGnPkvNp27+TPU6nHpMfLXaIjpE
|
|
erk5dRMxOfN1mPeisfshW1ne1a1577Y6x5R3U0zze31FOWI6ze0byU098kRlzbxM9qrMlPDpyRMR
|
|
Md5Vt/Ihp5898mWZm1pjftE91uCt7fCI7dWeHDEW3t723l6rslqxWZnasR+SYhFbzhnfxJ2jyeq9
|
|
lcGXWZcmW0zWKxHLaI7794eJx5fpfEKabT8t8l5isddo3l9S4VjrwrRUwzSJt3tav3pdOL6Y6dXD
|
|
j8HFWm+/KsU4NRXPvtWazHquWVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAa+fXYNP9u8b+kdZBsDkZOO135cWOZn4y5Wu4xqctbe9y19Kp4njt6vi+PDm8DFMWybbzPlV
|
|
5PiGtz67UxbNbeKTtWIjaIXYpnwuaftT5tXJT3vmi1pMsrU5qIrG1V1a+5DCa7b9GFbRr5J6Wnbt
|
|
Cu+Wmk0m8956z8ZWZNorbfzcbX5rZslazPux3hUt41NTntktObJ13+zX1bek01r4/HzVm0bxPXy/
|
|
+bNfDgjVa2uOY92kdfg6ufJOKvLXtttVVSqbcta2vM7zXtHpLQy5ZtMd+vWd+7Zy3mdJHXra3f0c
|
|
vUarw7zFY5rT2hH1Lavnrgx81p3U49Pk4nE5L35MO/StfNRXR5tXnrS8W67WvfyiPSPi7uLHFK1p
|
|
jrtSsbR5Lc4RzsXBaYreP4l45esRD2HD9fnw6evvWvO3Tfr0aGk0U55ra0TFInv6uzgrXFXlx0i0
|
|
77RPlC83Yj+JW7oddqr6vHzTTw9/f6dod+L1t9m0T8pcbFSmPHER3892W0zPuz+jSbVvidkcqmfP
|
|
Sel7bekrI4n4dZnPWIrHeYnZee2Wpy8dEaml4npNZblw5qzb8M9JbYgAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAABEzFYmZnaI7yCXL1XGa0jJXT0571nbee27DiXEprp8nhbxG20W8
|
|
5cbD0ikfnKO+urTPvjoZdXqctdsmTaPSvRpWmsdZ6yztfaGplvv3lWW1tyRlz1x0vkn7Vo5atTNe
|
|
Y0+1o79V2KsZsvX7Ne5mwxnyTNvsx2iGneM/rCdRSuOsTasTt5kRFtpjqmOH4t4nk7estiMNa97R
|
|
Hwhna0iuKTEdmGWa4672nZtRele1N59Zlq6vLOSsYorEc07qcW65euzRvtXvPZy52naZ7ujr6fXV
|
|
rWdukREK8+njHgmZmPc67bq6ivVWhxxgxZLztNrT1mZ/SP4VZs0zaOvfp84WUtNsXLvtv3699+rU
|
|
z7+Jtt5qURqMnPpctaR1rMSw4ZoK57eNk6xHaJRh97Ltt7lo5Z+L1HAPZvVauZ2nFTSzMTzeJEz8
|
|
to6xPfvsZntPZ9rXxabmxzefdrv0j1dXh/BcmstW1qxTHHasR3+b0GPhGl+kWmd64dNEVjf73T7X
|
|
y8vy+Ddx6O3iRakxTH5RXrMw1/lX+3Itw2MFIraN48qRHdZi0cUjmmPen9noox1iO0fNzdXEYrTt
|
|
stcmd9aX0bJ+HePmiKTitO8TMLZ1cVjrMfqpz6ys4pjfrPRWZ9rXXptUit6zO+23VyaRHEc05L1/
|
|
w9J9ys/en1ljqdVbwYw452tlnl3jyjzbmmiMeKtYjpEbLeTXPUU8ee/+qjJpsV5rbkrFqzE1tEbT
|
|
DpYNbW21Mnu29fKWna0KbqTdjXXjld0cvQ63ltGHNPSfs2n+HUbS9c2s2UASqAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAOVxPWe99HpP8ArmP4b+r1EabT3yT3iOkesvMVtN7za07zad5l
|
|
XV5GmM9vVfEstvDx0jtaVVMlq+UJ18b5cMRvPeSuK87bUt+i2Z3PtG7zXpjkzXt6R+TXyTMzvM7t
|
|
ydHqZ+zhv1+Cv/ZuqvPTHMfOYaTMil1a1K2vHSLTELq2v+KWzThGo84rH5rq8JzedqR+ZeI7WnOS
|
|
34pYTafWXR/2Pln/AMyrKOCWnvmiPyR6O1y9585lhWJvl557Q6eo4T4dYiMvW3b3UanhldHpJtGX
|
|
e09unmjsT7eb1l4trI2t0hsZfrdNO0bzy+nzU20/+NmkzO9esz+TZxWis9dttvPv+Tn21jjaW8zn
|
|
26bTG3mp1M/Wzv3t0jyWXiKZJmsTERaZhXXDbNl8WaztWenxZLstPp5pau8frDtVrNMM5cfTfpMf
|
|
3aunxxbes9d/R09Dp8ebJi09ptFr3jtt2WyrW9wy1Jx132mK+Xq9PotT0iIU19ntLtExa3T47T+q
|
|
6nBaYvsZstZ+cT/LeMnUi0TXffo1s2m8Ws2/OIMWk5Jib5L328rS2t94Sh5TV4ppklpW6PT6rh+P
|
|
NbebTHyas8E081mZy5P2W6OFhjxNTE/hr/LoRO0Kvo9dPqctKzMxEx1la5t3tdnjnMs4noievcrO
|
|
yZjeFF1OSnNV0OG62cn1GWffj7Mz5w05joovzY7xes7TE7w0xrjPeex6Ua+j1UarBFu1o6Wj0lsN
|
|
3JfQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrU5o0+nvlt92P3BxuM6nxNRGCs+7Tv8
|
|
2hToxm1r3m9utrTvMsonqyt7XTmcja0u3O6FMfi5t/u0/lzdJM81p9O3zdvHTwsUR5+bfPqOfX1h
|
|
dqV+3O7bs1+T31oqmI3TEM4rvCdkDGIIhlFd2daboS0NXG2bD6bufxXU1vlmu/u4us/N0+L1tTSx
|
|
kr9qk7w89j1FNZMV3jxLzvaJ8mer+LSOZqK2xZotbvljfr/89U453rXt9lse081xZtNjx7TGKu0t
|
|
DHlrevSevaN5Y6+tJ8c7VRNMt63n3ub+6/R54rERMztDYy4a5omclYmfxKcenrjtHLvtPrCnVmdb
|
|
eFe3JXmjy6eS/DrMuLVYsta9Mdt++6qLxO+0dEc8UmInr18iUfReHcXrqccb9Z27Q61Lb13eJ9nc
|
|
1Z35rTvE9avY4bTkpG8xEfB05vYxqybc07R281naGMREdoT5JQqy9mply7Q3bV3iXG1eXw7TWSka
|
|
c258t7+tpT5/BjT7MfHqndz12Z+M4lMMKyziUJJiN1WSu9fku23RaOgKNJqbaTU1t9yelo+D0cTE
|
|
xEx1iXmM1Nt3W4PqvFweDaffx9vjDbGvxz+TP66QDRiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAOJxzU73rp6z296zsZMkYsdr2naKxvLyObNOfNfJbvad1dXkaeOdpvsc2yuZVzfbfqybutwu
|
|
s5s8R92J3dvJb3tnO4HSMegtmt3nfZvYp8SZl0z45NfSK7onH1bNcfRFqnUKJr0Y7dVtq7prjEsK
|
|
0XVpEM6028mW20IHK41aPo3J6zs4ODhdcvPnvExFevNXpMOrxi/PlrTee7PLX6Pwa09uaNlKtHg9
|
|
dM3z5d7ReOu02nu0JzZMfblrv5R5uvrcdImZ26T1mYhxs1Os7RH93PZ7axuafNfLitvbaYU3yZYt
|
|
PXs9NwHhui1HBa5LVicsb81onrEuVqNNSuS8Y67dZ6xPZa59Il9uX41vEitImZme3q2Kxbxora0T
|
|
Md/ROSa4Ztkj7c9OafL5LuGYubmyX3iu/TfbdSfVnpvZLT/XZK233+Mbbva1xRXyiPk8pwbH4N6T
|
|
adq5a71n0tD1WDL4tPe6Xr0tDpz8YVnJHWEXYxbqlBedoef4tW0XraO09HdyztSZcbUz43C+ee9b
|
|
SVMaeOfqq7+jGckQ1Yz7+7v2RN/WXPXZPjci2+2yyJaVMuy+uSJlA2d+pNoVRbeDcSxyTE+TDDlt
|
|
pdRXLTynrHrDOyiyZeVFnY9TjvXJjres71tG8MnJ4Nqt4tp7T1jrV1nRL1x2cvABKAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAHJ49qfD09cNZ97JPX5PPw2uI6j6Vrsl/ux7tfk1mWr7dOM8iLdm
|
|
vfebREefRsWldw7SxqNbWbR7lPesrn3Vteo7dYjDpMGCvfbeXQ0uLlxRLRxROfUc34p6fCHYrXlr
|
|
EejqrjY8uzCYW7MZjdVKqK9VlaxCYrsnYExBMRMJRPZA8/xPHtmpP9W2xx76vhWOInvt/C7ike7N
|
|
vwzE9kcapGfhlevTaFbFo8RqJ5vy8/RoW09ek0msxHfp3dzNoLzp4zUmZpMbT8HJyYJi20X2n0lh
|
|
ZY1li/RaidBF4w2mK3jrHaFGp1lN+tptPp5IjBkid5mIp16TKu0abBPv33vPlM7z+iPdFNcWXU5I
|
|
tkrNce/b1W5db1nTaf3ax9q0fxDW1ebNk2phty1mOu09VOm8W19orEz23j1TwfSeERFuEYMddptW
|
|
d43dvBn21eKJ75KbW+cf/JcTgMxXTb3nbljz+TpcPmc2uyZO1KRtVtGVdi0bx07qJnllsRO6rNTe
|
|
N4XVamsy8mnvPwc3R2jPwe8TPbdlxXNOPSZfhWWpwO85OFzv57qrODkzeHntSe8Sn6Rv0a3EZ218
|
|
8nXekfr1a0ZLVnqx19dWb6demXybOO7lYMvNMdW9S/VVLo0us7tPHdtUtEwJiZU3jq2Jhham8CVG
|
|
PNODNTJXvWd3qcWSubFXJWd4tG8PK3pPd1OB6veLaa89Y61/u2xfxh5c/rsgNHOAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAANLimq+i6O0xPv392rdeZ4rq/pOqnlnelOkIt5F8Z7Wj27I2I6sb25YY
|
|
V1ImY3dbQ08LRc23vZp2j5OJG+XJWle9p2h6HHtbJXFT7OOIpX+7TxT31j5rycdTh+Dpz+XaG/sw
|
|
w18PHWseULN2trBE9UcrJKBhFU7JAQi0dEomegNDUYovM7x3jb5tO1ZvpbaTLtzRExWfWPJ08kbT
|
|
Ex5NXWYYyV5omYtHWJieyeDzuizfRs19Jn6TM7Ru1uMcJxZqTkw+5f4ebqa7SV1MR4tdrx2vEfy1
|
|
axqsNOTLjnLXytVXi3Xj8+nmsxTLM16d5npPyUzpekTtSK+U7vS6vQ/SYmK1vWPS1HOn2dvvvvE/
|
|
tDO5XlcO+LbfHSd/W3o6/BdDOXPTnj3Kz38rS6Wm4FNrRyRzTH3p6RH/AKvR8L4dXSzE3jmtHn5I
|
|
mbfqLV+m4dbLSsZInHjr3iI6zLpYaxS01rHuxHRHiT9mv6s67Vj1aqL6326MrWiYa+/Q54BxPaGe
|
|
XRZpj8MquB4+Xg8zPnB7SX30to379GxpK1xcHiKz5IS8xr8PLPixH2bftLTy05o6dHYyVjLhy0t1
|
|
izjZa3pMVv3iO/qz1G2L+NbSajbNyW7xLsY8kTDz+fJXFqKZN4iZnafi6WHL0iYlStI7OO+7axW2
|
|
crFl7dW9jvE9ULN+J3ZbdFGOy+AYWpEqN7afNXLj+1Wd23KrJVMvCzseh0+auow1yU7WhY4fCdV4
|
|
OadPefcvPuz6S7jol649Tl4AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAV581NPhtkvO0R+4NPi2
|
|
r8DB4dJ9+/7Q83Po2NTqLanNbLfvPaPSFDHV66sZ5ET0hRknyW2lTtMyouz0c8usx2n7s7vScKwx
|
|
zc1vu/y85p+maJh6Th+SOWeveXR4/wDLm8v+nX5mUWa9bbrInolmu5jdTNkxYFk2Isr3TuCzeGMz
|
|
+THdEyDDJO9Ja823rt2XWnya946pGvktDXta0ztWu/ybvLE9dkcoOf4GbJPWK1j49VmLh9JtE33v
|
|
Mevb9G7WsW8l1ccREISophiJ2jpDYpijbaOjOuOJ8ujOdqxsgVcsUjaETYvbaFFrgu5lVsm0yUtu
|
|
ryg43H5m+GIj1XcJzePoL4pnrWGtxmfchr8JvfHS1622if3QljzTTLes+qrNjrkiYtCzPMxnm095
|
|
YZJ6boS5teB49Tqscza97VtvWvlv8V/FOF34RrIxTM2xXjelp/eHoeA6XnzReY3ivX/0dfivDcfE
|
|
9HbDbaLx1pb0lOs+jO7K8Lis3cN+0NKcd9PmthzV5clJ2mF9J9GHHVL108dm1SznYr/Ft0tuhLb8
|
|
mNohFbMhLWy0mJ3rPXvDvcO1karBG8/WV6Wj+7kWrvDDBlvpdRGSnbzj1hpjX4z8mOx6UYYstc2O
|
|
uSk71tG7Ns5AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeXneJ62dVl5KT9VTt8Z9W9xbWclPo+O
|
|
fft9qfSHEU1pv48ftYST23ZTDC/p0YtlVuvVjMbM5+LCZjYGWGdrTPxiHY4ffaf3cjTxz1v6xMS6
|
|
Olty2iXVj/Dk8n+ndrkhnGRo1v8AFdW3RCrZ5uiYsqrboncSu508yjmZRYQt50TfowYTbYGVrKrT
|
|
uTZjvukQnYhMIGVY2ZxPVWyrHVCWzXpVXkt3TE7Va+W4K7X3jv1auTNy3jdba0RZpamfroQN7Hk3
|
|
6wr1GTaN2OOJiu6Mu98NvgDi8Wy74d/yZ8PiPAiO2zU4nb6qIn1bugjfFE/ASp1ke9u15mbbRDZ1
|
|
Mb823kx0Ontn1OOkedoJCvT8I03gaKsz9q/WW+isRWsVjtHRKyrhe0XCfpWL6Vgr9fjjrEfeh5fF
|
|
feH0V5Dj3DPoOo+k4a/U5J6xH3ZZ7z3228evytOk7NvFbo0cdols47bSybt7HbddHVqUs2aW3Qnq
|
|
xVeu8LILR3SlZw3V/R8nhXn6u0/pLuPMXjeHT4Zruf6jLPvR9mZ8/g1xrvpz+TH7HUAaMAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAABRq9VXSYJyW79qx6yvmdo3l5viGs+maqYrO+OnSvx+KLeLZz2te1rZL2v
|
|
ed7WneZYWnZl5K72YV1xEyxmeqJljzIEWlVkszvbZp5soN3h2SJz3pP3odCnuWmPRxuERfJrZmtZ
|
|
mtY96fR28kbX3dXj/wAuTyf6bmK+9YX1s0cNtm3Sd4LFY2K23W1s16StiUJW7bp22RW3RluBuruz
|
|
mWEgrmCGWyNkoExKE1QlPmsqRDKeyBjaejWy2W3ttDUyz1QKslvehVqKTNosyyTvELabXptIJpaP
|
|
B39Ia2mz+JGpr51jdZefDx2hzuHZObNq58poJaGtjxJ2+LoaKP8ADRPo5+T3skx5OhpOmC0fBNQ0
|
|
5yTbn+bt8A0u9raiY6RHLVwY62mI6zMvaaHBGn0mPHt1iN5+aYVsACBXqMFNTgviyxvW0bSsAeE1
|
|
mkvw7V2w5Ote9besJx2er4rw2nEdNNekZa9aW9JeQjnxZLYskTW9Z2mJY7zz26fHrrdpbZsY7NGt
|
|
mxjvso1b9NmUwpx33XRO4K7VUTE1nmrvEx1bVo2VWiJE/XY4frY1WPlt0y17x6/FuPM0m+HJGTHO
|
|
1qu9pNVXVYt46Xj7VfRtnXXL5MfzexsALsgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHM4jxOMFJphmJv529Dq
|
|
ZLfjDjPEIx450+K3v2+1MeUOHSOWFc3nJkmZnf4yujpVlqunOeFpV2nctLCZUXRM7MJtsWlRkv3Q
|
|
ky5NmpWt9RnrixVm17TtEQnJabXisRMzPSIew9n+CRoccajURvqLx5/chfOest642OGcIpoOG2w7
|
|
ROW9d72+LQvXevyejcPUU5M+SvpLeOataraw2a0dLbLqTtK1G3Es4lVWWUSoldFtmcXUbpidgXzK
|
|
GEW3TuCUSncnsDFMMLSms9EC6J6FpVzbZE5ALy0809ZbFr9GtfrEoFMzuuwz0Ueey3HbaBLDXe7i
|
|
tMOfwWnP9I+NZbuttvhs1uBRtXPb4SDm3iIvf57N7Dbl0VrS5+XrltEd+Z1Jx7cNms9N4TURRw3T
|
|
+PrcO3WszEvZOD7P6aYiMlvu16S7y1QAIAABxOPcLnUY/pWCv1tI96I+9DtgmXl68Biy7/NtUu3+
|
|
O8HnFa2s0tfd75KR5fFyMWTdhrPHVnX9R0cd21S3Rzsdm1iuqs256wrmGcT0RYSx5d047X02SMmO
|
|
esd49YRE9WcdSXhZ2O1p89NRji9J+cei1xMc3wXi+KZj1j1dTTaqmor06WjvWW+ddcu8XK8BZmAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAMMmWmKu952UZ9XFZmuP3revlDTtzWnmvO8q3XGmfHb9ZanV3yxtWeWn7y4es
|
|
vPNtDqZJ6Ts5mppvdl/XXRMyfGvSNlu/RVvtOzLfoipLT1VTKbSpvfogRkvtDVyZOhkyvQcA4Dzz
|
|
XV6yvTvTHMfvK+c9U3rkW+zvA/D21urr789cdZ8vi9KDb45rejl8Rry6iJ/FV1HP4vXbBTJEfYt1
|
|
+UpiHM295bXsqrO9l8QkZ0lZEqqLeyBZHZLGvZkhIndADKJ3TMoqWQMZ6pjsxll2jsCLSrmU2lFY
|
|
36gieyu0LJk3jbsga0wdqzK20QpyztQGprL/AFMrOE05NLkt6qdVWZxNrSe5o9vWBLiUjnzXn0vL
|
|
q555dHt8HOwV928/1z/LpzXxbYccRvzTB+jucOwxh0dI22mY3ltIrHLWIjyjZKyoAAAAACJiJjaY
|
|
3iXleM8InR5J1GniZw2n3oj7s/8Ao9Wi9a3rNbRE1mNpifNFnVs65XhcWTdt47bnFuF24dm8TFEz
|
|
p7T0/pn0a+HJux1OOrOux08d1ndqY7tillVkzExLOk7yd4YxGwluViJhE45raL0na0dtlWO0+bZr
|
|
1TKi+2zptZGTamT3b/tLacvJjiY3XaTWdYxZZ6/dtPm1zrv1z78fPcbwC7EAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABhkyV
|
|
xUm152iAZWtFazNp2iGhm1Vss8uP3aevnKrNntqLdelI7VRHRnrX/HRjx/tZREVjZXeybW6KbWZt
|
|
pCZ6S08tN7Nmbb7zCrJtyoS5145bSx5mWafelr3tsKmS/o08uXyhlly7RPV2+AcBnPNdZrK+53pS
|
|
fP4ytnPVda4y4BwHxOXV6uvu96Unz+MvVxG0bQRG0bR2G0nHLb2gCUDX12LxtFmpHeazt82wT1gH
|
|
mMN4tWs+rcr2aEV8DU5sM/cvO3yb+O0csLUTSdrLphRE8tlkZI7Atr2ZMazDJVKTYSCawi7Ksq7z
|
|
1QERvLK3ZGPrKbyCrbdnMcsbeaa18/RhvvM7oGEwTG0JmYYTIML22a2e28xELM19oURPNO4lOem+
|
|
n3ZY5+prVnMc2GYU4/L4A0a15cNf6rz/AC6fC6+NxCPOuOu/5tHJTbHj+F5/l1+BYumXJMd9o3/d
|
|
MRXYASgAAAAAAABhlxUz4rY8lYtS0bTEvH8R4ffhmo6bzhtPu29Pg9mq1Gnx6rDbFmrzVsizq2df
|
|
zXkMWTeIbNL7tbXaHLwzUctvexWn3bmPL8WFnHVL326VZ91MfFVjvvVlz79kLrcf2m7j7bNHH3bl
|
|
J2SirLQoy4t1++7G0dBC/RanxI8PJPv18/WG241+alovSdrV6w6mDNGfFF4/OPSW2b1zeTPL1aAs
|
|
zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAVZ9RXBTe3WZ7R6iZOpzZq4ac1p+UermZMl89+a/byj0Ra9815ted59PQ32hlrXXRjH
|
|
DpCLX6ML5NlNsm/ZRqstfdXzbsZt06sLZNvNB1Za8RDWyZdo7q8udq5Mu/mIMt4md2lmy7JzZuWJ
|
|
dHgfBL8RvGo1MTXTxPSPx/8AstJ1XWpIs4BwSdbeNVqq/URPu0n73/s9hEREbRG0QUpWlYrWIisR
|
|
tER5JbSccur2gCUAAAAPM8Sry8Uyz67fwuxbzVPGsE49XGbvF42V4M0TEL33ERnktsxpk3sumK2j
|
|
admFdPFZ33VS2Mdui2J3UU6LYlFSsN2O5NkCyJ6K7T1TEsbAsxdpReerKkTFGMxvYEz0rsqtbbpC
|
|
b2VT1QEzuwtbaGUxspuJU3neWdKoiu8rq12gCI92YatLcublnzbEz1aOptyZqTuDHLfxN6R0+t5X
|
|
qdJhjBp6UiPLeXl9NSMnEKxHa1+bb8nrlvxUAAAAAAAAAAABTqtNj1eC2LLXeto/R43VabJw/VTh
|
|
ydY+7b1h7ho8V4dXiGlmvbJXrS3xRZ1fGv5rzeHN02bEW3cys3xZJx5ImtqztMS3MeTeGFjqlb2O
|
|
8btql3NpbZtYsnSBLeiWfdTjtutid+ghherHS5p0+f3vsX6T8Fkw181d4lMvEWdnHaGnw/UeNh5L
|
|
T7+PpPxbjdyWcvAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAo1Oprgr63ntAmTqdRqK4K9etp7Q5d7Wy2m953lNrWyWm953mVd77R0
|
|
Za1104xxlN9lV8qnJl2a9s3xUXX2ybsJyRDWtl3YWydEC+2VRkzeW6q+T4tbJm+KRdfK1cmWZnlr
|
|
vNp7RC/R6HU8SycmCk7ed57Q9ZwvgOn4fEXtHi5/O9o7fJaZ6z1uRyOEezVstq6jiEbV71xevzer
|
|
rWtKxWsRFY6REeSRrJxz22gCUAAAAAANbX6aNVpL0npMRvWfSXlKamsRMVvXm+EvZXjmpaPWHzfL
|
|
oNRjzXicfWJ8phfPxFejx72x7xMzK+sXiNoiXlq+Pi6fWV/VfTNqfLJl/WTg9Pji8R70LqvMV1Gq
|
|
j/zcv6yz+lanzzZP1lWpelTET6S81Gp1P/Gyf90s412rjtnyfqql6asREdWM9+jz9eJ6yP8Az7uh
|
|
odZqMt458tpB1JvEViI3/RhzRt13/R1MNaziiZiJn5K9ZNceKZiIiQcu/WekT+iYrWI3lzdTrs+8
|
|
8uW0fJzcur1Np/zsn6g79phVaIeetqNR/wAXJ/3SwnUaj/i5P+6UD0ldonum161h5mNRqP8Ai5P1
|
|
lNtRqJjacuT9Qd22WN5aGeZyZd/KHJy59RHbLf8AVq31Gp/4uT9ZEvS8Lr/vSs2npzRtL1z53wK+
|
|
oza/HW2XJNd99pmX0Rb8VAAAAAAAAAAAAAAcHj/C5yV+l4I9+v24jzj1cLFk8nu5jeNpeW41wmdL
|
|
knU6ev1Vp96sfdn/ANFdTrXG+eq1q5F2LLtbZoY8m8d11bbSydErsYsm+zZrO/zcnBm226uhiyRK
|
|
EtrvCrJDOJTeu8A1MWX6Lqq5N/dnpb5O5ExMbx2cPNTeJb/DM/iYPDtPvY+nzhri/jDy5/W6AuwA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAa2p1UYo5adbz+xbxMlvqJ1OqjDHLXree0ejmzNrWm953tPmTPWbWneZ7yoy5YhjrXXTjH8s75N
|
|
mtkyxt0VZM2/m175N1V03yTKubMLXVXybeYLLX2VXy7eam+b0bOg4VquJW+rry4/O9uyZOq3UjVm
|
|
9r25axMzPaIdvhns1kzbZddM0p5Y47z8/R2+HcF03Doi1a8+Xzvbv+TotJnjDXkt+K8ODHp8cY8N
|
|
IpSO0RCwF2YAAAAAAAAACvUZYw6fJkntWN3k8dfHz2vLucdz8mkjFE9bz1+UOZosX1UzPm0nqI/W
|
|
MYo9FlcPNklfFGeH/NshLGun+Cz6PtHZtVZWlRLS+jxPkRpIn7rdoupHTdA5s6SI+7H6Mfo+32Y2
|
|
+To3neSIiZ7A0IjPXpXLePlMotGW3272t85datKzHZjbTVnsDj+FG/2Y/RlGP4R+jo20u7H6N1Ql
|
|
o+H8I/REY957R+jpfReiK6eOYHLtj2tttH6KrY/6Y/R2c+kjeJiFVtLG24hxpw7/AHY/RRkw9O37
|
|
O99Hrt1YX0tfOBLjcGp4XF8c+u8fs9c4dcVcGemSI61nd3IneN1orQAAAAAAAAAAAAABFqxes1tE
|
|
TE9JiUgPKcX4RbRXnNgiZwWnrH4XPi28PdXpW9JraImsxtMS8pxXhF9DecuGJtgmf+1TWW2N/la1
|
|
L7N7T5e3Vy6W3hsYcvLbqzbO9jvvCzvDR0+XeO7crO6FmGSvRThy/RtVXJ92elvk2rRvDUzU7pl4
|
|
izsd2J3jeBpcNz+Lg5LT7+Pp+Xk3W7js5eAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADs0NTrN96Yp6edkW8Wzm6+LNTq4pvTHO9vOfRoWtt
|
|
1mes95YWvs1s2fZldddOczLPLn2ju0MmebT3YZc2/mpm3qqllN1drsbZIhr3yzvtHf4AsvlYYseb
|
|
V5Yx4KTe0+UQ6nDvZ3UazbJqd8OKeu33peq0eh0+hxcmnxxWPOfOfm0mP+steT/ji8N9mKY9suum
|
|
L37+HHaPm9DSlaVitKxWsdohI0Y22gAgAAAAAAAAAABXnyRhw3yT92Nwef4xm8bVzET0rPJH5d12
|
|
CvLhho3rN9RWs9Z23n5y6O21YhrVYbdGOCfrrLPJRpv863zVS6FS09SvZj3lVZZRdPSqmnSWdrIE
|
|
ebOkK4ldTsgW1WKqd1oMZhEVZyRAImOjGI6rJ7IiATNd46qL02bHkiaxaoNGY2n4ImPgtyV2n0Vo
|
|
Gvlx7x2beiyTk08RPevSVUxux00+Fn2n7N+n5rRFb4AAAAAAAAAAAAAAACLVres1tETWekxKQHlu
|
|
L8InR2nPp43wz3j8P/s5dLveWrFqzW0bxPeJeV4xwmdFec+CJnDM9Y/CrY1xv8qvTZ+WYdbDk5oh
|
|
5zHk283U0eo3jaZZ2N5XYjrCnLSJhOK+8d1kxvCqzSwZvousrb7k9LfJ3nB1OLeJdLhufx9LEWn3
|
|
6e7LXN9Ofy5/W4AuxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAETaKxMzO0Qi9646Ta07RDmZ9VbPbaOlI7Qi3i+c3TPUaqcu9adKfy0722ZXvFa9
|
|
XO1OrjrESxt66ZJmcjPUanlidmhkzTZVfLN5VWvsC2b7R3U3yqrZZtO1esz2h2+F+zWTUcuXXTNM
|
|
feKR3n5+iZLVbqRzNJo9TxHLyaekz62ntD1fDOA6fQbZL7Zc/wCKY6R8odLBgxabFGPDSKUjyiFj
|
|
SZkYa3aALKAAAAAAAAAAAAAADQ4pl2pTFH3p3n5Q33E12Tn1eSfKscsLZ+orS00eJqbW+Lfnu1tF
|
|
XaJnZsz3WpCfsyp00fWSvmPdVYOmSUDd8kR3InoQosy7JmUX7MdwZ17ro7KKT1XRPRAsrO0rYndr
|
|
79V1ZBaQiJ6JgCSIJASwrO07MpV2nqBlrv1a1o2bf2qtfLXaQUTO0sb05o3jv3ZXhjS20xEphW5h
|
|
yeJjjf7UdJWNKLziyRePsz0lux1SgAQAAAAAAAAAAAAAADG9K5KTS8Rato2mJZAPIcU4ZbQZuekT
|
|
OC3afT4NXFkmlntc2GmoxWx5K71tG0vHa/RX0GpmlutJ61t6wrY2xr8dXS5uesN+tt4ef0eaa223
|
|
2dnHk3juyreM81OaFGiy/RtZET9jJ7s/2bdutd2jqKeic3iNTsd8a2h1H0jTVtP2o6W+bZbOO+gA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABje9cdJt
|
|
adohGTLXFTmvO0fy52bJfU23t0pHaqLeL5xdK9Rnvqb+cUjtCi94xxvK3JetKuHrdZvaa1ljb10y
|
|
cnIs1Wt3naJc++TmVWvMz1YWybfMGdsm3eWek0mo4jm8PT0mfW3lDf4V7P5tdMZdRviwfvZ6/TaX
|
|
DpMMYsFIpWPTzXmf+steT8jn8L4Dp+HxF77Zc/4pjpHydYGjC3oAAAAAAAAAAAAAAAAADG9opS1p
|
|
7RG7zszN6WtPe0zLua+3Joss/wBOzhzG2OsL5+IrY09dsSyYRijbHEMvOChb7KjF0yS2LQ169Mso
|
|
S24noyrPVXWejNVKbTuw3T3REdQWU6LYlVvsyiUDPfqupPRr79VuOQX1lZEqoZxIMksd0gT2VT0l
|
|
bPZVbuCaW8i8bwr32WxbcGnkjaZa9p2ndv5qbw5+aNugLItF6TEtvTX5sMb969HMpfazc0d9stqe
|
|
vVZDdAQAAAAAAAAAAAAAAAADV1+iprtPOO/2u9bektoB4TJTJpNRbHkja1Z6uto8viVht+0HDvpG
|
|
H6Tjj6zHHvbecONw7Ltfkmeqmo6Ma69DXbbZTkr1mGWO3RneOaGbZRoM30fVzSelMnT83aef1FZ7
|
|
x3h1tBqfpGnjmn369LNc3sc3kzy9bQCzIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAa+q1dNNXr7157VhGp1Xh70x+9f9ocy283m1p5rz3mVbrjXHjt91lz
|
|
5c9+fJ1nyjyhdM8lZlOOIiqrUXikd+kMreunnI5XEdX4dZiZcG+XmtNl/F83PeeWWHDOGanieSKY
|
|
q+5H2rz2hMzWd1Iqx1yajJXHhrNrW6REeb1nCPZumn2z62Ivl7xTyr/6uhwzhGn4Zj2xxzZJ+1kn
|
|
vLoNJnjHW7TbbsAszAAAAAAAAAAAAAAAAAAAAaPFrbaSK/itEOXt0rDf4xb/ACa/GZacRvaF58Q2
|
|
IjasQnzPIhCU92tMbZGzHmotG10C6nZkwpPRmipIllEbMIZIE7solgmJBnCyk9VMM6z1BtVllEqK
|
|
z0WRILYlluriWcSDJVbusV27gwInaSWM9ECyZ3hqamnSWxFmOSOaqRx725bNnSZNs9J+OynVY+WZ
|
|
YYr7TE+nVaIr0Ais81Yn1hKAAAAAAAAAAAAAAAAAABExvG09peU4nov9n66L0j6q/WPg9Y1OJaON
|
|
ZpL0+9HWs/EWzeVz9PbmrEtnyc3h9reHy26TWdnSr2YX6657ijLXpLX0+onSamL/AHJ6W+Tbv2aW
|
|
ekTv16JzeI1Ox6KJiYiY7Slz+E6jxdN4dp3vj6fl5Og2clnKACAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeQRMxEbzO0Q08uqtkma4ulfO3r8lefUePMxWf
|
|
cjy9WvlzVxV6T1Z61/x0Y8f7Wc7Ur1lqVy+LqOWJ2hp6rXddon5rOF1tfmz5OkT0qzb8dWbxjp1c
|
|
biuuilJ5Z6r+IcQrixzEy8zl1E6rNt1tMztFY81sztU1eRucN4ffi2p5esRM72n0h7rS6XFo8FcO
|
|
CkVpX082nwXh3+z9FWLxHi36328vg6TZyW9ABAAAAAAAAAAAAAAAAAAAAAADj8Unm1tK/hqppHvw
|
|
y1k8/EMk+m0GOPeafiFpCZYwolnXspvHvLa9mF46gmnZmwozRUiUCBKYYsoBLOFbKAX0llEqqyzi
|
|
QXRLOJVRLOOwLIljZMEgrlhKyYYTAK5nZPN0RZjugUanHzVlz6xtLq361c+9eXItPpXX0dubTU+E
|
|
bL2lw2++O1fSW6m/VYAISAAAAAAAAAAAAAAAAAp1GbwcfTreelYEydcuMcRrM/L9nnlsV6wqpi2r
|
|
tv133mfWVkRyRtEdGFva7MzkYZNoamWN4bV4mYa9qztKIujhVppxGI8r1mJegeZpknBqKZY+7L0t
|
|
LRekWrO8TG8Ns/HJ5ZypAWZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAADS12fp4VJ6z9qVuq1HgUiI+3bpDl589cOKZmevqprXPTbx477rDJlrhr1nq4+s182tMRP
|
|
RqaziXiZJrWekNG17ZbxWJ336M5LXRbI3dLTJrs07RMY6fan1dHLrowY+X7MVjt6N3R6Kul0EbWm
|
|
s7bz8Z+LnabQX43r7Y53php/mXj+Dnv0f1JO1x/8ZxbUzj02O15mfLtD13AvZqnDds+pmMmo26el
|
|
XX0Wh0/D8EYtNjilY7+s/NstpOOTW7QBKgAAAAAAAAAAAAAAAAAAAAAADG88tLW9I3BwJtz6nNf1
|
|
vK/DHVqYJ3pzT5y3MPZeojOWMQylEKpTVjZnDCwkqzYQyRRICATCITAJZQxhMAshnEq4ZQC2srKq
|
|
qrIBZCWNZZgwswmFloVyCu0dFcx1WyrtCBhv5NTPHXds2U5o3hIz4ffbPt+KHUcTSW5c9Jme0u2v
|
|
VYAKpAAAAAAAAAAAAAAAAYZctcVOa35R6tLrltN795/YvknNqrfhpPLH92V5isd9mWq6fHjk6rn0
|
|
ZxG8KK5Jm/wbVZiYZtqrmkqL023bkxvCiY3lJHNyRG81mHS4Rn5sNsNp64+3yaWaNrzOzHBl+i6q
|
|
mT7s9J+S+ay8mex6EIneN47SNXKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAImYiJme0JafEs3h6fkidrZOn5eaLeJk7eOdm1Hi2vmtPTry/CHmOJcUvmvOPF1n09Pm
|
|
6HF9ZGm01qxO3R5vSY7XwzmzTy47zzTEd7en5Mfvt2/PURWdo3tvPrPlKymbktFqTtMTvHzbOLDG
|
|
f63JXbFX7FdnoODcDprZpq9TjiMMTvSn4vj8l5fxnrk91saPSa7i2hpOfbTVt5x1m0fLydzR6PDo
|
|
dPGHBXasd585n1lsRERG0dIF5OOe6tAEqgAAAAAAAAAAAAAAAAAAAAAAADX11+TRZrf0y2Gjxe22
|
|
gtH4piP3TPpXKwxtjhuYo9xq442iIblI2pC1RET2ILd9kxCqRjZmwlCSEohIJAQAAJZISDKGUd2M
|
|
MoBnVbVVCyAWVWeSuqyOwIlXZZKue4MJV2WWYT2QKbKL9YlfdRdIo35b7/Hd3KTzUrPrDh27uxpb
|
|
c2mpPwX/ABX9XAKpAAAAAAAAAAAAAACekTIp1eTwtJmv+GkyJn1oafeazbfpMzLR4jq/o8b823zX
|
|
6XNF8ERCvTcNpxLV5LauvPhx9Irv3lhztdtv8TtaWLicXrt03jzjzb2k1nid56ty3s/w+a7Uwzjn
|
|
1raejlarhmbhl/FpbxMO/fzj5p/ixSeXOvTtRfeI280ZI26tfDm3pWe63LaZx7qtGvniJ6tPLvOK
|
|
fOa9WzbJvTbza02jl3n5SSljscK1MajSxWZ96nSW88xw/VfQ9XMT9nfa3yemid43jtLeXsce88qQ
|
|
EqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADia3UTm1l4j7OP3Y/u
|
|
7Vp2rM+kPJW1PhYcmS0+9MzKm/jbwz31weMzbV8UppazPL9q0/BF4rk1GLDSNqxPWPhCnHmnNrtT
|
|
qPKteWPm6U6OdHaZvO+SaRNvhv12Ub/q3FhtrNVj0uKOt56z6R5y9zix1w4qY6RtWsREOJ7L6OKa
|
|
S2rvX6zNM7T6Vh3mmZyOfya7eACzIAAAAAAAAAAAAAAAAAAAAAAAAAAczjVvqMVfW/8AZ03I41bf
|
|
Lp6/OVs/UVrY47NyOzUxd4bUJpEbb3Z7IiOrKIVSjZhMLJYyhKIgmGUQSDESIEbJEgQmCITEAmGU
|
|
IiGUAyhZVhDOoM4Wx2VQtqBKuyyWEgqlhKyyuyBVaGtkbNmvk7A15l1eH2300R6TMORPSXT4ZO+O
|
|
8fFefEX63gEAAAAAAAAAAAAAAAq1WPxdLlp+Kkx+y1Fvsz8gjhaDauGK8sx07y3OE3m1tT6RaP4c
|
|
vU6yMNKUx73zT0ilY3l2eF6a+m0kRl/zbzz3+Ez5M8z26fJruW6wzYq5sV8d43raNpZjRzPPaTmx
|
|
5b6bJ9rHO3zb2WJ8GWPEscY9bgzxH2t62n19GWW0eHOzHU5XbjXZ1x8WTnz2iZ7S2M1IjH2+LX0V
|
|
KTqs8zO9ot0j8nUthi1J3UaOFMTfLFo6xMbS9BwHWTqdHOO8+/hnln5eTjYMFo1WTH5VnePzXcIm
|
|
2k4zlpPSmXy/hfF5eMfJns69OA2cgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAADG/2LfJ874rW845mubliY7bPoto5qzHrDz0+yePNF41OotaJ7RWNtpV1OtfHqZ715fhu
|
|
j8adNpcVfeyzE2/vLuanhOu1nEctIxTTFa/+ZPbZ3eHcF0vDbTfFE2yzG03t32+DokynXl9+leDB
|
|
TTYKYccbUpWIhYCzEAAAAAAAAAAAAAAAAAAAAAAAAAAAAcXjE/4zDH9M/wAu04XF5/3jj/0f3Wz9
|
|
RUYmzDWxS2I7FSyjuzY1ZKpRKEygEwiWUIkGIk2QJNhKQhMIhkCYZQxhlAMoZwwZwgWQshVCyATL
|
|
CWc9ldpBhZXLOVdpQK7NfJPRdaWvknoDVvPvOnwuel4+TlXn3nS4VPvXj4QtEV0wAAAAAAAAAAAA
|
|
AAAAAVV02CmTxK4qRf8AFFeq0AAAanEsfPpZmO9Ji0NDLfkwdOsulrumiyzHlVzJrz4Ovoy26vB8
|
|
cTBa9NffLtMY77Rv8Yegx5ImkKdJoY1HC81Y+3OSbVn0mGGkmbY45u6tnrrTOu2xGO0RxCd+nNVj
|
|
qKxTV1vH2pjaGtnyzXXYdo96ZmGXEMk15b7/AGZiVerWPTYckZcNbx5wzc7hGbnxXxzPWk7x8pdF
|
|
0S9jh1OXgAlUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAcPjEf4/FP9H93ccXjMf4vDP9Mx+62fqKrx+S+GvibEFSsqyYwlVK
|
|
ZYsmIMoRKYJQIPIEiQ2ATCUQygCGUIhMAyhnDCGUIFkLIV1ZxIMpVWWSrsCuyqyyyq09ECq8tfJK
|
|
66jJ2Bp5J6upwn7dv9Lk5J951uE/av8AJaIrqAAAAAAAAAAAAAAAAAAAAAAq1Mc2myxPnWf4cmtu
|
|
XT9fR0tffk0WSe28bfq5Wbamm3326MtunwfK6PCv/AxPraZ/dz9PO97/AOqf5dHhdZrw7Dv3mOb9
|
|
XOxRFM+avpe38mvkPHf/AFWlrKba7Tzt99ZxKkfR7euyNXMTrtPHfa0z+zPiM/UR8Zj+Wbdu8HpN
|
|
M2bfzrV13M4dO2pyR61dNvj44/J/oAWZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADj8bj63BPzdhyeNx0wz8ZWz9RWri7Nmv
|
|
VrYu0NmqaRZHZlDGGSiwxZSgCEkCBCQSCQBMJRCYgEsoYx3Z17AlMIhlCBnDOGEM4AlhZZKq4KrK
|
|
7LLKrIFN2vdfZReAaObu6/CO9vk5OePR1uEd7fJeIrqAIAAAAAAAAAAAAAAAAAAAAGtxCk5NFliI
|
|
3mI32+XVyNTyZOHTee946PQKPoeDffw4777eW/yVs60xv+ZxOnr4Okx1t05KRv8Ao41Z5q3yed5m
|
|
XY1szXRZ5jvFJ/hxItP0aOSN9q7yrtr4f2tHFM5+KT16Yq/vK/iGSbXw4vO14UcPx5MGfNbPG18m
|
|
1oj4THRsTw7VanPXVYpi3gzMcnrvCnG11JOupwuN8+a3pEQ6jT4divjxWnJExa09pbjbM5HHu90A
|
|
JUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAHM41H1GOf6nTc/jEf4Ws+lls/UX45uGekNujTwdm5RNIthKIZKLDFlsiQIShIC
|
|
EgCUJ7AmGTGO7IDzZQhMSDJMMYZQgZwzhhDOATuqssmVdgVWVWWyqtCBTeVF19lF+wNLNG7q8I+9
|
|
8nLyupwnt+S8RXUAQAAAAAAAAAAAAAAAAAAAAAAItWL1mto3iY2lyrcLyUxzix2ia2nvPeK+jrCL
|
|
OrTVnxpanhuPPemSs8l6RtE7dJj0ldpNP9GwRSZ3neZmV4cR/Vs4AJQAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANHi1d9H
|
|
M+kt5ra+vPoskfDdOfqK4mn7Q3aNHBPZu0W0RdDOGFWcKLCJZeTGQQlCQSgASBsCYZQxhlAJTAmA
|
|
TsmAgGcM4YQyjsgRLC3VnaVcgwsrt3Z2V2QK7tbJ1bN5a9waeWO7p8Knt8nNyebpcK8vkvlFdQBA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK9RXmwZI+ErEWjesx6wQeZwejeo0cccuW8
|
|
elpblJaaRGxVnCuss4ZrMvJEgCAASISCQIBlCYYpieoM0wx8k7gzIRueYM4Z79FcSy3QEsLJmWFp
|
|
BjaVVpZWlXMoGNmvkXXlr3kGtknu6XCf7OXkl1OEdl8orqgIAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAHmskcmtzV/rls0U62OXiWX4zErcc9GmkRfWVkSqqziWayxCPIANwBIhIJSxS
|
|
CRG6dwZwlhEs4BluMdzfqgZxLLdXuy3AmVdpZTKuZBjaVVpWWV2QlhZRdfZRcGpl7urwfrzfJy8r
|
|
rcH61vPyWitdMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHA4nHLxKZ9awnH2ZcY
|
|
jbW459aq8fZpfiI2IZwrqzhmsz3Ebm4JN0AMhCQSIASndiAziWUSriWcAyRujc80DM3RCfIETLCW
|
|
UsZEsJYSslXZAwlTddPZTkBp5e7r8Gj6rJPxhx8k9Xa4PG2C8/FaK10QAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAcfjcbZMFvnDWx9m5x2PqcNvS+zSxT7sNPxH62YZQwqzhRZO6UCB
|
|
KUAJTux3SDIRuAncQAmJZRLBMSgZ7iIAZRKd2DICUSlAljLCYWMLIFVukNfI2bNbIDTyT7zu8Ijb
|
|
Sz/qcG/2nf4T/wCE/wD2WnxWt4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHL9oL
|
|
+Hw2cm28VvEuPptfgyVj6yIn0no7/FtJfW8NzYMe3PaPd39d3iMug1WktNc2C9dvPbeP1aZ9xF+v
|
|
T471tHu2iflK2HkqWmvaZj5Surqc9Ps5bx+alTHqYHm68S1Vf/NmfnC2vGNTXvyT84Ql6A3cSvHM
|
|
sfaxVn5Ssrxyv3sM/lKB1xza8bwT3pePyWV4tpZ+/MfOEjfGrXiGlt2zV/PotrqcN/s5aT/+wLRj
|
|
FontMSlAlKEgndO6IAZQljDIEgeQljLCzOVdkCu/SGrkbF56NPNeKxMzMRHxENe0+89DwuNtHHzl
|
|
5PJr8NcnLW3Pbf7r1nCZm2gpae8zMrz4i/W6AgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAETETG0xukB4HVaeMHEtRi26RedvkyjBSfX9W77QYvC4xz7dMlYlrU7M929dWJLFc6aPK0q
|
|
7YLxPS0S22FlP6q38Zac0yR92s/KVc3tHfFf8tpbcsLRvB/dR/8ALLVnU0r9uL1+dZI1mnmdvGpv
|
|
6TOy6ym+Oto2tWJ+cJ/tW+KLK5KW+zes/KU7tG+h01p64qx8Y6NXNo6Y+uPJlp8rLf0rfG7MXtHa
|
|
0x8pZxqs9e2a8f8A7Oj7HaTHn0+f6RWM23LETfr6vRW4PoL99NT8ui7F4+vEdXXtnt+fVbXjGsr/
|
|
AOZE/OsPS29nuH27YrV+VpeV9pdPXhOtw49NG9Mld55+vXcTPd42I47qo7xSfyWV9oM8d8VJ/VxM
|
|
d8l46xWF9cV7en6o/qLfxp2I9ob+eCv/AHMo9op89P8A/wBORGmyT5R+qfo2X8P7n9Q/jTsx7RR5
|
|
6ef+4/8AuHftg/8A6cWcOSO9J/WEbWr3pY7Efzp2Lcfv5YK/9zWy8d1E/ZpSv5Oba1/+Hb9lc+LP
|
|
bFt87I7E/wAabWbiurvEx4nL/pjZzc2bJkn372t85ZXx55/BX85lucC0vPxnTxlnnjm32mOiZqUu
|
|
LJ2p4TwnVavNWaYbRTfre0bQ99pcH0bT0xb78vmtiIiNojaErMwAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAHnfarF7umzRHaZrLjYrdIen9ocPi8JyTt1xzF4eUw23rCm3R4r6bMy
|
|
wt6kdTaWLdjswmNoZontsCm0K5XWjopnuDC0dGpqG5bs08/daKV672MjbSaif6oh6Z5f2LtvptRX
|
|
0tEvUN3Jfo8f7cYve0eX4zV7B5z20xc/C8eSPuZIRficfXlcPaG7ino08HWIbePpLF2NuiyOyrHK
|
|
3fZFSwuovHVfaVF4QK5YWTM9UT0EKry6Ps1Tn4zjn8NZn9nOtLseydObiWW34cf918fWfk+PYANn
|
|
KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq1WKM+ly4p+/WYeBxTNd6zG0xO0
|
|
vobw3FcP0bi2em20Tbmj5Srr418V9sa2Z7qKyzi07MXUylhaU7yjqhLCeiq3ddaFNxFYW7NLNG8t
|
|
zya+WO6Va9J7FW66mvwidnrXiPY3Ny8RyUn71Jj9Ht3RPjk19HK9pMHj8D1ER3rHN+jqqtTjjNps
|
|
uOe16zAifXzfTz7kNyndpYazS9qT0mszDdoxrsi6m8LazMq6zDOsq1ZEyrt1WWlXaUCqyq0rbKbi
|
|
Fdp6PReyFd8uqv8ACsfy83aXrPZHHto89/xX2/SP/dpj6y8vx6EBq5gAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAB5n2q03LfDqqx39y39npmlxbS/TOG5se29tuavzgWzeV4mtui2
|
|
O3RRSY2hdVhqO2MvI36iu9lUsrSrvDHn6spnmSiq5jooyV6tq1VV69RC32byTh43h8otMx+r6I+Z
|
|
aK/g8TwX7bXh9Mid4iW+fjl8n1ICWb57xLBOm4zqse20Tbmj8+qKdnS9q8PhcTw5tumSm0/OHMxz
|
|
0Za+uzx3sX1t0Zxurr1ZxvspWiZYWZbsbT0QK7KLrZVZJFaqt5vbezNOTg9J/FaZeJns93wCvLwb
|
|
T/GJn92uGHldIBowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuAPA67F9H4l
|
|
qMW20VvO3yRWW97T4fC4rXJHSMtI/WGhVlue3b473K2KzMML4+62tujG9pnozXaOSOVFMnVbmq1t
|
|
trJRW5E7wwvUxTvCyY6CHOt7moxz6Wh9PxTzYaT61h8x1MbZK/OH0zTf+Fxf6I/htj45vL9WgLMn
|
|
mvbPFvocGWO9L7fq85p5maw9d7VYvE4JkmPu2if3eW0+PasdFNOnxfF1Y2hlykRsmY+LJ0MZjZXa
|
|
eq2eyi8oQTO0KLdZWzPRjWu6VaqtHR73g0bcI0sf0Q8Nkq93wqNuFaWP+XDTDDytwBowAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAef9q8HNpcGaI60vtPyl56k9Iew49j8ThGe
|
|
PwxFv0l4zH2U26fDfTYiyJljvsjf4sm6vJ1hrXjq2MkqLdZEVbgbMx0auGdmzNt6iHN1Ub5af6of
|
|
TdPG2nxx6Vj+HzaaTm1+nx/iyVj930ysbViPRrj45vL9SAuyc7j1efguqj+jd4/T33rD3HEcPj8O
|
|
1GP8WOY/Z4TTT7sKadHhbcsZnaCJ3TPZk6VdrKbTutmP0U2nqgrGOsr8deiuI2X09EqKM1dt3uuG
|
|
f/jdN/06/wAPE546S9rwud+Gaaf+XH8NMMPK2wGjAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAABrcRp4nDtRWPPHP8PCYusPoWSvNjtX1iYfPuWaXtX8MzCuvjfw32siu8ptXoxi
|
|
0wy5t4YulReqmazu2skbquURWFInddM7VYRGyL291KFnCcfj8e0le/Lbmn8n0N4b2Ur4nHLWmPsY
|
|
5e5a5+OXyXugBZmiY3iY9Xz7NjnTa3Ph/BeYj5PoTxftFg8Hjk2iOmWkW/Psrr418V5WrWd2faFc
|
|
V2jdnEMXWxntupmN7NiYU27iWML6dVMVnddjgVqMsdHr+CW5uE6f4Rt+7yuSsTDv+zWXn0WTHP3L
|
|
/tK+GHl+O0A1c4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8Dn93W56/wDM
|
|
t/L3z59qp24jn+OS38lnpr4r7ZxHQ2TEstt3PXUrt27K57rr1VT0BjKnJPRbMqMs7QlV2fYvHvrd
|
|
VknyrEfu9m8f7FZI8fVU85iJewbT45NfQBKo817W4eulzxHaZrL0rje09ItwqbfhtBVs3leai8RD
|
|
KLw1sduesL606dWFdsZT1jdhNeq6K9DlhCVUU6s4jZnt1YzAhnM71dH2bycmszY/K1d/0c6OzY4R
|
|
fwuK4p8rTstn6z8k7HrwGzkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHz3
|
|
Vxvr80/8y38voTwGpj/F5/8AqT/JfjTx/WVeyY6FPspc9dZPVXaOq2WEwIUTVRmjo2rNfLHRI3vZ
|
|
DJycXtX8dZh7t879nsnhcbwz23tt+r6I2nxyb+gCVBzuPY/E4PqI9K7ui19fTxNBnp60n+Aj5/pJ
|
|
3jZu1aOnnltMNussdfXbm+l3ZM9URHREdZVXTuT1Nk7boQiOkJw28PU47/htEp5eivJPLMTCZ9Vv
|
|
x7mJ3iJ9UqNHk8XR4b+tIXuhxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD
|
|
weqjbWZ4/wCZP8vePCaz/wDIaiP+Zb+UX408f0r9lOxWOifJhXWjfyYWllPRXYQxnrCrJHRd3YZI
|
|
6A1NJecHEsN/S0T+76bE7xE+r5dk93LW3pL6ZpMni6PDf8VIn9m2fjm8s9rgFmQxvHNS0esbMiew
|
|
PnHLyai9fS0w2aNfUTtrs3+uf5bGPqy068fF227KtSsdFlKqNGMV6myyY6sbdIQI8tlOWOi6Jhhk
|
|
j3RD0vA8nicMx9etZmHRcT2Zyb6XNT8N9/2dt0T449T2AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAHhdfG3E9TH9cvdPEcXjk4zqI/q3L8aeP6xr2TsxpLOekMK6mFo6qpXSrm
|
|
OqBixvHSVmzC4OfqK7S9/wAByeLwbTW9K7fo8Fqo6Paeyl+fglI/Da0NcMPK7QC7AAB8313TiOf/
|
|
AKk/y2MHWrX4jG3E9R/1Lfyv0/aFNOrHxuU7LI7MMayGTVlHWUXhNe6Z6wIUsb9d1m20q7dkDpez
|
|
N9tRqKT5xEvRvKez9+Xis1/FSYerb5+OTyf6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAB43j9eXjN/jWJ/Z7J5L2mry8Upb8VIF8f6aGOey2eynHvOy7bowrrYSxZSwQJ2YXZ
|
|
92N4BoanrEvVexmTm4blr+HJ/aHltRHSXofYm/1Wrp5RaJaYY+X49WA0c4AD51xONuKan/qW/lbp
|
|
+0MOLRtxbU/9SU4J7KadWPjep2WQrr2WRPRk1TvsndXMpiRCb9FNu0rbTuqvKBscCjfi9PhWZeue
|
|
V9n434rafTHL1TfPxy+T/QAszAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHmv
|
|
avHtfTZfnV6VxPajHzcNrf8ABeJFs/XnMcr4no18c+6vr2YadkY2YM57sEDLyY37Mo7MMnYGlqO0
|
|
vQ+xNfqNVb1tEfs87qZ2rL0/sVX/AHdnt65P7Q0wx8vx6UBo5wAHz/jUbcX1PT78qtO2vaCnJxjP
|
|
8Zif2amnnspp04+OjWejKJ6MKdmcMmyJn4m5ZHzEVPMwtJv0VZLbQDqezcb8RzT6Y/7vUPM+ytZt
|
|
n1OTyiIh6Ztn45N/6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABocbxeLw
|
|
nUR5xXm/Rvq8+OMuDJjntaswEeBxT0bNZ6NatZpNqz3rO0rqsdO3PxlaWEMpY+aqWXkryT0ZT2V3
|
|
7A0dVPuy9f7G124NM/iyT/Z4zWT7sw957MYfB4Fp4/FE2/WWmGHldcBowAAeM9qKcvFeb8VIly9P
|
|
0nq7ntbTbVYL+tJj93CwT76unR4/jo0nozhhTsy3Y1sWljM9Ce7HyQIm3RRlttVbaWrnt0Sh6n2U
|
|
x8vD8mSfv3/h3XN4Bi8Lg2nj8Uc36y6TeOPXugCUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAPD8RxeBxXUU26Tbmj8+quro+02Lw+I4ssdslNvzhzazvDPbq8d7GW7Dfqz2VzG
|
|
0s2qd+iu/Zn5Ksk9BVztX1mI8930zh2LwOHabH+HHWP2fNYp4+vwYvxXiP3fUqxtWIjyjZtj45/L
|
|
faQFmQADzftfj3w6fJ6WmHmsP23rvaqnNwqLfhvEvIYZ+sV038bo0noy36MK9oZQxrdMyrlnMbMZ
|
|
QKrS1M07zEestq/RRjr4utwY/wAV4j91p9V18fQdJj8LR4ccfdpEfsuREbREJbuMAAAAAAAAAAAA
|
|
BAJAAAAEAJEAJQAJQAJEAJQAJQAJEACUJAQlAJEAJQAJQJAAAEAJEAJBAAAJAABAJEJAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwvanDzaPFmjvjv8A
|
|
tLztJ3h7HjGHx+FainnFeaPnHV4vFbeIU038VbHeGF+kso7Mb9mTdhKnLK3dRm7SIrHhGPxeP6Sv
|
|
9cT/AHfSnz72Zx+J7Q45/BWZ/Z9BbZ+OXyfQBZQABzeP4/E4NqI9Ii36S8Ng/wAx9C4jTxOH6ivr
|
|
jn+Hz3B/mQi/GvjdCnWNlsdI2V07LIlg6USrt2ZzZXMoFV+zPhGLxeOaavpbm/RVltEN72Yx+Jxm
|
|
b7dKUmf7L5+s9/HtRA2cqRACRACRACRACUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAACQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCQQCRACRACRCQBCQBCQB
|
|
ACRACRACRACRACL1i9LVntMbPATTwdRkxT3pea/u+gPE8Xx+DxrPHlaYt+qNfGvjvtXXsi0dOrKk
|
|
dEXjZg6VMtbP2bMtXUdpEV0/Y2nNxbNf8OP+727xvsXH+N1U/wBEfy9k3nxyb+gCVQAGOWvNivX1
|
|
rMPnGGOXNNfOJ2fSZ6w+dZKeHxDPX8N7R+6L8a+L63KdoZ7q6zvEMpnowdKJ6ywmWUyqvIKM0vQ+
|
|
x+D6rU55+9aKx+TzWa36vbezmDwODYenW+95/Nphj5L6dQBo5wAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEiAAAEoA
|
|
AAAAAAAAAAAAAEAkEAkRuAkQbgkQAkQAkQAkQAl5T2nx8nEMOT8dNv0l6pwfarHvpcGWPu32/WCr
|
|
YvK4mOem6b9mGKd4Z3idmFdka0y1c892zfpMtLPaNpEV6D2Kj/Eauf6YeweQ9ieuTVz8K/3evbT4
|
|
5NfQBKoAA8FxCvJxrUx/XMvevD8Zry8fz/Haf2RfjTx/6RSOnRMyypHu9kXjowrqVSrvPRnZVl6V
|
|
kK0775MsUjvadn0nT4ow6bFijtSsVfPuFYvpPGtNTy54mfy6vorXDm8l9pEC7JIgBIgBIgBIgBIg
|
|
BIgBIhIAgBIhIAgBIgBIIBIAAhIAhIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAAAAAAAAAAAAA
|
|
AAAAAAAAABAJQkAEAAAAAAAAAAjc3BIjdG4Mkbo5kcwMjdhzHMDPc3V8xzAs3N1fMjmBZubq+Y5g
|
|
Wbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmTzAz3N2HMnmBlu5ftFTx
|
|
OEZJ/DMW/d0t2rxKni8N1FPWkiZ9eS08e7Cy8dGGn6UhZaJljXZGnmc3UT3dPP2cnUT78xCIV6j2
|
|
H/8A9c/6f7vXPI+w8bU1U+vL/d63du5NfUiDcVSIAS8b7RV5eOb/AIqRL2TyXtNX/e2KfXH/AHlF
|
|
+NPH/pr4+2xcxx0hFpY11K7R16KM32ZWz3UaidqSgrc9kcPicWyZJjfw6T+727y3sXh2xarN+K0V
|
|
h6lvPjj3e0ASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJQAAAAAkQAkQAkAAAAAAAAAAAAAAA
|
|
EgAAAAAAAAAAAAAAAAAAAAAgAAABKDcAN0bgkY8xzAyRux5kcwM9zdXNkTcFm6OZXzMeYFvMibKu
|
|
ZHMC2bo51U2RuC2bom6rc3BZzom6sBZzI52ADPnOdggFnMc6skFnMc6rc3BbznOp3RzAv50c6nml
|
|
HMC/nOf4qOY5wX85zqOc5wbHOc7X5znBsc6edr85zg2ec52vzpi4NjmY5bROG+/bllVzsNTk5dLl
|
|
n0pP8BHmMHWNmzt0aum8obm08vVjfrtnxztR0mXHzTvaZdjVRMTLkZo6yiFen9iZ2pqY/wBP93rN
|
|
3kPY+/LfPX1rE/u9XzN3HfqzdO6vmTuIZ7m7Hc3Bnu8t7TR/vHBP9E/y9Pu837SV31umn+if5Rfi
|
|
/j/01MMb1hjkrtKzBG0bMsmOZY11tOYamr6Und0LUc7XT7u3rJPqL8er9lcPhcFpbzyWm39v7O00
|
|
+FYvA4Zpsc94xxu227jv1IAgAAAAAAAAABKAAAASgASgBIgBIgBIgBIhIAAAAAAAAAAAAAAAAAAC
|
|
UACUJAAAAAAAAAAAABIAAAAAAAAAAAAAAAAAAAAg3AEbomQZbo3YzLGbAz3RNlc3YzcFs2YzdVN2
|
|
M2Bdzom6nmNwW86JurTAMuY3REJ2BB1ZRVMVBhsbSsiqeUFXLucq3lTygp5TlXcpygp5TlXcpygp
|
|
5TlXcqOUFXKjlXcrGYBXysdlswiYBVMdUTCyY6sZBWxlnMMZgGLGZZSwkDdHMiWO4MuY5mEyjcFn
|
|
N1OdVzHMC3nTzqeY5gX85zqOZPMC+Lqdbk20eb/RKOZr8QybaK/XvtH7iZ9aGlp2luzT3fg19NHS
|
|
OjbmPcYX67XH1XSZ9XIzRvMuzrK7zLkZYmYnciunb9lZ5dTk+OP+71cXeP8AZnJ/ip2nf3J/l6iL
|
|
/Fu5L9bMWZczXi6YuIbEWTzKIuyiwLt3nuO25uI4a/hx7/rLuczg8TicvFLbfdpEK6+NPH/phhjo
|
|
stLGkctUWnoxrrU3j1cnWTzZq1jzl1clo5Zcu8c+txR63iP3Tn6pv4+g4o5cVI9IiGe7CJ2iE7t3
|
|
GyN2O6dwSINwSISAlAAlACRAAlAAlACRACRCQAAAAAAAAAASgASISAAAAAAAAAAAAACQAAAAAAAA
|
|
AAAAAASAAAAAAAAAAAAAAAAIAAAQCAJljuljsCJlhMs9mOwMJYys5TkBVsjZdyHICrZPKt5E8oK4
|
|
qmKrOVOwMIqyirPY2Bjyp2ZbAI2NmSARsbMgEbI2ZAMdjZICNkbMkSCNmOzJEgx2YyzljMAwlhKy
|
|
WEwCuWErJhhMArlhLOWEgxljMpljIImWMyTKJA3N0IBO5vux3NwZbnMx3NwZczT4jf3MdPW27a3a
|
|
fJOq1XNP2KdIRfi+J2trSYfcjeF+Wm1OicVeWIiN9kai8xjY12ORqultnI1Ecsujq79XP1FovWYI
|
|
rTgeq+j8QrWZ+3Mx+r2UXeC0WG2Ti2kiN5mL807eUREvbzbaejefHJv62Iv8WUXa0WTFhVtRdlF2
|
|
rz9WUXBtc7jR9dqc2T1ttHyhvZMvJitb0jdq6XHNcNenWVN3028U99WRj6Kb02be3Tq18/SN2Lpc
|
|
3UdN9nOmZrqKX/DaJ/d0svvTLRzV3jomK6+Pd1vvWJj0ZczT0mXxNJht60hfFnQ4qu3N1cWTEgs3
|
|
Tur5k7gz3N2O5uDM3Y7m4MtxBuCQASIASIASAAAAAAACRCQAAAAAAAAEoSAAAAAAAAAAAlAAlCQA
|
|
AAAAAAAAAAASAAAAAAAAAAAAIASgAAAEJAQJQCNkbMgGOyOVnsAw5TlZ7GwMOVPKy2NgY7GzIBGx
|
|
skA2AAAAAAAAAAQkBAEghEskAxYzDPZGwK5hjMLJhjMAqmGEwumrCagomFcw2JqqtUFEsLLrV82F
|
|
o7gqljKyYYTGwMZRKUSCAQAboJnaN5Bjkneu0d5W4ccViIiOzHFWbTzNumP1Zarr8eeRMbxDW1Mx
|
|
NO67NbkhzNVnmInqzaOZrL93JyZeV0M1++7S02jvxDWxhxx033tPpC8Z6rrezWjmZyazJG2/u03h
|
|
2vFibTHoqvamiwVwY+nLGzV0+SZ1Mx8G0/45tOhzJ5lXMc3UVXRdlF1HP+iYsDPLPPy49/tz1+Te
|
|
pSIr0ho6ak5Ms5J8o2q6NImOrHV7XX488ypzTtHXo0s9t6zG7c1G1qz6ubeZiZ3UatXJG3yauSO7
|
|
cvMTEx5tPLb3prPRMVr0HB8vicNxf0+7+kt+LOJwTJyY/Bnz3tH93X36N58cWvq6LSyiyndMSlC7
|
|
mZcymLJiwLosmJVRLKLAtiU7q4lMSCzc3YxJuDMRuAlKAEgAAAlAkAAAAAABKAEgAAAAAJAAAAAA
|
|
AAAAAAAEgAAAAAAAAAAAAAkAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAhIAAACAAAASgAAAAAAEAAAA
|
|
hGzJAImGMwzQDDZjNVuyNgUTVhNGxysZqDVmiu1G5NN2M4waM0+DCaN2cbGcQNGaMZq3JxMJxA1J
|
|
qx2bU4kU09slorWNwa20z02RXHbJbl26QvtFovbHWkxEdJt5y2MOHlr2U1W3jx+1hiw8vSO63lmI
|
|
XRTaEWmtY6snRHO1VpmJ+DjavpSZl2s8b7y4HFcnh0n0gha5ebJN55KRM2mdoiPN6fh+kpwXh0Wy
|
|
RHj5Otp/s5Ps1p62y31+em9aTMYt/OfVfxTiPjZ52naI7fBrI5t66xz5+a1rW7yx0eSL6iZjtEOX
|
|
qNbSletom3lENjh2fbHzbbWt3iVozruc+5ztWubf4M4ybpQ2Oboyrva0Vjza8WdDR4OkXt3n9ldX
|
|
kaePP9VtYqctYhdvt5oivTeCZ2YOxXk6ubqMfV0b9mrljfqlFcq88k7z2U5axeItDa1OPessuC8P
|
|
ya7XRWYnwqdbT/ZMilvIu4dpslNdixXja8Y5tt85djZdbDWnGOesRtXFtuw6T27No5Kx2OrKYQlC
|
|
ExKJgBnEpiyvdlEgsizKLKollFgWxLKJVRLKJBbEp3VxLKJBnuMWQJEbpBIAAAJAAAABIAAAAAAA
|
|
lAJAAAAAAAAAAAAAASAAAAAAAAAAAAAJAAAABAJABAlAAAAAAAAAAAAAAAAAAAAAAAAIAAAAAAAA
|
|
AAABAJQAAAAgAABAAI2EoBGyJhkgGPKxmqxAKpownHC+YRMdN5BrTj67R3bOn01o7p01Iv71u89o
|
|
b9a7LfBTfS1vWI2jf12VfQPSW8KX2mas+NC2iv6xMNfJpMnLtEbuuxtMRCtzF55NR5rPps1N/ctP
|
|
y6uHreE6nXZ4pak48X3rT06fB7fNeI33cbX6mI32R/MWu7XF116aDSRhxbRERs8f499bkyZeeKae
|
|
kzE2mdon81/tfxDLGOunwbzlzbx08oaHBvZHJlx48mrvaa94pu04y617576rNGLRRM0397JEd/lu
|
|
9Dw/S3x4qxffo6mm4NjwUiKY4iI9Ib1dHFY6QIaNabbrYrLfrpJtaK1rMzPZb/s+05IpP59OyLeJ
|
|
k7eNfRaOc1ue32I7fGXYpi5Y77M8OGMeOKxHSFsU3Y29deZMzirl6dlVvhLatCjJHeYQv1rXnps1
|
|
8k9/VsW6qLVmZIi1rzitlvFKRvaZ2h6TSaenC9FFY+3brM+sqeG8Prp4+kZ+lvuxPkr1mqm95nfp
|
|
DXM459676a2q1dsV7XietvNno78+CJn1cjX6mOeIm0bR33dfRU5NJjidt9t5afjG/V6JZ7I2QMNh
|
|
nyo2BhsMuVG3wAhMSbbQRAMolnE+iuGUSCyJZRKuGUSCyJZK4llEgyZMYTuCUsYSCQASISAAAlCQ
|
|
AAAAAAEoASCASAAAAAAAAAAAAlACRACQAAAAAAAAAEgCEoASCAAAAAAAAAAAAAAAAAAAAAAABAAA
|
|
AAAAAAAISAIAAAAAAQAAACASgAAAQJAQAAhIDHZhln3do7z0WS18mWsajHjmes7pg3dNi5aRMNqO
|
|
yvDHTpPRaigHZhN4hHRlaVN59JY3zRENLUavaO+yq0iNVlitJ6vNcR1MVi0zO0era1/Ea0rPvbz5
|
|
PM5MWp45qvo2GZrhmfrsnpHpHzTCseEcM/2vrr8Q1Eb4qzy44nziPN63HpYiIiI7LNHoqabBTFii
|
|
IpSNohuVxrKtWMEejPwY9G1FFmHB4mWJn7MdfnIM9JpIx15to5pbUaas/a6rqViI7MxPxqX0UT1r
|
|
O3wVzpbR2hviP5i03Y5s6a879FNtHljydhExCv8AMTPJXBnRZbz0iG5ptFjwe/l96zctMVamTJtE
|
|
yTMibu1VrdTzRMR0j0ed4lr64MVpm0RERvMz5NvX62uOJ69XhOKX1HH9bHDtFvNYnfJeOy0Z2ojX
|
|
6jjnEq6fRUmccTvN/J9H0eKcOnx45neaxEbubwHgOHg+milI3vP2resu3Wu0JQmITsmISDHZHKz2
|
|
JgFc1RMLJhGwK9iIZ7MZgEdgmAEwyiWCdwWRLKJVxKYsC2JTuriWUSDNlEsIlMAySx3SCRCQSIAS
|
|
AAACRACQAAAAAAASIASAAAAAAAAAAAAAAACRACRACQASIAAAAAAAAAAAAAAAAAAAAAAAAQCUAAAA
|
|
AAAAAAIAAAAAAAAQAAAAAACBICBICAAEJAQJQCJcLjuS2ny6fPG/LWdpd1o8T0X07SXx/e7wCdJx
|
|
Wa0jmneHQpxPDMdZmJfNtZm49weZrh0/j4o7VtSZ2+Uw0/8A7o49k92vBLc/ntFohFW9PqGXimOI
|
|
6Tu1L8T3eCx6r2t1O3JwvHjifO99v7t/Bwf2l1PXU6rS6eJ8qUm8x+so5TsekzcSjbvs4mt4rzW5
|
|
K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7
|
|
Vv8A0ej0uhxaXFGPFSK1j0bkY4jyZRVZVXFGUVWbGwKsk8mObekNrSW3pWf1a2aYjHbm7bNnQ1id
|
|
PW0TvuDdhJEbQABMsLW2R0ZTMQrvfbz2YWzVhpanUxEd0dWkW5c8R5uXxDX1w4pnfr5Q19XxKuOJ
|
|
2neXltVqtVxbV/RdJ715+1bypANfiOu1HENV9C0MTfNeesx2rD1PAeBYuE6aKx72W3W9/WVnBuB4
|
|
eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7
|
|
piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR
|
|
ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ
|
|
JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i
|
|
UiZidukulmxxlx2paN4mNng+K4+I8Hy2yaTfl37TXetoCPfRxfp1qi3F48ofKMvtvxak8s6LDv61
|
|
rZji9rPaLUf5PC+bfttS0q8q3p9W/wBrRMdpUZuKdN99nzvFqPbTVz7nD8OKs+do2/mW3h4D7Xaq
|
|
ZnPrtNpqz35aRaYOHY9Zk4pNt9rR+rl6zi+OnS+WN57Rv1lXp/YrNaYtruL6zNPnGO3hxP6O5w/2
|
|
f0HDuun09Yv55Le9afznqcOvO4tBreMTHu30unnva0bWt8on+70nDuE4OHYYx4Kbesz3tPrMuhGO
|
|
IjpDOKrK9YVpsyiGUQnYGOyUgI2SlAIEmwMWMs9kTAMJYzDOYRMArmGErZhhMArlHmzmGMwDE3Ts
|
|
bAbs4swj5pgFkSziVcM4BZEsolXDKAZwyhjCYBkACQhIAAAAAAAJAAAAAAAAAAAAAAAAAAAShIAA
|
|
AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
|
|
BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
|
|
SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
|
|
lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|
2Q==`;var J9="2.0.0";var mu,Ap,xp,Yi,Ji,gu,of,bp,lf,uf,df,pf,Jle=class{constructor(t){ra(this,mu,void 0);ra(this,Ap,void 0);ra(this,xp,void 0);ra(this,Yi,void 0);ra(this,Ji,void 0);ra(this,gu,void 0);this.analyze=(...t)=>{if(!pn(this,Ap))return;let n=this.tf.engine().state.numTensors,a=pn(this,mu);Ia(this,mu,n);let r=n-a;r!==0&&de(...t,r)};ra(this,of,t=>{if(!pn(this,xp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Be))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ra(this,bp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=Je();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&de("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&de("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&de("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&de(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&de("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&Qk();try{await this.tf.setBackend(this.config.backend)}catch(r){de("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(de("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&de(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(Je()-a)}});this.next=t=>Z9(t||this.result);ra(this,lf,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let u=0;u<r.length/3;u++)s+=r[3*u+2];a.dispose();let i=100*(Math.max(s,pn(this,Ji))/Math.min(s,pn(this,Ji))-1);Ia(this,Ji,s);let o=i<Math.max(this.config.cacheSensitivity,pn(this,gu));return Ia(this,gu,i>10*this.config.cacheSensitivity?0:i),o});ra(this,uf,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(rf);break;case"full":n=await t(sf);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});ra(this,df,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+rf;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+sf;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));ra(this,pf,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(rf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(sf)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&de("Warmup tfjs-node not loaded");return a});this.config=Ln(w5,t||{}),this.tf=dp,this.draw=i5,this.version=J9,this.state="idle",Ia(this,mu,0),Ia(this,Ap,!1),Ia(this,xp,!1),Ia(this,Yi,!0),Ia(this,gu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.image=n=>fu(n,this.config),this.faceTriangulation=c9,this.faceUVMap=h9,this.sysinfo=k5(),Ia(this,Ji,1)}similarity(t,n){return k2(t,n)}segmentation(t,n){return Y9(t,n,this.config)}enhance(t){return I2(t)}match(t,n,a=0){return g9(t,n,a)}async load(t){this.state="load";let n=Je();t&&(this.config=Ln(this.config,t)),pn(this,Yi)&&(this.config.debug&&de(`version: ${this.version}`),this.config.debug&&de(`tfjs version: ${this.tf.version_core}`),this.config.debug&&de("platform:",this.sysinfo.platform),this.config.debug&&de("agent:",this.sysinfo.agent),await pn(this,bp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&de("configuration:",this.config),this.config.debug&&de("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.movenet,this.models.nanodet,this.models.centernet,this.models.faceres,this.models.segmentation]=await Promise.all([this.models.face||(this.config.face.enabled?g2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?x2(this.config):null),this.models.handpose||(this.config.hand.enabled?L2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?$2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?J0(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?P9(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?X2(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?J2(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?n5(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?w2(this.config):null),this.models.segmentation||(this.config.segmentation.enabled?af(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await g2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await x2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await L2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await $2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await J0(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await J0(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await X2(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await J2(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await n5(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await w2(this.config)),this.config.segmentation.enabled&&!this.models.segmentation&&(this.models.segmentation=await af(this.config))),pn(this,Yi)&&(this.config.debug&&de("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ia(this,Yi,!1));let a=Math.trunc(Je()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r;this.config=Ln(this.config,n),this.state="check";let s=pn(this,of).call(this,t);s&&(de(s,t),a({error:s}));let i=Je();await pn(this,bp).call(this),await this.load(),r=Je();let o=fu(t,this.config);if(!o||!o.tensor){de("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.performance.image=Math.trunc(Je()-r),this.analyze("Get Image:"),r=Je(),this.config.skipFrame=await pn(this,lf).call(this,o.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(Je()-r),this.analyze("Check Changed:");let u,l,d,p,c;this.config.async?(u=this.config.face.enabled?N2(this,o.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Je(),u=this.config.face.enabled?await N2(this,o.tensor):[],c=Math.trunc(Je()-r),c>0&&(this.performance.face=c)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?F2(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?W2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?U2(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?K2(o.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Je(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await F2(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await W2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?await U2(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?await K2(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.body=c)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?P2(o.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Je(),d=this.config.hand.enabled?await P2(o.tensor,this.config):[],c=Math.trunc(Je()-r),c>0&&(this.performance.hand=c)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?Q2(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?a5(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Je(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await Q2(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await a5(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.object=c)),this.analyze("End Object:"),this.config.async&&([u,l,d,p]=await Promise.all([u,l,d,p]));let h=[];this.config.gesture.enabled&&(r=Je(),h=[...W9(u),...L9(l),...V9(d),...B9(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Je()-r)),this.performance.total=Math.trunc(Je()-i),this.state="idle",this.result={face:u,body:l,hand:d,gesture:h,object:p,performance:this.performance,canvas:o.canvas,timestamp:Date.now(),get persons(){var m;return K9(u,l,d,h,(m=o==null?void 0:o.tensor)==null?void 0:m.shape)}},he(o.tensor),a(this.result)})}async warmup(t){let n=Je();if(t&&(this.config=Ln(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await pn(this,uf).call(this):typeof Image!="undefined"?a=await pn(this,df).call(this):a=await pn(this,pf).call(this);let r=Je();return this.config.debug&&de("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};mu=new WeakMap,Ap=new WeakMap,xp=new WeakMap,Yi=new WeakMap,Ji=new WeakMap,gu=new WeakMap,of=new WeakMap,bp=new WeakMap,lf=new WeakMap,uf=new WeakMap,df=new WeakMap,pf=new WeakMap;export{Jle as Human,Jle as default};
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* 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 See the LICENSE file. */
|
|
//# sourceMappingURL=human.esm.js.map
|