mirror of https://github.com/vladmandic/human
5187 lines
1.3 MiB
5187 lines
1.3 MiB
|
|
/*
|
|
Human library
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
var VI=Object.defineProperty;var zm=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var v5=(e,t)=>{for(var n in t)VI(e,n,{get:t[n],enumerable:!0})};var w5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var pn=(e,t,n)=>(w5(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)=>(w5(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 Ke=()=>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 k5={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 I5(){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 ${process.version}`);return{platform:e,agent:t}}var dp={};v5(dp,{Abs:()=>mo,Acos:()=>go,Acosh:()=>yo,AdadeltaOptimizer:()=>wh,AdagradOptimizer:()=>kh,AdamOptimizer:()=>Ih,AdamaxOptimizer:()=>Sh,Add:()=>Or,AddN:()=>xs,All:()=>Ao,Any:()=>xo,ArgMax:()=>bs,ArgMin:()=>Fu,Asin:()=>bo,Asinh:()=>vo,Atan:()=>wo,Atan2:()=>Io,Atanh:()=>ko,AvgPool:()=>vs,AvgPool3D:()=>$u,AvgPool3DGrad:()=>Kp,AvgPoolGrad:()=>Xp,BackendWasm:()=>o4,BatchMatMul:()=>ws,BatchToSpaceND:()=>Du,Bincount:()=>Zp,BroadcastTo:()=>gb,Callback:()=>J8,CallbackList:()=>U4,Cast:()=>ks,Ceil:()=>Is,ClipByValue:()=>zr,Complex:()=>Yp,ComplexAbs:()=>Ou,Concat:()=>So,Conv2D:()=>Ss,Conv2DBackpropFilter:()=>Jp,Conv2DBackpropInput:()=>Ns,Conv3D:()=>zu,Conv3DBackpropFilterV2:()=>Qp,Conv3DBackpropInputV2:()=>ec,Cos:()=>Ts,Cosh:()=>No,CropAndResize:()=>To,Cumsum:()=>Cs,CustomCallback:()=>G4,DataStorage:()=>Up,DenseBincount:()=>tc,DepthToSpace:()=>Co,DepthwiseConv2dNative:()=>Es,DepthwiseConv2dNativeBackpropFilter:()=>nc,DepthwiseConv2dNativeBackpropInput:()=>ac,Diag:()=>rc,Dilation2D:()=>_u,Dilation2DBackpropFilter:()=>ic,Dilation2DBackpropInput:()=>sc,ENV:()=>sa,EarlyStopping:()=>ek,Einsum:()=>oc,Elu:()=>Eo,EluGrad:()=>lc,Environment:()=>fb,Equal:()=>Mo,Erf:()=>Ro,Exp:()=>Ms,ExpandDims:()=>Fo,Expm1:()=>$o,FFT:()=>uc,Fill:()=>Pu,FlipLeftRight:()=>Do,Floor:()=>Fs,FloorDiv:()=>$s,FromPixels:()=>Rc,FusedBatchNorm:()=>Ds,FusedConv2D:()=>fi,FusedDepthwiseConv2D:()=>mi,GPGPUContext:()=>Vh,GatherNd:()=>zo,GatherV2:()=>Oo,GraphModel:()=>Fk,Greater:()=>_o,GreaterEqual:()=>Os,History:()=>H4,IFFT:()=>dc,Identity:()=>zs,Imag:()=>pc,InputSpec:()=>zt,IsFinite:()=>Po,IsInf:()=>Lo,IsNan:()=>Wo,KernelBackend:()=>Eu,LRN:()=>Bu,LRNGrad:()=>hc,LayerVariable:()=>L4,LayersModel:()=>kr,LeakyRelu:()=>_s,Less:()=>Bo,LessEqual:()=>Vo,LinSpace:()=>cc,Log:()=>Ps,Log1p:()=>jo,LogSoftmax:()=>yb,LogicalAnd:()=>Uo,LogicalNot:()=>Lu,LogicalOr:()=>Wu,MathBackendCPU:()=>Eh,MathBackendWebGL:()=>Kl,Max:()=>Ls,MaxPool:()=>Bs,MaxPool3D:()=>Vu,MaxPool3DGrad:()=>mc,MaxPoolGrad:()=>fc,MaxPoolWithArgmax:()=>gc,Maximum:()=>Ws,Mean:()=>Vs,Min:()=>js,Minimum:()=>Us,MirrorPad:()=>Hs,Mod:()=>Ho,MomentumOptimizer:()=>Nh,Multinomial:()=>yc,Multiply:()=>Gs,Neg:()=>Go,NonMaxSuppressionV3:()=>Xo,NonMaxSuppressionV4:()=>Ko,NonMaxSuppressionV5:()=>Zo,NotEqual:()=>qo,OP_SCOPE_SUFFIX:()=>Fb,OneHot:()=>qs,OnesLike:()=>Yo,Optimizer:()=>xr,Pack:()=>Jo,PadV2:()=>Xs,Pool:()=>US,Pow:()=>Ks,Prelu:()=>Zs,Prod:()=>Qo,RMSPropOptimizer:()=>Th,RNN:()=>ar,Range:()=>ju,Rank:()=>Jm,Real:()=>Ac,RealDiv:()=>Rs,Reciprocal:()=>el,Reduction:()=>yn,Relu:()=>Ys,Relu6:()=>Qs,Reshape:()=>tl,ResizeBilinear:()=>Js,ResizeBilinearGrad:()=>bc,ResizeNearestNeighbor:()=>Uu,ResizeNearestNeighborGrad:()=>xc,Reverse:()=>ei,RotateWithOffset:()=>ml,Round:()=>ti,Rsqrt:()=>ni,SGDOptimizer:()=>kd,ScatterNd:()=>nl,Select:()=>al,Selu:()=>rl,Sequential:()=>ru,Sigmoid:()=>ri,Sign:()=>ol,Sin:()=>ai,Sinh:()=>il,Slice:()=>sl,Softmax:()=>oi,Softplus:()=>ll,SpaceToBatchND:()=>Hu,SparseFillEmptyRows:()=>vc,SparseReshape:()=>wc,SparseSegmentMean:()=>kc,SparseSegmentSum:()=>Ic,SparseToDense:()=>Sc,SplitV:()=>ul,Sqrt:()=>si,Square:()=>Gu,SquaredDifference:()=>li,Step:()=>Pr,StridedSlice:()=>dl,StringNGrams:()=>Nc,StringSplit:()=>Tc,StringToHashBucketFast:()=>Cc,Sub:()=>ui,Sum:()=>ii,SymbolicTensor:()=>Da,Tan:()=>di,Tanh:()=>pi,Tensor:()=>Be,TensorBuffer:()=>Lt,Tile:()=>_r,TopK:()=>pl,Transform:()=>cl,Transpose:()=>ci,Unique:()=>Ec,Unpack:()=>hl,UnsortedSegmentSum:()=>qu,Variable:()=>td,ZerosLike:()=>fl,_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:()=>h3,backend_util:()=>F,basicLSTMCell:()=>CC,batchNorm:()=>Ni,batchNorm2d:()=>y3,batchNorm3d:()=>A3,batchNorm4d:()=>x3,batchToSpaceND:()=>ud,bincount:()=>z1,booleanMaskAsync:()=>DM,broadcastTo:()=>Nl,browser:()=>oa,buffer:()=>Ve,callbacks:()=>Cie,cast:()=>ge,ceil:()=>_1,clipByValue:()=>Mn,clone:()=>Ha,complex:()=>Wr,concat:()=>lt,concat1d:()=>b3,concat2d:()=>Tl,concat3d:()=>v3,concat4d:()=>w3,constraints:()=>A4,conv1d:()=>Gc,conv2d:()=>mr,conv2dTranspose:()=>qc,conv3d:()=>L1,conv3dTranspose:()=>I3,copyRegisteredKernels:()=>qS,cos:()=>dd,cosh:()=>Xc,cosineWindow:()=>cg,cumsum:()=>Kc,customGrad:()=>qa,data:()=>$k,denseBincount:()=>S3,deprecationWarn:()=>k1,depthToSpace:()=>W1,depthwiseConv2d:()=>Cl,deregisterOp:()=>Rie,device_util:()=>ad,diag:()=>aE,dilation2d:()=>B1,disableDeprecationWarnings:()=>BT,dispose:()=>he,disposeVariables:()=>VT,div:()=>me,divNoNan:()=>V1,dot:()=>N3,dropout:()=>q3,einsum:()=>T3,elu:()=>El,enableDebugMode:()=>WT,enableProdMode:()=>LT,enclosingPowerOfTwo:()=>X3,engine:()=>fr,env:()=>te,equal:()=>Hr,erf:()=>j1,exp:()=>la,expandDims:()=>mn,expm1:()=>U1,eye:()=>H1,fft:()=>bd,fill:()=>Rl,findBackend:()=>I1,findBackendFactory:()=>KT,floor:()=>Ml,floorDiv:()=>Vc,forceHalfFloat:()=>Aw,fused:()=>Kr,gather:()=>Ti,gatherND:()=>G3,gather_util:()=>g1,getBackend:()=>qT,getGradient:()=>Xm,getKernel:()=>Mc,getKernelsForBackend:()=>yl,gpgpu_util:()=>Bv,grad:()=>$E,grads:()=>DE,greater:()=>Wn,greaterEqual:()=>qr,ifft:()=>Ol,imag:()=>Zc,image:()=>De,inTopKAsync:()=>HM,initializers:()=>S4,input:()=>m8,io:()=>En,irfft:()=>ch,isFinite:()=>C3,isInf:()=>E3,isNaN:()=>G1,keep:()=>Kt,kernel_impls:()=>Za,layers:()=>z4,leakyRelu:()=>pd,less:()=>Yc,lessEqual:()=>Xr,linalg:()=>i7,linspace:()=>R3,loadGraphModel:()=>ct,loadLayersModel:()=>Pre,localResponseNormalization:()=>q1,log:()=>Bn,log1p:()=>Jc,logSigmoid:()=>F3,logSoftmax:()=>eh,logSumExp:()=>Z1,logicalAnd:()=>xa,logicalNot:()=>cd,logicalOr:()=>th,logicalXor:()=>z3,losses:()=>S$,matMul:()=>je,math:()=>Xb,max:()=>Vn,maxPool:()=>hd,maxPool3d:()=>Y1,maxPoolWithArgmax:()=>_3,maximum:()=>Xa,mean:()=>Nt,memory:()=>Bc,meshgrid:()=>nR,metrics:()=>K8,min:()=>fd,minimum:()=>Fl,mirrorPad:()=>J1,mod:()=>Q1,model:()=>zre,models:()=>Z8,moments:()=>nh,movingAverage:()=>_M,mul:()=>B,multiRNNCell:()=>dR,multinomial:()=>P3,neg:()=>St,nextFrame:()=>Ch,norm:()=>gh,notEqual:()=>Ri,oneHot:()=>wl,ones:()=>jn,onesLike:()=>Un,op:()=>L,outerProduct:()=>mR,pad:()=>gr,pad1d:()=>AR,pad2d:()=>bR,pad3d:()=>wR,pad4d:()=>IR,pool:()=>L3,pow:()=>yr,prelu:()=>gd,print:()=>Vb,prod:()=>ah,profile:()=>jT,rand:()=>$R,randomGamma:()=>_R,randomNormal:()=>W3,randomUniform:()=>$l,range:()=>Dl,ready:()=>GT,real:()=>yd,reciprocal:()=>ng,registerBackend:()=>Il,registerCallbackConstructor:()=>Lre,registerGradient:()=>Ab,registerKernel:()=>gi,registerOp:()=>Eie,regularizers:()=>Y8,relu:()=>Ka,relu6:()=>rh,removeBackend:()=>XT,reshape:()=>q,reverse:()=>Hn,reverse1d:()=>GR,reverse2d:()=>XR,reverse3d:()=>ZR,reverse4d:()=>JR,rfft:()=>vd,round:()=>sh,rsqrt:()=>ih,scalar:()=>ke,scatterND:()=>H3,scatter_util:()=>y1,selu:()=>oh,separableConv2d:()=>ag,sequential:()=>_re,serialization:()=>re,setBackend:()=>HT,setPlatform:()=>ZT,setWasmPath:()=>Vee,setWasmPaths:()=>jee,setWebGLContext:()=>Oh,setdiff1dAsync:()=>B3,shared:()=>yg,sigmoid:()=>Rn,sign:()=>rg,signal:()=>I$,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:()=>k$,split:()=>Zt,sqrt:()=>an,square:()=>ot,squaredDifference:()=>hh,squeeze:()=>Vt,stack:()=>gn,step:()=>zl,stridedSlice:()=>ig,string:()=>vh,sub:()=>ye,sum:()=>Se,sumOutType:()=>zc,tan:()=>og,tanh:()=>Si,tensor:()=>ln,tensor1d:()=>Dt,tensor2d:()=>Ta,tensor3d:()=>Lc,tensor4d:()=>IM,tensor5d:()=>SM,tensor6d:()=>NM,tensor_util:()=>Sa,test_util:()=>d3,tidy:()=>V,tile:()=>Gr,time:()=>UT,topk:()=>lg,train:()=>Fi,transpose:()=>Qe,truncatedNormal:()=>fh,unique:()=>mh,unregisterGradient:()=>GS,unregisterKernel:()=>HS,unsortedSegmentSum:()=>ug,unstack:()=>Gn,upcastType:()=>Aa,util:()=>k,valueAndGrad:()=>OE,valueAndGrads:()=>zE,variable:()=>V3,variableGrads:()=>M3,version:()=>xle,version_converter:()=>$oe,version_core:()=>PT,version_cpu:()=>X7,version_layers:()=>Dy,version_wasm:()=>u4,version_webgl:()=>yw,webgl:()=>EV,webgl_util:()=>mv,where:()=>un,whereAsync:()=>dg,zeros:()=>$t,zerosLike:()=>Ge});var jI=Object.create,jp=Object.defineProperty,UI=Object.getOwnPropertyDescriptor,HI=Object.getOwnPropertyNames,GI=Object.getPrototypeOf,qI=Object.prototype.hasOwnProperty,XI=e=>jp(e,"__esModule",{value:!0}),co=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})},KI=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of HI(t))!qI.call(e,a)&&a!=="default"&&jp(e,a,{get:()=>t[a],enumerable:!(n=UI(t,a))||n.enumerable});return e},gs=e=>KI(XI(jp(e!=null?jI(GI(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),ZI=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=u(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=u(S,S<0?-1:0,!1),G&&(s[S]=O),O))}a.fromInt=o;function l(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?l(-S,z).neg():u(S%f|0,S/f|0,z)}a.fromNumber=l;function u(S,z,O){return new a(S,z,O)}a.fromBits=u;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=l(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=l(d(O,K));H=H.mul(Q).add(l(ne))}else H=H.mul(G),H=H.add(l(ne))}return H.unsigned=z,H}a.fromString=p;function c(S,z){return typeof S=="number"?l(S,z):typeof S=="string"?p(S,z):u(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=u(4294967295|0,2147483647|0,!1);a.MAX_VALUE=C;var E=u(4294967295|0,4294967295|0,!0);a.MAX_UNSIGNED_VALUE=E;var _=u(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=l(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=l(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,$.add=function(S){r(S)||(S=c(S));var z=this.high>>>16,O=this.high&65535,W=this.low>>>16,G=this.low&65535,H=S.high>>>16,J=S.high&65535,K=S.low>>>16,ne=S.low&65535,Q=0,se=0,Z=0,le=0;return le+=G+ne,Z+=le>>>16,le&=65535,Z+=W+K,se+=Z>>>16,Z&=65535,se+=O+J,Q+=se>>>16,se&=65535,Q+=z+H,Q&=65535,u(Z<<16|le,Q<<16|se,this.unsigned)},$.subtract=function(S){return r(S)||(S=c(S)),this.add(S.neg())},$.sub=$.subtract,$.multiply=function(S){if(this.isZero())return x;if(r(S)||(S=c(S)),n){var z=n.mul(this.low,this.high,S.low,S.high);return u(z,n.get_high(),this.unsigned)}if(S.isZero())return x;if(this.eq(_))return S.isOdd()?_:x;if(S.eq(_))return this.isOdd()?_:x;if(this.isNegative())return S.isNegative()?this.neg().mul(S.neg()):this.neg().mul(S).neg();if(S.isNegative())return this.mul(S.neg()).neg();if(this.lt(A)&&S.lt(A))return l(this.toNumber()*S.toNumber(),this.unsigned);var O=this.high>>>16,W=this.high&65535,G=this.low>>>16,H=this.low&65535,J=S.high>>>16,K=S.high&65535,ne=S.low>>>16,Q=S.low&65535,se=0,Z=0,le=0,oe=0;return oe+=H*Q,le+=oe>>>16,oe&=65535,le+=G*Q,Z+=le>>>16,le&=65535,le+=H*ne,Z+=le>>>16,le&=65535,Z+=W*Q,se+=Z>>>16,Z&=65535,Z+=G*ne,se+=Z>>>16,Z&=65535,Z+=H*K,se+=Z>>>16,Z&=65535,se+=O*Q+W*ne+G*K+H*J,se&=65535,u(le<<16|oe,se<<16|Z,this.unsigned)},$.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 u(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=l(O),Q=ne.mul(S);Q.isNegative()||Q.gt(W);)O-=K,ne=l(O,this.unsigned),Q=ne.mul(S);ne.isZero()&&(ne=b),G=G.add(ne),W=W.sub(Q)}return G},$.div=$.divide,$.modulo=function(S){if(r(S)||(S=c(S)),n){var z=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,S.low,S.high);return u(z,n.get_high(),this.unsigned)}return this.sub(this.div(S).mul(S))},$.mod=$.modulo,$.rem=$.modulo,$.not=function(){return u(~this.low,~this.high,this.unsigned)},$.and=function(S){return r(S)||(S=c(S)),u(this.low&S.low,this.high&S.high,this.unsigned)},$.or=function(S){return r(S)||(S=c(S)),u(this.low|S.low,this.high|S.high,this.unsigned)},$.xor=function(S){return r(S)||(S=c(S)),u(this.low^S.low,this.high^S.high,this.unsigned)},$.shiftLeft=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?u(this.low<<S,this.high<<S|this.low>>>32-S,this.unsigned):u(0,this.low<<S-32,this.unsigned)},$.shl=$.shiftLeft,$.shiftRight=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?u(this.low>>>S|this.high<<32-S,this.high>>S,this.unsigned):u(this.high>>S-32,this.high>=0?0:-1,this.unsigned)},$.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 u(O>>>S|z<<32-S,z>>>S,this.unsigned)}else return S===32?u(z,0,this.unsigned):u(z>>>S-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},$.toUnsigned=function(){return this.unsigned?this:u(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)}}),YI=xt(()=>{}),JI=xt((e,t)=>{(function(n,a,r){function s(u){var d=this,p=l();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(u),d.s0<0&&(d.s0+=1),d.s1-=p(u),d.s1<0&&(d.s1+=1),d.s2-=p(u),d.s2<0&&(d.s2+=1),p=null}function i(u,d){return d.c=u.c,d.s0=u.s0,d.s1=u.s1,d.s2=u.s2,d}function o(u,d){var p=new s(u),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 l(){var u=4022871197,d=function(p){p=p.toString();for(var c=0;c<p.length;c++){u+=p.charCodeAt(c);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return 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)}),QI=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:d+=l;for(var p=0;p<d.length+64;p++)u.x^=d.charCodeAt(p)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var d=new s(l),p=u&&u.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)}),eS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:d+=l;for(var p=0;p<d.length+64;p++)u.x^=d.charCodeAt(p)|0,p==d.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.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)}),tS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.x,c=u.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,u.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(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.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)}),nS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.w,c=u.X,h=u.i,m,f;return u.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,u.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(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.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)}),aS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):d+=l;for(var p=0;p<d.length+20;p++)u.b^=d.charCodeAt(p)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.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)}),S5=xt(()=>{}),rS=xt((e,t)=>{(function(n,a){var r=this,s=256,i=6,o=52,l="random",u=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=u,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[l]=S,z):S})($,E,"global"in w?w.global:this==a,w.state)}a["seed"+l]=m;function f(b){var w,N=b.length,C=this,E=0,_=C.i=C.j=0,$=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=S5()}catch(b){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),N5=xt((e,t)=>{var n=JI(),a=QI(),r=eS(),s=tS(),i=nS(),o=aS(),l=rS();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),Cu=xt(()=>{}),sS=xt(()=>{}),iS=xt(()=>{}),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||{};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 l(){return Z.buffer!=Ue&&tn(Z.buffer),na}function u(){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=co("fs")),O||(O=Cu()),T=O.normalize(T),z.readFileSync(T,R?null:"utf8")},$=function(T){var R=E(T,!0);return R.buffer||(R=new Uint8Array(R)),fe(R.buffer),R},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),f=process.argv.slice(2),process.on("uncaughtException",function(T){if(!(T instanceof Tu))throw T}),process.on("unhandledRejection",ur),y=function(T){process.exit(T)},d.inspect=function(){return"[Emscripten Module object]"};var W;try{W=sS()}catch(T){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),T}global.Worker=W.Worker}else b?(typeof read!="undefined"&&(E=function(T){return read(T)}),$=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=co("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=iS().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 po=0;if(Cn!=null&&Cn!==0){var b5=(Cn.length<<2)+1;po=oo(b5),nt(Cn,po,b5)}return po},array:function(Cn){var po=oo(Cn.length);return Ye(Cn,po),po}};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 uo=ve.apply(null,at);return uo=pe(uo),Gt!==0&&io(Gt),uo}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 Ye(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=[],eo=[],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());no(ma)}}function yu(){Va=!0,!w&&no(Nr)}function hf(){w||no(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());no(eo)}}function kp(T){ma.unshift(T)}function ff(T){eo.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 to="data:application/octet-stream;base64,";function Sp(T){return Ip(T,to)}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(){f5(T,R)},0)}};function Cp(){Ie.initRuntime()}function no(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(),lo>>2),X=0;if(j==T){var ce=Atomics.compareExchange(o(),lo>>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)l()[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)l()[X/4+R]=0;Atomics.store(l(),T+100>>2,X),Atomics.store(l(),T+40>>2,T),Dm(T,!x,1),h5(T)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ie.threadExitHandlers.length>0;)Ie.threadExitHandlers.pop()();w&&so()&&c5()},runExitHandlersAndDeinitThread:function(T,R){Atomics.store(l(),T+56>>2,1),Atomics.store(l(),T+60>>2,0),Ie.runExitHandlers(),Atomics.store(l(),T+4>>2,R),Atomics.store(l(),T+0>>2,1),Au(T+0,2147483647),Dm(0,0,0)},threadExit:function(T){var R=so();R&&(Ie.runExitHandlersAndDeinitThread(R,T),w&&postMessage({cmd:"exit"}))},threadCancel:function(){Ie.runExitHandlersAndDeinitThread(so(),-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()[x5>>2]=0;try{T()}finally{o()[x5>>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!=so()){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(l(),T.pthread.threadInfoStruct+64>>2);pe&&Ie.returnWorkerToPool(T)}else if(ce==="exitProcess")try{BI(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){y5(T,R),io(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()[d5()>>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(),lo>>2,T);;){if(X=performance.now(),X>ce)return ue=Atomics.exchange(o(),lo>>2,0),-73;if(ue=Atomics.exchange(o(),lo>>2,0),ue==0)break;if(Fm(),Atomics.load(o(),T>>2)!=R)return-6;ue=Atomics.exchange(o(),lo>>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?co("os").cpus().length:navigator.hardwareConcurrency}function Er(T,R){for(var j=arguments.length-2,X=Nu(),ce=j,ue=oo(ce*8),pe=ue>>3,ve=0;ve<j;ve++){var at=arguments[2+ve];u()[pe+ve]=at}var Gt=g5(T,ce,ue,R);return io(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?u()[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]=u()[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=oo(12);o()[pe>>2]=j,o()[pe+4>>2]=X,o()[pe+8>>2]=ce,$m(0,T,637534208,R,X,pe),io(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=oo(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),io(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]=so();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 ao={mappings:{},buffers:[null,[],[]],printChar:function(T,R){var j=ao.buffers[T];R===0||R===10?((T===1?G:H)(Pe(j,0)),j.length=0):j.push(R)},varargs:void 0,get:function(){ao.varargs+=4;var T=o()[ao.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++)ao.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(l(),pe+(64>>2),T.detached),Atomics.store(l(),pe+(100>>2),j),Atomics.store(l(),pe+(40>>2),ue.threadInfoStruct),Atomics.store(l(),pe+(80>>2),T.stackSize),Atomics.store(l(),pe+(76>>2),ce),Atomics.store(l(),pe+(104>>2),T.stackSize),Atomics.store(l(),pe+(104+8>>2),ce),Atomics.store(l(),pe+(104+12>>2),T.detached);var ve=p5(),at=ve+40;Atomics.store(l(),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 m5(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=A5(16,pe):(ve-=pe,fe(ve>0));for(var Pt=ms(228),Fr=0;Fr<228>>2;++Fr)l()[(Pt>>2)+Fr]=0;o()[T>>2]=Pt,o()[Pt+12>>2]=Pt;var uo=Pt+152;o()[uo>>2]=uo;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},u5=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)},ro=d._FloorDiv=function(){return(ro=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)},Je=d._Greater=function(){return(Je=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)},eI=d._MirrorPad=function(){return(eI=d._MirrorPad=d.asm.sa).apply(null,arguments)},tI=d._Multiply=function(){return(tI=d._Multiply=d.asm.ta).apply(null,arguments)},nI=d._Neg=function(){return(nI=d._Neg=d.asm.ua).apply(null,arguments)},aI=d._NonMaxSuppressionV3=function(){return(aI=d._NonMaxSuppressionV3=d.asm.va).apply(null,arguments)},rI=d._NonMaxSuppressionV4=function(){return(rI=d._NonMaxSuppressionV4=d.asm.wa).apply(null,arguments)},sI=d._NonMaxSuppressionV5=function(){return(sI=d._NonMaxSuppressionV5=d.asm.xa).apply(null,arguments)},iI=d._NotEqual=function(){return(iI=d._NotEqual=d.asm.ya).apply(null,arguments)},oI=d._OneHot=function(){return(oI=d._OneHot=d.asm.za).apply(null,arguments)},lI=d._PadV2=function(){return(lI=d._PadV2=d.asm.Aa).apply(null,arguments)},uI=d._Pow=function(){return(uI=d._Pow=d.asm.Ba).apply(null,arguments)},dI=d._Prelu=function(){return(dI=d._Prelu=d.asm.Ca).apply(null,arguments)},pI=d._Prod=function(){return(pI=d._Prod=d.asm.Da).apply(null,arguments)},cI=d._RealDiv=function(){return(cI=d._RealDiv=d.asm.Ea).apply(null,arguments)},hI=d._Relu=function(){return(hI=d._Relu=d.asm.Fa).apply(null,arguments)},fI=d._Relu6=function(){return(fI=d._Relu6=d.asm.Ga).apply(null,arguments)},mI=d._ResizeBilinear=function(){return(mI=d._ResizeBilinear=d.asm.Ha).apply(null,arguments)},gI=d._Reverse=function(){return(gI=d._Reverse=d.asm.Ia).apply(null,arguments)},yI=d._RotateWithOffset=function(){return(yI=d._RotateWithOffset=d.asm.Ja).apply(null,arguments)},AI=d._Round=function(){return(AI=d._Round=d.asm.Ka).apply(null,arguments)},xI=d._Rsqrt=function(){return(xI=d._Rsqrt=d.asm.La).apply(null,arguments)},bI=d._ScatterNd=function(){return(bI=d._ScatterNd=d.asm.Ma).apply(null,arguments)},vI=d._SelectV2=function(){return(vI=d._SelectV2=d.asm.Na).apply(null,arguments)},wI=d._Sigmoid=function(){return(wI=d._Sigmoid=d.asm.Oa).apply(null,arguments)},kI=d._Sin=function(){return(kI=d._Sin=d.asm.Pa).apply(null,arguments)},II=d._Softmax=function(){return(II=d._Softmax=d.asm.Qa).apply(null,arguments)},SI=d._Sqrt=function(){return(SI=d._Sqrt=d.asm.Ra).apply(null,arguments)},NI=d._Square=function(){return(NI=d._Square=d.asm.Sa).apply(null,arguments)},TI=d._SquaredDifference=function(){return(TI=d._SquaredDifference=d.asm.Ta).apply(null,arguments)},CI=d._Step=function(){return(CI=d._Step=d.asm.Ua).apply(null,arguments)},EI=d._StridedSlice=function(){return(EI=d._StridedSlice=d.asm.Va).apply(null,arguments)},RI=d._Sub=function(){return(RI=d._Sub=d.asm.Wa).apply(null,arguments)},MI=d._Sum=function(){return(MI=d._Sum=d.asm.Xa).apply(null,arguments)},FI=d._Tan=function(){return(FI=d._Tan=d.asm.Ya).apply(null,arguments)},$I=d._Tanh=function(){return($I=d._Tanh=d.asm.Za).apply(null,arguments)},DI=d._Tile=function(){return(DI=d._Tile=d.asm._a).apply(null,arguments)},OI=d._TopK=function(){return(OI=d._TopK=d.asm.$a).apply(null,arguments)},zI=d._Transform=function(){return(zI=d._Transform=d.asm.ab).apply(null,arguments)},_I=d._Transpose=function(){return(_I=d._Transpose=d.asm.bb).apply(null,arguments)},PI=d.__FusedMatMul=function(){return(PI=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)},d5=d.___errno_location=function(){return(d5=d.___errno_location=d.asm.fb).apply(null,arguments)},p5=d._emscripten_get_global_libc=function(){return(p5=d._emscripten_get_global_libc=d.asm.gb).apply(null,arguments)},so=d._pthread_self=function(){return(so=d._pthread_self=d.asm.hb).apply(null,arguments)},c5=d.___pthread_tsd_run_dtors=function(){return(c5=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)},LI=d._emscripten_current_thread_process_queued_calls=function(){return(LI=d._emscripten_current_thread_process_queued_calls=d.asm.kb).apply(null,arguments)},h5=d._emscripten_register_main_browser_thread_id=function(){return(h5=d._emscripten_register_main_browser_thread_id=d.asm.lb).apply(null,arguments)},f5=d.__emscripten_do_dispatch_to_thread=function(){return(f5=d.__emscripten_do_dispatch_to_thread=d.asm.mb).apply(null,arguments)},m5=d._emscripten_sync_run_in_main_thread_4=function(){return(m5=d._emscripten_sync_run_in_main_thread_4=d.asm.nb).apply(null,arguments)},g5=d._emscripten_run_in_main_runtime_thread_js=function(){return(g5=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)},WI=d._emscripten_tls_init=function(){return(WI=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)},io=d.stackRestore=function(){return(io=d.stackRestore=d.asm.tb).apply(null,arguments)},oo=d.stackAlloc=function(){return(oo=d.stackAlloc=d.asm.ub).apply(null,arguments)},y5=d._emscripten_stack_set_limits=function(){return(y5=d._emscripten_stack_set_limits=d.asm.vb).apply(null,arguments)},A5=d._memalign=function(){return(A5=d._memalign=d.asm.wb).apply(null,arguments)},x5=d.__emscripten_allow_main_runtime_queued_calls=9808,lo=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 BI(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)}),lS=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 l={},u;for(u in s)s.hasOwnProperty(u)&&(l[u]=s[u]);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=co("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 Je=new XMLHttpRequest;Je.open("GET",Y,!0),Je.responseType="arraybuffer",Je.onload=function(){if(Je.status==200||Je.status==0&&Je.response){ae(Je.response);return}Ce()},Je.onerror=Ce,Je.send(null)},w=function(Y){document.title=Y});var E=s.print||console.log.bind(console),_=s.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(s[u]=l[u]);l=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,Je,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(Je)for(var pr=0;pr<Je.length;pr++){var Wp=At[Ce[pr]];Wp?(dr===0&&(dr=Pp()),nn[pr]=Wp(Je[pr])):nn[pr]=Je[pr]}var Iu=qe.apply(null,nn);return Iu=He(Iu),dr!==0&&Lp(dr),Iu}function K(Y,ae,Ce,Je){Ce=Ce||[];var Ct=Ce.every(function(He){return He==="number"}),At=ae!=="string";return At&&Ct&&!Je?H(Y):function(){return J(Y,ae,Ce,arguments,Je)}}var ne=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function Q(Y,ae,Ce){for(var Je=ae+Ce,Ct=ae;Y[Ct]&&!(Ct>=Je);)++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,Je){if(!(Je>0))return 0;for(var Ct=Ce,At=Ce+Je-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 Ye(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 eo(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(!$&&(h||m)){if(typeof fetch=="function"&&!wp(Nn))return fetch(Nn,{credentials:"same-origin"}).then(function(Y){if(!Y.ok)throw"failed to load wasm binary file at '"+Nn+"'";return Y.arrayBuffer()}).catch(function(){return kp(Nn)});if(v)return new Promise(function(Y,ae){v(Nn,function(Ce){Y(new Uint8Array(Ce))},ae)})}return Promise.resolve().then(function(){return kp(Nn)})}function lr(){var Y={a:xf};function ae(He,qe){var nn=He.exports;s.asm=nn,z=s.asm.i,Ye(z.buffer),Ue=s.asm.o,eo("wasm-instantiate")}Tr("wasm-instantiate");function Ce(He){ae(He.instance)}function Je(He){return ff().then(function(qe){return WebAssembly.instantiate(qe,Y)}).then(He,function(qe){_("failed to asynchronously prepare wasm: "+qe),Va(qe)})}function Ct(){return!$&&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"),Je(Ce)})}):Je(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),Ye(z.buffer),1}catch(ae){}}function Ip(Y){var ae=gf(),Ce=2147483648;if(Y>Ce)return!1;for(var Je=1;Je<=4;Je*=2){var Ct=ae*(1+.2/Je);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 to={mappings:{},buffers:[null,[],[]],printChar:function(Y,ae){var Ce=to.buffers[Y];ae===0||ae===10?((Y===1?E:_)(Q(Ce,0)),Ce.length=0):Ce.push(ae)},varargs:void 0,get:function(){to.varargs+=4;var Y=ze[to.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,Je,Ct){}function Np(Y,ae,Ce,Je){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++)to.printChar(Y,Te[He+nn]);Ct+=qe}return ze[Je>>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)},no=s._init=function(){return(no=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.$).apply(null,arguments)},Qf=s._Minimum=function(){return(Qf=s._Minimum=s.asm.aa).apply(null,arguments)},em=s._MirrorPad=function(){return(em=s._MirrorPad=s.asm.ba).apply(null,arguments)},tm=s._Multiply=function(){return(tm=s._Multiply=s.asm.ca).apply(null,arguments)},et=s._Neg=function(){return(et=s._Neg=s.asm.da).apply(null,arguments)},nm=s._NonMaxSuppressionV3=function(){return(nm=s._NonMaxSuppressionV3=s.asm.ea).apply(null,arguments)},am=s._NonMaxSuppressionV4=function(){return(am=s._NonMaxSuppressionV4=s.asm.fa).apply(null,arguments)},rm=s._NonMaxSuppressionV5=function(){return(rm=s._NonMaxSuppressionV5=s.asm.ga).apply(null,arguments)},ao=s._NotEqual=function(){return(ao=s._NotEqual=s.asm.ha).apply(null,arguments)},Mp=s._OneHot=function(){return(Mp=s._OneHot=s.asm.ia).apply(null,arguments)},Fp=s._PadV2=function(){return(Fp=s._PadV2=s.asm.ja).apply(null,arguments)},$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)},u5=s._Round=function(){return(u5=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 ro;function Mm(Y){this.name="ExitStatus",this.message="Program terminated with exit("+Y+")",this.status=Y}or=function Y(){ro||ku(),ro||(or=Y)};function ku(Y){if(Y=Y||d,ma>0||(na(),ma>0))return;function ae(){ro||(ro=!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)}),uS=xt((e,t)=>{(function(n,a,r){function s(u){var d=this,p=l();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(u),d.s0<0&&(d.s0+=1),d.s1-=p(u),d.s1<0&&(d.s1+=1),d.s2-=p(u),d.s2<0&&(d.s2+=1),p=null}function i(u,d){return d.c=u.c,d.s0=u.s0,d.s1=u.s1,d.s2=u.s2,d}function o(u,d){var p=new s(u),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 l(){var u=4022871197,d=function(p){p=String(p);for(var c=0;c<p.length;c++){u+=p.charCodeAt(c);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return 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)}),dS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:d+=l;for(var p=0;p<d.length+64;p++)u.x^=d.charCodeAt(p)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var d=new s(l),p=u&&u.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)}),pS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:d+=l;for(var p=0;p<d.length+64;p++)u.x^=d.charCodeAt(p)|0,p==d.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.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)}),cS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.x,c=u.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,u.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(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.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)}),hS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.w,c=u.X,h=u.i,m,f;return u.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,u.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(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.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)}),fS=xt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):d+=l;for(var p=0;p<d.length+20;p++)u.b^=d.charCodeAt(p)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.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)}),mS=xt((e,t)=>{(function(n,a,r){var s=256,i=6,o=52,l="random",u=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=u,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[l]=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=S5()}catch(b){}}else typeof define=="function"&&define.amd?define(function(){return m}):r["seed"+l]=m})(typeof self!="undefined"?self:e,[],Math)}),T5=xt((e,t)=>{var n=uS(),a=dS(),r=pS(),s=cS(),i=hS(),o=fS(),l=mS();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),gS=xt(()=>{}),_m={};Fe(_m,{bin:()=>P5,browser:()=>U5,default:()=>yS,dependencies:()=>j5,description:()=>R5,devDependencies:()=>B5,jsdelivr:()=>D5,license:()=>W5,main:()=>F5,miniprogram:()=>_5,module:()=>$5,name:()=>C5,private:()=>M5,repository:()=>L5,scripts:()=>V5,types:()=>z5,unpkg:()=>O5,version:()=>E5});var C5="@tensorflow/tfjs",E5="3.7.0",R5="An open-source machine learning framework.",M5=!1,F5="dist/tf.node.js",$5="dist/index.js",D5="dist/tf.min.js",O5="dist/tf.min.js",z5="dist/index.d.ts",_5="dist/miniprogram",P5={"tfjs-custom-module":"dist/tools/custom_module/cli.js"},L5={type:"git",url:"https://github.com/tensorflow/tfjs.git"},W5="Apache-2.0",B5={"@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"},V5={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"},j5={"@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"},U5={"node-fetch":!1,util:!1,crypto:!1},yS={name:C5,version:E5,description:R5,private:M5,main:F5,module:$5,jsdelivr:D5,unpkg:O5,types:z5,miniprogram:_5,bin:P5,repository:L5,license:W5,devDependencies:B5,scripts:V5,dependencies:j5,browser:U5},Pm={};Fe(Pm,{browser:()=>lx,default:()=>AS,dependencies:()=>ox,description:()=>q5,devDependencies:()=>sx,engines:()=>nx,jsdelivr:()=>Z5,"jsnext:main":()=>Q5,license:()=>rx,main:()=>K5,miniprogram:()=>tx,module:()=>ex,name:()=>H5,private:()=>X5,repository:()=>ax,scripts:()=>ix,sideEffects:()=>ux,types:()=>J5,unpkg:()=>Y5,version:()=>G5});var H5="@tensorflow/tfjs-core",G5="3.7.0",q5="Hardware-accelerated JavaScript library for machine intelligence",X5=!1,K5="dist/tf-core.node.js",Z5="dist/tf-core.min.js",Y5="dist/tf-core.min.js",J5="dist/index.d.ts",Q5="dist/index.js",ex="dist/index.js",tx="dist/miniprogram",nx={yarn:">= 1.3.2"},ax={type:"git",url:"https://github.com/tensorflow/tfjs-core.git"},rx="Apache-2.0",sx={"@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"},ix={"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"},ox={"@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"},lx={"node-fetch":!1,util:!1,crypto:!1,worker_threads:!1},ux=["./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"],AS={name:H5,version:G5,description:q5,private:X5,main:K5,jsdelivr:Z5,unpkg:Y5,types:J5,"jsnext:main":Q5,module:ex,miniprogram:tx,engines:nx,repository:ax,license:rx,devDependencies:sx,scripts:ix,dependencies:ox,browser:lx,sideEffects:ux},Lm={};Fe(Lm,{browser:()=>Nx,default:()=>xS,dependencies:()=>Sx,description:()=>cx,devDependencies:()=>wx,jsdelivr:()=>mx,"jsnext:main":()=>Ax,license:()=>vx,main:()=>fx,miniprogram:()=>bx,module:()=>xx,name:()=>dx,peerDependencies:()=>Ix,private:()=>hx,scripts:()=>kx,types:()=>yx,unpkg:()=>gx,version:()=>px});var dx="@tensorflow/tfjs-data",px="3.7.0",cx="TensorFlow Data API in JavaScript",hx=!1,fx="dist/tf-data.node.js",mx="dist/tf-data.min.js",gx="dist/tf-data.min.js",yx="dist/index.d.ts",Ax="dist/index.js",xx="dist/index.js",bx="dist/miniprogram",vx="Apache-2.0",wx={"@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"},kx={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"},Ix={"@tensorflow/tfjs-core":"3.7.0",seedrandom:"~2.4.3"},Sx={"@types/node-fetch":"^2.1.2","node-fetch":"~2.6.1"},Nx={fs:!1,"node-fetch":!1,string_decoder:!1,crypto:!1},xS={name:dx,version:px,description:cx,private:hx,main:fx,jsdelivr:mx,unpkg:gx,types:yx,"jsnext:main":Ax,module:xx,miniprogram:bx,license:vx,devDependencies:wx,scripts:kx,peerDependencies:Ix,dependencies:Sx,browser:Nx},Wm={};Fe(Wm,{default:()=>bS,description:()=>Ex,devDependencies:()=>Lx,jsdelivr:()=>zx,"jsnext:main":()=>Dx,license:()=>Rx,main:()=>Fx,miniprogram:()=>Px,module:()=>Ox,name:()=>Tx,peerDependencies:()=>Bx,private:()=>Mx,scripts:()=>Wx,types:()=>$x,unpkg:()=>_x,version:()=>Cx});var Tx="@tensorflow/tfjs-layers",Cx="3.7.0",Ex="TensorFlow layers API in JavaScript",Rx="Apache-2.0 AND MIT",Mx=!1,Fx="dist/tf-layers.node.js",$x="dist/index.d.ts",Dx="dist/index.js",Ox="dist/index.js",zx="dist/tf-layers.min.js",_x="dist/tf-layers.min.js",Px="dist/miniprogram",Lx={"@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"},Wx={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"},Bx={"@tensorflow/tfjs-core":"3.7.0"},bS={name:Tx,version:Cx,description:Ex,license:Rx,private:Mx,main:Fx,types:$x,"jsnext:main":Dx,module:Ox,jsdelivr:zx,unpkg:_x,miniprogram:Px,devDependencies:Lx,scripts:Wx,peerDependencies:Bx},Bm={};Fe(Bm,{default:()=>vS,description:()=>Ux,devDependencies:()=>tb,jsdelivr:()=>Zx,"jsnext:main":()=>Gx,license:()=>Qx,main:()=>Hx,miniprogram:()=>Yx,module:()=>qx,name:()=>Vx,peerDependencies:()=>eb,repository:()=>Jx,scripts:()=>nb,types:()=>Xx,unpkg:()=>Kx,version:()=>jx});var Vx="@tensorflow/tfjs-converter",jx="3.7.0",Ux="Tensorflow model converter for javascript",Hx="dist/tf-converter.node.js",Gx="dist/index.js",qx="dist/index.js",Xx="dist/index.d.ts",Kx="dist/tf-converter.min.js",Zx="dist/tf-converter.min.js",Yx="dist/miniprogram",Jx={type:"git",url:"https://github.com/tensorflow/tfjs-converter.git"},Qx="Apache-2.0",eb={"@tensorflow/tfjs-core":"3.7.0"},tb={"@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"},nb={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"},vS={name:Vx,version:jx,description:Ux,main:Hx,"jsnext:main":Gx,module:qx,types:Xx,unpkg:Kx,jsdelivr:Zx,miniprogram:Yx,repository:Jx,license:Qx,peerDependencies:eb,devDependencies:tb,scripts:nb},wS=1e-7,kS=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?wS:kS}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 ab(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 IS(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 SS(e){return e%2==0?e:e+1}function NS(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function TS(e,t){let n=Math.random();return t*n+(1-n)*e}function CS(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 ES(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 RS(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 MS(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function FS(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return ab(t),t}function Mu(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function $S(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 DS(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 rb(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 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 throw new Error(`Unknown data type ${e}`);return n}function ib(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 ob(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 lb(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function OS(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 ub(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function $r(e){return typeof e=="string"||e instanceof String}function db(e){return typeof e=="boolean"}function pb(e){return typeof e=="number"}function Hp(e){return Array.isArray(e)?Hp(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":pb(e)?"float32":$r(e)?"string":db(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 ho(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 cb(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((l,u)=>l*u)*(a?2:1);for(let l=0;l<s;l++)r[l]=cb(e+l*o,i,n,a)}return r}function fo(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 cb(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 zS(e,t){let n=e.reduce((a,r)=>a*r,1);if(t==null||t==="float32")return fo(e,new Float32Array(n));if(t==="int32")return fo(e,new Int32Array(n));if(t==="bool")return fo(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 _S(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 PS(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 hb="tfjsflags",fb=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=LS,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);hb in e&&e[hb].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=BS(n,a)})}};function LS(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(WS(t,a[0],a[1]),a.join("="))),t}function WS(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function BS(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 ${e}.`)}function te(){return sa}var sa=null;function VS(e){sa=e}var Gm;function mb(){if(Gm==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Gm=e}return Gm}function jS(){let e=mb();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function qm(e,t){let n=jS();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var mo="Abs",go="Acos",yo="Acosh",Or="Add",xs="AddN",Ao="All",xo="Any",bs="ArgMax",Fu="ArgMin",bo="Asin",vo="Asinh",wo="Atan",ko="Atanh",Io="Atan2",vs="AvgPool",Xp="AvgPoolGrad",$u="AvgPool3D",Kp="AvgPool3DGrad",ws="BatchMatMul",Du="BatchToSpaceND",Zp="Bincount",gb="BroadcastTo",ks="Cast",Is="Ceil",zr="ClipByValue",Yp="Complex",Ou="ComplexAbs",So="Concat",Ss="Conv2D",Jp="Conv2DBackpropFilter",Ns="Conv2DBackpropInput",zu="Conv3D",Qp="Conv3DBackpropFilterV2",ec="Conv3DBackpropInputV2",Ts="Cos",No="Cosh",Cs="Cumsum",To="CropAndResize",tc="DenseBincount",Co="DepthToSpace",Es="DepthwiseConv2dNative",nc="DepthwiseConv2dNativeBackpropFilter",ac="DepthwiseConv2dNativeBackpropInput",rc="Diag",_u="Dilation2D",sc="Dilation2DBackpropInput",ic="Dilation2DBackpropFilter",Rs="RealDiv",oc="Einsum",Eo="Elu",lc="EluGrad",Ro="Erf",Mo="Equal",Ms="Exp",Fo="ExpandDims",$o="Expm1",uc="FFT",Pu="Fill",Do="FlipLeftRight",Fs="Floor",$s="FloorDiv",Ds="FusedBatchNorm",Oo="GatherV2",zo="GatherNd",_o="Greater",Os="GreaterEqual",zs="Identity",dc="IFFT",pc="Imag",Po="IsFinite",Lo="IsInf",Wo="IsNan",_s="LeakyRelu",Bo="Less",Vo="LessEqual",cc="LinSpace",Ps="Log",jo="Log1p",Uo="LogicalAnd",Lu="LogicalNot",Wu="LogicalOr",yb="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",Ho="Mod",yc="Multinomial",Gs="Multiply",Go="Neg",qo="NotEqual",Xo="NonMaxSuppressionV3",Ko="NonMaxSuppressionV4",Zo="NonMaxSuppressionV5",Yo="OnesLike",qs="OneHot",Jo="Pack",Xs="PadV2",US="Pool",Ks="Pow",Zs="Prelu",Qo="Prod",ju="Range",Ac="Real",el="Reciprocal",Ys="Relu",tl="Reshape",Uu="ResizeNearestNeighbor",xc="ResizeNearestNeighborGrad",Js="ResizeBilinear",bc="ResizeBilinearGrad",Qs="Relu6",ei="Reverse",ti="Round",ni="Rsqrt",nl="ScatterNd",al="Select",rl="Selu",sl="Slice",ai="Sin",il="Sinh",ol="Sign",ri="Sigmoid",ll="Softplus",si="Sqrt",ii="Sum",Hu="SpaceToBatchND",ul="SplitV",oi="Softmax",vc="SparseFillEmptyRows",wc="SparseReshape",kc="SparseSegmentMean",Ic="SparseSegmentSum",Sc="SparseToDense",li="SquaredDifference",Gu="Square",dl="StridedSlice",Nc="StringNGrams",Tc="StringSplit",Cc="StringToHashBucketFast",ui="Sub",di="Tan",pi="Tanh",_r="Tile",pl="TopK",cl="Transform",ci="Transpose",Ec="Unique",hl="Unpack",qu="UnsortedSegmentSum",fl="ZerosLike",Pr="Step",Rc="FromPixels",ml="RotateWithOffset",hi="_FusedMatMul",fi="FusedConv2D",mi="FusedDepthwiseConv2D",gl=qm("kernelRegistry",()=>new Map),Xu=qm("gradRegistry",()=>new Map);function Mc(e,t){let n=Km(e,t);return gl.get(n)}function Xm(e){return Xu.get(e)}function yl(e){let t=gl.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);gl.has(a)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),gl.set(a,e)}function Ab(e){let{kernelName:t}=e;Xu.has(t)&&te().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Xu.set(t,e)}function HS(e,t){let n=Km(e,t);if(!gl.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);gl.delete(n)}function GS(e){if(!Xu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Xu.delete(e)}function qS(e,t){yl(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:()=>ub,bytesPerElement:()=>Vm,checkConversionForErrors:()=>ob,clamp:()=>Ru,computeStrides:()=>ho,createScalarValue:()=>QS,createShuffledIndices:()=>FS,decodeString:()=>Dc,distSquared:()=>CS,encodeString:()=>Yu,fetch:()=>tN,fingerPrint64:()=>JS,flatten:()=>As,getArrayFromDType:()=>ib,getTypedArrayFromDType:()=>sb,hasEncodingLoss:()=>OS,hexToLong:()=>Ku,indexToLoc:()=>PS,inferDtype:()=>Hp,inferFromImplicitShape:()=>DS,isBoolean:()=>db,isFunction:()=>Dr,isInt:()=>qt,isNumber:()=>pb,isPromise:()=>Hm,isScalarShape:()=>ES,isString:()=>$r,isTypedArray:()=>on,isValidDtype:()=>lb,locToIndex:()=>_S,makeOnesTypedArray:()=>jm,makeZerosNestedTypedArray:()=>zS,makeZerosTypedArray:()=>qp,nearestDivisor:()=>Gp,nearestLargerEven:()=>SS,now:()=>Zu,parseAxisParam:()=>ya,randUniform:()=>TS,repeatedTry:()=>$S,rightPad:()=>Mu,shuffle:()=>ab,shuffleCombo:()=>IS,sizeFromShape:()=>Mt,sizeToSquarishShape:()=>MS,squeezeShape:()=>rb,sum:()=>NS,tanh:()=>RS,toNestedArray:()=>fo,toTypedArray:()=>$c});var xb=gs(ZI()),yi=xb.default||xb;function Ku(e){return yi.fromString(e,!0,16)}var bb=Ku("c3a5c85c97cb3127"),Ai=Ku("b492b66fbe98f273"),hn=Ku("9ae16a3b2f90404f");function Zm(e){return e.xor(e.shru(47))}function vb(e,t,n){let a=e.slice(t,t+n);return yi.fromBytes(Array.from(a),!0,!0)}function pt(e,t){return vb(e,t,8)}function wb(e,t){return vb(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 XS(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 XS(pt(e,t),pt(e,t+8),pt(e,t+16),pt(e,t+24),n,a)}function KS(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=wb(e,0);return Lr(a.shl(3).add(t),wb(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(bb.mul(i))).mul(hn)}return hn}function ZS(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 YS(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),l=Lr(o,a.add(Xt(r.add(hn),18)).add(s),n),u=pt(e,16).mul(n),d=pt(e,24),p=o.add(pt(e,t-32)).mul(n),c=l.add(pt(e,t-24)).mul(n);return Lr(Xt(u.add(d),43).add(Xt(p,30)).add(c),u.add(Xt(d.add(a),18)).add(p),n)}function JS(e,t=e.length){let n=yi.fromNumber(81,!0);if(t<=32)return t<=16?KS(e,t):ZS(e,t);if(t<=64)return YS(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 l=0,u=(t-1>>6)*64,d=u+(t-1&63)-63;do a=Xt(a.add(r).add(i[0]).add(pt(e,l+8)),37).mul(Ai),r=Xt(r.add(i[1]).add(pt(e,l+48)),42).mul(Ai),a=a.xor(o[1]),r=r.add(i[0]).add(pt(e,l+40)),s=Xt(s.add(o[0]),33).mul(Ai),i=Fc(e,l,i[1].mul(Ai),a.add(o[0])),o=Fc(e,l+32,s.add(o[1]),r.add(pt(e,l+16))),[s,a]=[a,s],l+=64;while(l!==u);let p=Ai.add(s.and(255).shl(1));return l=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,l+8)),37).mul(p),r=Xt(r.add(i[1]).add(pt(e,l+48)),42).mul(p),a=a.xor(o[1].mul(9)),r=r.add(i[0].mul(9).add(pt(e,l+40))),s=Xt(s.add(o[0]),33).mul(p),i=Fc(e,l,i[1].mul(p),a.add(o[0])),o=Fc(e,l+32,s.add(o[1]),r.add(pt(e,l+16))),[s,a]=[a,s],Lr(Lr(i[0],o[0],p).add(Zm(r).mul(bb)).add(s),Lr(i[1],o[1],p).add(a),p)}function QS(e,t){return t==="string"?Yu(e):$c([e],t)}function eN(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")&&ob(e,t),eN(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 tN(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 nN=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new rN)}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 l=a[o];l.data().then(u=>{aN(u,l.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 aN(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 rN=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?Mu(`${a}ms`,9):a.error,o=Mu(e,25),l=t.rank,u=t.size,d=Mu(t.shape.toString(),14),p="";for(let c in r){let h=r[c];if(h!=null){let m=h.shape||t.shape,f=m.length;p+=`${c}: ${f}D ${f>0?m:""} `}}console.log(`%c${o} %c${i} %c${l}D ${d} %c${u} %c${p} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function sN(e,t,n){let a={},r={};for(let l=0;l<t.length;l++)a[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],d=u.inputs;for(let p in d){let c=d[p],h=!1;for(let m=0;m<t.length;m++)if(a[c.id]){u.outputs.forEach(f=>a[f.id]=!0),h=!0,r[u.id]=!0;break}if(h)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],d=u.inputs;for(let p=0;p<u.outputs.length;p++)if(s[u.outputs[p].id]){for(let c in d)s[d[c].id]=!0,i[u.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let u=e[l];if(r[u.id]&&i[u.id]){let d={};for(let c in u.inputs){let h=u.inputs[c];a[h.id]&&(d[c]=h)}let p=Object.assign({},u);p.inputs=d,p.outputs=u.outputs,o.push(p)}}return o}function iN(e,t,n,a){for(let r=t.length-1;r>=0;r--){let s=t[r],i=[];if(s.outputs.forEach(l=>{let u=e[l.id];u!=null?i.push(u):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let l in s.inputs){if(!(l in o))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(o)}.`);let u=n(()=>o[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let d=s.inputs[l];if(!cr(u.shape,d.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${d.shape}'`);if(e[d.id]==null)e[d.id]=u;else{let p=e[d.id];e[d.id]=a(p,u),p.dispose()}}}}var kb=20,Ju=3,Ym=7;function oN(e,t,n,a){let r=ho(t),s=lN(e,t,n,r),i=t.length,o=Oc(e,t,n,r,s),l=["Tensor"];return a&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(u=>" "+u).join(`
|
|
`)),l.join(`
|
|
`)}function lN(e,t,n,a){let r=Mt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?ed(e):e;if(o>1)for(let u=0;u<r/s;u++){let d=u*s;for(let p=0;p<s;p++)i[p]=Math.max(i[p],Qu(l[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=`'${e}'`:n==="bool"?a=Ib(e):a=parseFloat(e.toFixed(Ym)).toString(),Mu(a,t)}function Ib(e){return e===0?"false":"true"}function Oc(e,t,n,a,r,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let f=ed(e);return[Qu(f[0],0,n)]}return n==="bool"?[Ib(e[0])]:[e[0].toString()]}if(l===1){if(o>kb){let g=Ju*i,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((o-Ju)*i,o*i));return n==="complex64"&&(y=ed(y),A=ed(A)),["["+y.map((x,v)=>Qu(x,r[v],n)).join(", ")+", ..., "+A.map((x,v)=>Qu(x,r[o-Ju+v],n)).join(", ")+"]"]}let f=n==="complex64"?ed(e):Array.from(e);return["["+f.map((g,y)=>Qu(g,r[y],n)).join(", ")+"]"]}let u=t.slice(1),d=a.slice(1),p=a[0]*i,c=[];if(o>kb){for(let f=0;f<Ju;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),u,n,d,r,!1))}c.push("...");for(let f=o-Ju;f<o;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),u,n,d,r,f===o-1))}}else for(let f=0;f<o;f++){let g=f*p,y=g+p;c.push(...Oc(e.slice(g,y),u,n,d,r,f===o-1))}let h=l===2?",":"";c[0]="["+c[0]+h;for(let f=1;f<c.length-1;f++)c[f]=" "+c[f]+h;let m=`,
|
|
`;for(let f=2;f<l;f++)m+=`
|
|
`;return c[c.length-1]=" "+c[c.length-1]+"]"+(s?"":m),c}function ed(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Lt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Mt(e),n!=null){let a=n.length;D(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||ib(t,this.size),this.strides=ho(e)}set(e,...t){t.length===0&&(t=[0]),D(t.length===this.rank,()=>`The number of provided coordinates (${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,Al=null,uN=null;function dN(e){ja=e}function pN(e){Al=e}function cN(e){uN=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=ho(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 Al.buffer(this.shape,this.dtype,e)}bufferSync(){return Al.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return fo(this.shape,e,this.dtype==="complex64")}arraySync(){return fo(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 Al.print(this,e)}clone(){return this.throwIfDisposed(),Al.clone(this)}toString(e=!1){let t=this.dataSync();return oN(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Al.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:()=>Sb,getTensorsInContainer:()=>a1,isTensorInList:()=>fN,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 hN={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 ${t}`)}return hN[e][t]}function zc(e){return Aa(e,"int32")}function It(e,t){if(e.dtype===t.dtype)return[e,t];let n=Aa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Sb(e,t){D(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function fN(e,t){return t.some(n=>n.id===e.id)}function a1(e){let t=[],n=new Set;return Nb(e,t,n),t}function Nb(e,t,n){if(e==null)return;if(e instanceof Be){t.push(e);return}if(!mN(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),Nb(s,t,n))}}function mN(e){return Array.isArray(e)||typeof e=="object"}function r1(e){return e.kernelName!=null}var Tb=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},nd=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Tb}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 nN(this.backendInstance),!0}setupRegisteredKernels(){yl(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){yl(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},l={dtype:i};return P.runKernel(ks,o,l)}}),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 '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=r1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(r1(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Mc(h,this.backendName);D(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:N}=v;return this.makeTensorFromDataId(b,w,N)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,p=r1(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Xm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&$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),l=ub(r);this.state.numBytes+=l-o.bytes,o.bytes=l}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=l=>(l=l.map((u,d)=>{if(u==null){let p=n[d],c=qp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return u}),a(l.length>1?l:l[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=sN(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?gN(r.shape):n,iN(i,s,l=>this.tidy(l),yN);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads: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 l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];D(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),D(u.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 u.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 Tb;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 gN(e){let t=jm(Mt(e),"float32");return P.makeTensor(t,e,"float32")}function Cb(){let e=mb();if(e._tfengine==null){let t=new fb(e);e._tfengine=new nd(t)}return VS(e._tfengine.ENV),dN(()=>e._tfengine),e._tfengine}var P=Cb();function yN(e,t){let n={a:e,b:t};return P.runKernel(Or,n)}var ad={};Fe(ad,{isBrowser:()=>Eb,isMobile:()=>xN});function AN(){return typeof navigator!="undefined"&&navigator!=null}function xN(e){if(e||AN()){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 Eb(){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",()=>Eb());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")&&Rb(e,a,[]),a}function Rb(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 ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)Rb(e[r],a,n.concat(r))}function Mb(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function M(e,t,n,a="numeric"){if(e instanceof Be)return Mb(a,e.dtype,t,n),e;let r=Hp(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),Mb(a,r,t,n),e==null||!on(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${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 Fb="__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+Fb;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 bN(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 ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return P.runKernel(Yp,r)}var Wr=L({complex_:bN});function Br(e,t,n,a){if(a==null&&(a=Hp(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!on(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Um(t);let r=Mt(t),s=Mt(n);D(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Mt(t.slice(i)):!0;D(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${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 vN(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],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let u={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let d=new Promise(async p=>{let c=await l.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(l.data());t!=null&&(u.group=t),n.push(u)}let s=await Promise.all(a);return{data:wN(s),specs:n}}function $b(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=Mt(l),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+u*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=CN()),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}': ${o}`);r+=u*c}else if(o==="string"){let p=Mt(s.shape);d=[];for(let c=0;c<p;c++){let h=new Uint32Array(e.slice(r,r+_c))[0];r+=_c;let m=new Uint8Array(e.slice(r,r+h));d.push(m),r+=h}}else{let p=s1[o],c=e.slice(r,r+u*p);if(o==="float32")d=new Float32Array(c);else if(o==="int32")d=new Int32Array(c);else if(o==="bool")d=new Uint8Array(c);else if(o==="complex64"){d=new Float32Array(c);let h=new Float32Array(d.length/2),m=new Float32Array(d.length/2);for(let y=0;y<h.length;y++)h[y]=d[y*2],m[y]=d[y*2+1];let f=ln(h,l,"float32"),g=ln(m,l,"float32");n[i]=Wr(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=u*p}o!=="complex64"&&(n[i]=ln(d,l,o))}return n}function wN(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 Db(e){return i1?Buffer.byteLength(e):new Blob([e]).size}function kN(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 IN(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 Ob(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:Db(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:Db(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function SN(){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 NN(){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 TN(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function CN(){let e=SN(),t=NN(),n=TN();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],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}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}},EN=e=>Et.registerSaveRouter(e),RN=e=>Et.registerLoadRouter(e),MN=e=>Et.getSaveHandlers(e),FN=(e,t)=>Et.getLoadHandlers(e,t),l1="tensorflowjs",u1=1,xi="models_store",Vr="model_info_store";function zb(){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=zb(),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=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=sd(t),o=s.transaction(Vr,"readwrite"),l=o.objectStore(Vr),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),d;u.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=>{l=o.objectStore(Vr);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(p.error)),h.onerror=m=>(s.close(),a(p.error))}},u.onerror=p=>(s.close(),a(u.error)),o.oncomplete=()=>{d==null?s.close():d.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};bi.URL_SCHEME="indexeddb://";var _b=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bi.URL_SCHEME)?$N(e.slice(bi.URL_SCHEME.length)):null;Et.registerSaveRouter(_b);Et.registerLoadRouter(_b);function $N(e){return new bi(e)}function DN(e){return e.startsWith(bi.URL_SCHEME)?e.slice(bi.URL_SCHEME.length):e}var ON=class{constructor(){this.indexedDB=zb()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(l1,u1);n.onupgradeneeded=()=>d1(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(Vr,"readonly"),s=r.objectStore(Vr).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=DN(e),new Promise((t,n)=>{let a=this.indexedDB.open(l1,u1);a.onupgradeneeded=()=>d1(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(Vr,"readwrite"),i=s.objectStore(Vr),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),d=()=>{l=r.transaction(xi,"readwrite");let p=l.objectStore(xi).delete(e);p.onsuccess=()=>t(o.result.modelArtifactsInfo),p.onerror=c=>n(o.error)};u.onsuccess=d,u.onerror=p=>(d(),r.close(),n(o.error))}},o.onerror=u=>(r.close(),n(o.error)),s.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},a.onerror=r=>n(a.error)})}},hr="/",xl="tensorflowjs_models",Pb="info",zN="model_topology",_N="weight_specs",PN="weight_data",LN="model_metadata";function Lb(e){return{info:[xl,e,Pb].join(hr),topology:[xl,e,zN].join(hr),weightSpecs:[xl,e,_N].join(hr),weightData:[xl,e,PN].join(hr),modelMetadata:[xl,e,LN].join(hr)}}function WN(e){let t=e.split(hr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(hr)}function BN(e){return e.startsWith(vi.URL_SCHEME)?e.slice(vi.URL_SCHEME.length):e}var vi=class{constructor(e){if(!te().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Lb(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=sd(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,kN(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=IN(s),t}};vi.URL_SCHEME="localstorage://";var Wb=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(vi.URL_SCHEME)?VN(e.slice(vi.URL_SCHEME.length)):null;Et.registerSaveRouter(Wb);Et.registerLoadRouter(Wb);function VN(e){return new vi(e)}var jN=class{constructor(){D(te().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),D(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=xl+hr,n=hr+Pb;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=WN(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=BN(e);let t=Lb(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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}},bl="://",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(bl)&&(e=e.slice(0,e.indexOf(bl))),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(bl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ia.getSchemes().join(",")}`);return{scheme:e.split(bl)[0],path:e.split(bl)[1]}}async function Bb(e,t,n=!1){D(e!==t,()=>`Old path and new path are the same: '${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,l=Pc(e).path,u=o===Pc(e).scheme,d=await r.load();n&&u&&await ia.getManager(o).removeModel(l);let p=await i.save(d);return n&&!u&&await ia.getManager(o).removeModel(l),p.modelArtifactsInfo}async function UN(){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+bl+r;t[s]=a[r]}}return t}async function HN(e){let t=Pc(e);return ia.getManager(t.scheme).removeModel(t.path)}async function GN(e,t){return Bb(e,t,!1)}async function qN(e,t){return Bb(e,t,!0)}var XN=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 XN);try{ia.registerManager(vi.URL_SCHEME,new jN)}catch(e){}try{ia.registerManager(bi.URL_SCHEME,new ON)}catch(e){}}var KN={importFetch:()=>YI()},p1,ZN=class{constructor(){this.util=co("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return te().global.fetch!=null?te().global.fetch(e,t):(p1==null&&(p1=KN.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 ZN);function Ve(e,t="float32",n){return t=t||"float32",Um(e),new Lt(e,t,n)}function YN(e,t){let n=M(e,"x","cast");if(!lb(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return P.runKernel(ks,a,r)}var ge=L({cast_:YN});function JN(e){let t={x:M(e,"x","clone","string_or_numeric")};return P.runKernel(zs,t)}var Ha=L({clone_:JN});function Vb(e,t=!1){console.log(e.toString(t))}Cb();var QN={buffer:Ve,cast:ge,clone:Ha,print:Vb};pN(QN);var En={};Fe(En,{browserFiles:()=>iT,browserHTTPRequest:()=>pT,concatenateArrayBuffers:()=>o1,copyModel:()=>GN,decodeWeights:()=>$b,encodeWeights:()=>vN,fromMemory:()=>hT,getLoadHandlers:()=>FN,getModelArtifactsInfoForJSON:()=>sd,getSaveHandlers:()=>MN,http:()=>f1,isHTTPScheme:()=>h1,listModels:()=>UN,loadWeights:()=>oT,moveModel:()=>qN,registerLoadRouter:()=>RN,registerSaveRouter:()=>EN,removeModel:()=>HN,weightsLoaderFactory:()=>Gb,withSaveHandler:()=>fT});var eT="model",tT=".json",nT=".weights.bin";function jb(e){return new Promise(t=>setTimeout(t)).then(e)}var vl=class{constructor(e){if(!te().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(vl.URL_SCHEME)&&(e=e.slice(vl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=eT),this.modelTopologyFileName=e+tT,this.weightDataFileName=e+nT}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 jb(()=>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 jb(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:sd(e)}}}};vl.URL_SCHEME="downloads://";var aT=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 l=i.weightsManifest;if(l==null){a(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(h){a(h);return}let d=[],p=[],c=[];l.forEach(h=>{h.paths.forEach(m=>{p.push(m),c.push(null)}),d.push(...h.weights)}),l.forEach(h=>{h.paths.forEach(m=>{let f=new FileReader;f.onload=g=>{let y=g.target.result,A=p.indexOf(m);if(c[A]=y,c.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:d,weightData:o1(c),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},f.onerror=g=>a(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(u[m])})})},r.onerror=s=>a(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>Ob(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=Ob(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},rT=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(vl.URL_SCHEME)?sT(e.slice(vl.URL_SCHEME.length)):null;Et.registerSaveRouter(rT);function sT(e="model"){return new vl(e)}function iT(e){return new aT(e)}function Ub(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=l=>(l.then(u=>{let d=n+ ++r/e.length*(a-n);return t(d),u}),l);function i(l){D(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){D(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),D(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),D(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function Hb(e,t){t==null&&(t={});let n=t.fetchFunc==null?te().platform.fetch:t.fetchFunc,a=e.map(u=>n(u,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await Ub(a,t.onProgress,r,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await Ub(i,t.onProgress,o,l)}async function oT(e,t="",n,a){return Gb(r=>Hb(r,{requestInit:a}))(e,t,n)}function Gb(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: ${o.join(", ")}.`)}let l=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),u=[];l.forEach(h=>{t[h].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;u.push(f)})});let d=await e(u),p={},c=0;return l.forEach(h=>{let m=t[h].paths.length,f=0;for(let x=0;x<m;x++)f+=d[c+x].byteLength;let g=new ArrayBuffer(f),y=new Uint8Array(g),A=0;for(let x=0;x<m;x++){let v=new Uint8Array(d[c+x]);y.set(v,A),A+=v.byteLength}s[h].forEach(x=>{let v=g.slice(x.groupOffset,x.groupOffset+x.sizeBytes),b=$b(v,[x.manifestEntry]);for(let w in b)p[w]=b[w]}),c+=m}),p}}var lT="application/octet-stream",uT="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:uT}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:lT}),"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,l=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 u,d;a!=null&&([u,d]=await this.loadWeights(a));let p={modelTopology:n,weightSpecs:u,weightData:d,generatedBy:r,convertedBy:s,format:i};o!=null&&(p.signature=o),l!=null&&(p.userDefinedMetadata=l);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]=dT(t),r=this.weightPathPrefix||n,s=[];for(let u of e)s.push(...u.weights);let i=[],o=[];for(let u of e)for(let d of u.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(d)):i.push(r+d+a);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await Hb(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,o1(l)]}};c1.URL_SCHEME_REGEX=/^https?:\/\//;function dT(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 qb=(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(qb);Et.registerLoadRouter(qb);function f1(e,t){return new c1(e,t)}function pT(e,t){return f1(e,t)}var m1=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},cT=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function hT(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 fT(e){return new cT(e)}var Xb={};Fe(Xb,{confusionMatrix:()=>xT});function mT(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_:mT});function gT(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 wl=L({oneHot_:gT});function yT(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_:yT});function AT(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=wl(ge(a,"int32"),n),i=wl(ge(r,"int32"),n),o=Qe(s),l=je(o,i);return ge(l,"int32")}var xT=L({confusionMatrix_:AT}),oa={};Fe(oa,{fromPixels:()=>NT,fromPixelsAsync:()=>IT,toPixels:()=>ST});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 kl;function Kb(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[l,u]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;i?d=e.getContext("2d").getImageData(0,0,l,u).data:a||n?d=e.data:(s||r||o)&&(kl==null&&(kl=document.createElement("canvas").getContext("2d")),kl.canvas.width=l,kl.canvas.height=u,kl.drawImage(e,0,0,l,u),d=kl.getImageData(0,0,l,u).data);let p;if(t===4)p=new Int32Array(d);else{let c=l*u;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,[u,l,t],"int32")}function bT(e){return e!=null&&e.data instanceof Uint8Array}function vT(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function wT(e){return e!=null&&e.width!==0&&e.height!==0}function kT(e){return vT()&&!(e instanceof ImageBitmap)&&wT(e)&&!bT(e)}async function IT(e,t=3){let n=null;if(te().getBool("WRAP_TO_IMAGEBITMAP")&&kT(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 Kb(n,t)}async function ST(e,t){let n=M(e,"img","toPixels");if(!(e instanceof Be)){let u=n;n=ge(u,"int32"),u.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[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,l=new Uint8ClampedArray(r*a*4);for(let u=0;u<a*r;++u){let d=[0,0,0,255];for(let c=0;c<s;c++){let h=i[u*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=u*4;l[p+0]=Math.round(d[0]),l[p+1]=Math.round(d[1]),l[p+2]=Math.round(d[2]),l[p+3]=Math.round(d[3])}if(t!=null){t.width=r,t.height=a;let u=t.getContext("2d"),d=new ImageData(l,r,a);u.putImageData(d,0,0)}return n!==e&&n.dispose(),l}var NT=L({fromPixels_:Kb}),g1={};Fe(g1,{prepareAndValidate:()=>Zb});function Zb(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,l=r.slice();l.pop();let u=1;for(let p=s;p<n;++p)u*=o[p],l.push(o[p]);let d=[...ho(e.shape).map(p=>p/u),1].slice(0,s);return[l,i,u,d]}var y1={};Fe(y1,{calculateShapes:()=>Yb,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: ${e.shape}`)}A1(n,t,e)}function Yb(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let p=r;p<s;++p)i*=n[p];let o=r<1?1:r,l=Mt(t.shape)/o,u=[...ho(n.slice(0,r)),1],d=Mt(n);return{sliceRank:r,numUpdates:l,sliceSize:i,strides:u,outputSize:d}}var fn={};Fe(fn,{assertParamsValid:()=>TT,computeFlatOffset:()=>ET,computeOutShape:()=>Jb,getNormalizedAxes:()=>n3,isSliceContinous:()=>CT,maskToAxes:()=>Wc,parseSliceParams:()=>l3,sliceInfo:()=>RT,startForAxis:()=>i3,startIndicesWithElidedDims:()=>a3,stopForAxis:()=>o3,stopIndicesWithElidedDims:()=>r3,stridesForAxis:()=>s3,stridesWithElidedDims:()=>Qb});function TT(e,t,n){let a=e.shape.length;D(a===t.length,()=>`Error in slice${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 Jb(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 Qb(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 e3(e,t,n){return n<=e?n:n-(t-1)}function t3(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function n3(e,t,n,a,r,s,i,o,l){let u=e.length,d=new Array(u),p=new Array(u),c=new Array(u);if(t.length&&n>0){let h=t[0],m=n+1;d=a3(i,h,m,a,e),p=r3(o,h,m,r,e),c=Qb(s,h,m,e)}else for(let h=0;h<u;h++)d[h]=i3(i,a,s,e,h,l),p[h]=o3(o,r,s,e,h,l),c[h]=s3(s,h,l);return{begin:d,end:p,strides:c}}function a3(e,t,n,a,r){let s=[...r],i=t3(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=e3(t,n,o),u=a[l];e&1<<l&&(u=0),s[o]=u}return s}function r3(e,t,n,a,r){let s=[...r],i=t3(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=e3(t,n,o),u=a[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),s[o]=u}for(let o=0;o<s.length;o++){let l=r[o];s[o]<0&&(s[o]+=l),s[o]=Ru(0,s[o],r[o])}return s}function s3(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}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.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),i=Ru(0,i,l-1),i}function o3(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 l=a[r];return i<0&&(i+=l),o>0?i=Ru(0,i,l):i=Ru(-1,i,l-1),i}function CT(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 ET(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 l3(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 ${o}.`),e.shape[o]-a[o])),[a,s]}function RT(e,t,n,a,r,s,i,o,l){let u=t.slice(),d=n.slice(),p=a;a==null&&(p=new Array(u.length));let c=Wc(i);if(c.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-u.length,m=Wc(o),f=e.slice();m.forEach(w=>{u[w]=0,d[w]=1,f.splice(w,0,1)});let{begin:g,end:y,strides:A}=n3(f,c,h,u,d,p,r,s,i);u=g,d=y,p=A;let x=Wc(l);x.forEach(w=>{d[w]=u[w]+1,p[w]=1});let v=Jb(u,d,p),b=v.filter((w,N)=>x.indexOf(N)===-1);return{nonStrided:p.every(w=>w===1),$begin:u,$end:d,$strides:p,size:v,newShape:f,outShape:b}}var re={};Fe(re,{Serializable:()=>u3,SerializationMap:()=>wi,registerClass:()=>jr});var u3=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 d3={};Fe(d3,{TEST_EPSILON_FLOAT16:()=>p3,encodeStrings:()=>c3,expectArrayBuffersEqual:()=>_T,expectArraysClose:()=>FT,expectArraysEqual:()=>DT,expectNumbersClose:()=>OT,expectPromiseToFail:()=>$T,expectValuesInRange:()=>zT,testEpsilon:()=>b1});var MT=.001,p3=.1;function FT(e,t,n){return n==null&&(n=b1()),v1(e,t,(a,r)=>w1(a,r,n))}function b1(){return P.backend.floatPrecision()===32?MT:p3}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: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
|
|
Actual: ${r}.
|
|
Expected: ${s}.`)}}function $T(e,t){e().then(()=>t.fail(),()=>t())}function DT(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 OT(e,t,n){if(n==null&&(n=b1()),!w1(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function w1(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function zT(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function _T(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function c3(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?c3(n):e[t]=Yu(n)}return e}var PT="3.7.0";function LT(){te().set("PROD",!0)}function WT(){te().set("DEBUG",!0)}function BT(){te().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function k1(e){te().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}cN(k1);function VT(){P.disposeVariables()}function fr(){return P}function Bc(){return P.memory()}function jT(e){return P.profile(e)}function V(e,t){return P.tidy(e,t)}function he(e){a1(e).forEach(t=>t.dispose())}function Kt(e){return P.keep(e)}function UT(e){return P.time(e)}function HT(e){return P.setBackend(e)}function GT(){return P.ready()}function qT(){return P.backendName}function XT(e){P.removeBackend(e)}function I1(e){return P.findBackend(e)}function KT(e){return P.findBackendFactory(e)}function Il(e,t,n=1){return P.registerBackend(e,t,n)}function h3(){return P.backend}function ZT(e,t){te().setPlatform(e,t)}function YT(e,t){let n=M(e,"a","add"),a=M(t,"b","add");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel(Or,r)}var ie=L({add_:YT});function JT(e,t){let n=M(e,"a","floorDiv"),a=M(t,"b","floorDiv");[n,a]=It(n,a);let r={a:n,b:a};return P.runKernel($s,r)}var Vc=L({floorDiv_:JT});function QT(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_:QT});function eC(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_:eC});function tC(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(mo,n)}}var Wt=L({abs_:tC});function nC(e){let t={x:M(e,"x","acos")};return P.runKernel(go,t)}var S1=L({acos_:nC});function aC(e){let t={x:M(e,"x","acosh")};return P.runKernel(yo,t)}var N1=L({acosh_:aC});function rC(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_:rC});function sC(e,t=null,n=!1){let a={x:M(e,"x","all","bool")},r={axis:t,keepDims:n};return P.runKernel(Ao,a,r)}var Uc=L({all_:sC});function iC(e,t=null,n=!1){let a={x:M(e,"x","any","bool")},r={axis:t,keepDims:n};return P.runKernel(xo,a,r)}var id=L({any_:iC});function oC(e,t=0){let n={x:M(e,"x","argMax")},a={axis:t};return P.runKernel(bs,n,a)}var ki=L({argMax_:oC});function lC(e,t=0){let n={x:M(e,"x","argMin")},a={axis:t};return P.runKernel(Fu,n,a)}var T1=L({argMin_:lC});function uC(e){let t={x:M(e,"x","asin")};return P.runKernel(bo,t)}var C1=L({asin_:uC});function dC(e){let t={x:M(e,"x","asinh")};return P.runKernel(vo,t)}var E1=L({asinh_:dC});function pC(e){let t={x:M(e,"x","atan")};return P.runKernel(wo,t)}var R1=L({atan_:pC});function cC(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(Io,r)}var M1=L({atan2_:cC});function hC(e){let t={x:M(e,"x","atanh")};return P.runKernel(ko,t)}var F1=L({atanh_:hC});function fC(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=g3(r);return od(e,o,n,s,a,null,null,l)}function f3(e,t,n,a,r,s,i="channelsLast"){let[o,l]=Hc(t),u;if(i==="channelsLast")u=[o,l,e[3],e[3]];else if(i==="channelsFirst")u=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return od(e,u,n,a,r,s,!1,i)}function mC(e,t,n,a,r,s,i="NDHWC"){let[o,l,u]=D1(t),d,p;if(i==="NDHWC")p="channelsLast",d=[o,l,u,e[4],e[4]];else if(i==="NCDHW")p="channelsFirst",d=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return m3(e,d,n,a,r,!1,p,s)}function od(e,t,n,a,r,s,i=!1,o="channelsLast"){let[l,u,d,p]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,d,p]=e;else if(o==="channelsFirst")[l,p,u,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[c,h,,m]=t,[f,g]=Hc(n),[y,A]=Hc(a),x=Sl(c,y),v=Sl(h,A),{padInfo:b,outHeight:w,outWidth:N}=AC(r,u,d,f,g,x,v,s,o),C=i?m*p:m,E;return o==="channelsFirst"?E=[l,C,w,N]:o==="channelsLast"&&(E=[l,w,N,C]),{batchSize:l,dataFormat:o,inHeight:u,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 m3(e,t,n,a,r,s=!1,i="channelsLast",o){let[l,u,d,p,c]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,d,p,c]=e;else if(i==="channelsFirst")[l,c,u,d,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,m,f,,g]=t,[y,A,x]=D1(n),[v,b,w]=D1(a),N=Sl(h,v),C=Sl(m,b),E=Sl(f,w),{padInfo:_,outDepth:$,outHeight:S,outWidth:z}=xC(r,u,d,p,y,A,x,N,C,E,o),O=s?g*c:g,W;return i==="channelsFirst"?W=[l,O,$,S,z]:i==="channelsLast"&&(W=[l,$,S,z,O]),{batchSize:l,dataFormat:i,inDepth:u,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 gC(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),l=Ii((i-t+2*a)/n+1,r);return[o,l]}function yC(e,t,n,a,r,s){r==null&&(r=$1(e,t,a));let i=e[0],o=e[1],l=e[2],u=Ii((i-t+2*r)/a+1,s),d=Ii((o-t+2*r)/a+1,s),p=Ii((l-t+2*r)/a+1,s);return[u,d,p,n]}function $1(e,t,n,a=1){let r=Sl(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 Sl(e,t){return t<=1?e:e+(e-1)*(t-1)}function AC(e,t,n,a,r,s,i,o,l){let u,d,p;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let c=gC([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;u={top:m,bottom:f,left:g,right:y,type:"SAME"}}else if(e==="valid")u={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=l==="channelsLast"?e[1][0]:e[2][0],h=l==="channelsLast"?e[1][1]:e[2][1],m=l==="channelsLast"?e[2][0]:e[3][0],f=l==="channelsLast"?e[2][1]:e[3][1];u={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:u,outHeight:d,outWidth:p}}function xC(e,t,n,a,r,s,i,o,l,u,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=yC([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+l-n,y=(m-1)*i+u-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-l+1)/s),m=Math.ceil((a-u+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 g3(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function bC(e,t){let n={x:M(e,"x","reshape","string_or_numeric")},a={shape:t};return P.runKernel(tl,n,a)}var q=L({reshape_:bC});function vC(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,l=!1;s.rank===3&&(l=!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 u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(vs,u,d);return p=ge(p,s.dtype),l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ld=L({avgPool_:vC});function wC(e,t,n,a,r,s="NDHWC"){let i=M(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!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 ${a}.`);let u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=P.runKernel($u,u,d);return p=ge(p,o.dtype),l?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var O1=L({avgPool3d_:wC});function kC(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(So,a,r)}var lt=L({concat_:kC});function IC(e){let t={x:M(e,"x","sigmoid")};return P.runKernel(ri,t)}var Rn=L({sigmoid_:IC});function SC(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(sl,r,s)}var Re=L({slice_:SC});function NC(e){let t={x:M(e,"x","tanh")};return P.runKernel(pi,t)}var Si=L({tanh_:NC});function TC(e,t,n,a,r,s){let i=M(e,"forgetBias","basicLSTMCell"),o=M(t,"lstmKernel","basicLSTMCell"),l=M(n,"lstmBias","basicLSTMCell"),u=M(a,"data","basicLSTMCell"),d=M(r,"c","basicLSTMCell"),p=M(s,"h","basicLSTMCell"),c=lt([u,p],1),h=je(c,o),m=ie(h,l),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 CC=L({basicLSTMCell_:TC});function EC(e,t,n){let a=M(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);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_:EC});function RC(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 MC(e,t,n,a,r,s){s==null&&(s=.001);let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;a!=null&&(d=M(a,"offset","batchNorm")),D(o.rank===l.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(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:RC(i),scale:u,offset:d,mean:o,variance:l},c={varianceEpsilon:s},h=P.runKernel(Ds,p,c);return q(h,i.shape)}var Ni=L({batchNorm_:MC});function FC(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=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(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&D(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.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,l,d,u,s)}var y3=L({batchNorm2d_:FC});function $C(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=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(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&D(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),d!=null&&D(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),Ni(i,o,l,d,u,s)}var A3=L({batchNorm3d_:$C});function DC(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=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(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&D(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.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,l,d,u,s)}var x3=L({batchNorm4d_:DC});function OC(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: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return P.runKernel(Zp,s,i)}var z1=L({bincount_:OC});function zC(e,t){let n=M(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=q(n,l)}let r=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Ha(n);let i={x:n},o={reps:s};return P.runKernel(_r,i,o)}var Nl=L({broadcastTo_:zC});function _C(e){let t={x:M(e,"x","ceil")};return P.runKernel(Is,t)}var _1=L({ceil_:_C});function PC(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_:PC});function LC(e){return lt(e,0)}var b3=L({concat1d_:LC});function WC(e,t){return lt(e,t)}var Tl=L({concat2d_:WC});function BC(e,t){return lt(e,t)}var v3=L({concat3d_:BC});function VC(e,t){return lt(e,t)}var w3=L({concat4d_:VC});function jC(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","conv2d"),l=M(t,"filter","conv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),D(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),D(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&D(qt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?u.shape[3]:u.shape[1];D(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.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:u,filter:l},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_:jC});function UC(e,t,n,a,r="NWC",s=1,i){let o=M(e,"x","conv1d"),l=M(t,"filter","conv1d"),u=o,d=!1;o.rank===2&&(d=!0,u=q(o,[1,o.shape[0],o.shape[1]])),D(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),D(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&D(qt(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),D(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.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 ${r} but only NWC is currently supported.`);let p=q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=q(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=mr(c,p,[1,n],a,"NHWC",[1,s],i);return d?q(h,[h.shape[2],h.shape[3]]):q(h,[h.shape[0],h.shape[2],h.shape[3]])}var Gc=L({conv1d_:UC});function HC(e,t,n,a,r,s="NHWC",i){D(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=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(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),D(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let d=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?l.shape[3]:l.shape[1];D(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${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:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=P.runKernel(Ns,c,h);return u?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var P1=L({conv2DBackpropInput_:HC});function GC(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_:GC});function qC(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=M(e,"x","conv3d"),o=M(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),D(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),D(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),D(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.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 ${r} but only NDHWC is currently supported.`);let d={x:l,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},c=P.runKernel(zu,d,p);return u?q(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var L1=L({conv3d_:qC});function XC(e,t,n,a,r){D(e.length===t.rank,()=>`Length of inShape (${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 l=s[4],u=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 ${n.rank}`),D(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),D(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) 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 k3=L({conv3DBackpropInput_:XC});function KC(e,t,n,a,r){let s=M(e,"x","conv3dTranspose"),i=M(t,"filter","conv3dTranspose");return k3(n,s,i,a,r)}var I3=L({conv3dTranspose_:KC});function ZC(e){let t={x:M(e,"x","cos")};return P.runKernel(Ts,t)}var dd=L({cos_:ZC});function YC(e){let t={x:M(e,"x","cosh")};return P.runKernel(No,t)}var Xc=L({cosh_:YC});function JC(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_:JC});function QC(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 S3=L({denseBincount_:QC});function eE(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},l={blockSize:t,dataFormat:n};return P.runKernel(Co,o,l)}var W1=L({depthToSpace_:eE});function tE(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","depthwiseConv2d"),l=M(t,"filter","depthwiseConv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),D(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),D(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),D(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&D(qt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:u,filter:l},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 Cl=L({depthwiseConv2d_:tE});function nE(e){let t={x:M(e,"x","diag")};return P.runKernel(rc,t)}var aE=L({diag_:nE});function rE(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 l=i,u=!1;i.rank===3&&(l=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let d={x:l,filter:o},p={strides:n,pad:a,dilations:r},c=P.runKernel(_u,d,p);return u?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var B1=L({dilation2d_:rE});function sE(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 iE(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(Mo,r)}var Hr=L({equal_:iE});function oE(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=Nl(s,i),l=Nl(a,i),u=Nl(r,i),d={condition:o,t:l,e:u};return P.runKernel(al,d)}var un=L({where_:oE});function lE(e){let t={x:M(e,"x","zerosLike")};return P.runKernel(fl,t)}var Ge=L({zerosLike_:lE});function uE(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_:uE});function dE(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 ${s}.`),n.rank===1&&a.rank===1){let i=q(n,[1,-1]),o=q(a,[-1,1]),l=je(i,o);return q(l,[])}else if(n.rank===1&&a.rank===2){let i=q(n,[1,-1]),o=q(a,[a.shape[0],a.shape[1]]),l=je(i,o);return q(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=q(a,[-1,1]),o=je(n,i);return q(o,[o.size])}else{let i=q(a,[a.shape[0],a.shape[1]]);return je(n,i)}}var N3=L({dot_:dE});function pE(e,...t){let n=t.map((r,s)=>M(r,`tensors${s}`,"einsum")),a={equation:e};return P.runKernel(oc,n,a)}var T3=L({einsum_:pE});function cE(e){let t={x:M(e,"x","elu")};return P.runKernel(Eo,t)}var El=L({elu_:cE});function hE(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(Ro,n)}var j1=L({erf_:hE});function fE(e){let t={x:M(e,"x","exp")};return P.runKernel(Ms,t)}var la=L({exp_:fE});function mE(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(Fo,a,r)}var mn=L({expandDims_:mE});function gE(e){let t={x:M(e,"x","expm1")};return P.runKernel($o,t)}var U1=L({expm1_:gE});function yE(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_:yE});function AE(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_:AE});function Rl(e,t,n){let a={shape:e,value:t,dtype:n};return P.runKernel(Pu,{},a)}function xE(e){let t={x:M(e,"x","floor")};return P.runKernel(Fs,t)}var Ml=L({floor_:xE});function bE(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(Oo,i,o)}var Ti=L({gather_:bE});function vE(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(_o,r)}var Wn=L({greater_:vE});function wE(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_:wE});function kE(e){let t={input:M(e,"input","imag")};return P.runKernel(pc,t)}var Zc=L({imag_:kE});function IE(e){let t={x:M(e,"x","isFinite")};return P.runKernel(Po,t)}var C3=L({isFinite_:IE});function SE(e){let t={x:M(e,"x","isInf")};return P.runKernel(Lo,t)}var E3=L({isInf_:SE});function NE(e){let t={x:M(e,"x","isNaN")};return P.runKernel(Wo,t)}var G1=L({isNaN_:NE});function TE(e,t=.2){let n={x:M(e,"x","leakyRelu")},a={alpha:t};return P.runKernel(_s,n,a)}var pd=L({leakyRelu_:TE});function CE(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(Bo,r)}var Yc=L({less_:CE});function EE(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(Vo,r)}var Xr=L({lessEqual_:EE});function R3(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 RE(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 l={x:i},u={depthRadius:t,bias:n,alpha:a,beta:r},d=P.runKernel(Bu,l,u);return o?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var q1=L({localResponseNormalization_:RE});function ME(e){let t={x:M(e,"x","log")};return P.runKernel(Ps,t)}var Bn=L({log_:ME});function FE(e){let t={x:M(e,"x","log1p")};return P.runKernel(jo,t)}var Jc=L({log1p_:FE});function $E(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 DE(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 OE(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 zE(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 M3(e,t){D(Dr(e),()=>"The f passed in variableGrads(f) must be a function"),D(t==null||Array.isArray(t)&&t.every(u=>u instanceof td),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in P.registeredVariables)t.push(P.registeredVariables[u])}let a=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),D(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=P.gradients(e,t,null,s);D(o.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),D(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((u,d)=>{o[d]!=null&&(l[u.name]=o[d])}),a!=null&&a.forEach(u=>l[u.name]=null),{value:i,grads:l}}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 _E(e){let t={x:M(e,"x","neg")};return P.runKernel(Go,t)}var St=L({neg_:_E});function PE(e){let t={x:M(e,"x","softplus")};return P.runKernel(ll,t)}var Ci=L({softplus_:PE});function LE(e){let t=M(e,"x","logSigmoid");return qa(n=>({value:St(Ci(St(n))),gradFunc:a=>B(a,Rn(St(n)))}))(t)}var F3=L({logSigmoid_:LE});function WE(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_:WE});function BE(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_:BE});function VE(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_:VE});function jE(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),l=ye(ge(o,"float32"),Bn(Se(la(o),t,s)));return r([l]),{value:l,gradFunc:(u,d)=>{let[p]=d,c=!0,h=la(p);return ye(u,B(Se(u,t,c),h))}}})(n)}var eh=L({logSoftmax_:jE});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 $3(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 D3(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 $3(e,n,t)}function UE(e,t,n){D(X1(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function O3(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 HE(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function GE(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),l=Se(o,r),u=Bn(l),d=ie(q(s,u.shape),u);if(n){let p=Ei(d.shape,r);return q(d,p)}return d}var Z1=L({logSumExp_:GE});function qE(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(Uo,r)}var xa=L({logicalAnd_:qE});function XE(e){let t={x:M(e,"x","logicalNot","bool")};return P.runKernel(Lu,t)}var cd=L({logicalNot_:XE});function KE(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_:KE});function ZE(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 z3=L({logicalXor_:ZE});function YE(e,t,n,a,r){let s=M(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!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 u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(Bs,u,d);return l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var hd=L({maxPool_:YE});function JE(e,t=[1,1,1],n,a,r,s="NDHWC"){let i=M(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!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 u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=P.runKernel(Vu,u,d);return l?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Y1=L({maxPool3d_:JE});function QE(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 _3=L({maxPoolWithArgmax_:QE});function eR(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_:eR});function tR(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_:tR});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 nR(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 aR(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_:aR});function rR(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 Fl=L({minimum_:rR});function sR(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_:sR});function iR(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(Ho,r)}var Q1=L({mod_:iR});function oR(e){let t=M(e,"x","square"),n={};return P.runKernel("Square",{x:t},n)}var ot=L({square_:oR});function lR(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_:lR});function uR(e,t,n,a){let r=M(t,"data","multiRNNCell"),s=rd(n,"c","multiRNNCell"),i=rd(a,"h","multiRNNCell"),o=r,l=[];for(let p=0;p<e.length;p++){let c=e[p](o,s[p],i[p]);l.push(c[0]),l.push(c[1]),o=c[1]}let u=[],d=[];for(let p=0;p<l.length;p+=2)u.push(l[p]),d.push(l[p+1]);return[u,d]}var dR=L({multiRNNCell_:uR});function pR(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},l={numSamples:t,seed:n,normalized:a},u=P.runKernel(yc,o,l);return i===1?q(u,[u.size]):u}var P3=L({multinomial_:pR});function cR(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(qo,r)}var Ri=L({notEqual_:cR});function hR(e){let t={x:M(e,"x","onesLike")};return P.runKernel(Yo,t)}var Un=L({onesLike_:hR});function fR(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 mR=L({outerProduct_:fR});function gR(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_:gR});function yR(e,t,n=0){return D(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),gr(e,[t],n)}var AR=L({pad1d_:yR});function xR(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 bR=L({pad2d_:xR});function vR(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 wR=L({pad3d_:vR});function kR(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 IR=L({pad4d_:kR});function SR(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,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${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_:SR});function NR(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,l=!1;i.rank===3&&(l=!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 u=f3(o.shape,t,s,r,a),d=[u.dilationHeight,u.dilationWidth],p;a==="same"?p=CR([u.filterHeight,u.filterWidth],d):p=[[0,0],[0,0]];let c=d[0]===1&&d[1]===1,[h,m]=TR([u.inHeight,u.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 l?q(A,[A.shape[1],A.shape[2],A.shape[3]]):A}function TR(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]),l=t.map((d,p)=>[a[p],o[p]]),u=t.map((d,p)=>[0,i[p]]);return[l,u]}function CR(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 L3=L({pool_:NR});function ER(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_:ER});function RR(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_:RR});function MR(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(Qo,r,s)}var ah=L({prod_:MR});function FR(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 $R=L({rand_:FR}),eg=gs(N5()),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}},DR=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)}},OR=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 zR(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 DR(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 _R=L({randomGamma_:zR});function PR(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 W3=L({randomNormal_:PR});function LR(e,t=0,n=1,a="float32",r){let s=Ve(e,a),i=new OR(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var $l=L({randomUniform_:LR});function Dl(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 WR(e){let t={input:M(e,"input","real")};return P.runKernel(Ac,t)}var yd=L({real_:WR});function BR(e){let t={x:M(e,"x","reciprocal")};return P.runKernel(el,t)}var ng=L({reciprocal_:BR});function VR(e){let t={x:M(e,"x","relu")};return P.runKernel(Ys,t)}var Ka=L({relu_:VR});function jR(e){let t={x:M(e,"x","relu6")};return P.runKernel(Qs,t)}var rh=L({relu6_:jR});function UR(e,t){let n={x:M(e,"x","reverse")},a={dims:t};return P.runKernel(ei,n,a)}var Hn=L({reverse_:UR});function HR(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 GR=L({reverse1d_:HR});function qR(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 XR=L({reverse2d_:qR});function KR(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 ZR=L({reverse3d_:KR});function YR(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 JR=L({reverse4d_:YR});function QR(e){let t={x:M(e,"x","round")};return P.runKernel(ti,t)}var sh=L({round_:QR});function eM(e){let t={x:M(e,"x","rsqrt")};return P.runKernel(ni,t)}var ih=L({rsqrt_:eM});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 tM(e){let t={x:M(e,"x","selu")};return P.runKernel(rl,t)}var oh=L({selu_:tM});function nM(e,t,n,a,r,s=[1,1],i="NHWC"){let o=M(e,"x","separableConv2d"),l=M(t,"depthwiseFilter","separableConv2d"),u=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(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),D(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),D(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),D(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let c=l.shape[2],h=l.shape[3];D(u.shape[2]===c*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${c*h}, but got ${u.shape[2]}.`);let m=Cl(d,l,a,r,i,s),f=mr(m,u,1,"valid",i);return p?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ag=L({separableConv2d_:nM});async function aM(e,t){let n=M(e,"x","setdiff1d"),a=M(t,"y","setdiff1d");D(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${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 (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let d=0;d<r.length;d++)i.has(r[d])||o++;let l=new Lt([o],n.dtype),u=new Lt([o],"int32");for(let d=0,p=0;d<r.length;d++)i.has(r[d])||(l.values[p]=r[d],u.values[p]=d,p++);return[l.toTensor(),u.toTensor()]}var B3=aM;function rM(e){let t={x:M(e,"x","sign")};return P.runKernel(ol,t)}var rg=L({sign_:rM});function sM(e){let t={x:M(e,"x","sin")};return P.runKernel(ai,t)}var lh=L({sin_:sM});function iM(e){let t={x:M(e,"x","sinh")};return P.runKernel(il,t)}var uh=L({sinh_:iM});function oM(e,t,n){let a=M(e,"x","slice1d");return D(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),Re(a,[t],[n])}var dh=L({slice1d_:oM});function lM(e,t,n){let a=M(e,"x","slice2d");return D(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var sg=L({slice2d_:lM});function uM(e,t,n){let a=M(e,"x","slice3d");return D(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var ph=L({slice3d_:uM});function dM(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_:dM});function pM(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_:pM});function cM(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_:cM});function hM(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 Ol=L({ifft_:hM});function fM(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=q(e,[n,t]);a=Ol(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),l=B(Hn(Re(i,[0,1],[n,t-2]),1),ke(-1)),u=lt([s,o],1),d=lt([i,l],1),p=q(Wr(u,d),[r[0],r[1]]);a=Ol(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_:fM});function mM(e,t,n=0){let a={x:M(e,"x","split")},r={numOrSizeSplits:t,axis:n};return P.runKernel(ul,a,r)}var Zt=L({split_:mM});function gM(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),l=Math.floor(n/2)+1,u=yd(o),d=Zc(o),p=Zt(u,[l,n-l],u.shape.length-1),c=Zt(d,[l,n-l],d.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,q(Wr(p[0],c[0]),h)}var vd=L({rfft_:gM});function yM(e){let t={x:M(e,"x","sqrt")};return P.runKernel(si,t)}var an=L({sqrt_:yM});function AM(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_:AM});function xM(e,t){let n=M(e,"x","squeeze");return q(n,rb(n.shape,t).newShape)}var Vt=L({squeeze_:xM});function bM(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(Jo,a,r)}var gn=L({stack_:bM});function vM(e,t=0){let n={x:M(e,"x","step")},a={alpha:t};return P.runKernel(Pr,n,a)}var zl=L({step_:vM});function wM(e,t,n,a,r=0,s=0,i=0,o=0,l=0){let u={x:M(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:a,beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return P.runKernel(dl,u,d)}var ig=L({stridedSlice_:wM});function kM(e){let t={x:M(e,"x","tan")};return P.runKernel(di,t)}var og=L({tan_:kM});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 IM(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 SM(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 NM(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 TM(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,l]=P.runKernel(pl,s,i);return{values:o,indices:l}}var lg=L({topk_:TM});function CM(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_:CM});function EM(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_:EM});function RM(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_:RM});function MM(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(hl,a,r)}var Gn=L({unstack_:MM});function V3(e,t=!0,n,a){return P.makeVariable(e,t,n,a)}function j3(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 FM(e){let t=M(e,"condition","whereAsync","bool"),n=await t.data(),a=j3(t.shape,n);return e!==t&&t.dispose(),a}var dg=FM;async function $M(e,t,n){let a=M(e,"tensor","boolMask"),r=M(t,"mask","boolMask","bool"),s=n==null?0:n,i=r.rank,o=a.shape;D(i>0,()=>"mask cannot be scalar"),cn(o.slice(s,s+i),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=s;f<s+i;f++)l*=o[f];let u=o.slice(0,s).concat([l],o.slice(s+i)),d=q(a,u),p=q(r,[-1]),c=await dg(p),h=Vt(c,[1]),m=Ti(d,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),d.dispose(),p.dispose(),c.dispose(),m}var DM=$M;function OM(e,t="euclidean",n=null,a=!1){e=M(e,"x","norm");let r=U3(e,t,n),s=r.shape;if(a){let i=ya(n,e.shape);s=Ei(r.shape,i)}return q(r,s)}function U3(e,t,n=null){if(e.rank===0)return Wt(e);if(e.rank!==1&&n===null)return U3(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_:OM});function zM(e,t,n,a,r=!0){let s=M(e,"v","movingAverage"),i=M(t,"x","movingAverage"),o=M(n,"decay","movingAverage");Sb(s,i),D(cr(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=ke(1),u=ye(l,o),d=B(ye(i,s),u);if(r){D(a!=null,()=>"When using zeroDebias: true, step is required.");let p=M(a,"step","movingAverage");d=me(d,ye(l,yr(o,p)))}return ie(s,d)}var _M=L({movingAverage_:zM});function PM(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(nl,s,i)}var H3=L({scatterND_:PM});function LM(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 WM(e,t,n,a=0){let r=M(e,"sparseIndices","sparseToDense","int32"),s=M(t,"sparseValues","sparseToDense"),i=M(a,"defaultValue","sparseToDense",s.dtype);LM(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return P.runKernel(Sc,o,l)}var pg=L({sparseToDense_:WM});function BM(e,t){let n=M(t,"indices","gatherND","int32"),a={params:M(e,"x","gatherND","string_or_numeric"),indices:n};return P.runKernel(zo,a)}var G3=L({gatherND_:BM});function VM(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 jM(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 ${t}.`),t===0)return e instanceof Be?r.clone():r;let s=VM(r,n),i=1-t,o=me(Ml(ie($l(s,0,1,"float32",a),i)),i);return B(r,o)}var q3=L({dropout_:jM});function X3(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 UM(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(),[l,u]=[i.length/s,s],d=sb("bool",l);for(let p=0;p<l;p++){let c=p*u,h=i.subarray(c,c+u),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 HM=UM,Kr={};Fe(Kr,{conv2d:()=>XM,depthwiseConv2d:()=>JM,matMul:()=>eF});function GM(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 l=t;l.rank===3&&(l=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(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),D(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],d=s==="NHWC"?l.shape[3]:l.shape[1];D(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${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:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(Jp,p,c)}var hg=L({conv2DBackpropFilter_:GM});function yh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return B(e,zl(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 El(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 qM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(l=l||"linear",bh(P.state.gradientDepth,l)===!1){let b=mr(e,t,n,a,r,s,i);return o!=null&&(b=ie(b,o)),xh(b,l,u,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 ${r} but only NHWC is currently supported.`);let f=od(h.shape,c.shape,n,s,a,i),g;o!=null&&(g=M(o,"bias","fused conv2d"),[g]=It(g,p),mt(f.outShape,g.shape));let y;u!=null&&(y=M(u,"prelu weights","fused conv2d"));let A=(b,w)=>{let[N,C,E,_]=w,$=yh(b,E,l);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:l,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 XM=L({fusedConv2d_:qM});function KM(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 l=t;l.rank===3&&(l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return P.runKernel(nc,u,d)}var K3=L({depthwiseConv2dNativeBackpropFilter_:KM});function ZM(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},p=P.runKernel(ac,u,d);return l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Z3=L({depthwiseConv2dNativeBackpropInput_:ZM});function YM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(bh(P.state.gradientDepth,l)===!1){let b=Cl(e,t,n,a,r,s,i);return o!=null&&(b=ie(b,o)),xh(b,l,u,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 ${a}.`);let f=od(h.shape,c.shape,n,s,a,i,!0),g;o!=null&&(g=M(o,"bias","fused conv2d"),[g]=It(g,p),mt(f.outShape,g.shape));let y;u!=null&&(y=M(u,"prelu weights","fused depthwiseConv2d"));let A=(b,w)=>{D(Ur(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,C,E,_]=w,$=yh(b,E,l),S=Z3(C.shape,$,N,n,a,s,i),z=K3(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:l,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 JM=L({fusedDepthwiseConv2d_:YM});function QM({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 l=M(e,"a","fused matMul"),u=M(t,"b","fused matMul");[l,u]=It(l,u);let d=n?l.shape[l.rank-2]:l.shape[l.rank-1],p=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=Mt(m),y=Mt(f);D(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),D(cr(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),D(d===p,()=>`Error in fused matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let A=l.shape.slice(0,-2).concat([c,h]),x=n?q(l,[g,d,c]):q(l,[g,c,d]),v=a?q(u,[y,h,p]):q(u,[y,p,h]),b;r!=null&&(b=M(r,"bias","fused matMul"),[b]=It(b,l),mt(A,b.shape));let w;i!=null&&(w=M(i,"prelu weights","fused matMul"));let N=(_,$)=>{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 eF=L({fusedMatMul_:QM});function tF(e){return cg(e,.54,.46)}var nF=L({hammingWindow_:tF});function aF(e){return cg(e,.5,.5)}var Y3=L({hannWindow_:aF});function rF(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,l=lt([Re(e,s,t-o),Rl([o],r)]);i.push(l),s+=n}return i.length===0?Ta([],[0,t]):q(lt(i),[i.length,t])}var J3=L({frame_:rF});function sF(e,t,n,a,r=Y3){a==null&&(a=X3(t));let s=J3(e,t,n),i=B(s,r(t));return vd(i,a)}var iF=L({stft_:sF});function oF(e,t,n,a,r="bilinear",s=0){let i=M(e,"image","cropAndResize"),o=M(t,"boxes","cropAndResize","float32"),l=M(n,"boxInd","cropAndResize","int32"),u=o.shape[0];D(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),D(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),D(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] 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:l},p={method:r,extrapolationValue:s,cropSize:a};return P.runKernel(To,d,p)}var lF=L({cropAndResize_:oF});function uF(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(Do,n,{})}var dF=L({flipLeftRight_:uF});function pF(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 ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(ml,s,i)}var cF=L({rotateWithOffset_:pF});function _l(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),D(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${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 hF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppression"),i=M(t,"scores","nonMaxSuppression"),o=_l(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:a,scoreThreshold:r};return P.runKernel(Xo,{boxes:s,scores:i},l)}var fF=L({nonMaxSuppression_:hF});function mF(e,t,n){let a=gF(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function gF(e,t,n){return AF(e,t,n||yF)}function yF(e,t){return e>t?1:e<t?-1:0}function AF(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 Q3(e,t,n,a,r){return fg(e,t,n,a,r,0)}function e7(e,t,n,a,r,s){return fg(e,t,n,a,r,0,!1,s,!0)}function t7(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,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>r&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(n7);let d=s>0?-.5/s:0,p=[],c=[];for(;p.length<n&&u.length>0;){let g=u.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=xF(e,A,p[b]);if(w>=a){v=!0;break}if(g.score=g.score*bF(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&&mF(u,g,n7))}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),l&&(f.validOutputs=h),f}function xF(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]),l=Math.max(a[1],a[3]),u=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)*(l-i),m=(p-u)*(c-d);if(h<=0||m<=0)return 0;let f=Math.max(s,u),g=Math.max(i,d),y=Math.min(o,p),A=Math.min(l,c),x=Math.max(y-f,0)*Math.max(A-g,0);return x/(h+m-x)}function bF(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function n7(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function vF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppressionAsync"),i=M(t,"scores","nonMaxSuppressionAsync"),o=_l(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],d=l[1],{selectedIndices:p}=Q3(u,d,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),Dt(p,"int32")}var wF=vF;function kF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),l=_l(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},d={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},p=P.runKernel(Zo,u,d);return{selectedIndices:p[0],selectedScores:p[1]}}var IF=L({nonMaxSuppressionWithScore_:kF});async function SF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),l=_l(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),d=u[0],p=u[1],{selectedIndices:c,selectedScores:h}=t7(d,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Dt(c,"int32"),selectedScores:Dt(h)}}var NF=SF;function TF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),l=_l(i,o,n,a,r,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:d,scoreThreshold:p,padToMaxOutputSize:s},m=P.runKernel(Ko,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var CF=L({nonMaxSuppressionPadded_:TF});async function EF(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),l=_l(i,o,n,a,r,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,[c,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=e7(c,h,u,d,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Dt(m,"int32"),validOutputs:ke(f,"int32")}}var RF=EF;function MF(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},l={alignCorners:n,halfPixelCenters:a,size:t},u=P.runKernel(Js,o,l);return i?q(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var a7=L({resizeBilinear_:MF});function FF(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},l={alignCorners:n,halfPixelCenters:a,size:t},u=P.runKernel(Uu,o,l);return i?q(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var r7=L({resizeNearestNeighbor_:FF});function $F(e,t="binary",n=!1,a=.5){let r=M(e,"image","threshold"),s=.2989,i=.587,o=.114,l=r.shape[0]*r.shape[1],u=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 ${t}`),r.shape[2]===3){[d,p,c]=Zt(r,[1,1,1],-1);let f=B(d,s),g=B(p,i),y=B(c,o);h=ie(ie(f,g),y)}else h=e;if(t==="otsu"){let f=z1(ge(sh(h),"int32"),ln([]),256);u=DF(f,l)}let m=n?Xr(h,u):Wn(h,u);return ge(B(m,255),"int32")}function DF(e,t){let n=Dt([-1]),a=Dt([0]),r=Dt([0]),s,i,o,l,u,d;for(let p=0;p<e.size-1;p++){s=Re(e,0,p+1),i=Re(e,p+1),u=me(Se(s),t),d=me(Se(i),t);let c=Se(B(s,Dl(0,s.size)));o=me(c,Se(s));let h=Rl(i.shape,s.size),m=ie(Dl(0,i.size),h),f=B(i,m);l=me(Se(f),Se(i));let g=ye(o,l),y=ye(o,l),A=B(u,d);r=B(B(A,g),y);let x=Wn(r,a);a=un(x,r,a),n=un(x,Dt([p]),n)}return n}var OF=L({threshold_:$F});function zF(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 l={image:i,transforms:o},u={interpolation:n,fillMode:a,fillValue:r,outputShape:s};return P.runKernel(cl,l,u)}var _F=L({transform_:zF});function PF(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 (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=q(Dl(0,s,1,"int32"),[-1,1]),l=Dl(0,i,1,"int32"),u=ye(o,l),d=xa(Xr(u,ke(+t,"int32")),qr(u,ke(-n,"int32"))),p=$t([s,i],a.dtype);return q(gn(Gn(q(a,[-1,s,i])).map(c=>un(d,c,p))),r)}var LF=L({bandPart_:PF});function WF(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 BF=L({gramSchmidt_:WF});function VF(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 s7(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),a=Gn(q(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[u,d]=s7(l,t);r.push(u),s.push(d)});let i=q(gn(r,0),e.shape),o=q(gn(s,0),e.shape);return[i,o]}}function s7(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),l=n>=a?a:n;for(let u=0;u<l;++u){let d=s,p=o,c=r;[o,s,r]=P.tidy(()=>{let h=Re(s,[u,u],[n-u,1]),m=gh(h),f=Re(s,[u,u],[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,[u,0],[n-u,a]),b=B(x,o),w=Qe(o);if(u===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],[u,a]),E],0)}let N=Qe(b),C=Re(r,[0,u],[n,r.shape[1]-u]);if(u===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,u]),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 jF=L({qr_:VF}),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 UF(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: ${n}`)}var Ar=L({computeWeightedLoss_:UF});function HF(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=M(n,"weights","absoluteDifference")),cn(r.shape,s.shape,"Error in absoluteDifference: ");let o=Wt(ye(r,s));return Ar(o,i,a)}var GF=L({absoluteDifference_:HF});function qF(e,t,n,a,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","cosineDistance"),i=M(t,"predictions","cosineDistance"),o=null;a!=null&&(o=M(a,"weights","cosineDistance")),cn(s.shape,i.shape,"Error in cosineDistance: ");let l=ke(1),u=ye(l,Se(B(s,i),n,!0));return Ar(u,o,r)}var XF=L({cosineDistance_:qF});function KF(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),cn(r.shape,s.shape,"Error in hingeLoss: ");let o=ke(1);r=ye(B(ke(2),r),o);let l=Ka(ye(o,B(r,s)));return Ar(l,i,a)}var ZF=L({hingeLoss_:KF});function YF(e,t,n,a=1,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),cn(s.shape,i.shape,"Error in huberLoss: ");let l=ke(a),u=Wt(ye(i,s)),d=Fl(u,l),p=ye(u,d),c=ie(B(ke(.5),ot(d)),B(l,p));return Ar(c,o,r)}var JF=L({huberLoss_:YF});function QF(e,t,n,a=1e-7,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),cn(s.shape,i.shape,"Error in logLoss: ");let l=ke(1),u=ke(a),d=St(B(s,Bn(ie(i,u)))),p=B(ye(l,s),Bn(ie(ye(l,i),u))),c=ye(d,p);return Ar(c,o,r)}var e$=L({logLoss_:QF});function t$(e,t,n,a=yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),cn(r.shape,s.shape,"Error in meanSquaredError: ");let o=hh(r,s);return Ar(o,i,a)}var n$=L({meanSquaredError_:t$});function a$(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 r$(e,t,n,a=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),cn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=ke(a),d=ke(1),p=ke(.5);s=ie(B(s,ye(d,u)),B(p,u))}let l=a$(s,i);return Ar(l,o,r)}var s$=L({sigmoidCrossEntropy_:r$});function i$(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 ${n}`);return qa((a,r,s)=>{let i=Z1(r,[n],!0),o=ye(ge(r,"float32"),i);s([a,o]);let l=St(B(o,a));return{value:Se(l,[n]),gradFunc:(u,d)=>{let[p,c]=d,h=Ei(u.shape,[n]);return[B(q(u,h),ye(ge(p,"float32"),la(c))),B(q(u,h),ye(la(c),ge(p,"float32")))]}}})(e,t)}function o$(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 u=ke(a),d=ke(1),p=ke(s.shape[1]);s=ie(B(s,ye(d,u)),me(u,p))}let l=i$(s,i);return Ar(l,o,r)}var l$=L({softmaxCrossEntropy_:o$});function u$(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 ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},u=P.runKernel(vc,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var d$=L({sparseFillEmptyRows_:u$});function p$(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 ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=P.runKernel(wc,i);return{outputIndices:o[0],outputShape:o[1]}}var c$=L({sparseReshape_:p$});function h$(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
|
|
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(kc,i)}var f$=L({sparseSegmentMean_:h$});function m$(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
|
|
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(Ic,i)}var g$=L({sparseSegmentSum_:m$});function y$(e,t,n,a,r,s,i,o){let l=M(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=M(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:n,nGramWidths:a,leftPad:r,rightPad:s,padWidth:i,preserveShortSequences:o},p={data:l,dataSplits:u},c=P.runKernel(Nc,p,d);return{nGrams:c[0],nGramsSplits:c[1]}}var A$=L({stringNGrams_:y$});function x$(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 ${r.shape}`);let s={skipEmpty:n},i={input:a,delimiter:r},o=P.runKernel(Tc,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var b$=L({stringSplit_:x$});function v$(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 w$=L({stringToHashBucketFast_:v$}),k$={fft:bd,ifft:Ol,rfft:vd,irfft:ch},I$={hammingWindow:nF,hannWindow:Y3,frame:J3,stft:iF},De={flipLeftRight:dF,resizeNearestNeighbor:r7,resizeBilinear:a7,rotateWithOffset:cF,cropAndResize:lF,nonMaxSuppression:fF,nonMaxSuppressionAsync:wF,nonMaxSuppressionWithScore:IF,nonMaxSuppressionWithScoreAsync:NF,nonMaxSuppressionPadded:CF,nonMaxSuppressionPaddedAsync:RF,threshold:OF,transform:_F},i7={bandPart:LF,gramSchmidt:BF,qr:jF},S$={absoluteDifference:GF,computeWeightedLoss:Ar,cosineDistance:XF,hingeLoss:ZF,huberLoss:JF,logLoss:e$,meanSquaredError:n$,sigmoidCrossEntropy:s$,softmaxCrossEntropy:l$},wd={sparseFillEmptyRows:d$,sparseReshape:c$,sparseSegmentMean:f$,sparseSegmentSum:g$},vh={stringNGrams:A$,stringSplit:b$,stringToHashBucketFast:w$},xr=class extends u3{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 M3(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:`${t}/accum_var`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;V(()=>{let l=ie(B(i,this.rho),B(ot(s),1-this.rho)),u=B(me(an(ie(o,this.epsilon)),an(ie(i,this.epsilon))),s),d=ie(B(o,this.rho),B(ot(u),1-this.rho));i.assign(l),o.assign(d);let p=ie(B(u,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(he(this.accumulatedGrads.map(e=>e.variable)),he(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};wh.className="Adadelta";jr(wh);var kh=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:V(()=>Rl(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 l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=ie(B(u,this.beta1),B(l,1-this.beta1)),c=ie(B(d,this.beta2),B(ot(l),1-this.beta2)),h=me(p,n),m=me(c,a);u.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 l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=ie(B(u,this.beta1),B(l,1-this.beta1)),c=B(d,this.beta2),h=Wt(l),m=Xa(c,h);u.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 l=ie(B(i,this.decay),B(ot(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,d=ie(B(u,this.decay),B(s,1-this.decay)),p=me(B(s,this.learningRate),an(ye(l,ie(ot(d),this.epsilon)))),c=ie(B(o,this.momentum),p);i.assign(l),u.assign(d),o.assign(c);let h=ye(a,c);a.assign(h)}else{let u=ie(B(i,this.decay),B(ot(s),1-this.decay)),d=ie(B(o,this.momentum),me(B(s,this.learningRate),an(ie(u,this.epsilon))));i.assign(u),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},N$=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Ch(){return new Promise(e=>N$(()=>e()))}var F={};Fe(F,{ERF_A1:()=>_$,ERF_A2:()=>P$,ERF_A3:()=>L$,ERF_A4:()=>W$,ERF_A5:()=>B$,ERF_P:()=>z$,PARALLELIZE_THRESHOLD:()=>mg,SELU_SCALE:()=>l7,SELU_SCALEALPHA:()=>o7,applyActivation:()=>xh,assertAndGetBroadcastShape:()=>mt,assertAxesAreInnerMostDims:()=>UE,assertParamsConsistent:()=>T$,assignToTypedArray:()=>K$,axesAreInnerMostDims:()=>X1,calculateShapes:()=>Yb,checkEinsumDimSizes:()=>tD,combineLocations:()=>$3,complexWithEvenIndex:()=>G$,complexWithOddIndex:()=>q$,computeConv2DInfo:()=>od,computeConv3DInfo:()=>m3,computeDefaultPad:()=>$1,computeDilation2DInfo:()=>fC,computeOptimalWindowSize:()=>E$,computeOutAndReduceShapes:()=>D3,computeOutShape:()=>C$,computePool2DInfo:()=>f3,computePool3DInfo:()=>mC,convertConv2DDataFormat:()=>g3,decodeEinsumEquation:()=>Q$,eitherStridesOrDilationsAreOne:()=>Ga,expandShapeToKeepDim:()=>Ei,exponent:()=>Y$,exponents:()=>Z$,fromStringArrayToUint8:()=>dD,fromUint8ToStringArray:()=>uD,getAxesPermutation:()=>O3,getBroadcastDims:()=>sE,getComplexWithIndex:()=>X$,getEinsumComputePath:()=>nD,getEinsumPermutation:()=>eD,getFusedBiasGradient:()=>Ah,getFusedDyActivation:()=>yh,getImageCenter:()=>R$,getInnerMostAxes:()=>HE,getPermuted:()=>F$,getReductionAxes:()=>Bt,getReshaped:()=>M$,getReshapedPermuted:()=>$$,getSliceBeginCoords:()=>D$,getSliceSize:()=>O$,getUndoAxesPermutation:()=>K1,isIdentityPermutation:()=>aD,log:()=>j$,mergeRealAndImagArrays:()=>U$,prepareAndValidate:()=>Zb,prepareSplitSize:()=>sD,segment_util:()=>p7,shouldFuse:()=>bh,slice_util:()=>fn,splitRealAndImagArrays:()=>H$,tupleValuesAreOne:()=>Ur,upcastType:()=>Aa,validateInput:()=>x1,validateUpdateShape:()=>A1,warn:()=>V$});function T$(e,t){let n=e[0].length;e.forEach((r,s)=>{D(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),D(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)D(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function C$(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var mg=30;function E$(e){return e<=mg?e:Gp(e,Math.floor(Math.sqrt(e)))}function R$(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function M$(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function F$(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function $$(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 D$(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function O$(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 o7=1.7580993408473768,l7=1.0507009873554805,z$=.3275911,_$=.254829592,P$=-.284496736,L$=1.421413741,W$=-1.453152027,B$=1.061405429;function V$(...e){te().getBool("IS_TEST")||console.warn(...e)}function j$(...e){te().getBool("IS_TEST")||console.log(...e)}function U$(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function H$(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 G$(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function q$(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 X$(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function K$(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function Z$(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function Y$(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="->",J$=/->/g,u7=",",d7="...";function Q$(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(J$,"").length)/gg.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${gg}").`);let[a,r]=e.split(gg);D(a.indexOf(d7)===-1,()=>`The ellipsis notation ("${d7}") is not supported yet.`);let s=a.split(u7),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!==u7&&o.push(h)}let l=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.`);l[c]=[];for(let h=0;h<s[c].length;++h)l[c].push(o.indexOf(s[c][h]))}let u=o.length,d=r.length,p=[];for(let c=d;c<u;++c)p.push(c);return{allDims:o,summedDims:p,idDims:l}}function eD(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 tD(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 ${s[i]}`)}}function nD(e,t){let n=e,a=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let i=0;i<r;++i)a.push([]);let s=[];for(let i=0;i<n.length;++i){let o=n[i],l=rD(t,o);for(let u of l)s.indexOf(u)===-1&&(a[i].push(u),s.push(u))}return{path:n,steps:a}}function aD(e){return e.every((t,n)=>t===n)}function rD(e,t){let n=[];for(let a=0;a<e.length;++a)(e[a].length===0||e[a].indexOf(t)!==-1||t===-1)&&n.push(a);return n}function sD(e,t,n=0){let a=[];if(typeof t=="number")D(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);D(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}D(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}var p7={};Fe(p7,{collectGatherOpShapeInfo:()=>lD,computeOutShape:()=>oD,segOpComputeOptimalWindowSize:()=>iD});function iD(e,t){let n=!1,a;for(e<=mg?(a=e,n=!0):a=Gp(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=Gp(e,a+1);return a}function oD(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function lD(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${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=[],l=1,u=1,d=1;for(let p=0;p<a;++p)o.push(e.shape[p]),l*=e.shape[p];for(let p=a;p<n;p++)o.push(e.shape[p]),u*=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:l,sliceSize:d,outerSize:u,dimSize:i,outputShape:o}}function uD(e){try{return e.map(t=>Dc(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function dD(e){return e.map(t=>Yu(t))}var Za={};Fe(Za,{nonMaxSuppressionV3Impl:()=>Q3,nonMaxSuppressionV4Impl:()=>e7,nonMaxSuppressionV5Impl:()=>t7,whereImpl:()=>j3});function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var pD=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 pD(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Eh.nextDataId=0;var yg={};Fe(yg,{addImpl:()=>h7,bincountImpl:()=>xg,bincountReduceImpl:()=>f7,ceilImpl:()=>m7,concatImpl:()=>bg,equalImpl:()=>g7,expImpl:()=>A7,expm1Impl:()=>b7,floorImpl:()=>v7,gatherNdImpl:()=>w7,gatherV2Impl:()=>k7,greaterEqualImpl:()=>S7,greaterImpl:()=>I7,lessEqualImpl:()=>T7,lessImpl:()=>N7,linSpaceImpl:()=>C7,logImpl:()=>E7,maxImpl:()=>R7,maximumImpl:()=>M7,minimumImpl:()=>F7,multiplyImpl:()=>vg,negImpl:()=>$7,notEqualImpl:()=>D7,prodImpl:()=>O7,rangeImpl:()=>kg,rsqrtImpl:()=>z7,simpleAbsImpl:()=>c7,sliceImpl:()=>Fh,sparseFillEmptyRowsImpl:()=>_7,sparseReshapeImpl:()=>P7,sparseSegmentReductionImpl:()=>Ig,squaredDifferenceImpl:()=>L7,stridedSliceImpl:()=>W7,stringNGramsImpl:()=>B7,stringSplitImpl:()=>V7,stringToHashBucketFastImpl:()=>j7,subImpl:()=>U7,tileImpl:()=>H7,topKImpl:()=>G7,transposeImpl:()=>wg,uniqueImpl:()=>q7});function c7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var cD=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=c7(r),n.makeOutput(a,t.shape,"float32")},hD={kernelName:mo,backendName:"cpu",kernelFunc:cD};function Ot(e){return(t,n,a,r,s)=>{let i=F.assertAndGetBroadcastShape(t,n),o=i.length,l=k.computeStrides(i),u=k.sizeFromShape(i),d=k.getTypedArrayFromDType(s,u),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,l),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"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(a.shape,"float32",s),imag:n.makeTensorInfo(r.shape,"float32",i)},o}var fD={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 mD={kernelName:zs,backendName:"cpu",kernelFunc:Ya};function $i(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var gD={kernelName:Ac,backendName:"cpu",kernelFunc:$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"}}),l=qn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}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),[l,u]=Ot((d,p)=>d!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var yD={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,l=s;we([i,o],e);let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,p=i.dtype==="string"?F.fromUint8ToStringArray(u):u,c=i.dtype==="string"?F.fromUint8ToStringArray(d):d,h=a||i.dtype,[m,f]=t(i.shape,o.shape,p,c,h);return l.makeTensorInfo(f,h,m)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=Zr({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),d=l.data.get(u.dataId),p=d.complexTensorInfos.real,c=d.complexTensorInfos.imag,h=l.data.get(p.dataId).values,m=l.data.get(c.dataId).values,f=Zr({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(f.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,v=l.data.get(A.dataId).values,[b,w,N]=n(i.shape,o.shape,h,m,x,v),C=l.makeTensorInfo(N,"float32",b),E=l.makeTensorInfo(N,"float32",w),_=qn({inputs:{real:C,imag:E},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(C),l.disposeIntermediateTensorInfo(E),_}else{let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,p=a||i.dtype,[c,h]=t(i.shape,o.shape,u,d,p);return l.makeTensorInfo(h,p,c)}}}function Ag(e){return(t,n,a,r,s,i)=>{let o=F.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(o),u=o.length,d=k.computeStrides(o),p=k.getTypedArrayFromDType("float32",l),c=k.getTypedArrayFromDType("float32",l),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,u,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 h7=Ot((e,t)=>e+t),AD=Ag((e,t,n,a)=>({real:e+n,imag:t+a})),Id=Yt(Or,h7,AD),xD={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 l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function f7(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 l=0;l<s;l++){let u=e.get(o,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(a?i.set(1,o,u):t.size>0?i.set(i.get(o,u)+t.get(o,l),o,u):i.set(i.get(o,u)+1,o,u))}return i}function Pl(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,l=o.data.get(i.dataId).values,u=k.sizeFromShape(i.shape),d=n||i.dtype,p=k.getArrayFromDType(d,u);for(let c=0;c<u;++c)p[c]=t(l[c],r);return o.makeTensorInfo(i.shape,d,p)}}function Ll(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,l=o.data.get(i.dataId).values,u=n||i.dtype,d=t(l,u,r);return o.makeTensorInfo(i.shape,u,d)}}var m7=Pl(e=>Math.ceil(e)),bD=Ll(Is,m7),vD={kernelName:Is,backendName:"cpu",kernelFunc:bD};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,l=0;for(let u=0;u<i.shape[0];++u){let d=u*t[1]+s;for(let p=0;p<i.shape[1];++p)r[d+p]=o[l++]}s+=i.shape[1]})}return r}var g7=Ot((e,t)=>e===t?1:0),y7=Yt(Mo,g7,null,"bool"),wD={kernelName:Mo,backendName:"cpu",kernelFunc:y7},A7=Pl(e=>Math.exp(e)),x7=Ll(Ms,A7),kD={kernelName:Ms,backendName:"cpu",kernelFunc:x7},b7=Pl(e=>Math.expm1(e)),ID=Ll($o,b7),SD={kernelName:$o,backendName:"cpu",kernelFunc:ID},v7=Pl(e=>Math.floor(e)),ND=Ll(Fs,v7),TD={kernelName:Fs,backendName:"cpu",kernelFunc:ND};function w7(e,t,n,a,r,s,i,o,l){let u=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>=l/s)throw new Error(`Invalid indices: ${p} does not index into ${o}`);for(let h=0;h<s;h++)u.values[d*s+h]=t.get(...t.indexToLoc(c*s+h))}return u}function k7(e,t,n){let a=Ve(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);a.values[r]=e.values[u]}return a}var I7=Ot((e,t)=>e>t?1:0),CD=Yt(_o,I7,null,"bool"),ED={kernelName:_o,backendName:"cpu",kernelFunc:CD},S7=Ot((e,t)=>e>=t?1:0),RD=Yt(Os,S7,null,"bool"),MD={kernelName:Os,backendName:"cpu",kernelFunc:RD},N7=Ot((e,t)=>e<t?1:0),FD=Yt(Bo,N7,null,"bool"),$D={kernelName:Bo,backendName:"cpu",kernelFunc:FD},T7=Ot((e,t)=>e<=t?1:0),DD=Yt(Vo,T7,null,"bool"),OD={kernelName:Vo,backendName:"cpu",kernelFunc:DD};function C7(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 E7=Pl(e=>Math.log(e)),zD=Ll(Ps,E7),_D={kernelName:Ps,backendName:"cpu",kernelFunc:zD};function R7(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 l=0;l<t;++l){let u=e[i+l];(Number.isNaN(u)||u>o)&&(o=u)}r[s]=o}return r}var M7=Ot((e,t)=>Math.max(e,t)),PD=Yt(Ws,M7),LD={kernelName:Ws,backendName:"cpu",kernelFunc:PD},F7=Ot((e,t)=>Math.min(e,t)),WD=Yt(Us,F7),BD={kernelName:Us,backendName:"cpu",kernelFunc:WD},vg=Ot((e,t)=>e*t),VD=Ag((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),Mh=Yt(Gs,vg,VD),jD={kernelName:Gs,backendName:"cpu",kernelFunc:Mh};function $7(e,t,n){let a=k.createScalarValue(-1,n);return vg([],t,a,e,n)}function UD(e){let{inputs:t,backend:n}=e,{x:a}=t;we(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=$7(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var HD={kernelName:Go,backendName:"cpu",kernelFunc:UD},D7=Ot((e,t)=>e!==t?1:0),GD=Yt(qo,D7,null,"bool"),qD={kernelName:qo,backendName:"cpu",kernelFunc:GD};function wg(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),l=k.computeStrides(r),u=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,l);u[h]=e[d]}return u}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 l=a.data.get(r.dataId).values,u=wg(l,r.shape,r.dtype,s,o);return{dataId:a.write(u,o,r.dtype),shape:o,dtype:r.dtype}}var XD={kernelName:ci,backendName:"cpu",kernelFunc:ua};function O7(e,t,n,a){let[r,s]=F.computeOutAndReduceShapes(e,a),i=Aa(t,"int32"),o=k.makeZerosTypedArray(k.sizeFromShape(r),i),l=k.sizeFromShape(s);for(let u=0;u<o.length;++u){let d=u*l,p=1;for(let c=0;c<l;++c)p*=n[d+c];o[u]=p}return{outVals:o,outShape:r,outDtype:i}}function KD(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,l=k.parseAxisParam(s,r.shape),u=F.getAxesPermutation(l,o),d=l,p=r,c=[];u!=null&&(p=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),c.push(p),d=F.getInnerMostAxes(d.length,o));let h=n.data.get(p.dataId).values,{outVals:m,outShape:f,outDtype:g}=O7(p.shape,p.dtype,h,d),y=f;return i&&(y=F.expandShapeToKeepDim(f,l)),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(y,g,m)}var ZD={kernelName:Qo,backendName:"cpu",kernelFunc:KD};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)),l=k.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var z7=Pl(e=>1/Math.sqrt(e)),YD=Ll(ni,z7),JD={kernelName:ni,backendName:"cpu",kernelFunc:YD};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 l=r==="string"?F.fromUint8ToStringArray(e):e,u=Ve(a,r,l),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(u.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,l]=fn.parseSliceParams(r,s,i);fn.assertParamsValid(r,o,l);let u=n.data.get(r.dataId).values,d=Fh(u,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}var QD={kernelName:sl,backendName:"cpu",kernelFunc:Di};function _7(e,t,n,a,r,s,i){let o=t[0],l=s[0],u=new Array(l),d=new Array(o),p=t[1];if(l===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
|
|
indices.shape[0] = ${o}`);let g=k.getArrayFromDType(n,0),y=k.getArrayFromDType(r,0);return[g,[0,p],y,u,d]}let c=!0,h=0,m=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*p];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${l}`);++m[y],c=c&&y>=h,h=y}let f=!0;for(let g=0;g<l;++g){let y=m[g]===0;u[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,u,d]}else{let g=m[l-1],y=k.getArrayFromDType(n,g*p),A=k.getArrayFromDType(r,g),x=new Array(l).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<l;++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,u,d]}}function P7(e,t,n,a,r){let s=k.sizeFromShape(a),i=t[0],o=r.length,l=[],u=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,l.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);u*=y,l.push(y)}}if(d!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(s/u);if(u*g!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
|
|
dense values, but the requested shape requires a multiple of ${u}. inputShape=${a} outputShape= ${l}`);l[d]=g}let p=k.sizeFromShape(l);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=${l}`);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]*l[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],l]}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 l=[t[0],e.length/t[0]],u=l[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, ${d}), possibly because segmentIds input is not sorted.`);y>g&&h.fill(i,g*u,y*u);for(let x=m;x<f;++x){let v=a[x];if(v<0||v>=l[0])throw new Error(`Bad: indices[${x}] == ${a[x]} out of range [0, ${l[0]})`);for(let b=0;b<u;b++)h[y*u+b]+=e[v*u+b]}if(s)for(let x=0;x<u;x++)h[y*u+x]/=f-m;if(m=f,++f,g=y+1,y=A,f>o)break}return g<d&&h.fill(i,g*u,d*u),[h,p]}var L7=Ot((e,t)=>{let n=e-t;return n*n}),eO=Yt(li,L7),tO={kernelName:li,backendName:"cpu",kernelFunc:eO};function W7(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 l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var nO=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),l=Math.max(0,o-i),u=Math.max(0,o-(r-(i+1))),d=s-(l+u),p=t+(l>0?0:i-o),c=0;c+=l*this.leftPad.length;for(let g=0;g<d;++g)c+=e[p+g].length;c+=u*this.rightPad.length,c+=(l+u+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<l;++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<u;++g)f(this.separator),f(this.rightPad)}else{for(let g=0;g<u-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 l=1;l<a;++l){let u=t[l]>=o;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${n}]`);o=t[l]}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 l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(d=>{u+=this.getNumNGrams(l,d)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(d=>{let p=t[o+1]-t[o],c=this.getNumNGrams(p,d);this.createNGrams(e,l,i,u,c,d),u+=c}),this.preserveShort&&u===s[o]){let d=t[o+1]-t[o];if(d===0)continue;let p=d+2*this.padWidth,c=1;this.createNGrams(e,l,i,u,c,p)}}return[i,s]}};function B7(e,t,n,a,r,s,i,o){return new nO(n,a,r,s,i,o).compute(e,t)}function aO(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 l=e.subarray(0,o);(!n||l.length!==0)&&i.push(l),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 V7(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=aO(e[c],t,n),m=h.length;o[c]=m,s+=m,i=Math.max(i,m),r.push(...h)}let l=k.getArrayFromDType("int32",s*2),u=new Array(s),d=[a,i],p=0;for(let c=0;c<a;++c)for(let h=0;h<o[c];++h)l[p*2]=c,l[p*2+1]=h,u[p]=r[p],++p;return[l,u,d]}function j7(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 U7=Ot((e,t)=>e-t),rO=Ag((e,t,n,a)=>({real:e-n,imag:t-a})),Sg=Yt(ui,U7,rO),sO={kernelName:ui,backendName:"cpu",kernelFunc:Sg};function H7(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 l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}function G7(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*a),u=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=l.subarray(f,f+a),y=u.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,l),Ve(d,"int32",u)]}function q7(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]),l=new Lt(s,a,e),u=[],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(l.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,u.push(m)}}let p=s.slice();p[1]=Object.keys(i).length;let c=new Lt(p,a);u.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)c.set(l.get(g,m,y),g,f,y)});let h=n.slice();return h[r]=p[1],{outputValues:c.values,outputShape:h,indices:o}}var X7="3.7.0";Il("cpu",()=>new Eh,1);var K7=rt(Eo,e=>e>=0?e:Math.exp(e)-1),iO={kernelName:Eo,backendName:"cpu",kernelFunc:K7};function Z7(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,l=k.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(r.shape,"float32",l)}var oO={kernelName:_s,backendName:"cpu",kernelFunc:Z7},lO=Ot((e,t)=>e<0?t*e:e);function Y7(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,l]=lO(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var uO={kernelName:Zs,backendName:"cpu",kernelFunc:Y7},J7=rt(Ys,e=>Math.max(0,e)),dO={kernelName:Ys,backendName:"cpu",kernelFunc:J7},Q7=rt(Qs,e=>Math.min(Math.max(0,e),6)),pO={kernelName:Qs,backendName:"cpu",kernelFunc:Q7},ev=rt(ri,e=>1/(1+Math.exp(-e))),cO={kernelName:ri,backendName:"cpu",kernelFunc:ev};function Ng(e,t,n,a,r){if(n==="linear")return Ya({inputs:{x:t},backend:e});if(n==="relu")return J7({inputs:{x:t},backend:e});if(n==="elu")return K7({inputs:{x:t},backend:e});if(n==="relu6")return Q7({inputs:{x:t},backend:e});if(n==="prelu")return Y7({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return Z7({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return ev({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let d=u.complexTensorInfos.real,p=u.complexTensorInfos.imag;d.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var hO={kernelName:tl,backendName:"cpu",kernelFunc:gt};function tv(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;we([r,s],"matMul");let l=r.shape.length,u=s.shape.length,d=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),A=g===y||g===1||y===1;k.assert(l>=2&&u>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${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 Ye=Te;Ye<ze;Ye++){let ht=Math.min(xe,g-1)*G,Ue=Math.min(xe,y-1)*Q,In=S[ht+tt*H+Ye*J],kt=z[Ye*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 fO={kernelName:ws,backendName:"cpu",kernelFunc:tv};function mO(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a,c,h,m,f=[];c=tv({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},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 gO={kernelName:hi,backendName:"cpu",kernelFunc:mO},yO=rt(go,e=>Math.acos(e)),AO={kernelName:go,backendName:"cpu",kernelFunc:yO},xO=rt(yo,e=>Math.acosh(e)),bO={kernelName:yo,backendName:"cpu",kernelFunc:xO};function vO(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 l=r[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var wO={kernelName:xs,backendName:"cpu",kernelFunc:vO};function kO(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),l=o,u=F.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("all",l,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,l),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}u!=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 IO={kernelName:Ao,backendName:"cpu",kernelFunc:kO};function SO(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),l=o,u=F.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("any",l,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,l),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}u!=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 NO={kernelName:xo,backendName:"cpu",kernelFunc:SO};function TO(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),l=r,u=[];o!=null&&(l=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,p]=F.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.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 u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var CO={kernelName:bs,backendName:"cpu",kernelFunc:TO};function EO(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),l=r,u=[];o!=null&&(l=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,p]=F.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.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 u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var RO={kernelName:Fu,backendName:"cpu",kernelFunc:EO},MO=rt(bo,e=>Math.asin(e)),FO={kernelName:bo,backendName:"cpu",kernelFunc:MO},$O=rt(vo,e=>Math.asinh(e)),DO={kernelName:vo,backendName:"cpu",kernelFunc:$O},OO=rt(wo,e=>Math.atan(e)),zO={kernelName:wo,backendName:"cpu",kernelFunc:OO},_O=Ot((e,t)=>Math.atan2(e,t)),PO=Yt(Io,_O),LO={kernelName:Io,backendName:"cpu",kernelFunc:PO},WO=rt(ko,e=>Math.atanh(e)),BO={kernelName:ko,backendName:"cpu",kernelFunc:WO};function Tg(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=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+=l){let se=w+Q*a[1];for(let Z=W;Z<G;Z+=u){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 nv(e,t,n,a,r=!1,s=!1){let i=Ve(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=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+=u;let b=Math.min(a.inHeight,p+x);for(let w=0;w<a.outWidth;++w){let N=w*l-m,C=N;for(;C<0;)C+=d;let E=Math.min(a.inWidth,c+N),_=Number.NEGATIVE_INFINITY,$=-1;for(let S=v;S<b;S+=u){let z=S-x;for(let O=C;O<E;O+=d){let W=O-N,G=f.get(g,S,O,y);G>_&&(_=G,r?$=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 av(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=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,$=E*a[0];for(let S=0;S<r.inChannels;++S)for(let z=0;z<r.outDepth;++z){let O=z*i-f,W=O;for(;W<0;)W+=u;let G=Math.min(r.inDepth,c+O),H=_+z*w;for(let J=0;J<r.outHeight;++J){let K=J*o-g,ne=K;for(;ne<0;)ne+=d;let Q=Math.min(r.inHeight,h+K),se=H+J*N;for(let Z=0;Z<r.outWidth;++Z){let le=Z*l-y,oe=le;for(;oe<0;)oe+=p;let xe=Math.min(r.inWidth,m+le),fe=se+Z*C,Ne=A,Te=0,Oe=0;for(let ze=W;ze<G;ze+=u){let tt=$+ze*a[1];for(let nt=ne;nt<Q;nt+=d){let it=tt+nt*a[2];for(let Ye=oe;Ye<xe;Ye+=p){let ht=it+Ye*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 VO(e,t){let n=Ve(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=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,u+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;)$+=l;let S=Math.min(t.inWidth,p+_),z=Number.NEGATIVE_INFINITY,O=-1;for(let W=x;W<v;W+=i){let G=W-A;for(let H=N;H<C;H+=o){let J=H-w;for(let K=$;K<S;K+=l){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 jO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l),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 UO={kernelName:vs,backendName:"cpu",kernelFunc:jO};function HO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;we(r,"avgPool3d");let d=F.computePool3DInfo(r.shape,s,i,1,o,l,u),p=n.data.get(r.dataId).values,c=av(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var GO={kernelName:$u,backendName:"cpu",kernelFunc:HO};function qO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;we([r,s],"avgPool3DGrad");let d=F.computePool3DInfo(s.shape,i,o,1,l,u),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,v=d.effectiveFilterDepth,b=d.effectiveFilterHeight,w=d.effectiveFilterWidth,N=v-1-d.padInfo.front,C=w-1-d.padInfo.left,E=b-1-d.padInfo.top,_=Ve(s.shape,"float32"),$=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 XO={kernelName:Kp,backendName:"cpu",kernelFunc:qO};function KO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;we([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=F.computePool2DInfo(i.shape,o,l,1,u),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 ZO={kernelName:Xp,backendName:"cpu",kernelFunc:KO};function YO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.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,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let d=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(l.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++]+u),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 JO={kernelName:Ds,backendName:"cpu",kernelFunc:YO};function QO(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),l=F.getReshaped(r.shape,s,o),u=F.getPermuted(l.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:l}}),m=ua({inputs:{x:h},backend:n,attrs:{perm:u}}),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 ez={kernelName:Du,backendName:"cpu",kernelFunc:QO};function tz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=xg(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var nz={kernelName:Zp,backendName:"cpu",kernelFunc:tz},az=rt(zr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),rz={kernelName:zr,backendName:"cpu",kernelFunc:az},sz=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,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],p=l[u];a[u]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},iz={kernelName:Ou,backendName:"cpu",kernelFunc:sz};function Wl(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 oz={kernelName:pc,backendName:"cpu",kernelFunc:Wl};function Bl(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 l=o.map(f=>f.shape);if(F.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>$i({inputs:{input:v},backend:n})),g=o.map(v=>Wl({inputs:{input:v},backend:n})),y=Bl({inputs:f,backend:n,attrs:{axis:s}}),A=Bl({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 u=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return gt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),d=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=F.computeOutShape(u.map(f=>f.shape),1);let p=u[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 u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var lz={kernelName:So,backendName:"cpu",kernelFunc:Bl};function rv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a;we([r,s],"conv2d");let p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,s.shape,i,u,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 Ye=0;Ye<c.inChannels;++Ye){let ht=W[nt+Ye*_];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 uz={kernelName:Ss,backendName:"cpu",kernelFunc:rv};function dz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a;we([r,s],"conv2dBackpropFilter");let p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,d,i,1,o,u,!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 pz={kernelName:Jp,backendName:"cpu",kernelFunc:dz};function cz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a;we([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),c=k.computeStrides(r.shape),h=F.convertConv2DDataFormat(u),m=F.computeConv2DInfo(i,s.shape,o,1,l,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,Ye=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=Ye;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 hz={kernelName:Ns,backendName:"cpu",kernelFunc:cz};function fz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let u=F.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:p,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,v=new Lt(u.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;_<u.batchSize;++_){let $=_*C[0],S=_*v.strides[0];for(let z=0;z<u.outDepth;++z){let O=S+z*v.strides[1],W=z*u.strideDepth-y;for(let G=0;G<d;++G){let H=W+G*h;if(H<0||H>=u.inDepth)continue;let J=G*E[0],K=$+H*C[1];for(let ne=0;ne<u.outHeight;++ne){let Q=O+ne*v.strides[2],se=ne*u.strideHeight-x;for(let Z=0;Z<p;++Z){let le=se+Z*m;if(le<0||le>=u.inHeight)continue;let oe=J+Z*E[1],xe=K+le*C[2];for(let fe=0;fe<u.outWidth;++fe){let Ne=Q+fe*u.outChannels,Te=fe*u.strideWidth-A;for(let Oe=0;Oe<c;++Oe){let Pe=Te+Oe*f;if(Pe<0||Pe>=u.inWidth)continue;let ze=oe+Oe*E[2],tt=xe+Pe*u.inChannels,nt=ze;for(let it=0;it<u.inChannels;++it){let Ye=b[tt+it];for(let ht=0;ht<u.outChannels;++ht)N[Ne+ht]+=Ye*w[nt+ht];nt+=u.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var mz={kernelName:zu,backendName:"cpu",kernelFunc:fz};function gz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;we([r,s],"conv3dBackpropFilterV2");let u=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(r.shape,l,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,_,$,S]=d,z=n.data.get(r.dataId).values,[O,W,G,H]=u,J=p.padInfo.front,K=p.padInfo.left,ne=p.padInfo.top;for(let Q=0;Q<f;++Q){let se=Math.max(0,Math.ceil((J-Q)/c)),Z=Math.min(p.outDepth,(p.inDepth+J-Q)/c),le=Q*v;for(let oe=0;oe<g;++oe){let xe=Math.max(0,Math.ceil((ne-oe)/h)),fe=Math.min(p.outHeight,(p.inHeight+ne-oe)/h),Ne=oe*b+le;for(let Te=0;Te<y;++Te){let Oe=Math.max(0,Math.ceil((K-Te)/m)),Pe=Math.min(p.outWidth,(p.inWidth+K-Te)/m),ze=Te*w+Ne;for(let tt=0;tt<p.inChannels;++tt){let nt=tt*N+ze;for(let it=0;it<p.outChannels;++it){let Ye=0;for(let ht=0;ht<p.batchSize;++ht){let Ue=ht*O,In=ht*E;for(let kt=se;kt<Z;++kt){let ta=(Q+kt*c-J)*W+Ue,en=kt*_+In;for(let Sn=xe;Sn<fe;++Sn){let na=(oe+Sn*h-ne)*G+ta,Pn=Sn*$+en;for(let dn=Oe;dn<Pe;++dn){let tn=(Te+dn*m-K)*H+na,Ba=dn*S+Pn;Ye+=z[tn+tt]*C[Ba+it]}}}}x[nt+it]=Ye}}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var yz={kernelName:Qp,backendName:"cpu",kernelFunc:gz};function Az(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let u=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(l,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]=u,N=n.data.get(s.dataId).values,[C,E,_,$]=d,{batchSize:S,filterDepth:z,filterHeight:O,filterWidth:W,inChannels:G,inDepth:H,inHeight:J,inWidth:K,outChannels:ne,outDepth:Q,outHeight:se,outWidth:Z,strideDepth:le,strideHeight:oe,strideWidth:xe}=p,fe=z-1-p.padInfo.front,Ne=O-1-p.padInfo.top,Te=W-1-p.padInfo.left;for(let Oe=0;Oe<S;++Oe)for(let Pe=0;Pe<G;++Pe)for(let ze=0;ze<H;++ze){let tt=ze-fe,nt=Math.max(0,Math.ceil(tt/le)),it=Math.min(Q,(z+tt)/le);for(let Ye=0;Ye<J;++Ye){let ht=Ye-Ne,Ue=Math.max(0,Math.ceil(ht/oe)),In=Math.min(se,(O+ht)/oe);for(let kt=0;kt<K;++kt){let ta=kt-Te,en=Math.max(0,Math.ceil(ta/xe)),Sn=Math.min(Z,(W+ta)/xe),na=0;for(let Pn=nt;Pn<it;++Pn){let dn=Pn*le-tt;for(let tn=Ue;tn<In;++tn){let Ba=tn*oe-ht;for(let fa=en;fa<Sn;++fa){let ma=fa*xe-ta,Nr=x*Oe+v*Pn+b*tn+w*fa,or=C*(z-1-dn)+E*(O-1-Ba)+_*(W-1-ma)+$*Pe;for(let Tr=0;Tr<ne;++Tr){let eo=A[Nr+Tr],Va=N[or+Tr];na+=eo*Va}}}}h[m*Oe+f*ze+g*Ye+y*kt+Pe]=na}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var xz={kernelName:ec,backendName:"cpu",kernelFunc:Az},bz=rt(Ts,e=>Math.cos(e)),vz={kernelName:Ts,backendName:"cpu",kernelFunc:bz},wz=rt(No,e=>Math.cosh(e)),kz={kernelName:No,backendName:"cpu",kernelFunc:wz};function Iz(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=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+$)*(p-1);if(H<0||H>p-1){for(let J=0;J<g;J++)for(let K=0;K<h;K++){let ne=K+J*w[2]+G*w[1]+N*w[0];y.values[ne]=u}continue}if(l==="bilinear"){let J=Math.floor(H),K=Math.ceil(H),ne=H-J;for(let Q=0;Q<g;Q++){let se=g>1?_*(c-1)+Q*W:.5*(_+S)*(c-1);if(se<0||se>c-1){for(let xe=0;xe<h;xe++){let fe=xe+Q*w[2]+G*w[1]+N*w[0];y.values[fe]=u}continue}let Z=Math.floor(se),le=Math.ceil(se),oe=se-Z;for(let xe=0;xe<h;xe++){let fe=xe+Z*b[2]+J*b[1]+z*b[0],Ne=v[fe];fe=xe+le*b[2]+J*b[1]+z*b[0];let Te=v[fe];fe=xe+Z*b[2]+K*b[1]+z*b[0];let Oe=v[fe];fe=xe+le*b[2]+K*b[1]+z*b[0];let Pe=v[fe],ze=Ne+(Te-Ne)*oe,tt=Oe+(Pe-Oe)*oe;fe=xe+Q*w[2]+G*w[1]+N*w[0],y.values[fe]=ze+(tt-ze)*ne}}}else for(let J=0;J<g;++J){let K=g>1?_*(c-1)+J*W:.5*(_+S)*(c-1);if(K<0||K>c-1){for(let se=0;se<h;se++){let Z=se+J*w[2]+G*w[1]+N*w[0];y.values[Z]=u}continue}let ne=Math.round(K),Q=Math.round(H);for(let se=0;se<h;se++){let Z=se+ne*b[2]+Q*b[1]+z*b[0],le=se+J*w[2]+G*w[1]+N*w[0];y.values[le]=v[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Sz={kernelName:To,backendName:"cpu",kernelFunc:Iz};function Nz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=F.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=ua({inputs:{x:r},backend:n,attrs:{perm:l}}));let d=F.getInnerMostAxes(1,r.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let p=Aa(u.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(u.shape),p),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<h.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)c[x]=i?0:h[x];else{let v=f(y,A-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(u.shape,p,c);if(l!=null){let y=F.getUndoAxesPermutation(l),A=ua({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),A}return g}var Tz={kernelName:Cs,backendName:"cpu",kernelFunc:Nz};function Cz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,d=xg(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=f7(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Ez={kernelName:tc,backendName:"cpu",kernelFunc:Cz};function Rz(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],l=r.shape[1],u=r.shape[2],d=r.shape[3],p=l*s,c=u*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+u*(x+l*y));f[g++]=m[_]}}}return n.makeTensorInfo([o,p,c,h],r.dtype,f)}var Mz={kernelName:Co,backendName:"cpu",kernelFunc:Rz};function sv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a;we([r,s],"depthwiseConv2DNative");let d=k.computeStrides(r.shape),p=k.computeStrides(s.shape),c=l;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,u,!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 Fz={kernelName:Es,backendName:"cpu",kernelFunc:sv};function $z(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=F.computeConv2DInfo(r.shape,d,i,o,l,u,!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,$,W,G)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Dz={kernelName:nc,backendName:"cpu",kernelFunc:$z};function Oz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,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,l,u,!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 Ye=Te;Ye<Oe;++Ye){let ht=Ye*ne-Ne;for(let Ue=tt;Ue<nt;++Ue){let In=Ue*Q-ze,kt=v*oe+b*Ye+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 zz={kernelName:ac,backendName:"cpu",kernelFunc:Oz};function _z(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 u=0;u<s.length;u++)o[u*r+u]=s[u];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var Pz={kernelName:rc,backendName:"cpu",kernelFunc:_z},Lz={kernelName:_u,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,d=a.shape.length,p=l.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=u[xe]+p[fe];Ne>ne&&(ne=Ne)}}}let Q=k.locToIndex([O,W,H,K],S,k.computeStrides(_));z[Q]=ne}}}return{dataId:l.write(k.toTypedArray(z,a.dtype),_,a.dtype),shape:_,dtype:a.dtype}}},Wz={kernelName:ic,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=k.toNestedArray(a.shape,u.data.get(a.dataId).values),p=k.toNestedArray(r.shape,u.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",l);k.assert(s.rank===E.length,()=>`Error in ${ic}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let _=k.toNestedArray(E,u.data.get(s.dataId).values),$=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)}}}$[K][ne][H]+=_[S][z][W][H]}}}return{dataId:u.write(k.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Bz={kernelName:sc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=k.toNestedArray(a.shape,u.data.get(a.dataId).values),p=k.toNestedArray(r.shape,u.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",l);k.assert(s.rank===E.length,()=>`Error in ${sc}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let _=k.toNestedArray(E,u.data.get(s.dataId).values),$=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)}}}$[S][K][ne][H]+=_[S][z][W][H]}}}return{dataId:u.write(k.toTypedArray($,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 l=o.shape.length,u=k.parseAxisParam(s,o.shape),d=F.getAxesPermutation(u,l),p=u,c=o;d!=null&&(c=ua({inputs:{x:o},backend:n,attrs:{perm:d}}),p=F.getInnerMostAxes(p.length,l)),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,u),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 Vz={kernelName:ii,backendName:"cpu",kernelFunc:Sd};function jz(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=F.getEinsumComputePath(o,l),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,l[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&&(u[f]>=0&&(c=Sd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Uz={kernelName:oc,backendName:"cpu",kernelFunc:jz};function Hz(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 l=0;l<i.length;++l){let u=i[l];u>=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var Gz={kernelName:lc,backendName:"cpu",kernelFunc:Hz},qz=F.ERF_P,Xz=F.ERF_A1,Kz=F.ERF_A2,Zz=F.ERF_A3,Yz=F.ERF_A4,Jz=F.ERF_A5,Qz=rt(Ro,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+qz*n);return t*(1-((((Jz*a+Yz)*a+Zz)*a+Kz)*a+Xz)*a*Math.exp(-n*n))}),e_={kernelName:Ro,backendName:"cpu",kernelFunc:Qz};function $h(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),gt({inputs:{x:r},backend:n,attrs:{shape:o}})}var t_={kernelName:Fo,backendName:"cpu",kernelFunc:$h},n_=Ot((e,t)=>e/t),Cg=Yt(Rs,n_),Eg={kernelName:Rs,backendName:"cpu",kernelFunc:Cg};function iv(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],d=k.sizeFromShape(u),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:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=qn({inputs:{real:y,imag:A},backend:n}),{real:v,imag:b}=a_(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(u,"float32",p),m=n.makeTensorInfo(u,"float32",c),f=qn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function a_(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(r_(a)){let o=Rg(s,i,a,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",o.real),d=n.makeTensorInfo(l,"float32",o.imag),p=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),c=Ya({inputs:{x:p},backend:n}),h=Eg.kernelFunc({inputs:{a:u,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(u),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),l=s_(o,a,t);return F.splitRealAndImagArrays(l)}}function r_(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),l=o.real,u=o.imag,d=[l.length],p=r.makeTensorInfo(d,"float32",l),c=r.makeTensorInfo(d,"float32",u),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(l,u,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=$i({inputs:{input:xe},backend:r}),Te=Wl({inputs:{input:oe},backend:r}),Oe=Wl({inputs:{input:xe},backend:r}),Pe=Bl({inputs:[fe,Ne],backend:r,attrs:{axis:0}}),ze=Bl({inputs:[Te,Oe],backend:r,attrs:{axis:0}}),tt=r.data.get(Pe.dataId).values,nt=r.data.get(ze.dataId).values;return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(E),r.disposeIntermediateTensorInfo(_),r.disposeIntermediateTensorInfo($),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 s_(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 l=F.exponent(r*o,t,n),u=F.getComplexWithIndex(e,o);s+=u.real*l.real-u.imag*l.imag,i+=u.real*l.imag+u.imag*l.real}n&&(s/=t,i/=t),F.assignToTypedArray(a,s,i,r)}return a}function i_(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]}}),l=iv(o,!1,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var o_={kernelName:uc,backendName:"cpu",kernelFunc:i_};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 u_(o,r,i),t.makeTensorInfo(a,i,o)}var l_={kernelName:Pu,backendName:"cpu",kernelFunc:Mg};function u_(e,t,n){e.fill(t)}var d_={kernelName:Do,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,l,u]=a.shape,d=r.data.get(a.dataId).values;for(let p=0;p<i;p++){let c=p*l*o*u;for(let h=0;h<o;h++){let m=h*(l*u);for(let f=0;f<l;f++){let g=f*u;for(let y=0;y<u;y++){let A=[i,h,f,y][2],x=Math.round(l-A),v=c+m+g+y,b=d[v];if(x>=0&&x<l){let w=x*u,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}}},p_=Ot((e,t)=>Math.floor(e/t)),c_=Yt($s,p_,null,"int32"),h_={kernelName:$s,backendName:"cpu",kernelFunc:c_};function f_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=rv({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,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 m_={kernelName:fi,backendName:"cpu",kernelFunc:f_};function g_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=sv({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,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 y_={kernelName:mi,backendName:"cpu",kernelFunc:g_};function A_(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],[l,u,d,p]=F.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=w7(c,h,a.dtype,u,o,d,p,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var x_={kernelName:zo,backendName:"cpu",kernelFunc:A_};function b_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;we([r,s],"gatherV2");let l=o;o==null&&(l=0);let u=k.sizeFromShape(s.shape),d=k.parseAxisParam(i,r.shape)[0],p=F.segment_util.collectGatherOpShapeInfo(r,s,d,l),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,u/p.batchSize]}}),m=[p.batchSize,p.outerSize,u/p.batchSize,p.sliceSize],f=n.bufferSync(h),g=n.bufferSync(c),y=k7(g,f,m);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(p.outputShape,y.dtype,y.values)}var v_={kernelName:Oo,backendName:"cpu",kernelFunc:b_};function w_(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]}}),l=iv(o,!0,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var k_={kernelName:dc,backendName:"cpu",kernelFunc:w_},I_=rt(Po,e=>Number.isFinite(e)?1:0,"bool"),S_={kernelName:Po,backendName:"cpu",kernelFunc:I_},N_=rt(Lo,e=>Math.abs(e)===Infinity?1:0,"bool"),T_={kernelName:Lo,backendName:"cpu",kernelFunc:N_},C_=rt(Wo,e=>Number.isNaN(e)?1:0,"bool"),E_={kernelName:Wo,backendName:"cpu",kernelFunc:C_};function R_(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=C7(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var M_={kernelName:cc,backendName:"cpu",kernelFunc:R_},F_=rt(jo,e=>Math.log1p(e)),$_={kernelName:jo,backendName:"cpu",kernelFunc:F_},D_=Ot((e,t)=>e&&t),O_=Yt(Uo,D_,null,"bool"),z_={kernelName:Uo,backendName:"cpu",kernelFunc:O_},__=rt(Lu,e=>e?0:1,"bool"),P_={kernelName:Lu,backendName:"cpu",kernelFunc:__},L_=Ot((e,t)=>e||t),W_=Yt(Wu,L_,null,"bool"),B_={kernelName:Wu,backendName:"cpu",kernelFunc:W_};function V_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;we(r,"LRN");let u=r.shape[3],d=u-1,p=n.data.get(r.dataId).values,c=k.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,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,-l);h[f]=y}return n.makeTensorInfo(r.shape,r.dtype,h)}var j_={kernelName:Bu,backendName:"cpu",kernelFunc:V_};function U_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,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=u*w+l;for(let N=v;N<b;N++){let C=-2*u*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 H_={kernelName:hc,backendName:"cpu",kernelFunc:U_};function ov(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,l=r.shape,u=l.length,d=k.parseAxisParam(s,l),p=d,c=F.getAxesPermutation(p,u),h=o.data.get(r.dataId).values;if(c!=null){let v=new Array(u);for(let b=0;b<v.length;b++)v[b]=l[c[b]];h=wg(h,l,r.dtype,c,v),p=F.getInnerMostAxes(p.length,u),l=v}we(r,"max"),F.assertAxesAreInnerMostDims("max",p,u);let[m,f]=F.computeOutAndReduceShapes(l,p),g=k.sizeFromShape(f),y=R7(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 G_={kernelName:Ls,backendName:"cpu",kernelFunc:ov};function q_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l),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 X_={kernelName:Bs,backendName:"cpu",kernelFunc:q_};function K_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;we(r,"maxPool3d");let d=F.computePool3DInfo(r.shape,s,i,1,o,l,u),p=n.data.get(r.dataId).values,c=av(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var Z_={kernelName:Vu,backendName:"cpu",kernelFunc:K_};function Y_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;we([r,s],"maxPool3DGrad");let d=F.computePool3DInfo(s.shape,i,o,1,l,u),p=n.bufferSync(s),c=VO(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 J_={kernelName:mc,backendName:"cpu",kernelFunc:Y_};function Q_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,l,u,1,d,p),h=n.data.get(o.dataId).values,m=Ve(c.outShape,o.dtype,nv(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 eP={kernelName:fc,backendName:"cpu",kernelFunc:Q_};function tP(e,t,n,a,r){let s=k.computeStrides(t),i=Tg(e,t,n,s,r,"max"),o=nv(e,t,n,r,!0,a);return[i.values,o.values]}var nP={kernelName:gc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;we(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,d=F.computePool2DInfo(a.shape,r,s,[1,1],i),[p,c]=tP(u,a.shape,a.dtype,o,d),h=l.write(p,d.outShape,a.dtype),m=l.write(c,d.outShape,a.dtype);return[{dataId:h,shape:d.outShape,dtype:a.dtype},{dataId:m,shape:d.outShape,dtype:"int32"}]}};function aP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=F.computeOutAndReduceShapes(r.shape,o)[1],u=k.sizeFromShape(l),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([u]));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 rP={kernelName:Vs,backendName:"cpu",kernelFunc:aP};function sP(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),l=o,u=F.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",l,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,l),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}u!=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 iP={kernelName:js,backendName:"cpu",kernelFunc:sP};function oP(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]),l=s.map(A=>A[0]),u=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]<l[b]?x[b]=l[b]*2-x[b]-d:x[b]>=u[b]&&(x[b]=(u[b]-1)*2-x[b]+d);x=x.map((b,w)=>b-l[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 lP={kernelName:Hs,backendName:"cpu",kernelFunc:oP},uP=Ot((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),dP=Yt(Ho,uP),pP={kernelName:Ho,backendName:"cpu",kernelFunc:dP},cP=gs(N5());function lv(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 l=k.parseAxisParam([o],r.shape),u=ov({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),d=F.expandShapeToKeepDim(u.shape,l),p=gt({inputs:{x:u},backend:n,attrs:{shape:d}}),c=Sg({inputs:{a:r,b:p},backend:n}),h=x7({inputs:{x:c},backend:n}),m=Sd({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=gt({inputs:{x:m},backend:n,attrs:{shape:d}}),g=Cg({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var hP={kernelName:oi,backendName:"cpu",kernelFunc:lv};function fP(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;we(r,"multinomial");let l=o?r:lv({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],d=l.shape[1],p=n.data.get(l.dataId).values,c=[u,s],h=k.makeZerosTypedArray(k.sizeFromShape(c),"int32");for(let m=0;m<u;++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=cP.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(l),n.makeTensorInfo(c,"int32",h)}var mP={kernelName:yc,backendName:"cpu",kernelFunc:fP},gP=Za.nonMaxSuppressionV3Impl;function yP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;we(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:p}=gP(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var AP={kernelName:Xo,backendName:"cpu",kernelFunc:yP},xP=Za.nonMaxSuppressionV4Impl;function bP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a;we(r,"NonMaxSuppressionPadded");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=xP(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var vP={kernelName:Ko,backendName:"cpu",kernelFunc:bP},wP=Za.nonMaxSuppressionV5Impl;function kP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a;we(r,"NonMaxSuppressionWithScore");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=wP(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var IP={kernelName:Zo,backendName:"cpu",kernelFunc:kP};function SP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;we(r,"oneHot");let l=k.sizeFromShape(r.shape),u=new Float32Array(l*s);u.fill(o);let d=n.data.get(r.dataId).values;for(let p=0;p<l;++p)d[p]>=0&&d[p]<s&&(u[p*s+d[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",u)}var NP={kernelName:qs,backendName:"cpu",kernelFunc:SP};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=Wl({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),l=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Mg({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var TP={kernelName:fl,backendName:"cpu",kernelFunc:Dh};function uv(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=uv({inputs:{x:r},backend:n}),i=Wl({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),l=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Mg({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var CP={kernelName:Yo,backendName:"cpu",kernelFunc:uv};function dv(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $h({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=$h({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=Bl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var EP={kernelName:Jo,backendName:"cpu",kernelFunc:dv};function RP(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]),l=s.map(y=>y[0]),u=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+l[b]),x=k.locToIndex(A,m,f);g[x]=u[y]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var pv={kernelName:Xs,backendName:"cpu",kernelFunc:RP},MP=Ot((e,t)=>Math.pow(e,t)),FP=Yt(Ks,MP),$P={kernelName:Ks,backendName:"cpu",kernelFunc:FP};function DP(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=kg(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var OP={kernelName:ju,backendName:"cpu",kernelFunc:DP},zP=rt(el,e=>1/e),_P={kernelName:el,backendName:"cpu",kernelFunc:zP};function PP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeBilinear");let l=k.computeStrides(r.shape),[u,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,u,d,m])),y=[s&&u>1?c-1:c,s&&d>1?h-1:h],A=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=0,v=y[0]/A[0],b=y[1]/A[1];for(let w=0;w<p;w++)for(let N=0;N<u;N++){let C;i?C=v*(N+.5)-.5:C=v*N;let E=Math.max(0,Math.floor(C)),_=C-E,$=Math.min(c-1,Math.ceil(C)),S=w*l[0]+E*l[1],z=w*l[0]+$*l[1];for(let O=0;O<d;O++){let W;i?W=b*(O+.5)-.5:W=b*O;let G=Math.max(0,Math.floor(W)),H=W-G,J=Math.min(h-1,Math.ceil(W)),K=S+G*l[2],ne=z+G*l[2],Q=S+J*l[2],se=z+J*l[2];for(let Z=0;Z<m;Z++){let le=f[K+Z],oe=f[ne+Z],xe=f[Q+Z],fe=f[se+Z],Ne=le+(xe-le)*H,Te=oe+(fe-oe)*H,Oe=Ne+(Te-Ne)*_;g[x++]=Oe}}}return n.makeTensorInfo([p,u,d,m],"float32",g)}var LP={kernelName:Js,backendName:"cpu",kernelFunc:PP};function WP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,u,d,p]=r.shape,[,c,h]=s.shape,m=new Float32Array(l*u*d*p),f=[i&&c>1?u-1:u,i&&h>1?d-1:d],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/g[0],A=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let b=0;b<l;b++){let w=b*o[0];for(let N=0;N<c;N++){let C=N*y,E=Math.floor(C),_=Math.min(Math.ceil(C),u-1),$=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([l,d,u,p],"float32",m)}var BP={kernelName:bc,backendName:"cpu",kernelFunc:WP};function VP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeNearestNeighbor");let l=k.computeStrides(r.shape),[u,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(p*u*d*m),y=[s&&u>1?c-1:c,s&&d>1?h-1:h],A=[s&&u>1?u-1:u,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*l[0];for(let C=0;C<u;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 $=N+_*l[1];for(let S=0;S<d;S++){let z=i?v*(S+.5):v*S,O=Math.min(h-1,s?Math.round(z):Math.floor(z));i&&(O=Math.max(0,O));let W=$+O*l[2];for(let G=0;G<m;G++){let H=f[W+G];g[b++]=H}}}}return n.makeTensorInfo([p,u,d,m],r.dtype,g)}var jP={kernelName:Uu,backendName:"cpu",kernelFunc:VP};function UP(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),l=k.computeStrides(s.shape),[u,d,p,c]=r.shape,[,h,m]=s.shape,f=new Float32Array(u*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<u;E++){let _=E*o[0];for(let $=0;$<d;$++){let S=_+$*o[1],z=Math.floor($*b),O=Math.floor(z-N/2);for(let W=0;W<p;W++){let G=S+W*o[2],H=Math.floor(W*w),J=Math.floor(H-C/2);for(let K=0;K<c;K++){let ne=0;for(let Q=0;Q<N;Q++){let se=Q+O;if(se<0||se>=h)continue;let Z=_+se*l[1],le=se*x,oe=Math.min(d-1,i?Math.round(le):Math.floor(le));if($===oe)for(let xe=0;xe<C;xe++){let fe=xe+J;if(fe<0||fe>=m)continue;let Ne=Z+fe*l[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 HP={kernelName:xc,backendName:"cpu",kernelFunc:UP};function GP(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 l=new Lt(r.shape,r.dtype),u=n.bufferSync(r);for(let d=0;d<l.size;d++){let p=l.indexToLoc(d),c=p.slice();o.forEach(h=>c[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...p)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var qP={kernelName:ei,backendName:"cpu",kernelFunc:GP},XP={kernelName:ml,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[u,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<u;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 _=[u,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;l[G]=W}}}}return{dataId:o.write(l,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},KP=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}),ZP={kernelName:ti,backendName:"cpu",kernelFunc:KP};function cv(e,t,n,a,r,s,i,o,l,u){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(l);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++)u?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 YP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=cv(h,m,i,p,u,l,o,d,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var JP={kernelName:nl,backendName:"cpu",kernelFunc:YP};function QP(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,l=n.data.get(r.dataId).values,u=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++]=l[m]:p[c++]=u[m];return n.makeTensorInfo(r.shape,d,p)}var eL={kernelName:al,backendName:"cpu",kernelFunc:QP},tL=F.SELU_SCALEALPHA,nL=F.SELU_SCALE,aL=rt(rl,e=>e>=0?nL*e:tL*(Math.exp(e)-1)),rL={kernelName:rl,backendName:"cpu",kernelFunc:aL},sL=rt(ol,e=>e<0?-1:e>0?1:0),iL={kernelName:ol,backendName:"cpu",kernelFunc:sL},oL=rt(ai,e=>Math.sin(e)),lL={kernelName:ai,backendName:"cpu",kernelFunc:oL},uL=rt(il,e=>Math.sinh(e)),dL={kernelName:il,backendName:"cpu",kernelFunc:uL},pL=11920928955078125e-23,hv=Math.log(pL)+2,cL=rt(ll,e=>{let t=e>-hv,n=e<hv,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),hL={kernelName:ll,backendName:"cpu",kernelFunc:cL};function fL(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),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let u=pv.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=F.getReshaped(u.shape,s,o,!1),p=F.getPermuted(d.length,s.length,!1),c=F.getReshapedPermuted(u.shape,s,o,!1),h=gt({inputs:{x:u},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(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var mL={kernelName:Hu,backendName:"cpu",kernelFunc:fL};function gL(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,l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values[0],[p,c,h,m,f]=_7(o,a.shape,a.dtype,l,r.dtype,u,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 yL={kernelName:vc,backendName:"cpu",kernelFunc:gL};function AL(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,l=Array.from(n.data.get(s.dataId).values),[u,d,p]=P7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var xL={kernelName:wc,backendName:"cpu",kernelFunc:AL};function bL(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,l=n.data.get(s.dataId).values,[u,d]=Ig(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(d,a.dtype,u)}var vL={kernelName:kc,backendName:"cpu",kernelFunc:bL};function wL(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,l=n.data.get(s.dataId).values,[u,d]=Ig(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(d,a.dtype,u)}var kL={kernelName:Ic,backendName:"cpu",kernelFunc:wL};function IL(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,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=cv(m,f,o,c,d,u,l,p,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var SL={kernelName:Sc,backendName:"cpu",kernelFunc:IL};function NL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=F.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),d=r.shape.slice();return l.map(p=>{let c=[...d];c[o]=p;let h=Di({inputs:{x:r},backend:n,attrs:{begin:u,size:c}});return u[o]+=p,h})}var TL={kernelName:ul,backendName:"cpu",kernelFunc:NL},CL=rt(si,e=>Math.sqrt(e)),EL={kernelName:si,backendName:"cpu",kernelFunc:CL},RL={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}}},ML=rt(Pr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),FL={kernelName:Pr,backendName:"cpu",kernelFunc:ML};function $L(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a;we(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=fn.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=gt({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=Di({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=gt({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))v=n.makeTensorInfo(A,r.dtype,[]);else{let w=n.bufferSync(x),N=W7(A,w,f,m);v=n.makeTensorInfo(N.shape,N.dtype,N.values)}let b=gt({inputs:{x:v},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var DL={kernelName:dl,backendName:"cpu",kernelFunc:$L};function OL(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:d,dataSplits:p}=t,c=n.data.get(d.dataId).values,h=n.data.get(p.dataId).values,[m,f]=B7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var zL={kernelName:Nc,backendName:"cpu",kernelFunc:OL};function _L(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,l=n.data.get(i.dataId).values[0],[u,d,p]=V7(o,l,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var PL={kernelName:Tc,backendName:"cpu",kernelFunc:_L};function LL(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=j7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var WL={kernelName:Cc,backendName:"cpu",kernelFunc:LL},BL=rt(di,e=>Math.tan(e)),VL={kernelName:di,backendName:"cpu",kernelFunc:BL},jL=rt(pi,e=>Math.tanh(e)),UL={kernelName:pi,backendName:"cpu",kernelFunc:jL};function HL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;we(r,"tile");let i=H7(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var GL={kernelName:_r,backendName:"cpu",kernelFunc:HL};function qL(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,[l,u]=G7(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var XL={kernelName:pl,backendName:"cpu",kernelFunc:qL};function KL(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[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(l);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=fv(W,c,o),J=fv(G,p,o);switch(i){case"nearest":z=tW(w,p,c,A,x,v,C,J,H,S,l);break;case"bilinear":z=nW(w,p,c,A,x,v,C,J,H,S,l);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 ZL={kernelName:cl,backendName:"cpu",kernelFunc:KL};function fv(e,t,n){switch(n){case"reflect":return YL(e,t);case"wrap":return JL(e,t);case"nearest":return eW(e,t);case"constant":default:return QL(e,t)}}function YL(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 JL(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 QL(e,t){return e}function eW(e,t){return k.clamp(0,e,t-1)}function Nd(e,t,n,a,r,s,i,o,l,u,d){let p=i*a+o*r+l*s+u;return 0<=o&&o<t&&0<=l&&l<n?e[p]:d}function tW(e,t,n,a,r,s,i,o,l,u,d){let p=Math.round(o),c=Math.round(l);return Nd(e,t,n,a,r,s,i,p,c,u,d)}function nW(e,t,n,a,r,s,i,o,l,u,d){let p=Math.floor(o),c=Math.floor(l),h=p+1,m=c+1,f=(m-l)*Nd(e,t,n,a,r,s,i,p,c,u,d)+(l-c)*Nd(e,t,n,a,r,s,i,p,m,u,d),g=(m-l)*Nd(e,t,n,a,r,s,i,h,c,u,d)+(l-c)*Nd(e,t,n,a,r,s,i,h,m,u,d);return(h-o)*f+(o-p)*g}function aW(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:l,indices:u}=q7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var rW={kernelName:Ec,backendName:"cpu",kernelFunc:aW};function sW(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],l=new Array(i-1),u=0;for(let h=0;h<i;h++)h!==s&&(l[u++]=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:l}}),n.disposeIntermediateTensorInfo(m)}return c}var iW={kernelName:hl,backendName:"cpu",kernelFunc:sW};function oW(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,l=s.shape.length,u=[],d=[],p=o-l,c=s;for(let m=0;m<p;++m){let f=$h({inputs:{input:c},backend:n,attrs:{dim:m+1}});c=f,d.push(f)}for(let m=0;m<i;++m){let f=k.createScalarValue(m,"int32"),g=n.makeTensorInfo([],"int32",f),y=y7({inputs:{a:g,b:c},backend:n}),A=Zr({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=Mh({inputs:{a:A,b:r},backend:n}),v=Sd({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});u.push(v),d.push(g),d.push(y),d.push(A),d.push(x),d.push(v)}let h=dv({inputs:u,backend:n,attrs:{axis:0}});return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var lW={kernelName:qu,backendName:"cpu",kernelFunc:oW},uW=[gO,hD,AO,bO,xD,wO,IO,NO,CO,RO,FO,DO,zO,LO,BO,UO,GO,XO,ZO,fO,JO,ez,nz,yD,vD,rz,fD,iz,lz,pz,hz,uz,yz,xz,mz,vz,kz,Sz,Tz,Ez,Mz,Fz,Dz,zz,Pz,Lz,Bz,Wz,Eg,Uz,iO,Gz,wD,e_,kD,t_,SD,o_,l_,d_,TD,h_,m_,y_,x_,v_,ED,MD,mD,k_,oz,S_,T_,E_,oO,$D,OD,M_,_D,$_,z_,P_,B_,j_,H_,LD,X_,Z_,J_,eP,nP,G_,rP,iP,BD,lP,pP,mP,jD,HD,AP,vP,IP,qD,NP,CP,EP,pv,$P,uO,ZD,OP,gD,_P,dO,pO,hO,LP,BP,jP,HP,qP,XP,ZP,JD,JP,eL,rL,cO,iL,lL,dL,QD,hP,hL,mL,yL,xL,vL,kL,SL,TL,EL,RL,tO,FL,DL,zL,PL,WL,sO,Vz,VL,UL,GL,XL,XD,ZL,rW,iW,lW,TP];for(let e of uW)gi(e);var mv={};Fe(mv,{assertNotComplex:()=>jl,bindCanvasToFramebuffer:()=>vW,bindColorTextureToFramebuffer:()=>_h,bindTextureToProgramUniformSampler:()=>Rv,bindTextureUnit:()=>Tv,bindVertexBufferToProgramAttribute:()=>Dg,callAndCheck:()=>be,canBeRepresented:()=>gv,createFragmentShader:()=>xv,createFramebuffer:()=>Nv,createProgram:()=>bv,createStaticIndexBuffer:()=>kv,createStaticVertexBuffer:()=>wv,createTexture:()=>Iv,createVertexShader:()=>Av,getBatchDim:()=>zi,getExtensionOrThrow:()=>Rd,getFramebufferErrorMessage:()=>Mv,getMaxTexturesInShader:()=>Ov,getNumChannels:()=>xW,getProgramUniformLocation:()=>Ev,getProgramUniformLocationOrThrow:()=>Cv,getRowsCols:()=>_i,getShapeAs3D:()=>Ph,getTextureShapeFromLogicalShape:()=>$v,getWebGLDisjointQueryTimerVersion:()=>zv,getWebGLErrorMessage:()=>yv,getWebGLMaxTextureSize:()=>Dv,hasExtension:()=>pa,isCapableOfRenderingToFloatTexture:()=>_v,isDownloadFloatTextureEnabled:()=>Pv,isReshapeFree:()=>Fd,isWebGLFenceEnabled:()=>Lv,isWebGLVersionEnabled:()=>zg,linkProgram:()=>vv,resetMaxTextureSize:()=>wW,resetMaxTexturesInShader:()=>kW,unbindColorTextureFromFramebuffer:()=>Og,unbindTextureUnit:()=>bW,validateFramebuffer:()=>Md,validateProgram:()=>zh,validateTextureSize:()=>Sv});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=pW(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 dW(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 pW(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=dW(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 cW(e,t){return e*t}function Ed(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Vl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function hW(e,t){let[n,a]=Vl(e,t);return n*a*4}function $g(e,t){let n=e,a,r,s,i,o,l,u,d,p,c;return te().getNumber("WEBGL_VERSION")===2?(a=n.R32F,r=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,u=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,u=4,d=4,p=t!=null?t.HALF_FLOAT_OES:null,c=e.FLOAT),l=e.RGBA,{internalFormatFloat:a,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:d,textureTypeHalfFloat:p,textureTypeFloat:c}}function be(e,t){let n=t();return te().getBool("DEBUG")&&fW(e),n}function fW(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+yv(e,t))}var mW=596e-10,gW=65504;function gv(e){return!!(te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||mW<Math.abs(e)&&Math.abs(e)<gW)}function yv(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 Av(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 xv(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 AW(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var yW=/ERROR: [0-9]+:([0-9]+):/g;function AW(e,t){let n=yW.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 l=i.slice(0,a-1),u=i.slice(a-1,a),d=i.slice(a);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${k.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(d.join(`
|
|
`))}function bv(e){return br(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function vv(e,t){if(be(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function zh(e,t){if(be(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function wv(e,t){let n=br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function kv(e,t){let n=br(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function xW(){return te().getNumber("WEBGL_VERSION")===2?1:4}function Iv(e){return br(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Sv(e,t){let n=te().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${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 Nv(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 Tv(e,t,n){Fv(e,n),be(e,()=>e.activeTexture(e.TEXTURE0+n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function bW(e,t){Fv(e,t),be(e,()=>e.activeTexture(e.TEXTURE0+t)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Cv(e,t,n){return br(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Ev(e,t,n){return e.getUniformLocation(t,n)}function Rv(e,t,n,a){be(e,()=>Tv(e,t,a)),be(e,()=>e.uniform1i(n,a))}function vW(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: "+Mv(e,t))}function Mv(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 ${t}`}}function br(e,t,n){let a=be(e,()=>t());if(a==null)throw new Error(n);return a}function Fv(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${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 $v(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 Dv(e){if(Wh==null){let t=Ja(e);Wh=t.getParameter(t.MAX_TEXTURE_SIZE)}return Wh}function wW(){Wh=null}function kW(){Bh=null}function Ov(e){if(Bh==null){let t=Ja(e);Bh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Bh)}function zv(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 _v(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 Pv(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 IW(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 IW(e,t){let n=$g(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function Lv(e){return e!==2?!1:Ja(e).fenceSync!=null}function jl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${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",()=>Dv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Ov(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Me.getNumber("WEBGL_VERSION");return e===0?0:zv(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",()=>_v(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",()=>Pv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Lv(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,l,u;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)
|
|
`,l="",u=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",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));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}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 Wv=`
|
|
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;
|
|
}
|
|
`,SW=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;
|
|
}
|
|
`}},NW=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;
|
|
}
|
|
`}},TW=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
|
|
${Wv}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},CW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
|
|
${Wv}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},EW=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.);
|
|
}
|
|
`}},RW=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 l=0;l<=1;l++)for(let u=0;u<=1;u++){let d=l*2+u;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${u} < ${e[2]}) {
|
|
localCoords[2] += ${u};
|
|
if(localCoords[1] + ${l} < ${e[1]}) {
|
|
localCoords[1] += ${l};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${s};
|
|
c = imod(flatIndex, ${s});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${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} = ${o};
|
|
}
|
|
`}},Bv={};Fe(Bv,{bindVertexProgramAttributeStreams:()=>Zv,createBufferFromOutputTexture:()=>Qv,createFloat16MatrixTexture:()=>Gv,createFloat16PackedMatrixTexture:()=>Kv,createFloat32MatrixTexture:()=>Hv,createIndexBuffer:()=>Uv,createPackedMatrixTexture:()=>Xv,createUnsignedBytesMatrixTexture:()=>qv,createVertexBuffer:()=>jv,createVertexShader:()=>Vv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>tw,downloadFloat32MatrixFromBuffer:()=>ew,downloadMatrixFromPackedOutputTexture:()=>aw,downloadPackedMatrixFromBuffer:()=>nw,getInternalFormatForFloat16MatrixTexture:()=>Wg,getInternalFormatForFloat16PackedMatrixTexture:()=>jg,getInternalFormatForFloat32MatrixTexture:()=>Lg,getInternalFormatForPackedMatrixTexture:()=>Vg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Bg,uploadDenseMatrixToTexture:()=>Yv,uploadPixelDataToTexture:()=>Jv});function Vv(e){let t=An(),n=`${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 Av(e,n)}function jv(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 wv(e,t)}function Uv(e){let t=new Uint16Array([0,1,2,2,1,3]);return kv(e,t)}function $d(e,t,n,a,r,s){Sv(t,n);let i=Iv(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 Hv(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 Gv(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 qv(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 Xv(e,t,n,a){let[r,s]=Vl(t,n);return $d(e,r,s,Vg(a),e.RGBA,e.FLOAT)}function jg(e){return e.internalFormatPackedHalfFloat}function Kv(e,t,n,a){let[r,s]=Vl(t,n);return $d(e,r,s,jg(a),e.RGBA,a.textureTypeHalfFloat)}function Zv(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 Yv(e,t,n,a,r,s){be(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),be(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Jv(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 Qv(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 ew(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 tw(e,t,n,a){let[r,s]=Cd(t,n),i=4,o=new Uint8Array(cW(t*n,i));return be(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function nw(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(hW(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function aw(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=jv(this.gl),this.indexBuffer=Uv(this.gl),this.framebuffer=Nv(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(),Hv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Gv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),qv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Jv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Yv(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Kv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Xv(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,()=>tw(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return nw(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return ew(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=Qv(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,()=>aw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=xv(t,e);this.vertexShader==null&&(this.vertexShader=Vv(t));let a=bv(t);return be(t,()=>t.attachShader(a,this.vertexShader)),be(t,()=>t.attachShader(a,n)),vv(t,a),this.debug&&zh(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Zv(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?Cv(this.gl,e,t):Ev(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(),Rv(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Vl(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=MW(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 MW(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:rw}=F;function FW(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=>$W(h,t,a)).join(`
|
|
`),o=t.texShape,l=An(),u=zW(l),d,p,c=LW(l);return t.isPacked?(d=DW(t.logicalShape,o),p=PW(l)):(d=OW(t.logicalShape,o),p=_W(l)),a&&(c+=jW),[c,u,p,s,d,i,n].join(`
|
|
`)}function Ul(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return tB(e);case 1:return aB(e);case 2:return sB(e);case 3:return oB(e);case 4:return uB(e);case 5:return dB(e);case 6:return pB(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function sw(e){switch(e.shapeInfo.logicalShape.length){case 0:return eB(e);case 1:return nB(e);case 2:return rB(e);case 3:return iB(e);default:return lB(e)}}function $W(e,t,n=!1){let a="";n?a+=sw(e):a+=Ul(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=cB(e,t):a+=hB(e,t)),a}function DW(e,t){switch(e.length){case 0:return iw();case 1:return UW(e,t);case 2:return JW(e,t);case 3:return GW(e,t);default:return XW(e,t)}}function OW(e,t){switch(e.length){case 0:return iw();case 1:return HW(e,t);case 2:return QW(e,t);case 3:return qW(e,t);case 4:return KW(e,t);case 5:return ZW(e,t);case 6:return YW(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function zW(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function _W(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function PW(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function LW(e){return`${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);
|
|
}
|
|
|
|
${WW}
|
|
${BW}
|
|
${VW}
|
|
`}var WW=`
|
|
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);
|
|
}
|
|
`,BW=`
|
|
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);
|
|
}
|
|
`,VW=`
|
|
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);
|
|
}
|
|
`,jW=`
|
|
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 iw(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function UW(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 HW(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function GW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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, ${a}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function qW(e,t){let n=Pi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function XW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${l} = index / ${s};
|
|
index -= b${l} * ${s};
|
|
`+i,o=`b${l}, `+o;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec${e.length}(${o});
|
|
}
|
|
`}function KW(e,t){let n=Pi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function ZW(e,t){let n=Pi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function YW(e,t){let n=Pi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function JW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${a}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function QW(e,t){return k.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 eB(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 tB(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 nB(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 aB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Hl(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 rB(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 l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],u=Math.ceil(t[1]/2);return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${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;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=Gl(e,o),c=["row","col"];return`
|
|
${Ul(p)}
|
|
float ${a}(int row, int col) {
|
|
return ${a}(${ql(c,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${Hl(e)}
|
|
}
|
|
`;let l=r[0],u=r[1],d=Li(n);return u===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) / ${l}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:l===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) / ${u}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${l}, ${u}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function iB(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=Gl(e,p),m=["b","row","col"];return`
|
|
${sw(h)}
|
|
vec4 ${a}(int b, int row, int col) {
|
|
return ${a}(${ql(m,c)});
|
|
}
|
|
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),d=An();return`
|
|
vec4 ${a}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${u}, ${l}, b, row, col);
|
|
return ${d.texture2D}(${n}, uv);
|
|
}
|
|
`}function oB(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),l=i;if(l.length<t.length){let m=Gl(e,l),f=["row","col","depth"];return`
|
|
${Ul(m)}
|
|
float ${a}(int row, int col, int depth) {
|
|
return ${a}(${ql(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)));
|
|
${Hl(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,d=u[0],p=u[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 lB(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],l=i[1],u=Math.ceil(t[n-1]/2),d=u*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${d} + (row / 2) * ${u} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${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 / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
|
|
return ${h.texture2D}(${a}, uv);
|
|
}
|
|
`}function uB(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:l}=k.squeezeShape(t);if(o.length<t.length){let m=Gl(e,o),f=["row","col","depth","depth2"];return`
|
|
${Ul(m)}
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
return ${a}(${ql(f,l)});
|
|
}
|
|
`}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)));
|
|
${Hl(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],c=d[1];if(c===i&&u==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&&u==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 dB(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:l,keptDims:u}=k.squeezeShape(t);if(l.length<t.length){let f=Gl(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${Ul(f)}
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${a}(${ql(g,u)});
|
|
}
|
|
`}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;
|
|
${Hl(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 pB(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=Gl(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Ul(g)}
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${a}(${ql(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;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}, ${u}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Hl(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===d&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${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 * ${u} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
|
|
vec2 uv = uvFromFlat(${h}, ${m}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Hl(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function cB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=rw(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,d,p=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${p[g+u]} = 0;`).join(`
|
|
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+u]}`).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}() {
|
|
${l} coords = getOutputCoords();
|
|
${d}
|
|
vec4 outputValue = get${a}(${c});
|
|
${h}
|
|
}
|
|
`}function hB(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,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let u=ut(l),d=rw(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(`
|
|
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
|
|
float ${r}() {
|
|
${u} 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 ${e} is not yet supported`)}function Gl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ql(e,t){return t.map(n=>e[n]).join(", ")}function fB(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=FW(s,o,r,t.packedInputs),u=e.createProgram(l),d=null,p=e.getUniformLocation(u,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let c={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;c[m]=e.getUniformLocation(u,m,f),c[`offset${m}`]=e.getUniformLocation(u,`offset${m}`,f)}return{program:t,source:l,webGLProgram:u,uniformLocations:c,inShapeInfos:i,outShapeInfo:o,infLoc:d,nanLoc:p}}function ow(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,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function mB(e,t,n,a,r){ow(t.inShapeInfos,n),ow([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`];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,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function gB(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:yB,bincountImpl:lw,bincountReduceImpl:AB,ceilImpl:xB,concatImpl:bB,equalImpl:vB,expImpl:wB,expm1Impl:kB,floorImpl:IB,gatherNdImpl:SB,gatherV2Impl:NB,greaterImpl:TB,greaterEqualImpl:CB,lessImpl:EB,lessEqualImpl:RB,linSpaceImpl:MB,logImpl:FB,maxImpl:$B,maximumImpl:DB,minimumImpl:OB,multiplyImpl:zB,negImpl:_B,notEqualImpl:PB,prodImpl:LB,rangeImpl:WB,rsqrtImpl:BB,simpleAbsImpl:uw,sliceImpl:VB,sparseFillEmptyRowsImpl:jB,sparseReshapeImpl:UB,sparseSegmentReductionImpl:dw,stridedSliceImpl:HB,stringNGramsImpl:GB,stringSplitImpl:qB,stringToHashBucketFastImpl:XB,subImpl:KB,tileImpl:ZB,topKImpl:YB,transposeImpl:Ug,uniqueImpl:JB}=yg;function pw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function xn(e,t){return t===1?[e]:pw(e,t)}function QB(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 eV=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=nV(t,e,n),s=aV(t,e[e.length-1],e[e.length-2],n),i=rV(e,n);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function tV(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 nV(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 aV(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 rV(e,t){let n=e.length,a=tV(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 cw=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=`
|
|
${sV(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 sV(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var iV=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=fw(t,n),r=mw(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=hw(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=fw(n,a),s=mw(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=hw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=te().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function oV(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 hw(e,t,n,a,r){let s=lV(t,a),i;if(r){let[l,u]=Vl(e[0],e[1]);i=l*u}else{let[l,u]=Cd(e[0],e[1]);i=l*u}let o=oV(n,s);return i*o}function lV(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 uV(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 fw(e,t){if(e===da.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return uV(t);if(e===da.DOWNLOAD||e===da.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function mw(e,t,n){return`${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;",dV="return x;",gw="return abs(x);",pV="return (x >= 0.0) ? x : (exp(x) - 1.0);",cV=Ca+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,hV=Ca+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,jh="return x;",fV="return 1.0 / (1.0 + exp(-1.0 * x));",mV="return x;",gV=`
|
|
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;
|
|
`,yV=`
|
|
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;
|
|
`,AV=`
|
|
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;
|
|
`,xV="return 1.0 / (1.0 + exp(-1.0 * x));",Xl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},bV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=xn("rc",t),a=ut(t),r=QB(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},vV=Za.whereImpl,wV=1e-7,kV=1e-4,Hg={};function IV(e){return e in Hg||(Hg[e]={}),Hg[e]}var SV=te().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),NV=600;function TV(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*NV/1024/1024}var Kl=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=IV(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 iV(this.gpgpu),this.numMBBeforeWarning=TV(),this.texData=new Up(this,fr())}nextDataId(){return Kl.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 Xl(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 l=this.activeTimers!=null,u;l&&(u=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 l&&(this.downloadWaitMs+=k.now()-u),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 Xl(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 l=null,u;if(s!=="complex64"&&te().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=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(l==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}u!=null&&this.disposeIntermediateTensorInfo(u);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(!gv(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...Ed(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?Ph(t):t,o=s?new CW(i):new TW(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return 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,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=SV){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 vV(e.shape,t)}packedUnaryOp(e,t,n){let a=new Xl(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=uw(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,gw,e.dtype);let t=new Yr(e.shape,gw),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 bV(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new eV(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 cw(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 NW(s):i=new SW(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.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=[],l=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 u={shape:s.shape,texData:i,isUniform:!1},d=gB(e,l,u),p=this.getAndSaveBinary(d,()=>fB(this.gpgpu,e,l,u)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),mB(this.gpgpu,p,l,u,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?wV:kV}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 l=this.activeTimers!=null,u;l&&(u=k.now());let d=t.texShape;if(d==null&&(d=$v(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]=Vl(d[0],d[1]),c=new RW(p,[m,h],f)):c=new EW(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,l&&(this.uploadWaitMs+=k.now()-u)}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=CV(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)}};Kl.nextDataId=0;function CV(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 yw="3.7.0";function Aw(){te().set("WEBGL_FORCE_F16_TEXTURES",!0)}ad.isBrowser()&&Il("webgl",()=>new Kl,2);var EV={forceHalfFloat:Aw},xw=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Zl=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 RV={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}),l=Xn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var MV={kernelName:Yp,backendName:"webgl",kernelFunc:Jr},bw="return (a < 0.) ? b * a : a;",vw=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function FV(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(vw,r.shape,i.shape):new Zl(bw,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var $V={kernelName:_s,backendName:"webgl",kernelFunc:FV},ww="return (a < 0.) ? b * a : a;",kw=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function DV(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(kw,a.shape,r.shape):new Zl(ww,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var OV={kernelName:Zs,backendName:"webgl",kernelFunc:DV},Iw="if (isnan(x)) return x;",zV=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,_V=`
|
|
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 Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Xl(i.shape,t):d=new Yr(i.shape,e),o.runWebGLProgram(d,[i],l)}}function sn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(a&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.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:l.shape},N={dataId:b.dataId,dtype:b.dtype,shape:u.shape},C=new Zl(e,l.shape,u.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(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&r!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?F.fromUint8ToStringArray(m):m,y=l.dtype==="string"?F.fromUint8ToStringArray(f):f,[A,x]=r(l.shape,u.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,l.shape,u.shape,n):h=new Zl(e,l.shape,u.shape),d.runWebGLProgram(h,[l,u],p)}}function Hh(e,t=!1){if(e==="linear")return t?mV:dV;if(e==="relu")return t?yV:cV;if(e==="elu")return t?gV:pV;if(e==="relu6")return t?AV:hV;if(e==="prelu")return t?kw:ww;if(e==="leakyrelu")return t?vw:bw;if(e==="sigmoid")return t?xV:fV;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Sw=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=a?e[1]:e[2],d=Math.ceil(u/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}
|
|
}`:l?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"),l&&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);
|
|
}
|
|
`}},Nw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Tw=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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},Cw="return a * b;";function Gg(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=F.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Tw(Nw.REAL,a.shape,r.shape),d=new Tw(Nw.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Jr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,d]=zB(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=u,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Dd(Cw,a.shape,r.shape):i=new Zl(Cw,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var PV={kernelName:Gs,backendName:"webgl",kernelFunc:Gg};function LV(e,t,n){let a=[zi(e.shape),..._i(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[zi(t),..._i(t)],i=new cw(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),u=k.sizeFromShape(l);k.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${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,l)&&!(d.texture!==null&&Fd(d.shape,l))?LV(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var WV={kernelName:tl,backendName:"webgl",kernelFunc:Ae},Ew=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,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";r%n>0&&(u=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${u}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},BV=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,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 < ${u}; 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 + ${u};
|
|
if (${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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function VV(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=VV(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],d,p;n==="mean"?d=i===0?new Ew({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Ew({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new BV({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var jV=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=UV(t);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function UV(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 HV=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ut(this.rank),r=pw("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Gh(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HV(e.shape,t):new jV(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function GV(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=F.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Gh(e,l,a),o=F.getInnerMostAxes(o.length,s)),F.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=F.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=F.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),y=zc(e.dtype),A=Wi(g,y,"sum",a),x=Ae({inputs:{x:A},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),u&&a.disposeIntermediateTensorInfo(d),x}function qh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return GV(r,s,i,n)}var qV={kernelName:ii,backendName:"webgl",kernelFunc:qh};function bn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=r.shape[s[d]];let u;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=Ug(d,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=p}else u=Gh(r,s,i);return u}var XV={kernelName:ci,backendName:"webgl",kernelFunc:bn},Rw=1e3;function Xh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),A=k.sizeFromShape(g),x=y===A||y===1||A===1;k.assert(u>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${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=l==="leakyrelu",W=l!=null?Hh(l,!0):null,G=S||z||O||W!=null,H;if((h===1||m===1)&&$>Rw&&G===!1){let K=N,ne=C;n&&(K=bn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(ne=bn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let Q=m!==1,se=m===1,Z=K;Q&&(Z=Ae({inputs:{x:K},backend:r,attrs:{shape:[_,$,1]}}),E.push(Z));let le=m===1?2:1,oe=ne;se&&(oe=Ae({inputs:{x:ne},backend:r,attrs:{shape:[_,1,$]}}),E.push(oe));let xe=Gg({inputs:{a:Z,b:oe},backend:r});H=qh({inputs:{x:xe},backend:r,attrs:{axis:le,keepDims:!0}}),E.push(xe)}else{let K=Aa(e.dtype,t.dtype),ne=new Sw(b,w,[_,h,m],n,a,S,W,z,O),Q=[N,C];if(s!=null&&Q.push(s),z&&Q.push(i),O){let se=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(se),E.push(se)}H=r.runWebGLProgram(ne,Q,K)}let J=Ae({inputs:{x:H},backend:r,attrs:{shape:v}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return J}function KV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a;return Xh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var ZV={kernelName:hi,backendName:"webgl",kernelFunc:KV},Mw="return abs(x);";function YV(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=uw(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Xl(a.shape,Mw):r=new Yr(a.shape,Mw),n.runWebGLProgram(r,[a],a.dtype)}var JV={kernelName:mo,backendName:"webgl",kernelFunc:YV},QV=Ca+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,ej=Ze({opSnippet:QV}),tj={kernelName:go,backendName:"webgl",kernelFunc:ej},nj=Ca+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,aj=Ze({opSnippet:nj}),rj={kernelName:yo,backendName:"webgl",kernelFunc:aj},Fw="return a + b;",sj=sn({opSnippet:Fw,packedOpSnippet:Fw,supportsComplex:!0,cpuKernelImpl:yB}),ij={kernelName:Or,backendName:"webgl",kernelFunc:sj},oj=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}},lj=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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),l=Kh({inputs:a.slice(0,o),backend:n}),u=Kh({inputs:a.slice(o),backend:n});return Kh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>Aa(o,l)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new lj(a[0].shape,s):new oj(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var uj={kernelName:xs,backendName:"webgl",kernelFunc:Kh};function dj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,o)),F.assertAxesAreInnerMostDims("all",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),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,l);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 pj={kernelName:Ao,backendName:"webgl",kernelFunc:dj};function cj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,o)),F.assertAxesAreInnerMostDims("any",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),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,l);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 hj={kernelName:xo,backendName:"webgl",kernelFunc:cj},fj=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 ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},mj=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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,l=ut(o),u=xn("coords",o),d,p;if(s===1){p=o+1;let N=ut(p);d=`
|
|
${N} sourceLocR = ${N}(${u.join()}, 0);
|
|
++${u[o-1]};
|
|
${N} sourceLocG = ${N}(${u.join()}, 0);
|
|
++${u[o-2]};
|
|
${N} sourceLocA = ${N}(${u.join()}, 0);
|
|
--${u[o-1]};
|
|
${N} sourceLocB = ${N}(${u.join()}, 0);
|
|
--${u[o-2]};`}else p=o,d=`
|
|
${l} sourceLocR = coords;
|
|
++${u[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[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() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${u[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 $w(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=F.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new fj(o,n,a==null),u=[t];a!=null&&u.push(a);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let p=$w(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function Dw(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=F.computeOptimalWindowSize(s),o=new mj(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=Dw(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}return u}function Ow(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),l=k.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let d=$w(e,u,a);s.push(d);let p=Ae({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return Dw(e,t,a)}function gj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=Ow(n,l,i[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var yj={kernelName:bs,backendName:"webgl",kernelFunc:gj};function Aj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=Ow(n,l,i[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var xj={kernelName:Fu,backendName:"webgl",kernelFunc:Aj},bj=Ca+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,vj=Ze({opSnippet:bj}),wj={kernelName:bo,backendName:"webgl",kernelFunc:vj},kj=Ca+"return log(x + sqrt(x * x + 1.0));",Ij=Ze({opSnippet:kj}),Sj={kernelName:vo,backendName:"webgl",kernelFunc:Ij},Nj=Ca+`
|
|
return atan(x);
|
|
`,Tj=Ze({opSnippet:Nj}),Cj={kernelName:wo,backendName:"webgl",kernelFunc:Tj},Ej=zV+`
|
|
return atan(a, b);
|
|
`,Rj=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+_V+`
|
|
return result;
|
|
`,Mj=sn({opSnippet:Ej,packedOpSnippet:Rj}),Fj={kernelName:Io,backendName:"webgl",kernelFunc:Mj},$j=Ca+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Dj=Ze({opSnippet:$j}),Oj={kernelName:ko,backendName:"webgl",kernelFunc:Dj},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,l=e.dilationHeight,u=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 += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${u}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${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 += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${v}; wC += 4) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${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 + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
} else if (${b===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, 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,l=e.strideWidth,u=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}, ${l});
|
|
const ivec3 pads = ivec3(${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 += ${u}) {
|
|
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}, ${l});
|
|
const ivec3 pads = ivec3(${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 += ${u}) {
|
|
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 zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;jl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l);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 _j={kernelName:vs,backendName:"webgl",kernelFunc:zj};function Pj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new qg(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Lj={kernelName:$u,backendName:"webgl",kernelFunc:Pj},Wj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${d});
|
|
const float avgMultiplier = float(${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 < ${l};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Bj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${u}) {
|
|
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 Vj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,l,p,u,d),h=new Bj(c);return n.runWebGLProgram(h,[r],i.dtype)}var jj={kernelName:Kp,backendName:"webgl",kernelFunc:Vj};function Uj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;jl([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=F.computePool2DInfo(i.shape,o,l,1,u),p=new Wj(d);return n.runWebGLProgram(p,[r],i.dtype)}var Hj={kernelName:Xp,backendName:"webgl",kernelFunc:Uj};function Gj(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 qj={kernelName:ws,backendName:"webgl",kernelFunc:Gj},Xj=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)));
|
|
}
|
|
`}},Kj=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);
|
|
}
|
|
`}},Zj=({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:l}=n;l==null&&(l=.001);let u=[a,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let p=null;o!=null&&(p=o.shape,u.push(o));let c=te().getBool("WEBGL_PACK_NORMALIZATION")?new Kj(a.shape,r.shape,s.shape,d,p,l):new Xj(a.shape,r.shape,s.shape,d,p,l);return t.runWebGLProgram(c,u,u[0].dtype)},Yj={kernelName:Ds,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=Qj(this.rank),r,s=e.map((i,o)=>`sourceLoc.${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 Qj(e){if(e===1)return"sourceLoc";if(e<=6)return Xg.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var eU=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=xn("coords",this.rank),a=xn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function tU(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 l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function zd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=fn.parseSliceParams(r,s,i);if(fn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=VB(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),d=fn.isSliceContinous(r.shape,o,l);if(u||!d){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eU(l):new Jj(l),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),tU(r,o,l,n)}var nU={kernelName:sl,backendName:"webgl",kernelFunc:zd},aU=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),l=F.getReshaped(r.shape,s,o),u=F.getPermuted(l.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:l}}),f=bn({inputs:{x:m},backend:n,attrs:{perm:u}}),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},rU={kernelName:Du,backendName:"webgl",kernelFunc:aU};function sU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=lw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var iU={kernelName:Zp,backendName:"webgl",kernelFunc:sU},oU="return float(a != b);",zw=sn({opSnippet:oU,cpuKernelImpl:PB,dtype:"bool"}),lU={kernelName:qo,backendName:"webgl",kernelFunc:zw};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 uU={kernelName:Ac,backendName:"webgl",kernelFunc:_d},dU="return float(int(x));";function pU(e,t){let n=new Yr(e.shape,dU),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"}}),l=Jr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}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 pU(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=zw({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 cU={kernelName:ks,backendName:"webgl",kernelFunc:Kg},_w="return ceil(x);",hU=Ze({opSnippet:_w,packedOpSnippet:_w,cpuKernelImpl:xB}),fU={kernelName:Is,backendName:"webgl",kernelFunc:hU},mU=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)}}},gU=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 yU(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 gU(r.shape):o=new mU(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var AU={kernelName:zr,backendName:"webgl",kernelFunc:yU},xU=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 Pw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function bU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new xU(a.shape),i=[Pw(a,r.complexTensorInfos.real),Pw(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var vU={kernelName:Ou,backendName:"webgl",kernelFunc:bU},wU=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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},kU=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=F.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=xn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${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 l=i[t],u=i.slice(-2),d=i.join(),p=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${d}), vec2(${u.join()}));
|
|
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
|
|
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
|
|
return getChannel(
|
|
getT${m}(${Zh(i,l,f)}),
|
|
vec2(${Zh(u,l,f)}));
|
|
}`}let c=o.length,h=o[o.length-1];p+=`
|
|
return getChannel(
|
|
getT${c}(${Zh(i,l,h)}),
|
|
vec2(${Zh(u,l,h)}));`,this.userCode=`
|
|
float getValue(${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 IU={kernelName:pc,backendName:"webgl",kernelFunc:Yh};function Yl(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=Yl(d,t,n),h=Yl(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=bB(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=Yl(e.slice(0,d),t,n),c=Yl(e.slice(d),t,n),h=Yl([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 kU(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=SU(e,t,n),o=new wU(s.map(d=>d.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function SU(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 Lw(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(u=>u.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>k.sizeFromShape(u.shape)>0);if(o.length===1)return Xn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return F.assertParamsConsistent(l,s),Yl(o,s,n)}var NU={kernelName:So,backendName:"webgl",kernelFunc:Lw},Ww=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,l=e.strideWidth,u=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}, ${l});
|
|
const ivec2 pads = ivec2(${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 * ${u};
|
|
|
|
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}
|
|
${v}
|
|
setOutput(result);
|
|
}
|
|
`}},TU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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 * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${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);
|
|
}
|
|
`}},CU=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:l,dilationWidth:u,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 < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${h};
|
|
d0 = offsetY + ${d} * (pos / ${m});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${c}.);
|
|
d1 = offsetX + ${u} * (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 Bw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),d=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],A=(p===1||c===1)&&d>Rw,x=l[2]%2!=0&&!!u.isPacked;if(A||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[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?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(Fd(u.shape,b.shape),()=>`packed reshape ${u.shape} to ${b.shape} isn't free`);let N=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let C=Xh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=a.texData.get(C.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,E.shape=n.outShape,g=Xn({inputs:{x:C},backend:a}),g.shape=n.outShape,y.push(C)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function Vw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*d,g=c*p,y=[f,g],A=!0,x=!1,v=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let N=new CU(y,b.shape,n),C=a.runWebGLProgram(N,[b],"float32"),E=Ae({inputs:{x:C},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(C),v.push(E);let _=r!=null,$=s!=null,S=o==="leakyrelu",z=o?Hh(o,!0):null,O=new Sw(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 EU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,s.shape,i,u,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=Bw({x:r,filter:s,convInfo:c,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=Vw({x:r,filter:s,convInfo:c,backend:n});else{let f=new Ww(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 RU={kernelName:Ss,backendName:"webgl",kernelFunc:EU},MU=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);
|
|
}
|
|
`}},FU=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,l=s?1:2,u=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[${l}], coords[${u}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${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);
|
|
}
|
|
`}},$U=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},DU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${u});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${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 OU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,d,i,1,o,u,!1,p),h=new MU(c);return n.runWebGLProgram(h,[r,s],"float32")}var zU={kernelName:Jp,backendName:"webgl",kernelFunc:OU};function _U(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p),h=new FU(c);return n.runWebGLProgram(h,[r,s],"float32")}var PU={kernelName:Ns,backendName:"webgl",kernelFunc:_U};function LU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=F.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new TU(u);return n.runWebGLProgram(d,[r,s],"float32")}var WU={kernelName:zu,backendName:"webgl",kernelFunc:LU};function BU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=F.computeConv3DInfo(r.shape,l,i,1,o),d=new $U(u);return n.runWebGLProgram(d,[r,s],"float32")}var VU={kernelName:Qp,backendName:"webgl",kernelFunc:BU};function jU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=F.computeConv3DInfo(l,s.shape,o,1,i),d=new DU(u);return n.runWebGLProgram(d,[r,s],"float32")}var UU={kernelName:ec,backendName:"webgl",kernelFunc:jU},HU=Iw+`
|
|
return cos(x);
|
|
`,GU=Ze({opSnippet:HU}),qU={kernelName:Ts,backendName:"webgl",kernelFunc:GU},XU=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,KU=Ze({opSnippet:XU}),ZU={kernelName:No,backendName:"webgl",kernelFunc:KU},YU=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${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(${c} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},JU=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new YU(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},QU={kernelName:To,backendName:"webgl",kernelFunc:JU},jw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Uw(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 = ${Hw(a,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${Hw(a,"coords")} = idx;
|
|
val += getX(${Uw(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 Uw(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 ${e} is not yet supported`)}function Hw(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function eH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=F.getAxesPermutation([s],l),d=r;u!=null&&(d=bn({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=F.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Xn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new jw(d.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new jw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=F.getUndoAxesPermutation(u),f=bn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var tH={kernelName:Cs,backendName:"webgl",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=lw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=AB(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var aH={kernelName:tc,backendName:"webgl",kernelFunc:nH},rH=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 sH(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],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new rH(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var iH={kernelName:Co,backendName:"webgl",kernelFunc:sH},Gw=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,l=e.padInfo.left,u=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=`
|
|
${g}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${d});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${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}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},qw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,g=f,y=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
|
|
vec4 xTexelC${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&&(u%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=u%2==0?k.nearestLargerEven(h):h;h%2==0&&u%2==1||h%2!=0&&u%2!=1?(y+=`
|
|
xCOffset = xC + ${u%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&&(u%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}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${u});
|
|
|
|
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 oH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,d=l;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,u,!0),c;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new qw(p):c=new Gw(p),n.runWebGLProgram(c,[r,s],"float32")}var lH={kernelName:Es,backendName:"webgl",kernelFunc:oH},uH=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},dH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function pH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,p=F.computeConv2DInfo(r.shape,d,i,o,l,u,!0),c=new uH(p);return n.runWebGLProgram(c,[r,s],"float32")}var cH={kernelName:nc,backendName:"webgl",kernelFunc:pH};function hH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,p=F.computeConv2DInfo(d,s.shape,i,o,l,u,!0),c=new dH(p);return n.runWebGLProgram(c,[r,s],"float32")}var fH={kernelName:ac,backendName:"webgl",kernelFunc:hH},mH=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function gH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new mH(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var yH={kernelName:rc,backendName:"webgl",kernelFunc:gH},AH=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=a;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${u};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function xH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=F.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,p=new AH(u);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),c}var bH={kernelName:_u,backendName:"webgl",kernelFunc:xH};function vH(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=F.getEinsumComputePath(o,l),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,l[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&&(u[f]>=0&&(c=qh({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var wH={kernelName:oc,backendName:"webgl",kernelFunc:vH},kH="return (x >= 0.0) ? x : (exp(x) - 1.0);",IH=`
|
|
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;
|
|
`,SH=Ze({opSnippet:kH,packedOpSnippet:IH}),NH={kernelName:Eo,backendName:"webgl",kernelFunc:SH},TH="return (b >= 1.0) ? a : a * (b + 1.0);",CH=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,EH=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(CH,a.shape,r.shape):new Zl(TH,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},RH={kernelName:lc,backendName:"webgl",kernelFunc:EH},MH=`
|
|
return vec4(equal(a, b));
|
|
`,FH="return float(a == b);",$H=sn({opSnippet:FH,packedOpSnippet:MH,dtype:"bool",cpuKernelImpl:vB}),DH={kernelName:Mo,backendName:"webgl",kernelFunc:$H},OH=`
|
|
// 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 = ${F.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,zH=Ze({opSnippet:OH}),_H={kernelName:Ro,backendName:"webgl",kernelFunc:zH},Xw="return exp(x);",Kw=Ze({opSnippet:Xw,packedOpSnippet:Xw,cpuKernelImpl:wB}),PH={kernelName:Ms,backendName:"webgl",kernelFunc:Kw};function Zg(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var LH={kernelName:Fo,backendName:"webgl",kernelFunc:Zg},Zw="return exp(x) - 1.0;",WH=Ze({opSnippet:Zw,packedOpSnippet:Zw,cpuKernelImpl:kB}),BH={kernelName:$o,backendName:"webgl",kernelFunc:WH},Yw=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 Jw(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]}}),l=o.shape,u=new Yw("real",l,t),d=new Yw("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,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 VH(e){let{inputs:t,backend:n}=e,{input:a}=t;return Jw(a,!1,n)}var jH={kernelName:uc,backendName:"webgl",kernelFunc:VH},UH=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 UH(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var HH={kernelName:Pu,backendName:"webgl",kernelFunc:Yg},GH=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},qH={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new GH(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Qw="return floor(x);",XH=Ze({opSnippet:Qw,packedOpSnippet:Qw,cpuKernelImpl:IB}),KH={kernelName:Fs,backendName:"webgl",kernelFunc:XH},ZH=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,YH=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,JH=sn({opSnippet:ZH,packedOpSnippet:YH,dtype:"int32"}),QH={kernelName:$s,backendName:"webgl",kernelFunc:JH},eG=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 = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},tG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=An(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},nG={kernelName:Rc,backendName:"webgl",kernelFunc:aG},Jl;function aG(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,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],p=[u,l,s];(o||i)&&(Jl==null&&(Jl=document.createElement("canvas").getContext("2d")),Jl.canvas.width=l,Jl.canvas.height=u,Jl.drawImage(r,0,0,l,u),r=Jl.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 tG(p):new eG(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function rG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=F.convertConv2DDataFormat(d),g=F.computeConv2DInfo(r.shape,s.shape,l,p,u,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=Bw({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=Vw({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 Ww(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 sG={kernelName:fi,backendName:"webgl",kernelFunc:rG};function iG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=F.computeConv2DInfo(r.shape,s.shape,l,f,u,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 qw(g,v,A,b,w):N=new Gw(g,v,A,b,w);let C=n.runWebGLProgram(N,x,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var oG={kernelName:mi,backendName:"webgl",kernelFunc:iG},lG=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 uG(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),[l,u,d,p]=F.prepareAndValidate(a,r),c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,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=SB(y,A,a.dtype,u,i,d,p,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new lG(i,p,[u,d]),f=n.runWebGLProgram(m,[h,c],h.dtype),g=Ae({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var dG={kernelName:zo,backendName:"webgl",kernelFunc:uG},pG=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=cG(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function cG(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(`${n[r]}`);return a.join()}function hG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=F.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=k.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,d/u.batchSize]}});p.push(c),p.push(h);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let A=n.bufferSync(h),x=n.bufferSync(c),v=NB(x,A,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new pG(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);p.push(g);let y=Ae({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var fG={kernelName:Oo,backendName:"webgl",kernelFunc:hG},mG="return float(a > b);",gG=`
|
|
return vec4(greaterThan(a, b));
|
|
`,yG=sn({opSnippet:mG,packedOpSnippet:gG,cpuKernelImpl:TB,dtype:"bool"}),AG={kernelName:_o,backendName:"webgl",kernelFunc:yG},xG="return float(a >= b);",bG=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,vG=sn({opSnippet:xG,packedOpSnippet:bG,dtype:"bool",cpuKernelImpl:CB}),wG={kernelName:Os,backendName:"webgl",kernelFunc:vG};function kG(e){let{inputs:t,backend:n}=e,{input:a}=t;return Jw(a,!0,n)}var IG={kernelName:dc,backendName:"webgl",kernelFunc:kG},SG="return float(!isnan(x) && !isinf(x));",NG=Ze({opSnippet:SG,dtype:"bool"}),TG={kernelName:Po,backendName:"webgl",kernelFunc:NG},CG="return float(isinf(x));",EG=Ze({opSnippet:CG,dtype:"bool"}),RG={kernelName:Lo,backendName:"webgl",kernelFunc:EG},MG="return float(isnan(x));",FG=Ze({opSnippet:MG,dtype:"bool"}),$G={kernelName:Wo,backendName:"webgl",kernelFunc:FG},DG="return float(a < b);",OG=`
|
|
return vec4(lessThan(a, b));
|
|
`,zG=sn({opSnippet:DG,packedOpSnippet:OG,cpuKernelImpl:EB,dtype:"bool"}),_G={kernelName:Bo,backendName:"webgl",kernelFunc:zG},PG="return float(a <= b);",LG=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,WG=sn({opSnippet:PG,packedOpSnippet:LG,cpuKernelImpl:RB,dtype:"bool"}),BG={kernelName:Vo,backendName:"webgl",kernelFunc:WG};function VG(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=MB(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var jG={kernelName:cc,backendName:"webgl",kernelFunc:VG},UG=`if (x < 0.0) return NAN;
|
|
return log(x);`,HG=`
|
|
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;
|
|
`,GG=Ze({opSnippet:UG,packedOpSnippet:HG,cpuKernelImpl:FB}),qG={kernelName:Ps,backendName:"webgl",kernelFunc:GG},XG="return log(1.0 + x);",KG=Ze({opSnippet:XG}),ZG={kernelName:jo,backendName:"webgl",kernelFunc:KG},YG="return float(a >= 1.0 && b >= 1.0);",JG=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,QG=sn({opSnippet:YG,packedOpSnippet:JG,dtype:"bool"}),eq={kernelName:Uo,backendName:"webgl",kernelFunc:QG},tq="return float(!(x >= 1.0));",nq=Ze({opSnippet:tq}),aq={kernelName:Lu,backendName:"webgl",kernelFunc:nq},rq="return float(a >= 1.0 || b >= 1.0);",sq=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,iq=sn({opSnippet:rq,packedOpSnippet:sq,dtype:"bool"}),oq={kernelName:Wu,backendName:"webgl",kernelFunc:iq},lq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${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 * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},uq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${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);
|
|
}
|
|
`}},dq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=te().getBool("WEBGL_PACK_NORMALIZATION")?new uq(r.shape,s,i,o,l):new lq(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},pq={kernelName:Bu,backendName:"webgl",kernelFunc:dq},cq=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 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},hq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,p=new cq(r.shape,o,l,u,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},fq={kernelName:hc,backendName:"webgl",kernelFunc:hq};function mq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,e.dtype,"max",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function e6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=F.getAxesPermutation(u,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let A=n.texData.get(h.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[d[w]];let v=Ug(A,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=v}else h=Gh(r,d,n);u=F.getInnerMostAxes(u.length,o)}F.assertAxesAreInnerMostDims("max",u,o);let[m,f]=F.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=F.expandShapeToKeepDim(m,l));let y;if(c){let A=n.texData.get(h.dataId).values,x=$B(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=mq(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var gq={kernelName:Ls,backendName:"webgl",kernelFunc:e6},yq=xw+`
|
|
return max(a, b);
|
|
`,Aq=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Uh+`
|
|
return result;
|
|
`,xq=sn({opSnippet:yq,packedOpSnippet:Aq,cpuKernelImpl:DB}),bq={kernelName:Ws,backendName:"webgl",kernelFunc:xq};function vq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;jl(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l);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 wq={kernelName:Bs,backendName:"webgl",kernelFunc:vq};function kq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new qg(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Iq={kernelName:Vu,backendName:"webgl",kernelFunc:kq},Sq=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,l=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 = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Nq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 < ${l};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${u};
|
|
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 * ${l} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Tq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,l,p,u,d),h=new qg(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Nq(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Cq={kernelName:mc,backendName:"webgl",kernelFunc:Tq};function Eq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;jl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,l,u,1,d,p),h=!0,m=new Od(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Sq(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Rq={kernelName:fc,backendName:"webgl",kernelFunc:Eq};function Mq(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 Fq={kernelName:gc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];k.assert(F.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=F.computePool2DInfo(a.shape,r,s,u,i),[p,c]=Mq(a,o,d,l);return[p,c]}};function $q(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,"float32","mean",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Dq={kernelName:Vs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,d=F.getAxesPermutation(u,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let N=0;N<v.length;N++)v[N]=a.shape[d[N]];let b=Ug(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=Gh(a,d,i);h.push(m),u=F.getInnerMostAxes(u.length,o)}F.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=F.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=F.expandShapeToKeepDim(f,l));let A=$q(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return A}};function Oq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),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,l);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 zq={kernelName:js,backendName:"webgl",kernelFunc:Oq},_q=xw+`
|
|
return min(a, b);
|
|
`,Pq=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Uh+`
|
|
return result;
|
|
`,Lq=sn({opSnippet:_q,packedOpSnippet:Pq,cpuKernelImpl:OB}),Wq={kernelName:Us,backendName:"webgl",kernelFunc:Lq},Bq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=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 - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
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] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},Vq=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),l=xn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.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;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.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;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${d});
|
|
}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.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);
|
|
}
|
|
`}},jq=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vq(a.shape,r,s):new Bq(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Uq={kernelName:Hs,backendName:"webgl",kernelFunc:jq},Hq=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Gq=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Uh+`
|
|
return result;
|
|
`,qq=sn({opSnippet:Hq,packedOpSnippet:Gq}),Xq={kernelName:Ho,backendName:"webgl",kernelFunc:qq},Kq=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)}}},Zq=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Yq=`
|
|
// 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;
|
|
`,t6=sn({opSnippet:Zq,packedOpSnippet:Yq,checkOutOfBounds:!0}),Jq={kernelName:Rs,backendName:"webgl",kernelFunc:t6},n6="return a - b;",a6=sn({opSnippet:n6,packedOpSnippet:n6,supportsComplex:!0,cpuKernelImpl:KB}),Qq={kernelName:ui,backendName:"webgl",kernelFunc:a6};function r6(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=e6({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=F.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),d=a6({inputs:{a:r,b:u},backend:n}),p=Kw({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:l}}),m=t6({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var eX={kernelName:oi,backendName:"webgl",kernelFunc:r6};function tX(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:r6({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new Kq(u,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var nX={kernelName:yc,backendName:"webgl",kernelFunc:tX},s6="return -x;";function aX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=_B(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Xl(a.shape,s6):r=new Yr(a.shape,s6),n.runWebGLProgram(r,[a],a.dtype)}var rX={kernelName:Go,backendName:"webgl",kernelFunc:aX},sX=Za.nonMaxSuppressionV3Impl;function iX(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:l}=a,u=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=sX(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var oX={kernelName:Xo,backendName:"webgl",kernelFunc:iX},lX=Za.nonMaxSuppressionV4Impl;function uX(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:l,padToMaxOutputSize:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=lX(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var dX={kernelName:Ko,backendName:"webgl",kernelFunc:uX},pX=Za.nonMaxSuppressionV5Impl;function cX(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:l,softNmsSigma:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=pX(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var hX={kernelName:Zo,backendName:"webgl",kernelFunc:cX},fX=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)));
|
|
}
|
|
`}},mX=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),u=new fX(l,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[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},gX={kernelName:qs,backendName:"webgl",kernelFunc:mX};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}),l=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var yX={kernelName:fl,backendName:"webgl",kernelFunc:Jh};function i6(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=i6({inputs:{x:r},backend:n}),i=Yh({inputs:{input:a},backend:n}),o=Jh({inputs:{x:i},backend:n}),l=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var AX={kernelName:Yo,backendName:"webgl",kernelFunc:i6};function xX(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=[],l=t.map(d=>{let p=Zg({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=Lw({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var bX={kernelName:Jo,backendName:"webgl",kernelFunc:xX},vX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).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)}}},wX=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),l=xn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
|
|
if(${u}) {
|
|
`,a===1?"":`}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
|
|
if(${u}) {`],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(${l.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)}}},o6=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 wX(r.shape,s,i):new vX(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},kX={kernelName:Xs,backendName:"webgl",kernelFunc:o6},IX=`
|
|
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);
|
|
`,SX=`
|
|
// 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;
|
|
`,NX=sn({opSnippet:IX,packedOpSnippet:SX}),TX={kernelName:Ks,backendName:"webgl",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=k.parseAxisParam(s,r.shape),d=u,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),l.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}=LB(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}}),l.push(y),l.push(x)}if(i){l.push(h);let m=F.expandShapeToKeepDim(h.shape,u);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var EX={kernelName:Qo,backendName:"webgl",kernelFunc:CX},l6=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=WB(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},RX={kernelName:ju,backendName:"webgl",kernelFunc:l6},MX="return 1.0 / x;",FX=Ze({opSnippet:MX}),$X={kernelName:el,backendName:"webgl",kernelFunc:FX},DX=Ca+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,OX=`
|
|
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;
|
|
`,zX=Ze({opSnippet:DX,packedOpSnippet:OX}),_X={kernelName:Ys,backendName:"webgl",kernelFunc:zX},PX=Ca+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,LX=`
|
|
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;
|
|
`,WX=Ze({opSnippet:PX,packedOpSnippet:LX}),BX={kernelName:Qs,backendName:"webgl",kernelFunc:WX},VX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[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(
|
|
${u[0]/d[0]},
|
|
${u[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${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);
|
|
}
|
|
`}},jX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[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(
|
|
${u[0]/d[0]},
|
|
${u[1]/d[1]},
|
|
${u[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${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 < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function UX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jX(r.shape,l,u,s,i):new VX(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var HX={kernelName:Js,backendName:"webgl",kernelFunc:UX},GX=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],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,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(${u});
|
|
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 qX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new GX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var XX={kernelName:bc,backendName:"webgl",kernelFunc:qX},KX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[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(
|
|
${u[0]/d[0]},
|
|
${u[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${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);
|
|
}
|
|
`}},ZX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[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(
|
|
${u[0]/d[0]},
|
|
${u[1]/d[1]},
|
|
${u[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${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 < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function YX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZX(r.shape,l,u,s,i):new KX(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var JX={kernelName:Uu,backendName:"webgl",kernelFunc:YX},QX=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
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(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function eK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new QX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var tK={kernelName:xc,backendName:"webgl",kernelFunc:eK},nK=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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}));
|
|
}
|
|
`}},aK=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 = ${l(a.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${u(a.slice())};
|
|
if(${r}) {
|
|
result.a = ${d(a.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,A)=>c(A,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function rK(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 l=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aK(r.shape,o):new nK(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var sK={kernelName:ei,backendName:"webgl",kernelFunc:rK},iK=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},oK={kernelName:ml,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new iK(a.shape,s),[u,d]=F.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(u,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},lK=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,uK=Ze({opSnippet:lK}),dK={kernelName:ti,backendName:"webgl",kernelFunc:uK},pK="return inversesqrt(x);",cK=Ze({opSnippet:pK,cpuKernelImpl:BB}),hK={kernelName:ni,backendName:"webgl",kernelFunc:cK},u6=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let d=`getIndices(${u})`,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() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${d});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${c};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function fK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new u6(l,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 mK={kernelName:nl,backendName:"webgl",kernelFunc:fK},gK=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.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 yK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new gK(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Aa(r.dtype,s.dtype))}var AK={kernelName:al,backendName:"webgl",kernelFunc:yK},xK=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${F.SELU_SCALEALPHA};
|
|
float scale = ${F.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,bK=Ze({opSnippet:xK}),vK={kernelName:rl,backendName:"webgl",kernelFunc:bK},wK="return 1.0 / (1.0 + exp(-1.0 * x));",kK=Ze({opSnippet:wK}),IK={kernelName:ri,backendName:"webgl",kernelFunc:kK},SK=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,NK=Ze({opSnippet:SK}),TK={kernelName:ol,backendName:"webgl",kernelFunc:NK},CK=Iw+`
|
|
return sin(x);
|
|
`,EK=Ze({opSnippet:CK}),RK={kernelName:ai,backendName:"webgl",kernelFunc:EK},MK=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,FK=Ze({opSnippet:MK}),$K={kernelName:il,backendName:"webgl",kernelFunc:FK},DK=`
|
|
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;
|
|
`,OK=Ze({opSnippet:DK}),zK={kernelName:ll,backendName:"webgl",kernelFunc:OK},_K=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),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],d=o6({inputs:{x:r},backend:n,attrs:{paddings:l,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 u.push(d),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},PK={kernelName:Hu,backendName:"webgl",kernelFunc:_K};function LK(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),l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=jB(o,a.shape,a.dtype,l,r.dtype,u,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 WK={kernelName:vc,backendName:"webgl",kernelFunc:LK};function BK(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),l=Array.from(n.readSync(s.dataId)),[u,d,p]=UB(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var VK={kernelName:wc,backendName:"webgl",kernelFunc:BK};function jK(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),l=n.readSync(s.dataId),[u,d]=dw(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(d,a.dtype,u)}var UK={kernelName:kc,backendName:"webgl",kernelFunc:jK};function HK(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
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=dw(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(d,a.dtype,u)}var GK={kernelName:Ic,backendName:"webgl",kernelFunc:HK};function qK(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:p}=F.calculateShapes(s,r,o),c=!1,h=new u6(u,l,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var XK={kernelName:Sc,backendName:"webgl",kernelFunc:qK};function KK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=F.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=zd({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var ZK={kernelName:ul,backendName:"webgl",kernelFunc:KK},YK="return sqrt(x);",JK=Ze({opSnippet:YK}),QK={kernelName:si,backendName:"webgl",kernelFunc:JK},eZ="return x * x;",tZ=Ze({opSnippet:eZ}),nZ={kernelName:Gu,backendName:"webgl",kernelFunc:tZ},d6="return (a - b) * (a - b);",aZ=sn({opSnippet:d6,packedOpSnippet:d6}),rZ={kernelName:li,backendName:"webgl",kernelFunc:aZ};function sZ({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ca+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Yr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var iZ={kernelName:Pr,backendName:"webgl",kernelFunc:sZ},oZ=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((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function lZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,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,l,u,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=HB(A,N,f,m);v=n.makeTensorInfo(A,x.dtype,C.values)}else{let w=new oZ(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 uZ={kernelName:dl,backendName:"webgl",kernelFunc:lZ};function dZ(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:d,dataSplits:p}=t,c=n.readSync(d.dataId),h=n.readSync(p.dataId),[m,f]=GB(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var pZ={kernelName:Nc,backendName:"webgl",kernelFunc:dZ};function cZ(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),l=n.readSync(i.dataId)[0],[u,d,p]=qB(o,l,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var hZ={kernelName:Tc,backendName:"webgl",kernelFunc:cZ};function fZ(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=XB(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var mZ={kernelName:Cc,backendName:"webgl",kernelFunc:fZ},gZ="return tan(x);",yZ=Ze({opSnippet:gZ}),AZ={kernelName:di,backendName:"webgl",kernelFunc:yZ},xZ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,bZ=Ze({opSnippet:xZ}),vZ={kernelName:pi,backendName:"webgl",kernelFunc:bZ},wZ=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=kZ(e);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function kZ(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]}, ${e[r]})`);return a.join()}function p6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,u=Ve(r.shape,r.dtype,l),d=ZB(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new wZ(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var IZ={kernelName:_r,backendName:"webgl",kernelFunc:p6};function SZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=YB(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var NZ={kernelName:pl,backendName:"webgl",kernelFunc:SZ},TZ=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${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 CZ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],g=[d,m,f,h],y=new TZ(p,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var EZ={kernelName:cl,backendName:"webgl",kernelFunc:CZ};function RZ(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;jl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=JB(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var MZ={kernelName:Ec,backendName:"webgl",kernelFunc:RZ};function FZ(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,l=r.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);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:u}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var $Z={kernelName:hl,backendName:"webgl",kernelFunc:FZ},DZ=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",l="sumValue",u=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 < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function OZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,d=F.getAxesPermutation([u],o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),l.push(p),u=F.getInnerMostAxes(1,o)[0]);let c=F.segment_util.computeOutShape(p.shape,u,i),h=k.sizeFromShape([p.shape[u]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.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 DZ(S,b),O=n.compileAndRun(z,[v,w],N);if(l.push(O),O.shape[1]===C)return O;let W=l6({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),G=p6({inputs:{x:W},backend:n,attrs:{reps:[_/$]}});return l.push(W),l.push(G),g(O,b,G,N,C)},y=g(m,"unsortedSegmentSum",s,f,i),A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}}),x=A;if(d!=null){l.push(A);let v=F.getUndoAxesPermutation(d);x=bn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var zZ={kernelName:qu,backendName:"webgl",kernelFunc:OZ},_Z=[pq,fq,ZV,JV,tj,rj,ij,uj,pj,hj,yj,xj,wj,Sj,Fj,Cj,Oj,Lj,_j,jj,Hj,qj,Yj,rU,iU,cU,fU,AU,vU,MV,NU,zU,PU,RU,VU,UU,WU,qU,ZU,QU,tH,aH,iH,cH,fH,lH,yH,bH,wH,NH,RH,DH,_H,PH,LH,BH,jH,HH,qH,KH,QH,nG,sG,oG,dG,fG,AG,wG,RV,IG,IU,TG,RG,$G,$V,_G,BG,jG,ZG,qG,eq,aq,oq,gq,Iq,wq,Cq,Rq,Fq,bq,Dq,zq,Wq,Uq,Xq,nX,PV,rX,oX,dX,hX,lU,gX,AX,bX,kX,TX,OV,EX,RX,uU,Jq,$X,BX,_X,WV,HX,XX,JX,tK,sK,oK,dK,hK,mK,AK,vK,IK,TK,RK,$K,nU,eX,zK,PK,WK,VK,UK,GK,XK,ZK,QK,nZ,rZ,iZ,uZ,pZ,hZ,mZ,Qq,qV,AZ,vZ,IZ,NZ,EZ,XV,MZ,$Z,zZ,yX];for(let e of _Z)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 c6;function PZ(e){c6=e.wasm.cwrap(hi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function LZ(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:l,transposeB:u,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=l?r.shape[2]:r.shape[1],A=u?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 c6(c,w,r.shape.length,h,N,s.shape.length,l,u,g,m,f,p||0,b),v}var WZ={kernelName:hi,backendName:"wasm",setupFunc:PZ,kernelFunc:LZ};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,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var BZ=vn(mo);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:l}=i,{a:u,b:d}=l,p=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:u.dtype,m=F.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,c,y,d.shape.length,Fn[u.dtype],A);if(t&&u.dtype==="float32")return x(),f;let v=F.getBroadcastDims(u.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 ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var VZ=!0,jZ=wn(Or,VZ),h6;function UZ(e){h6=e.wasm.cwrap(xs,null,["array","number","number","number"])}function HZ(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 h6(s,r.length,Fn[a.dtype],i),a}var GZ={kernelName:xs,backendName:"wasm",setupFunc:UZ,kernelFunc:HZ};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 qZ={kernelName:zs,backendName:"wasm",kernelFunc:Qh},f6;function XZ(e){f6=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]=ZZ(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=KZ(t.x.shape,a.perm),l={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 u=n.makeOutput(o,l.dtype),d=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(u.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return f6(d,h,l.shape.length,Fn[l.dtype],p,c,s.length),u}function KZ(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function ZZ(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 YZ={kernelName:ci,backendName:"wasm",kernelFunc:e0,setupFunc:XZ};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),l=null,u=!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),l=e0({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var m6;function JZ(e){m6=e.wasm.cwrap(Ao,null,["number, number, number"])}function QZ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let h=l.shape.length;F.assertAxesAreInnerMostDims("all",d,h);let[m,f]=F.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;m6(o,g,A)}if(c&&t.disposeData(u.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var eY={kernelName:Ao,backendName:"wasm",setupFunc:JZ,kernelFunc:QZ},g6;function tY(e){g6=e.wasm.cwrap(xo,null,["number, number, number"])}function nY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let h=l.shape.length;F.assertAxesAreInnerMostDims("any",d,h);let[m,f]=F.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;g6(o,g,A)}if(c&&t.disposeData(u.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var aY={kernelName:xo,backendName:"wasm",setupFunc:tY,kernelFunc:nY},y6;function rY(e){y6=e.wasm.cwrap(bs,null,["number","number","number","number","number"])}function sY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:p}=Qr(s,r,t);if(p){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let c=l.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[d[0]];return y6(o,Fn[l.dtype],f,g,m),p&&t.disposeData(u.dataId),h}var iY={kernelName:bs,backendName:"wasm",kernelFunc:sY,setupFunc:rY},A6;function oY(e){A6=e.wasm.cwrap(vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lY(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=F.computePool2DInfo(r.shape,i,o,1,l,u),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 A6(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,g,y,A,x,b),v}var uY={kernelName:vs,backendName:"wasm",setupFunc:oY,kernelFunc:lY};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: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var dY={kernelName:tl,backendName:"wasm",kernelFunc:Ea},x6;function pY(e){x6=e.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number"])}function cY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,d=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),A=g===y||g===1||y===1;k.assert(l>=2&&u>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${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 x6(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 hY={kernelName:ws,backendName:"wasm",setupFunc:pY,kernelFunc:cY};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 fY={kernelName:ks,backendName:"wasm",kernelFunc:t0},mY=vn(Is),b6;function gY(e){b6=e.wasm.cwrap(zr,null,["number","number","number","number"])}function yY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return b6(o,s,i,u),l}var AY={kernelName:zr,backendName:"wasm",setupFunc:gY,kernelFunc:yY};function v6(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 l=k.sizeFromShape(s[0].shape.slice(0,a)),u=0,d=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*u;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 xY={kernelName:So,backendName:"wasm",kernelFunc:v6},w6;function bY(e){w6=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 vY(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:l,dilations:u,pad:d,dimRoundingMode:p,dataFormat:c}=n,h=F.convertConv2DDataFormat(c),m=F.computeConv2DInfo(r.shape,s.shape,l,u,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:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(m.outShape,"float32"),z=a.dataIdMap.get(S.dataId).id;return w6(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,A,x,v,$,b,w,N,C,E,_,z),S}var wY={kernelName:Ss,backendName:"wasm",setupFunc:bY,kernelFunc:vY},k6;function kY(e){k6=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 IY(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=a,p=1,c=F.convertConv2DDataFormat(l),h=F.computeConv2DInfo(d,s.shape,i,p,o,u,!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 k6(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 SY={kernelName:Ns,backendName:"wasm",setupFunc:kY,kernelFunc:IY},NY=vn(Ts),Jg;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Jg||(Jg={}));var I6;function TY(e){I6=e.wasm.cwrap(To,null,["number","number","number","number","array","number","number","number","number","number"])}function CY(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:u}=n,d=l.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(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(o.shape).buffer);return I6(g,y,A,d,b,p,c,Jg[r],s,v),f!=null&&t.disposeData(f.dataId),x}var EY={kernelName:To,backendName:"wasm",setupFunc:TY,kernelFunc:CY},S6;function RY(e){S6=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number"])}function MY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=F.getAxesPermutation([s],l),d=r;u!==null&&(d=e0({inputs:{x:r},attrs:{perm:u},backend:n}));let p=F.getInnerMostAxes(1,l)[0];F.assertAxesAreInnerMostDims("cumsum",[p],l);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;S6(m,i?1:0,o?1:0,h,f,Fn[r.dtype]);let g=c;if(u!==null){let y=F.getUndoAxesPermutation(u);g=e0({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(d.dataId),n.disposeData(c.dataId)}return g}var FY={kernelName:Cs,backendName:"wasm",setupFunc:RY,kernelFunc:MY},N6;function $Y(e){N6=e.wasm.cwrap(Co,null,["number","number","number","array","number","array","array","number","number"])}function DY(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: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return N6(g,s,i==="NHWC"?1:0,y,r.shape.length-1,A,x,m.length,v),f}var OY={kernelName:Co,backendName:"wasm",setupFunc:$Y,kernelFunc:DY},T6;function zY(e){T6=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 _Y(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:l,dilations:u,pad:d,dimRoundingMode:p}=n,c=u==null?[1,1]:u,h=F.computeConv2DInfo(r.shape,s.shape,l,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 T6(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,A,x,_,v,b,w,N,C,E,S),$}var PY={kernelName:Es,backendName:"wasm",setupFunc:zY,kernelFunc:_Y},LY=!1,WY=wn(Mo,LY,"bool"),BY=vn(Ms);function Qg(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ea({inputs:{x:r},backend:a,attrs:{shape:o}})}var VY={kernelName:Fo,backendName:"wasm",kernelFunc:Qg};function jY(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var UY={kernelName:Pu,backendName:"wasm",kernelFunc:jY},C6;function HY(e){C6=e.wasm.cwrap(Do,null,["number","number","number","number","number","number"])}function GY(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,d]=a.shape;return C6(s,o,l,u,d,i),r}var qY={kernelName:Do,backendName:"wasm",kernelFunc:GY,setupFunc:HY},XY=vn(Fs),KY=!1,ZY=wn($s,KY),E6;function YY(e){E6=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number"])}function JY(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.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 E6(d,p,c,h,m,r,g),f}var QY={kernelName:Ds,backendName:"wasm",setupFunc:YY,kernelFunc:JY},R6;function eJ(e){R6=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 tJ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=F.computeConv2DInfo(r.shape,s.shape,l,d,u,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:'${p}'. Please use 'NHWC'.`);let ne=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(ne.dataId).id,se=o==null?0:a.dataIdMap.get(o.dataId).id;return R6(y,H,J,K,A,b,w,v,N,C,E,_,G,$,S,z,O,W,x,g,se,m||0,Q),ne}var nJ={kernelName:fi,backendName:"wasm",setupFunc:eJ,kernelFunc:tJ},M6;function aJ(e){M6=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 rJ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=F.computeConv2DInfo(r.shape,s.shape,l,d,u,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:'${p}'. Please use 'NHWC'.`);let ne=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(ne.dataId).id,se=o==null?0:a.dataIdMap.get(o.dataId).id;return M6(y,H,J,K,A,b,w,v,N,C,E,_,G,$,S,z,O,W,x,g,se,m||0,Q),ne}var sJ={kernelName:mi,backendName:"wasm",setupFunc:aJ,kernelFunc:rJ},F6;function iJ(e){F6=e.wasm.cwrap(zo,null,["number","number","number","number","number","number","array","number"])}function oJ(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=g1.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;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(l).buffer),f=t.dataIdMap.get(u.dataId).id;return F6(c,Fn[a.dtype],h,i,p,o,m,f),u}var lJ={kernelName:zo,backendName:"wasm",setupFunc:iJ,kernelFunc:oJ},$6;function uJ(e){$6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function dJ(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=F.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=Ea({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),c=Ea({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.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 $6(g,Fn[r.dtype],x,f,y,u.batchSize,v,A),t.disposeData(d.dataId),t.disposeData(c.dataId),m.shape=u.outputShape,m}var pJ={kernelName:Oo,backendName:"wasm",setupFunc:uJ,kernelFunc:dJ},cJ=!1,hJ=wn(_o,cJ,"bool"),fJ=!1,mJ=wn(Os,fJ,"bool"),D6;function gJ(e){D6=e.wasm.cwrap(_s,null,["number","number","number"])}function yJ(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;D6(r,n,i)}return s}var AJ={kernelName:_s,backendName:"wasm",setupFunc:gJ,kernelFunc:yJ},xJ=!1,bJ=wn(Bo,xJ,"bool"),vJ=!1,wJ=wn(Vo,vJ,"bool"),kJ=vn(Ps),IJ=!1,SJ=wn(Uo,IJ,"bool"),O6;function NJ(e){O6=e.wasm.cwrap(Ls,null,["number, number, number"])}function TJ(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:c}=Qr(i,r,t);if(c){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let h=l.shape.length;F.assertAxesAreInnerMostDims("max",d,h);let[m,f]=F.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;O6(o,g,A)}if(c&&t.disposeData(u.dataId),s){let A=F.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var CJ={kernelName:Ls,backendName:"wasm",setupFunc:NJ,kernelFunc:TJ},EJ=!1,RJ=wn(Ws,EJ),z6;function MJ(e){z6=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function FJ(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=F.computePool2DInfo(r.shape,i,o,1,l,u),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 z6(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 $J={kernelName:Bs,backendName:"wasm",setupFunc:MJ,kernelFunc:FJ},_6;function DJ(e){_6=e.wasm.cwrap(Vs,null,["number, number, number"])}function OJ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=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&&(u=d,l=v,m=F.getInnerMostAxes(m.length,u.shape.length))}F.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=F.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=t0({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;_6(l,y,v)}if(h&&t.disposeData(d.dataId),s){let v=F.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var zJ={kernelName:Vs,backendName:"wasm",setupFunc:DJ,kernelFunc:OJ},P6;function _J(e){P6=e.wasm.cwrap(js,null,["number, number, number"])}function PJ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=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&&(u=d,l=x)}let m=u.shape.length;F.assertAxesAreInnerMostDims("min",p,m);let[f,g]=F.computeOutAndReduceShapes(u.shape,p),y=k.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;P6(l,y,x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var LJ={kernelName:js,backendName:"wasm",setupFunc:_J,kernelFunc:PJ},WJ=!1,BJ=wn(Us,WJ),ey;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(ey||(ey={}));var L6;function VJ(e){L6=e.wasm.cwrap(Hs,null,["number","array","number","number","array","array","number","number"])}function jJ(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),l=n.dataIdMap.get(o.dataId).id,u=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 L6(i,u,t.shape.length,Fn[t.dtype],c,h,ey[r],l),o}var UJ={kernelName:Hs,backendName:"wasm",kernelFunc:jJ,setupFunc:VJ},HJ=!0,GJ=wn(Gs,HJ),qJ=vn(Go);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 W6;function XJ(e){W6=e.wasm.cwrap(Xo,"number",["number","number","number","number","number"])}function KJ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,p=W6(u,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 ZJ={kernelName:Xo,backendName:"wasm",setupFunc:XJ,kernelFunc:KJ},B6;function YJ(e){B6=e.wasm.cwrap(Ko,"number",["number","number","number","number","number","bool"])}function JJ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,c=B6(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 QJ={kernelName:Ko,backendName:"wasm",setupFunc:YJ,kernelFunc:JJ},V6;function eQ(e){V6=e.wasm.cwrap(Zo,"number",["number","number","number","number","number","number"])}function tQ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,c=V6(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 nQ={kernelName:Zo,backendName:"wasm",setupFunc:eQ,kernelFunc:tQ},aQ=!1,rQ=wn(qo,aQ,"bool"),j6;function sQ(e){j6=e.wasm.cwrap(qs,null,["number","number","number","number","number"])}function iQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return j6(d,s,i,o,u),l}var oQ={kernelName:qs,backendName:"wasm",setupFunc:sQ,kernelFunc:iQ};function lQ(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var uQ={kernelName:Yo,backendName:"wasm",kernelFunc:lQ};function dQ(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=[],l=t.map(d=>{let p=Qg({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=v6({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeData(d.dataId)),u}var pQ={kernelName:Jo,backendName:"wasm",kernelFunc:dQ},U6;function cQ(e){U6=e.wasm.cwrap(Xs,null,["number","array","number","number","array","array","number","number"])}function hQ(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),l=n.dataIdMap.get(o.dataId).id,u=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 U6(i,u,t.shape.length,Fn[t.dtype],c,h,r,l),o}var fQ={kernelName:Xs,backendName:"wasm",kernelFunc:hQ,setupFunc:cQ},mQ=!1,gQ=wn(Ks,mQ),H6;function yQ(e){H6=e.wasm.cwrap(Zs,null,["number","number","number"])}function AQ(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"),l=n.dataIdMap.get(o.dataId).id;return H6(s,i,l),o}var xQ={kernelName:Zs,backendName:"wasm",setupFunc:yQ,kernelFunc:AQ},G6;function bQ(e){G6=e.wasm.cwrap(Qo,null,["number","number","number","number"])}function vQ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=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&&(u=d,l=x,m=F.getInnerMostAxes(m.length,u.shape.length))}F.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=F.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;G6(l,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 wQ={kernelName:Qo,backendName:"wasm",setupFunc:bQ,kernelFunc:vQ},kQ=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=kg(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},IQ={kernelName:ju,backendName:"wasm",kernelFunc:kQ},SQ=!0,NQ=wn(Rs,SQ),TQ=vn(Ys),CQ=vn(Qs),q6;function EQ(e){q6=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number"])}function RQ(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[d,p,c,h]=r.shape,m=[d,l,u,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 q6(y,d,p,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var MQ={kernelName:Js,backendName:"wasm",setupFunc:EQ,kernelFunc:RQ},X6;function FQ(e){X6=e.wasm.cwrap(ei,null,["number","array","number","array","number","number"])}function $Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Qh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);X6(l,d,i.length,p,r.shape.length,u);let c=Ea({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var DQ={kernelName:ei,backendName:"wasm",kernelFunc:$Q,setupFunc:FQ},K6;function OQ(e){K6=e.wasm.cwrap(ml,null,["number","number","number","number","number","number","number","number","array","number","number"])}function zQ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,d=n.dataIdMap.get(l.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 K6(u,p,c,h,m,s,f,g,v,x.length,d),l}var _Q={kernelName:ml,backendName:"wasm",kernelFunc:zQ,setupFunc:OQ},PQ=vn(ti),LQ=vn(ni),Z6;function WQ(e){Z6=e.wasm.cwrap(nl,null,["number","number","number","number","number","number","array","number","number"])}function BQ(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:l,numUpdates:u,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 Z6(h,m,Fn[s.dtype],l,u,d,f,c,g),o}var VQ={kernelName:nl,backendName:"wasm",setupFunc:WQ,kernelFunc:BQ},Y6;function jQ(e){Y6=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function UQ(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,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),d=n.dataIdMap.get(u.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 Y6(i,o,l,h,d),u}var HQ={kernelName:al,backendName:"wasm",kernelFunc:UQ,setupFunc:jQ},J6;function GQ(e){J6=e.wasm.cwrap(ri,null,["number","number"])}function qQ(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||J6(a,s),r}var XQ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:GQ,kernelFunc:qQ},KQ=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),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),d=k.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=fn.computeFlatOffset(s,d);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+k.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Fh(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)ZQ(l,d[0],c,s,i);else if(h===3)YQ(l,d[0],d[1],c,s,i);else if(h===4)JQ(l,d[0],d[1],d[2],c,s,i);else{let m=Fh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function ZQ(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u<l;u++){let d=u*t+o;n.set(e.subarray(d,d+r[1]),s),s+=r[1]}}function YQ(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],u=r[2],d=o+s[0],p=l+s[1];for(let c=o;c<d;c++)for(let h=l;h<p;h++){let m=c*t+h*n+u;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function JQ(e,t,n,a,r,s,i){let o=0,l=s[0],u=s[1],d=s[2],p=l+i[0],c=u+i[1],h=d+i[2],m=s[3];for(let f=l;f<p;f++)for(let g=u;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 QQ={kernelName:sl,backendName:"wasm",kernelFunc:n0},Q6;function eee(e){Q6=e.wasm.cwrap(oi,null,["number","number","number","number"])}function tee(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],l=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||Q6(r,i,o,l),s}var nee={kernelName:oi,backendName:"wasm",setupFunc:eee,kernelFunc:tee};function aee(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],l=F.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),d=r.shape.slice();return l.map(p=>{let c=[...d];c[o]=p;let h=n0({inputs:{x:r},attrs:{begin:u,size:c},backend:a});return u[o]+=p,h})}var ree={kernelName:ul,backendName:"wasm",kernelFunc:aee},see=vn(si),iee=vn(Gu),oee=!0,lee=wn(li,oee),e4;function uee(e){e4=e.wasm.cwrap(Pr,null,["number","number","number"])}function dee(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),l=t.dataIdMap.get(o.dataId).id;return e4(i,r,l),o}var pee={kernelName:Pr,backendName:"wasm",setupFunc:uee,kernelFunc:dee},t4;function cee(e){t4=e.wasm.cwrap(dl,null,["number","array","number","array","array","array","array","array","number","number"])}function hee(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:l,endMask:u,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,l,u,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,$=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),S=new Uint8Array(new Int32Array(s).buffer),z=new Uint8Array(new Int32Array(i).buffer),O=new Uint8Array(new Int32Array(o).buffer),W=new Uint8Array(new Int32Array(N).buffer),G=new Uint8Array(new Int32Array(k.computeStrides(N)).buffer),H=t.dataIdMap.get(C.dataId).id;t4(_,$,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 fee={kernelName:dl,backendName:"wasm",setupFunc:cee,kernelFunc:hee},mee=!0,gee=wn(ui,mee),n4;function yee(e){n4=e.wasm.cwrap(ii,null,["number, number, number"])}function Aee(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=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&&(u=d,l=x,m=F.getInnerMostAxes(m.length,u.shape.length))}F.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=F.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;n4(l,y,x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var xee={kernelName:ii,backendName:"wasm",setupFunc:yee,kernelFunc:Aee},bee=vn(di),vee=vn(pi),a4;function wee(e){a4=e.wasm.cwrap(_r,null,["number","array","number","array","number","number"])}function kee(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 l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),d=n.makeOutput(o,r.dtype),p=n.dataIdMap.get(d.dataId).id;return a4(s,l,r.shape.length,u,o.length,Fn[d.dtype],p),d}var Iee={kernelName:_r,backendName:"wasm",setupFunc:wee,kernelFunc:kee},r4;function See(e){r4=e.wasm.cwrap(pl,null,["number","array","number","number","number","bool","number","number"])}var Nee=({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),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),d=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),c=t.dataIdMap.get(p.dataId).id;return r4(i,o,a.shape.length,Fn[a.dtype],r,s,d,c),[u,p]},Tee={kernelName:pl,backendName:"wasm",setupFunc:See,kernelFunc:Nee},s4;function Cee(e){s4=e.wasm.cwrap(cl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Eee(e){let{backend:t,inputs:n,attrs:a}=e,{image:r,transforms:s}=n,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[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 s4(v,b,s.shape[0]>1,d,m,f,h,c,p,y,r.shape.length-1,w,N,l,x),A}var Ree={kernelName:cl,backendName:"wasm",setupFunc:Cee,kernelFunc:Eee};function Mee(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,l=new Array(o-1),u=0;for(let h=0;h<o;h++)h!==s&&(l[u++]=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:l}))}var Fee={kernelName:hl,backendName:"wasm",kernelFunc:Mee};function $ee(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var Dee={kernelName:fl,backendName:"wasm",kernelFunc:$ee},Oee=[BZ,jZ,GZ,eY,aY,iY,uY,hY,fY,mY,AY,xY,wY,SY,NY,EY,FY,OY,PY,WY,BY,VY,UY,qY,XY,ZY,WZ,QY,nJ,sJ,lJ,pJ,hJ,mJ,qZ,AJ,bJ,wJ,kJ,SJ,CJ,RJ,$J,zJ,LJ,BJ,UJ,GJ,qJ,ZJ,QJ,nQ,rQ,oQ,uQ,pQ,fQ,gQ,xQ,wQ,IQ,NQ,TQ,CQ,dY,MQ,DQ,_Q,LQ,PQ,VQ,HQ,XQ,KQ,QQ,nee,ree,see,iee,lee,pee,fee,gee,xee,bee,vee,Iee,Tee,Ree,YZ,Fee,Dee];for(let e of Oee)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 i4=gs(oS()),zee='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()}}}}',_ee=gs(lS()),o4=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 u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=k.sizeFromShape(n),o=i*k.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape: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 Wee(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 ${t}`)}}};function Pee(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function l4(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 Lee(){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,l)=>{if(o.endsWith(".worker.js")){let u=zee,d=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(d)}return o.endsWith(".wasm")?l4(e,t,Ld!=null?Ld:l):l+o},ay&&(r.instantiateWasm=Pee(l4(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 = "+i4.default.toString()],{type:"text/javascript"}),i=(0,i4.default)(r)):i=(0,_ee.default)(r),i.then(o=>{s=!0,Bd=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function Wee(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 Bee=["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 Vee(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 jee(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=Bee.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 u4="3.7.0",Uee=2;Il("wasm",async()=>{let{wasm:e}=await Lee();return new o4(e)},Uee);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(),Nl(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(),Cl(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(),N3(this,e)};ee().prototype.elu=function(){return this.throwIfDisposed(),El(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(),Ml(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(),Ol(this)};ee().prototype.irfft=function(){return this.throwIfDisposed(),ch(this)};ee().prototype.isFinite=function(){return this.throwIfDisposed(),C3(this)};ee().prototype.isInf=function(){return this.throwIfDisposed(),E3(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(),F3(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(),z3(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(),Fl(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(),wl(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(),L3(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(),a7(this,e,t,n)};ee().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),r7(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(),zl(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 d4={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,zl(ge(n,"float32"),-1))}}},Hee={kernelName:go,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))}}}},Gee={kernelName:yo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(ye(ot(ge(n,"float32")),1));return me(e,a)}}}},qee={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)}}}},Xee={kernelName:xs,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},Kee={kernelName:bs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},Zee={kernelName:Fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},Yee={kernelName:bo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,an(ye(ke(1),ot(ge(n,"float32")))))}}},Jee={kernelName:vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(ie(ke(1),ot(ge(n,"float32"))));return me(e,a)}}}},Qee={kernelName:Io,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)}}}},ete={kernelName:wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ie(ot(ge(n,"float32")),1))}}},tte={kernelName:ko,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ye(ke(1),ot(ge(n,"float32"))))}}};function nte(e,t,n,a,r,s){let i=M(e,"dy","avgPool3dGrad"),o=M(t,"input","avgPool3dGrad"),l=i,u=o,d=!1;o.rank===4&&(d=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),u=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),D(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),D(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),s!=null&&D(qt(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let p={dy:l,input:u},c={filterSize:n,strides:a,pad:r,dimRoundingMode:s},h=P.runKernel(Kp,p,c);return d?q(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var ate=L({avgPool3dGrad_:nte}),rte={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>ate(e,a,r,s,i,o)}}};function ste(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,l=s,u=!1;i.rank===3&&(u=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),D(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),D(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let d={dy:l,input:o},p={filterSize:n,strides:a,pad:r},c=P.runKernel(Xp,d,p);return u?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var ite=L({avgPoolGrad_:ste}),ote={kernelName:vs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>ite(e,a,r,s,i)}}},lte={kernelName:ws,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>je(e,r,!1,!0),b:()=>je(a,e,!0,!1)}:!s&&i?{a:()=>je(e,r,!1,!1),b:()=>je(e,a,!0,!1)}:s&&!i?{a:()=>je(r,e,!1,!0),b:()=>je(a,e,!1,!1)}:{a:()=>je(r,e,!0,!0),b:()=>je(e,a,!0,!0)}}},ute={kernelName:Du,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>md(e,a,r)}}},dte={kernelName:gb,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let l=r.length-1;l>=0;l--)if(r[l]===s[l])i[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>Se(e,o,!0)}}},pte={kernelName:ks,gradFunc:e=>({x:()=>e.clone()})},cte={kernelName:Is,gradFunc:e=>({x:()=>Ge(e)})},hte={kernelName:zr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>un(xa(qr(a,r),Xr(a,s)),e,Ge(e))}}},fte={kernelName:Ou,inputsToSave:["x"],gradFunc:d4.gradFunc},mte={kernelName:So,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=ya(r,t[0].shape)[0],i=a.map(o=>o[s]);return Zt(e,i,s).map(o=>()=>o)}},gte={kernelName:Ss,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return D(Ur(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>P1(a.shape,e,r,i,o,l),filter:()=>hg(a,e,r.shape,i,o,l)}}},yte={kernelName:Ns,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>mr(e,r,s,i,o,1,l),filter:()=>hg(e,a,r.shape,s,i,o,l)}}};function Ate(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},l={strides:a,pad:r,filterShape:n};return P.runKernel(Qp,o,l)}var xte=L({conv3DBackpropFilter_:Ate}),bte={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 '${a}'`);let[i,o]=t;return{x:()=>k3(i.shape,e,o,r,s),filter:()=>xte(i,e,o.shape,r,s)}}},vte={kernelName:Ts,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(St(lh(ge(n,"float32"))),e)}}},wte={kernelName:No,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(uh(ge(n,"float32")),e)}}},kte={kernelName:Cs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=O3([r],a.rank),l=Kc(e,r,s,!i);return o!=null&&(l=Qe(l,o)),l}}}},Ite={kernelName:Es,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;D(Ur(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return D(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),D(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),D(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),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 ${s}.`),{x:()=>Z3(l.shape,e,u,r,s,a,i),filter:()=>K3(l,e,u.shape,r,s,a,i)}}},Ste={kernelName:_u,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,s={x:a,filter:r,dy:e},i={x:a,filter:r,dy:e};return{x:()=>P.runKernel(sc,s,n),filter:()=>P.runKernel(ic,i,n)}}},Nte={kernelName:Eo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>P.runKernel(lc,a)}}},Tte={kernelName:Ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(la(St(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>B(e,a)}}},Cte={kernelName:Ms,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,n)}}},Ete={kernelName:Fo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>q(e,n.shape)}}},Rte={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,la(n))}}},Mte={kernelName:Fs,gradFunc:e=>({x:()=>Ge(e)})},Fte={kernelName:$s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=me(e,ge(a,"float32")),i=Bt(n.shape,r);return i.length>0?q(Se(s,i),n.shape):s},b:()=>{let s=B(e,ge(n,"float32")),i=Bt(a.shape,r);i.length>0&&(s=q(Se(s,i),a.shape));let o=ot(a);return St(me(s,ge(o,"float32")))}}}},$te={kernelName:Ds,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?ke(1):o,u=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,l),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)),l),r.shape):q(B(B(e,h),l),r.shape),mean:()=>{let f=B(B(h,ke(-1)),c);return s.rank===1&&(f=Se(f,u)),q(f,s.shape)},variance:()=>{let f=B(B(m,p),c);return s.rank===1&&(f=Se(f,u)),q(f,s.shape)},scale:()=>{let f=B(p,h),g=B(e,f);return s.rank===1&&(g=Se(g,u)),q(g,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Se(f,u)),q(f,s.shape)}}}},Dte={kernelName:Oo,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,l=r.size,u=o.slice(0,i),d=u.length,p=o.slice(s,o.length).slice(1),c=p.length,h=p4(0,d),m=p4(d+1,d+1+c),f=c4([u,[l],p]),g=q(e,f),y=q(r,[l]),A=c4([[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 p4(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function c4(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 Ote={kernelName:Os,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>Ge(n),b:()=>Ge(a)}}},zte={kernelName:zs,gradFunc:e=>({x:()=>ge(e,"float32")})},_te={kernelName:Po,gradFunc:e=>({x:()=>Ge(e)})},Pte={kernelName:Lo,gradFunc:e=>({x:()=>Ge(e)})},Lte={kernelName:Wo,gradFunc:e=>({x:()=>Ge(e)})},Wte={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))}}},Bte={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ie(n,1))}}},Vte={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ge(n,"float32"))}}},jte={kernelName:yb,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 Ute(e,t,n,a=5,r=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:a,bias:r,alpha:s,beta:i};return P.runKernel(hc,o,l)}var Hte=L({localResponseNormalizationBackprop_:Ute}),Gte={kernelName:Bu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>Hte(a,r,e,s,i,o,l)}}};function h4(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 f4={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),l=h4(e,i,s,o);return{x:()=>l.x()}}},qte={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 Xte(e,t,n,a,r,s,i){let o=M(e,"dy","maxPool3dGrad"),l=M(t,"input","maxPool3dGrad"),u=M(n,"output","maxPool3dGrad"),d=o,p=l,c=u,h=!1;l.rank===4&&(h=!0,d=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),p=q(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),c=q(u,[1,u.shape[0],u.shape[1],u.shape[2],u.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 ${s}.`);let m={dy:d,input:p,output:c},f={filterSize:a,strides:r,pad:s,dimRoundingMode:i},g=P.runKernel(mc,m,f);return h?q(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var Kte=L({maxPool3dGrad_:Xte}),Zte={kernelName:Vu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>Kte(e,a,r,s,i,o,l)}}};function Yte(e,t,n,a,r,s,i){let o=M(e,"dy","maxPoolGrad"),l=M(t,"input","maxPoolGrad"),u=M(n,"output","maxPoolGrad");D(l.rank===o.rank,()=>`Rank of input (${l.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(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&D(qt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let d={dy:o,input:l,output:u},p={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return P.runKernel(fc,d,p)}var Jte=L({maxPoolGrad_:Yte}),Qte={kernelName:Bs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>Jte(e,a,r,s,i,o)}}},ene={kernelName:Vs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=ya(r,a.shape),i=D3(a.shape,s)[1],o=Mt(i);return{x:()=>{let l=a.shape.slice();s.forEach(d=>{l[d]=1});let u=q(e,l);return me(B(u,jn(a.shape,"float32")),o)}}}},tne={kernelName:js,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=ya(r,s.shape),l=h4(e,i,s,o);return{x:()=>l.x()}}},nne={kernelName:Us,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>B(e,ge(Xr(n,a),"float32")),b:()=>B(e,ge(Wn(n,a),"float32"))}}},ane={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Re(e,s,a.shape)}}},rne={kernelName:Ho,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=Bt(n.shape,r);return s.length>0?q(Se(e,s),n.shape):e},b:()=>{let s=B(e,St(Ml(me(n,a)))),i=Bt(a.shape,r);return i.length>0?q(Se(s,i),a.shape):s}}}},sne={kernelName:Gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=B(e,ge(a,"float32")),i=Bt(n.shape,r);return i.length>0?q(Se(s,i),n.shape):s},b:()=>{let s=B(e,ge(n,"float32")),i=Bt(a.shape,r);return i.length>0?q(Se(s,i),a.shape):s}}}},ine={kernelName:Go,gradFunc:e=>({x:()=>St(e)})},one={kernelName:qs,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>$t(n.shape,"float32")}}},lne={kernelName:Yo,gradFunc:e=>({x:()=>Ge(e)})},une={kernelName:Jo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return Gn(e,a).map(r=>()=>r)}},m4={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)}}},dne={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 l=ge(i,"float32"),u=B(e,B(l,yr(s,ye(l,ke(1))))),d=Bt(s.shape,o);return d.length>0&&(u=Se(u,d)),q(u,s.shape)},b:()=>{let l=Wn(s,0),u=un(l,Bn(s),Ge(s)),d=B(e,B(r,u)),p=Bt(i.shape,o);return p.length>0&&(d=Se(d,p)),q(d,i.shape)}}}},pne={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)}}}},cne={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")))}}}},hne={kernelName:el,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,St(ot(n)))}}},fne={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(Xr(n,6),zl(n));return{x:()=>B(e,ge(a,"float32"))}}},mne={kernelName:Ys,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,ge(zl(n),"float32"))}}},gne={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>q(e,n.shape)}}},yne={kernelName:Js,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(bc,r,n)}}},Ane={kernelName:Uu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(xc,r,n)}}},xne={kernelName:ei,gradFunc:(e,t,n)=>{let{dims:a}=n,r=ya(a,e.shape);return{x:()=>Hn(e,r)}}},bne={kernelName:ti,gradFunc:e=>({x:()=>Ge(e)})},vne={kernelName:ni,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(me(e,B(yr(n,1.5),2)))}}},wne={kernelName:al,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))}}},kne={kernelName:rl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Wn(n,ke(0)),r=ke(o7),s=ke(l7),i=B(e,s),o=B(B(e,r),la(ge(n,"float32")));return un(a,i,o)}}}},Ine={kernelName:ri,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(n,ye(ke(1),n)))}}},Sne={kernelName:ol,gradFunc:e=>({x:()=>Ge(e)})},Nne={kernelName:ai,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(dd(ge(n,"float32")),e)}}},Tne={kernelName:il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(Xc(ge(n,"float32")),e)}}},Cne={kernelName:sl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=l3(a,r,s),u=[];for(let d=0;d<e.rank;d++)u.push([o[d],i[d]-o[d]-l[d]]);return{x:()=>gr(e,u)}}},Ene={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))}}},Rne={kernelName:ll,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,Rn(n))}}},g4={kernelName:Hu,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>ud(e,a,r)}}},y4={kernelName:ul,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>lt(e,a)}}},Mne={kernelName:si,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,B(an(ge(n,"float32")),2))}}},Fne={kernelName:Gu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(ge(n,"float32"),2))}}},$ne={kernelName:li,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ke(2);return{a:()=>B(e,B(r,ye(n,a))),b:()=>B(e,B(r,ye(a,n)))}}},Dne={kernelName:Pr,gradFunc:e=>({x:()=>Ge(e)})},One={kernelName:ui,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=mt(n.shape,a.shape);return{a:()=>{let s=e,i=Bt(n.shape,r);return i.length>0&&(s=Se(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Bt(a.shape,r);return i.length>0&&(s=Se(s,i)),q(St(s),a.shape)}}}},zne={kernelName:ii,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;ya(s,a.shape).forEach(l=>{r[l]=1});let i=q(e,r),o=B(i,jn(a.shape,"float32"));return{x:()=>o}}},_ne={kernelName:di,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ot(dd(n)))}}},Pne={kernelName:pi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(ye(ke(1),ot(n)),e)}}},Lne={kernelName:_r,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=Ge(a);if(a.rank===1)for(let i=0;i<r[0];++i)s=ie(s,Re(e,[i*a.shape[0]],[a.shape[0]]));else if(a.rank===2)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1]],[a.shape[0],a.shape[1]]));else if(a.rank===3)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2]],[a.shape[0],a.shape[1],a.shape[2]]));else if(a.rank===4)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)for(let u=0;u<r[3];++u)s=ie(s,Re(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2],u*a.shape[3]],[a.shape[0],a.shape[1],a.shape[2],a.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${a.rank} tensors yet.`);return s}}}},Wne={kernelName:ci,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=K1(r);return{x:()=>Qe(e,s)}}},Bne={kernelName:hl,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>gn(e,r)}}},Vne={kernelName:qu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>jne(e,n)}}};function jne(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 Une={kernelName:fl,gradFunc:e=>({x:()=>Ge(e)})},Hne=[d4,Hee,Gee,qee,Xee,Kee,Zee,Yee,Jee,Qee,ete,tte,rte,ote,lte,ute,dte,pte,cte,hte,fte,mte,yte,gte,bte,vte,wte,kte,Ite,Ste,cne,Nte,Tte,Cte,Ete,Rte,Fte,Mte,$te,Dte,Ote,zte,_te,Pte,Lte,Wte,Bte,Vte,jte,Gte,f4,f4,qte,Zte,Qte,ene,tne,nne,ane,rne,sne,ine,one,lne,une,m4,m4,dne,pne,hne,fne,mne,gne,yne,Ane,xne,bne,vne,wne,kne,Ine,Sne,Nne,Tne,Cne,Ene,Rne,g4,g4,y4,y4,Mne,$ne,Fne,Dne,One,zne,_ne,Pne,Lne,Wne,Bne,Vne,Une];for(let e of Hne)Ab(e);var A4={};Fe(A4,{maxNorm:()=>Kne,minMaxNorm:()=>Jne,nonNeg:()=>Yne,unitNorm:()=>Zne});var ry;function jt(){return ry==null&&(ry=h3().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)}},x4=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,x4.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 x4(t)}function b4(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,l;if(i in n?[o,l]=n[i]:i in ba?[o,l]=ba.className:i in t&&([o,l]=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(l!=null){let u={};for(let h of Object.keys(ba))u[h]=ba[h];for(let h of Object.keys(n))u[h]=n[h];let d=s.config;d.customObjects=u;let p=Object.assign({},ba);for(let h of Object.keys(n))ba[h]=n[h];iy(s.config);let c=l(o,s.config,n,r);return ba=Object.assign({},p),c}else{let u=Object.assign({},ba);for(let p of Object.keys(n))ba[p]=n[p];let d=new o(s.config);return ba=Object.assign({},u),d}}}function Gne(e,t){return e<t?-1:e>t?1:0}function r0(e,t){return-1*Gne(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 qne(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 ${v4(e)}.`)}function v4(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>v4(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function Xne(e,t){let n=k.now(),a;return(...r)=>{let s=k.now();return s-n<t||(n=s,a=e(...r)),a}}function w4(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 k4={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ut(e){return sy(e)}function I4(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 k4?k4[e]:e,config:{}};return I4(t)}else return e instanceof jd?e:I4(e)}function Kne(e){return new uy(e)}function Zne(e){return new dy(e)}function Yne(){return new py}function Jne(e){return new cy(e)}var S4={};Fe(S4,{constant:()=>vae,glorotNormal:()=>Cae,glorotUniform:()=>Tae,heNormal:()=>Eae,heUniform:()=>Rae,identity:()=>Sae,leCunNormal:()=>Mae,leCunUniform:()=>Fae,ones:()=>bae,orthogonal:()=>$ae,randomNormal:()=>kae,randomUniform:()=>wae,truncatedNormal:()=>Iae,varianceScaling:()=>Nae,zeros:()=>xae});var Qne=["channelsFirst","channelsLast"],eae=["nearest","bilinear"],tae=["valid","same","causal"],nae=["max","avg"],aae=["sum","mul","concat","ave"],Ql=new Map;function Ft(e){ji(Qne,"DataFormat",e)}function rae(e){ji(eae,"InterpolationFormat",e)}function ca(e){ji(tae,"PaddingMode",e)}function N4(e){ji(nae,"PoolMode",e)}var Ud=[],T4="/";function Ui(e,t){Ud.push(e);try{let n=t();return Ud.pop(),n}catch(n){throw Ud.pop(),n}}function sae(){return Ud.length===0?"":Ud.join(T4)+T4}function C4(e){if(!R4(e))throw new Error("Not a valid tensor name: '"+e+"'");return sae()+e}function E4(e){if(!R4(e))throw new Error("Not a valid tensor name: '"+e+"'");Ql.has(e)||Ql.set(e,0);let t=Ql.get(e);if(Ql.set(e,Ql.get(e)+1),t>0){let n=`${e}_${t}`;return Ql.set(n,1),n}else return e}var iae=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function R4(e){return!!e.match(iae)}function oae(e){return e===parseInt(e.toString(),10)}function ts(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let a=1;for(let r=t;r<n;++r)a*=e[r];return a}function eu(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a<t&&(t=a)}return t}function ns(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a>t&&(t=a)}return t}function Fa(e,t){if(t<e)throw new U(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let a=e;a<t;++a)n.push(a);return n}function Hd(e,t){return e.asType(t)}function Gd(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function lae(e,t){return V(()=>{if(e.shape.length!==2)throw new U(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Gd(e,1);return my(n,[1,t,1])})}function uae(e){let t=[ts(e.shape)];return e.reshape(t)}function dae(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 M4(e,t){switch(e.rank){case 1:return b3([e,t]);case 2:return Tl([e,t],0);case 3:return v3([e,t],0);case 4:return w3([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 W3(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(),l=i.pop(),u=[...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([l,-1]);let p=[...r,...u],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 F4(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 $a(e,t,n){return V(()=>(n==null&&(n=Ra()),Ft(n),e.add(gy(e.rank,t,n))))}function pae(e,t=1){if(t!==1)throw new _e(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return El(e)}function cae(e){return V(()=>me(e,Wt(e).add(1)))}function $4(e,t,n,a){return V(()=>q3(e,t,n,a))}function hae(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 fae=["fanIn","fanOut","fanAvg"],mae=["normal","uniform","truncatedNormal"];function gae(e){ji(fae,"FanMode",e)}function yae(e){ji(mae,"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 $l(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 Aae(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: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,gae(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,yae(this.distribution),this.seed=e.seed}apply(e,t){let n=Aae(e),a=n[0],r=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,a):this.mode==="fanOut"?s/=Math.max(1,r):s/=Math.max(1,(a+r)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new _e(`${this.getClassName()} does not support dType ${t}.`);return fh(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return $l(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Dn.className="VarianceScaling";re.registerClass(Dn);var l0=class extends Dn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};l0.className="GlorotUniform";re.registerClass(l0);var u0=class extends Dn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};u0.className="GlorotNormal";re.registerClass(u0);var d0=class extends Dn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};d0.className="HeNormal";re.registerClass(d0);var p0=class extends Dn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};p0.className="HeUniform";re.registerClass(p0);var c0=class extends Dn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};c0.className="LeCunNormal";re.registerClass(c0);var h0=class extends Dn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Dn.className}};h0.className="LeCunNormal";re.registerClass(h0);var ky=class extends va{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new _e("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return V(()=>{if(e.length<2)throw new _e("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${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=i7.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 D4={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 O4(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 D4?D4[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={},O4(n)}}else return e instanceof va?e:O4(e)}function xae(){return new yy}function bae(){return new o0}function vae(e){return new Ay(e)}function wae(e){return new xy(e)}function kae(e){return new by(e)}function Iae(e){return new vy(e)}function Sae(e){return new wy(e)}function Nae(e){return new Dn(e)}function Tae(e){return new l0(e)}function Cae(e){return new u0(e)}function Eae(e){return new d0(e)}function Rae(e){return new p0(e)}function Mae(e){return new c0(e)}function Fae(e){return new h0(e)}function $ae(e){return new ky(e)}var z4={};Fe(z4,{Layer:()=>Xe,RNN:()=>ar,RNNCell:()=>ap,activation:()=>mse,add:()=>Ise,alphaDropout:()=>oie,average:()=>Sse,averagePooling1d:()=>BA,averagePooling2d:()=>VA,averagePooling3d:()=>jA,avgPool1d:()=>Dse,avgPool2d:()=>zse,avgPool3d:()=>Pse,avgPooling1d:()=>Ose,avgPooling2d:()=>_se,avgPooling3d:()=>Lse,batchNormalization:()=>Mse,bidirectional:()=>Qse,concatenate:()=>Nse,conv1d:()=>ise,conv2d:()=>ose,conv2dTranspose:()=>lse,conv3d:()=>use,conv3dTranspose:()=>dse,convLstm2d:()=>Kse,convLstm2dCell:()=>Zse,cropping2D:()=>cse,dense:()=>gse,depthwiseConv2d:()=>fse,dot:()=>Rse,dropout:()=>yse,elu:()=>ese,embedding:()=>kse,flatten:()=>xse,gaussianDropout:()=>iie,gaussianNoise:()=>sie,globalAveragePooling1d:()=>Wse,globalAveragePooling2d:()=>Bse,globalMaxPool1d:()=>tie,globalMaxPool2d:()=>nie,globalMaxPooling1d:()=>H8,globalMaxPooling2d:()=>G8,gru:()=>jse,gruCell:()=>Use,input:()=>m8,inputLayer:()=>Qre,layerNormalization:()=>Fse,leakyReLU:()=>nse,lstm:()=>Hse,lstmCell:()=>Gse,masking:()=>lie,maxPool1d:()=>aie,maxPool2d:()=>rie,maxPooling1d:()=>q8,maxPooling2d:()=>X8,maxPooling3d:()=>Vse,maximum:()=>Tse,minimum:()=>Cse,multiply:()=>Ese,permute:()=>wse,prelu:()=>ase,reLU:()=>tse,repeatVector:()=>bse,reshape:()=>vse,rnn:()=>Yse,separableConv2d:()=>pse,simpleRNN:()=>qse,simpleRNNCell:()=>Xse,softmax:()=>rse,spatialDropout1d:()=>Ase,stackedRNNCells:()=>Jse,thresholdedReLU:()=>sse,timeDistributed:()=>eie,upSampling2d:()=>hse,zeroPadding2d:()=>$se});var Dae=0;function _4(){return Dae++}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 P4="Variable",L4=class{constructor(e,t="float32",n=P4,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=_4(),n=n==null?P4:n,this.originalName=C4(n),this.name=E4(this.originalName),this.trainable_=a,this.constraint=r,this.val=V3(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Oae(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 Oae(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=_4(),s!=null&&(this.originalName=C4(s),this.name=E4(this.originalName)),this.rank=t.length}},zae=0,A0=class{constructor(e,t){this.callArgs=t,this.id=zae++,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}}},_ae=0,Xe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_ae++,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 l=Number(o),u=r.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],l=a.shape[i];if(o!=null&&l!=null&&o!==l)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 l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);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=Pae(e),i=this.computeOutputShape(s),o,l=Lae(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new Da(l,u,this,yt(e),t,this.name,d)):o=new Da(l,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),l=new L4(o,n,e,s,i);return o.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=yt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=yt(e);t=yt(t),n=yt(n),a=yt(a),r=g0(r),s=g0(s);let l=[],u=[],d=[];for(let p of o)l.push(p.sourceLayer),u.push(p.nodeIndex),d.push(p.tensorIndex);new A0({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Pae(e){e=yt(e);let t=[];for(let n of e)t.push(n.shape);return $n(t)}function Lae(e){return"float32"}function W4(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],l=a.nodeIndices[s],u=W4(i,o,l);for(let d of u)r.indexOf(d)===-1&&r.push(d)}return r}}}var tu=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}}};tu.className="InputLayer";re.registerClass(tu);function B4(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 tu({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 V4(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var j4;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(j4||(j4={}));var Wae=125,nu=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){}},U4=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)}},Bae=class extends nu{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])}))}},H4=class extends nu{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]}},G4=class extends nu{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Wae),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=Xne(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 q4(e,t){return e==null&&(e={}),e instanceof nu?[e]:Array.isArray(e)&&e[0]instanceof nu?e:yt(e).map(n=>new G4(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 X4(e,t,n,a,r,s,i,o,l){let u=new H4,d=[new Bae,...wa.createCallbacks(t)];e!=null&&d.push(...e),d.push(u);let p=new U4(d);return p.setParams({epochs:n,initialEpoch:a,samples:r,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:p,history:u}}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=Rl(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 au(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 Vae(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 jae(e,t){return V(()=>{let n=Xa(0,ye(1,B(e,t)));return Nt(qd(n),-1)})}function Uae(e,t){return V(()=>{let n=Xa(0,ye(1,B(e,t)));return Nt(n,-1)})}function Hae(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 Gae(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=Ml(uae(e)).toInt();t=Mn(t,jt(),1-jt());let r=t.shape,s=wl(a,r[r.length-1]).reshape(r);return Kd(s,t,n)})}function qae(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(qae(e,n),-1)})}function Xae(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 Kae(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:au,meanSquaredLogarithmicError:Vae,squaredHinge:jae,hinge:Uae,categoricalHinge:Hae,logcosh:Gae,categoricalCrossentropy:Kd,sparseCategoricalCrossentropy:v0,binaryCrossentropy:w0,kullbackLeiblerDivergence:Xae,poisson:Kae,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 K4(e,t){return V(()=>xa(e.equal(1),t.equal(1)).sum().cast("float32"))}function Zae(e,t){return V(()=>xa(e.equal(1),t.equal(0)).sum().cast("float32"))}function Yae(e,t){return V(()=>xa(e.equal(0),t.equal(1)).sum().cast("float32"))}function Z4(e,t){return V(()=>{let n=K4(e,t),a=Yae(e,t),r=n.add(a);return un(Wn(r,0),n.div(r),0).cast("float32")})}function Jae(e,t){return V(()=>{let n=K4(e,t),a=Zae(e,t),r=n.add(a);return un(Wn(r,0),n.div(r),0).cast("float32")})}function Y4(e,t){return w0(e,t)}function J4(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 Qae=Gi,ere=Gi,tre=b0,nre=b0,are=au,rre=au,My=Kd,sre=Ty,Q4=v0,I0={binaryAccuracy:Ey,categoricalAccuracy:Ry,precision:Z4,categoricalCrossentropy:My,sparseCategoricalCrossentropy:Q4,mse:Qae,MSE:ere,mae:tre,MAE:nre,mape:are,MAPE:rre,cosine:sre};function ire(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 ore(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 ${e}`)}var e8=1*1024*1024;function t8(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!Fy(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>e8&&console.warn(`User-defined metadata of model "${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 <= ${e8}.`)}}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 lre(e,t,n,a=console.log){let r=dre(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?pre(o[d],n,a):cre(o[d],n,i,a),a((d===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=ure(e),u=y0(e.nonTrainableWeights);a(`Total params: ${l+u}`),a(`Trainable params: ${l}`),a(`Non-trainable params: ${u}`),a("_".repeat(t))}function ure(e){let t;return e.collectedTrainableWeights!=null?t=y0(e.collectedTrainableWeights):t=y0(e.trainableWeights),t}function dre(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function N0(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function pre(e,t,n){let a;try{a=JSON.stringify(e.outputShape)}catch(o){a="multiple"}let r=e.name,s=e.getClassName(),i=[`${r} (${s})`,a,e.countParams().toString()];N0(i,t,n)}function cre(e,t,n,a){let r;try{r=JSON.stringify(e.outputShape)}catch(d){r="multiple"}let s=[];for(let d of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(d)===-1))for(let p=0;p<d.inboundLayers.length;++p){let c=d.inboundLayers[p].name,h=d.nodeIndices[p],m=d.tensorIndices[p];s.push(`${c}[${h}][${m}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],u=[`${i} (${o})`,r,e.countParams().toString(),l];N0(u,t,a);for(let d=1;d<s.length;++d)N0(["","","",s[d]],t,a)}function n8(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];n8(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 $y(e,t){if(e==null)return null;if(typeof e=="string")return wr(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];n8(t,r,s)?n.push(s):n.push($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]=$y(r,a)}return n}}var Dy="3.7.0";function hre(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ge(t,e.dtype)}catch(n){throw new U(`The dtype of the feed (${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]=hre(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: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&he(this.id2Mask)}},Oy={},a8={};function Yd(e,t,n,a){let r=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(m=>m.name),l=[],u=t.names();for(let m of o)u.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);a!=null&&(a.maxNumTensors=-Infinity,a.minNumTensors=Infinity);let d=o.join(",")+"|"+t.names().join(","),p,c;if(Oy[d]==null){let m=fre(i,t);p=m.sorted,c=m.recipientCounts,Oy[d]=p,a8[d]=c}p=Oy[d],c={},r||Object.assign(c,a8[d]);let h=new qi(t);for(let m=0;m<p.length;++m){if(a!=null){let E=Bc().numTensors;E>a.maxNumTensors&&(a.maxNumTensors=E),E<a.minNumTensors&&(a.minNumTensors=E)}let f=p[m],g=f.sourceLayer;if(g instanceof tu)continue;let y=[],A=[],x=[],v=!1;for(let E of f.inputs){let _=h.getValue(E),$=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=gre(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&&(l[_]=b[E])}r||he(x)}return h.disposeMasks(),s?l:l[0]}function fre(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=r8(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=r8(s,t);for(let l of i)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in o)a[l]==null&&(a[l]=new Set),o[l].forEach(u=>a[l].add(u))}}return{sorted:n,recipientCounts:mre(a)}}function mre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function r8(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 l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),a.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(o.name),!n.has(u.name)&&s.push(u)}}return{sorted:a,recipientMap:r}}function gre(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 tu))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)},l=[],u=[];for(let y of this.outputs)o(y,l,u);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 ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(r0);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let d=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],A=`${f.name}_${g}_${y}`,x=n[A];d.push(x)}let p=u.computeOutputShape($n(d)),c=g0(p),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;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 l=this.inputs[o],u=e[o],d=t[o];n[l.id]=[u,d]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(r0);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,p=u.inputTensors,c=u.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(u.callArgs!=null&&(m=u.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[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}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(),l=[];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: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],A=p.tensorIndices[f],x=tr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,A,h])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];a.push([i.name,u,d])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];r.push([i.name,u,d])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],A;for(let x of g){let v=x[0],b=x[1],w=x[2];if(A=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let N=r[v];if(N.inboundNodes.length<=b){i(f,g);return}let C=N.inboundNodes[b];y.push(C.outputTensors[w])}y.length>0&&f.apply($n(y),A)}function l(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 u=t.name,d=t.layers;for(let f of d)l(f);for(;!qne(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:u})}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 yre(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: ${JSON.stringify(e)}`)}function s8(e,t){return yre(e,t,"classWeight")}async function i8(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=V(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 Are(e,t){return B(e,t)}var xre=32;function o8(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=l8("input",e.inputNames,n),i=l8("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 l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function l8(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 bre(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 vre(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(u8(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=bre(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=q4(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=X4(d,p,n.epochs,null,null,wre(t,n),null,r,u);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}=o8(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 _=s8(n.classWeight,e.outputNames);for(let $=0;$<_.length;++$)N.push(await i8(b[$],null,_[$]))}let C=v.concat(b).concat(N),E=o(C);he(C);for(let _=0;_<l.length;++_){let $=l[_],S=E[_];w[$]=S,Kt(S)}await c.onBatchEnd(A,w),V4(w),A++,y++}if(a?y>=n.batchesPerEpoch:x.done){if(r){let v;u8(n.validationData)?v=yt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=yt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?xre: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 wre(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function u8(e){return typeof e.iterator=="function"}function kre(e){return typeof e.next=="function"}async function Ire(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=kre(t)?t:await t.iterator(),o=0,l=0;for(;a?l<n.batches:!0;){let u=await i.next();if(s=V(()=>{if(u.value){let{xs:d,ys:p}=o8(e,u.value),c=d.concat(p),h=V(()=>r(c));if(he(c),l===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))),l>0&&he(y)}he(h),o+=m,++l}return s}),u.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 u=0;u<s.length;++u){let d=s[u];s[u]=me(s[u],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 ${e}`)}function Jd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Hi(a,t,n-t)):Hi(e,t,n-t)}function _y(e,t){return V(()=>e==null?null:Array.isArray(e)?e.map(n=>_y(n,t)):F4(e,t.dtype==="int32"?t:t.toInt()))}function Py(e,t){let n=[],a=0,r=null;for(;a<e;)r=a+t,r>=e&&(r=e),n.push([a,r]),a=r;return n}async function Sre(e,t,n,a,r,s,i,o,l,u,d,p,c,h,m){r==null&&(r=32),s==null&&(s=1),d==null&&(d=!0),c==null&&(c=0);let f=!1;if(l!=null&&u!=null&&(f=!0),m!=null&&(f=!0,h==null))throw new U("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),y;g!=null&&(y=Fa(0,g)),i==null&&(i=1);let{callbackList:A,history:x}=X4(o,i,s,c,g,h,r,f,p);A.setModel(e),e.history=x,await A.onTrainBegin(),e.stopTraining_=!1;for(let v=c;v<s;++v){await A.onEpochBegin(v);let b={};if(h!=null)throw new _e("stepsPerEpoch mode is not implemented yet.");{if(d==="batch")throw new _e("batch shuffling is not implemneted yet");d&&k.shuffle(y);let w=Dt(y),N=Py(g,r);for(let C=0;C<N.length;++C){let E={};if(await A.onBatchBegin(C,E),V(()=>{let _=N[C][0],$=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(l,u,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),V4(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 Nre(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,l,u,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);l=N[0],u=N[1],f=l.concat(u)}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];l=Jd(r,w,N),r=Jd(r,0,w),u=Jd(s,w,N),s=Jd(s,0,w),f=l.concat(u)}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=q4(a.callbacks,a.yieldEvery);return await Sre(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(l,i),Xi(u,o),d!=null&&he(d)}}function d8(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 Tre(e){return e instanceof Be}function Ly(e){return Array.isArray(e)}function p8(e){return!Tre(e)&&!Ly(e)}function c8(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(p8(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 ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(p8(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new U(`No data provided for "${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=d8(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 l=0;l<n[i].length;++l){if(l===0&&!a)continue;let u=o.shape[l],d=n[i][l];if(d!=null&&d>=0&&u!==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 Cre(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 Ere(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 l=s.shape.slice(1),u=o.slice(1);for(let d=0;d<l.length;++d){let p=l[d],c=u[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 h8(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 l=0;l<n[i].length;++l){if(l===0&&!a)continue;let u=o.shape[l],d=n[i][l];if(d!=null&&d!==u)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 Rre(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: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var Mre="layers-model",kr=class extends tr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new U("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");lre(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=ore(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof xr))throw new U("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new U(`Unknown entry in loss dictionary: "${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=Rre(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 l="",u,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=Y4):this.lossFunctions[s]===v0?["accuracy","acc"].indexOf(c)!==-1?d=J4:["crossentropy","ce"].indexOf(c)!==-1&&(d=Q4):["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,u=l+f}else p=ire(c),u=l+S0(c);let h;Ui(u,()=>{h=p}),r(s,u,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,l=this.testLoop(o,i,a,n.verbose,n.steps);return $n(l)}finally{Xi(s[0],e),Xi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),Ire(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 l=e[o.name];if(l==null)throw new U(`No value is provided for the model's input ${o.name}`);s.add(o,l)}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],l=r[i][1],u=Jd(e,o,l),d=[];if(Array.isArray(u))for(let c=0;c<u.length;++c)d.push({key:this.inputs[c],value:u[c]});else d.push({key:this.inputs[0],value:u});let p=new qi(d);return Yd(this.outputs,p)}).forEach((o,l)=>s[l].push(o));return $n(s.map(i=>lt(i,0)))})}predict(e,t={}){let n=d8(e);h8(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){h8(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=c8(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=c8(t,this.feedOutputNames,r,!1,"target"),Cre(e,t,null),Ere(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: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,a,r=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,r,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(a!=null){let u=s8(a,this.outputNames);l=[];for(let d=0;d<u.length;++d)l.push(await i8(o[d],null,u[d]))}return[i,o,l]}testLoop(e,t,n,a=0,r){return V(()=>{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new _e("Verbose mode is not implemented yet.");if(r!=null)throw new _e("steps mode in testLoop() is not implemented yet");{let o=Py(s,n),l=Dt(Fa(0,s));for(let u=0;u<o.length;++u){let d=o[u][0],p=o[u][1],c=Hi(l,d,p-d),h=_y(t,c),m=e(h);if(u===0)for(let f=0;f<m.length;++f)i.push(ke(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=ie(i[f],B(p-d,g))}}for(let u=0;u<i.length;++u)i[u]=me(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;b4(e,a)>1&&(r+=`_${b4(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 u=[];for(let h=0;h<this.inputs.length;++h)u.push({key:this.inputs[h],value:n[h]});let d=new qi(u),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=Are(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(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,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 l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new qi(s),o=Yd(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Nt(u(r[l],o[l]));l===0?n=d:n=ie(n,d),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],p=Nt(u(r[d],o[d]));t.push(p)}return t})}async fit(e,t,n={}){return Nre(this,e,t,n)}async fitDataset(e,t){return vre(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 l=await o.data();i.push(l[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:Mre,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:l}=await En.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=En.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;t8(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){t8(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};kr.className="Model";re.registerClass(kr);var f8=class extends kr{};f8.className="Functional";re.registerClass(f8);async function Fre(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 $re(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Dre(e,void 0,t)}async function Dre(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),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),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:u,optimizerWeights:d}=Ore(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),he(u),he(d.map(p=>p.tensor))}return o}function Ore(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 ru=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 ru||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=B4({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=W4(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 ru))throw new _e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Oa(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};ru.className="Sequential";re.registerClass(ru);function zre(e){return new kr(e)}function _re(e){return new ru(e)}function Pre(e,t){return t==null&&(t={}),$re(e,t)}function m8(e){return B4(e)}function Lre(e,t){wa.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},g8=class extends On{apply(e,t=1){return pae(e,t)}};g8.className="elu";re.registerClass(g8);var y8=class extends On{apply(e){return oh(e)}};y8.className="selu";re.registerClass(y8);var A8=class extends On{apply(e){return Ka(e)}};A8.className="relu";re.registerClass(A8);var x8=class extends On{apply(e){return V(()=>Fl(6,Ka(e)))}};x8.className="relu6";re.registerClass(x8);var b8=class extends On{apply(e){return e}};b8.className="linear";re.registerClass(b8);var v8=class extends On{apply(e){return Rn(e)}};v8.className="sigmoid";re.registerClass(v8);var w8=class extends On{apply(e){return hae(e)}};w8.className="hardSigmoid";re.registerClass(w8);var k8=class extends On{apply(e){return Ci(e)}};k8.className="softplus";re.registerClass(k8);var I8=class extends On{apply(e){return cae(e)}};I8.className="softsign";re.registerClass(I8);var S8=class extends On{apply(e){return Si(e)}};S8.className="tanh";re.registerClass(S8);var Wy=class extends On{apply(e,t=-1){return xd(e,t)}};Wy.className="softmax";re.registerClass(Wy);var N8=class extends On{apply(e,t=-1){return eh(e,t)}};N8.className="logSoftmax";re.registerClass(N8);var T8=class extends On{apply(e,t=1){return V(()=>Rn(e.mul(t)).mul(e))}};T8.className="swish";re.registerClass(T8);var C8=class extends On{apply(e){return V(()=>B(e,Si(Ci(e))))}};C8.className="mish";re.registerClass(C8);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: ${e}`)}var E8=class extends re.Serializable{},Qd=class extends E8{constructor(e){super();Vy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=$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 Wre(e){return Vy(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function Bre(e){return Vy(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var R8={l1l2:"L1L2"};function dt(e){return sy(e)}function M8(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 R8?R8[e]:e,config:{}};return M8(t)}else return e instanceof E8?e:M8(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 El(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 su(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(!oae(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 F8(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function Vre(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 $8(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 l=Ky(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Kr.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Qe(l,[0,3,1,2])),l})}function jre(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 ${e.rank}.`);let o=F8(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=L1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=$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=su(t.kernelSize,e,"kernelSize"),this.strides=su(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=su(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=w4(this.activation.getClassName());if(r!=null&&this.rank===2)n=$8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Vre(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=$8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=jre(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],l=a[i],u=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=nr(o,p,u,this.padding),m=nr(l,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],l=this.strides[1];return t[n]=this.filters,t[a]=nr(t[a],o,s,this.padding),t[r]=nr(t[r],l,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 l=a[o],u=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(l,m,p,this.padding),A=nr(u,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=I3(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=$a(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=nr(t[a],u,i,this.padding),t[r]=nr(t[r],d,o,this.padding),t[s]=nr(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jy.className="Conv3DTranspose";re.registerClass(Jy);var D8=class extends ep{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${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}};D8.className="SeparableConv";var Qy=class extends D8{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,rae(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 Ure(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=Cl(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=Ure(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 O8(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 z8(e,t,n,a=!1,r,s,i=!1,o=!1){return V(()=>{let l=t.shape.length;if(l<3)throw new U(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Fa(2,l));if(t=Qe(t,u),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===l-1&&(r=mn(r,-1)),r=Qe(r,u)),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=O8(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),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,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 ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=z8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return V(()=>{let t=$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=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([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=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([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 ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let u=er(e,this.kernel.read());this.useBias&&(u=$a(u,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(u,3,u.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);l=this.activation.apply(ie(g,x));let v=ie(B(i,a),B(ie(1,St(i)),l));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=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([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 l=r.apply([s]),u=new o0().apply([s]),d=r.apply([s*2]);return M4(M4(l,u),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 ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),p=ie(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=$a(p,this.bias.read()));let[c,h,m,f]=Zt(p,4,p.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=ie(B(l,r),B(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=B(d,this.activation.apply(u));return[g,g,u]})}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=()=>$4(t(),n),i=()=>Xd(s,t,a);return!r||r<=1?Kt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Kt(o.clone()))}var Hre=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},_8=class extends ar{constructor(e){if(e.unroll)throw new _e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new _e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new zt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$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",l=e[o?3:2],u=e[o?4:3],d=za(l,a[0],r,s[0],i[0]),p=za(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};_8.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=su(n,2,"kernelSize"),this.kernelSize.forEach(o=>Jt(o,"kernelSize")),this.strides=su(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=su(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 l=this.biasInitializer,u=this.filters;o=new(t=class extends va{apply(d,p){let c=l.apply([u]),h=jn([u]),m=l.apply([u*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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(K,ne,Q)=>!ne||!ne[Q]?K:B(ne[Q],K),u=l(a,o,0),d=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,v,b,w]=Zt(this.kernel.read(),i,A),[N,C,E,_]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,v,C,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,w,_,this.padding);let[$,S,z,O]=Zt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let W=this.recurrentActivation.apply(ie(u,m)),G=this.recurrentActivation.apply(ie(d,f)),H=ie(B(G,s),B(W,this.activation.apply(ie(p,g)))),J=B(this.recurrentActivation.apply(ie(c,y)),this.activation.apply(H));return[J,J,H]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Hre(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=mr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$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 _8{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(()=>$4(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=w4(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 dae(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),lae(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 l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new U("Can only specifiy one unknown dimension.");else r*=l}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")),F4(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: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&es(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ns(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Gd(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],p=u.slice(1).concat([d]),c=o.reshape([d].concat(ts(u.slice(1))));c=Qe(c,[1,0]),c=c.reshape(p),n.push(c),r=!0}else if(l>1){let u=Fa(1,l).concat([0]);n.push(Qe(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=Qe(s.reshape([-1,u]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Fa(0,i-1));s=Qe(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=es(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${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=Fl(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 Gre(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 l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=o.squeeze(u)}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])),Gre(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($l(n),this.rate);o=Hd(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>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=y3(e,t,n,a,r,s);else if(e.rank===3)i=A3(e,t,n,a,r,s);else if(e.rank===4)i=x3(e,t,n,a,r,s);else throw new _e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function qre(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 Xre(e,t,n,a,r=.001){return V(()=>{let s=nh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Fa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=i.reshape(l),d=o.reshape(l),p=t==null?null:t.reshape(l),c=n==null?null:n.reshape(l);return[ip(e,u,d,c,p,r),i,o]})}function Kre(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Fa(0,e.rank-1))?qre(e,t,n,a,r):Xre(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 l=Bi(1,s);l[o]=r[o];let u=i.slice();u.sort();let d=!k.arraysEqual(u,Fa(0,s).slice(0,s-1)),p=()=>{if(d){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),A=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):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]=Kre(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),l=Bi(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,d=u(this.gamma.read()),p=u(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 Zre(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(()=>Zre(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),N4(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 P8(e,t,n,a,r,s){return V(()=>{Ft(r),N4(s),ca(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ra()),s==null&&(s="max"),e=F8(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 L8=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 L8{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 L8{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 W8=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 W8{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 W8{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 B8=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 B8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),P8(e,t,n,a,r,"max")}};$A.className="MaxPooling3D";re.registerClass($A);var DA=class extends B8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),P8(e,t,n,a,r,"avg")}};DA.className="AveragePooling3D";re.registerClass(DA);var V8=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 V8{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 V8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Le(e);return Vn(n,1)})}};zA.className="GlobalMaxPooling1D";re.registerClass(zA);var j8=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 j8{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 j8{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 U8=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 U8{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),z8((n,a)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};LA.className="TimeDistributed";re.registerClass(LA);function Yre(e){ji(aae,"BidirectionalMergeMode",e)}var Jre="concat",WA=class extends U8{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?Jre:e.mergeMode,Yre(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=O8(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 l=n.length;if(l%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 u=n.map(d=>new zt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new _e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Da;for(let l of s)if(l 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 l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let p=super.apply(l,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),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}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 Qre(e){return new tu(e)}function ese(e){return new Gy(e)}function tse(e){return new jy(e)}function nse(e){return new Uy(e)}function ase(e){return new Hy(e)}function rse(e){return new Xy(e)}function sse(e){return new qy(e)}function ise(e){return new T0(e)}function ose(e){return new tp(e)}function lse(e){return new Yy(e)}function use(e){return new np(e)}function dse(e){return new Jy(e)}function pse(e){return new Qy(e)}function cse(e){return new eA(e)}function hse(e){return new tA(e)}function fse(e){return new nA(e)}function mse(e){return new dA(e)}function gse(e){return new lA(e)}function yse(e){return new F0(e)}function Ase(e){return new oA(e)}function xse(e){return new uA(e)}function bse(e){return new pA(e)}function vse(e){return new cA(e)}function wse(e){return new hA(e)}function kse(e){return new mA(e)}function Ise(e){return new gA(e)}function Sse(e){return new AA(e)}function Nse(e){return new vA(e)}function Tse(e){return new xA(e)}function Cse(e){return new bA(e)}function Ese(e){return new yA(e)}function Rse(e){return new wA(e)}function Mse(e){return new NA(e)}function Fse(e){return new TA(e)}function $se(e){return new CA(e)}function BA(e){return new RA(e)}function Dse(e){return BA(e)}function Ose(e){return BA(e)}function VA(e){return new FA(e)}function zse(e){return VA(e)}function _se(e){return VA(e)}function jA(e){return new DA(e)}function Pse(e){return jA(e)}function Lse(e){return jA(e)}function Wse(e){return new OA(e)}function Bse(e){return new _A(e)}function H8(e){return new zA(e)}function G8(e){return new PA(e)}function q8(e){return new EA(e)}function X8(e){return new MA(e)}function Vse(e){return new $A(e)}function jse(e){return new rA(e)}function Use(e){return new E0(e)}function Hse(e){return new sA(e)}function Gse(e){return new rp(e)}function qse(e){return new aA(e)}function Xse(e){return new C0(e)}function Kse(e){return new iA(e)}function Zse(e){return new M0(e)}function Yse(e){return new ar(e)}function Jse(e){return new R0(e)}function Qse(e){return new WA(e)}function eie(e){return new LA(e)}var tie=H8,nie=G8,aie=q8,rie=X8;function sie(e){return new kA(e)}function iie(e){return new IA(e)}function oie(e){return new SA(e)}function lie(e){return new fA(e)}var K8={};Fe(K8,{MAPE:()=>xie,MSE:()=>wie,binaryAccuracy:()=>uie,binaryCrossentropy:()=>die,categoricalAccuracy:()=>cie,categoricalCrossentropy:()=>hie,cosineProximity:()=>gie,mape:()=>bie,meanAbsoluteError:()=>yie,meanAbsolutePercentageError:()=>Aie,meanSquaredError:()=>vie,mse:()=>kie,precision:()=>fie,recall:()=>mie,sparseCategoricalAccuracy:()=>pie});function uie(e,t){return Ey(e,t)}function die(e,t){return Y4(e,t)}function pie(e,t){return J4(e,t)}function cie(e,t){return Ry(e,t)}function hie(e,t){return My(e,t)}function fie(e,t){return Z4(e,t)}function mie(e,t){return Jae(e,t)}function gie(e,t){return Ty(e,t)}function yie(e,t){return b0(e,t)}function Aie(e,t){return au(e,t)}function xie(e,t){return au(e,t)}function bie(e,t){return au(e,t)}function vie(e,t){return Gi(e,t)}function wie(e,t){return Gi(e,t)}function kie(e,t){return Gi(e,t)}var Z8={};Fe(Z8,{modelFromJSON:()=>Fre});var Y8={};Fe(Y8,{l1:()=>Sie,l1l2:()=>Iie,l2:()=>Nie});function Iie(e){return new Qd(e)}function Sie(e){return Wre(e)}function Nie(e){return Bre(e)}var J8=class extends nu{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 Q8(e,t){return e>t}var ek=class extends J8{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=Q8:this.monitor.indexOf("acc")!==-1?this.monitorFunc=Q8: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 Tie(e){return new ek(e)}var Cie={earlyStopping:Tie},_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 tk;(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={}))})(tk||(tk={}));var UA={};function Eie(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};UA[e]=n}function nk(e){return UA[e]}function Rie(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,l=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,l).map(p=>kn(p,n,a,r));let u=kn(t.inputNames.slice(o)[0],n,a,r),d=u.dataSync();return s.type==="number"?d[0]:k.toNestedArray(u.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 Mie(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 ak={};Fe(ak,{json:()=>Fie});var Fie=[{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}]}],rk={};Fe(rk,{json:()=>$ie});var $ie=[{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}]}],sk={};Fe(sk,{json:()=>Die});var Die=[{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"}]}],ik={};Fe(ik,{json:()=>Oie});var Oie=[{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"}]}],ok={};Fe(ok,{json:()=>zie});var zie=[{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"}]}],lk={};Fe(lk,{json:()=>_ie});var _ie=[{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}]}],uk={};Fe(uk,{json:()=>Pie});var Pie=[{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"}]}],dk={};Fe(dk,{json:()=>Lie});var Lie=[{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"}]}],pk={};Fe(pk,{json:()=>Wie});var Wie=[{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"}]}],ck={};Fe(ck,{json:()=>Bie});var Bie=[{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"}]}],hk={};Fe(hk,{json:()=>Vie});var Vie=[{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}]}],fk={};Fe(fk,{json:()=>jie});var jie=[{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"}]}],mk={};Fe(mk,{json:()=>Uie});var Uie=[{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}]}],gk={};Fe(gk,{json:()=>Hie});var Hie=[{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"}]}],yk={};Fe(yk,{json:()=>Gie});var Gie=[{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}]}],Ak={};Fe(Ak,{json:()=>qie});var qie=[{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"}]}],xk={};Fe(xk,{json:()=>Xie});var Xie=[{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}]}],bk={};Fe(bk,{json:()=>Kie});var Kie=[{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"}]}],vk={};Fe(vk,{json:()=>Zie});var Zie=[{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:[]}],wk=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[ak,rk,sk,ik,ok,lk,uk,dk,pk,ck,hk,fk,mk,gk,yk,Ak,xk,bk,vk],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=[],l=[],u={},d={};t!=null&&(u=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&&l.push(f)}):Object.keys(d).forEach(m=>{let[f]=Ir(m),g=i[f];g!=null&&(g.signatureKey=d[m],l.push(g))}),Object.keys(u).length>0?Object.keys(u).forEach(m=>{let[f]=Ir(m),g=i[f];g&&(g.signatureKey=u[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:l,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=nk(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=Ik(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Ik(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return a[r.name]={value:i,type:s},a},{})),n}mapFunction(e){let t=e.nodeDef,n=[],a=[],r={};t!=null&&(r=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&a.push(u[d.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[d]=Ir(u.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:XA(u.type),type:"dtype"}},children:[]};p.signatureKey=u.name,s.push(p),r[d]=p}),Object.keys(r).forEach(u=>{let d=r[u];d.inputNames.forEach((p,c)=>{let[h,,m]=Ir(p),f=r[h];if(f.outputs!=null){let g=f.outputs.indexOf(m);if(g!==-1){let y=`${h}:${g}`;d.inputNames[c]=y}}d.inputs.push(f),f.children.push(d)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[d,p]=Ir(o[u.name]),c=r[d];c!=null&&(c.defaultOutput=p,i.push(c))});let l=this.mapArgsToSignature(e);return{nodes:r,inputs:s,outputs:i,weights:a,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function Yie(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 kk(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Yie(e);return t?n:n.toLowerCase()}function HA(e,t,n,a=!1){let r=e[t];return r!=null?kk(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 Ik(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 Sk(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?Sk(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=>kk(s,a)):n}function e2(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>Sk(r)):n}function t2(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var Jie=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}},Qie=(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[Fl(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`)}},eoe=(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[El(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[Ml(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 Nk(e){return!(typeof e=="number"||e.some(t=>t<0))}function op(e,t,n){let a=n2(e,n),r=!Nk(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)}),!Nk(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 toe=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 l=o===0?0:a[o-1],u=[0,l,0],d=[1,e[o],r];s[o]=q(Re(t,u,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 noe(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 aoe(e,t,n){return new lp([],e,t,n)}function roe(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,l)=>{s.setItem(o,i[l])}),s}function soe(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,l=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}),u=new lp([],n,e.dtype,t.length);for(let d=0;d<l.length;d++)u.setItem(d,l[d]);return u}var ioe=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),l=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let u=s;for(;l[0];){let d=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let p=u.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(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return u}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),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),d=new toe(u,r,a,s,l,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=roe(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=aoe(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=noe(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=soe(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Tk(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",l=a==="fusedbatchnorm",u=I("numArgs",e,t,n);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let 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 ooe=(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:l,activationFunc:u,leakyreluAlpha:d}=Tk(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:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=Tk(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:u,preluActivationWeights:l,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[Cl(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:l}=_3(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,l]}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],l=s[1],u=s[2];return[B1(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},loe=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[Rl(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[R3(a,r,s)]}case"Multinomial":{let a=I("logits",e,t,n),r=I("numSamples",e,t,n),s=I("seed",e,t,n);return[P3(a,r,s)]}case"OneHot":{let a=I("indices",e,t,n),r=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[wl(a,r,s,i)]}case"Ones":return[jn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Un(I("x",e,t,n))];case"RandomUniform":return[$l(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[Dl(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),l=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var uoe=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=a2(e,t,n),u=await De.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=a2(e,t,n),l=I("padToMaxOutputSize",e,t,n),u=await De.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[u.selectedIndices,u.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 B3(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},doe=(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`)}},poe=(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 u=I("x",e,t,n);return[Sr(u)]}case"IdentityN":return I("x",e,t,n).map(u=>Sr(u));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(u=>Dt(u.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),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},coe=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],l=a[i];Kt(l),this.tensorMap.set(o,l)}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}`)}},hoe=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 coe(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`)}},foe=(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),l=I("extrapolationValue",e,t,n);return[De.cropAndResize(a,r,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},moe=(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`)}},goe=(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[T3(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),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,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:u,activation:r,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yoe=(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`)}},Aoe=(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),l=I("reverse",e,t,n);return[Kc(I("x",e,t,n),i,o,l)]}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),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[S3(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xoe=(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),l=I("ellipsisMask",e,t,n),u=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,l,u,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(l=>{let u=k.arraysEqual(l.shape,s);if(!u&&!k.arraysEqual(Vt(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:q(l,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[H3(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[G3(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`)}},boe=(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`)}},voe=(e,t,n)=>{switch(e.op){case"FFT":return[bd(I("x",e,t,n))];case"IFFT":return[Ol(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`)}},woe=(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`)}},koe=(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[Nl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ck(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return V(()=>Qie(s,i,o));case"basic_math":return V(()=>eoe(s,i,o));case"control":return ioe(s,i,o);case"convolution":return V(()=>ooe(s,i,o));case"creation":return V(()=>loe(s,i,o));case"dynamic":return uoe(s,i,o);case"evaluation":return V(()=>doe(s,i,o));case"image":return V(()=>foe(s,i,o));case"graph":return V(()=>poe(s,i,o));case"logical":return V(()=>moe(s,i,o));case"matrices":return V(()=>goe(s,i,o));case"normalization":return V(()=>yoe(s,i,o));case"reduction":return V(()=>Aoe(s,i,o));case"slice_join":return V(()=>xoe(s,i,o));case"sparse":return V(()=>boe(s,i,o));case"spectral":return V(()=>voe(s,i,o));case"string":return V(()=>woe(s,i,o));case"transformation":return V(()=>koe(s,i,o));case"hash_table":return hoe(s,i,o,a);case"custom":let l=nk(s.op);if(l&&l.customExecutor)return l.customExecutor(new Jie(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 Ek=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 Rk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=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((Mk(c)||Coe(c)||Eoe(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&&u.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ioe(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 l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return u}var Soe=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Noe=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Toe=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Mk(e){return Soe.indexOf(e.op)>=0}function Coe(e){return Noe.indexOf(e.op)>=0}function Eoe(e){return Toe.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=Rk(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 [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return Ioe(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 l={},u={};return V(()=>{let d=new Ek(this.weightMap,l,u,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=Ck(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 l=Mie(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let d=i[u.id];d===1?(u.dispose(),delete i[u.id]):d!=null&&i[u.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 Ek(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>kn(p,i,s)),l=o.map(p=>p.id),u=Object.keys(e).map(p=>e[p].id),d=new Set([...l,...u,...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:l,missingInputs:u,dynamicNode:d,syncInputs:p}=Rk(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,l);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=>!Mk(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: [${u}]. ${A}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&I("isConstant",d.node,a,n)&&([p]=Ir(d.node.name,n)),a[d.node.name]==null){let c=Ck(d.node,a,n,this._resourceManager);p||([p]=Ir(d.node.name,n));let h=n.currentContext;k.isPromise(c)?u.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l))}else this.processChildNodes(d.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Kn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${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 '${t}' is not found in the graph`)})}},Roe=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Moe="?tfjs-format=file",Foe="model.json",Fk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Roe}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=En.browserHTTPRequest(e,this.loadOptions);else{let t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new r2(wk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=wk.Instance.transformGraph(e.modelInitializer);this.initializer=new r2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=En.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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}${Foe}${Moe}`);let n=new Fk(e,t);return await n.load(),n}var $oe="3.7.0",$k={};Fe($k,{CSVDataset:()=>Hk,Dataset:()=>ou,FileDataSource:()=>Jk,TextLineDataset:()=>Vk,URLDataSource:()=>Qk,array:()=>nle,csv:()=>hle,func:()=>fle,generator:()=>mle,microphone:()=>yle,version_data:()=>Ale,webcam:()=>gle,zip:()=>ale});var Doe=gs(T5()),Ooe=gs(T5());function zoe(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(iu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=_0(o,t,n,a);s[i]=l}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 _oe(e,t=Ok){return Dk(e,t)}function Dk(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(iu(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=Dk(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function Ok(e){return e===null?null:iu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function zk(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 iu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Be))}function Poe(e){return e==null||Loe(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Be||k.isTypedArray(e)}function Loe(e){return e===null||typeof e!="object"&&typeof e!="function"}function Woe(e){return zoe(e,Boe)}function Boe(e){return e instanceof Be?{value:e.clone(),recurse:!1}:iu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var _k=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 _k{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 Pk(e){return new Uoe(e)}function i2(e){return new Hoe(e)}function Voe(e,t){return new Wk(e,t)}function joe(e,t=os.FAIL){return new ele(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 Joe(this,e)}filter(e){return new Zoe(this,e)}map(e){return new Yoe(this,e)}mapAsync(e){return new Lk(this,e)}serialMapAsync(e){return new Lk(this,e).serial()}flatmap(e){return new Qoe(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 Koe(this,e,t)}columnMajorBatch(e,t=!0,n=Ok){return this.rowMajorBatch(e,t).map(a=>_oe(a,n))}concatenate(e,t){return new Wk(Pk([this,e]),t)}take(e){return e<0||e==null?this:new Xoe(this,e)}skip(e){return e<0||e==null?this:new qoe(this,e)}prefetch(e){return new Bk(this,e)}shuffle(e,t){return new tle(this,e,t)}serial(){return new Goe(this)}},Uoe=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:Woe(e),done:!1}}},Hoe=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: ${e.message}`,e}}},Goe=class extends Qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},qoe=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;he(e.value)}return this.upstream.next()}},Xoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Koe=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Zoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},Yoe=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Joe=class extends Qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Lk=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},o2=class extends Qt{constructor(){super();this.outputQueue=new s2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Qoe=class extends o2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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}},Wk=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 ele=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 zk(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 ${this.count}.`);case os.SHORTEST:return{value:null,done:!0};case os.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Bk=class extends Qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new _k(t)}summary(){return`${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()}},tle=class extends Bk{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ooe.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}}},ou=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Zn(async()=>(await n.iterator()).columnMajorBatch(e,t,rle),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Zn(async()=>(await t.iterator()).filter(a=>V(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Zn(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return Zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Zn(async()=>{let a=i2(async()=>({value:await t.iterator(),done:!1}));return Voe(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=Doe.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()}};ou.MAX_BUFFER_SIZE=1e4;function Zn(e,t=null){return new class extends ou{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function nle(e){return Zn(async()=>Pk(e),e.length)}function ale(e){if(!iu(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 zk(e,a=>{if(a instanceof ou)return{value:a.iterator(),recurse:!1};if(iu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return joe(n,os.SHORTEST)},t)}function rle(e){if(e===null)return null;let t=e[0];return Poe(t)?{value:sle(e),recurse:!1}:{value:null,recurse:!0}}function sle(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 Vk=class extends ou{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"),jk=Symbol("field"),L0=Symbol("quote"),l2=Symbol("quoteafterquote"),Uk=Symbol("quoteinquote"),Hk=class extends ou{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 Vk(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],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?a[s]=l:n[s]=l}}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=jk,a=i;break}break;case jk: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=Uk;break}break;case Uk: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}},Gk=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 Gk(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)}},qk=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 qk(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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=mn(ge(e,"float32"),0),n;n=De.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return q(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Xk=class{},Kk=class extends Qt{split(e){return new ile(this,e)}},ile=class extends Kk{constructor(e,t){super();this.upstream=e,this.impl=new ole(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ole=class extends o2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},lle=class extends Qt{decodeUTF8(){return new ule(this)}},ule=class extends Kk{constructor(e){super();this.upstream=e,this.impl=new dle(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dle=class extends o2{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=gS();this.decoder=new t("utf8")}}summary(){return`${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}},Zk=class extends lle{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 ple(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=cle(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new Zk(s,t)}else throw new Error(r.statusText)}var cle=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 Yk(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Jk=class extends Xk{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Yk(this.input)&&te().get("IS_NODE")){let e=co("fs");this.input=e.readFileSync(this.input.substr(7))}return new Zk(this.input,this.options)}},Qk=class extends Xk{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Yk(this.url)?new Jk(this.url,this.fileOptions).iterator():ple(this.url,this.fileOptions)}};function hle(e,t={}){return new Hk(new Qk(e),t)}function fle(e){let t=i2(e);return Zn(async()=>t)}function mle(e){return Zn(async()=>{let t=await e();return i2(()=>t.next())})}async function gle(e,t){return qk.create(e,t)}async function yle(e){return Gk.create(e)}var Ale="3.7.0",xle={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":X7||void 0,"tfjs-backend-webgl":yw||void 0,"tfjs-backend-wasm":u4||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 e9(){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);Il(Yn.name,()=>new Kl(e),Yn.priority)}catch(e){de("error: cannot register WebGL backend:",e);return}try{yl("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 t9(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 lu(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function uu(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=lu(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=lu(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 n9=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 ble(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 ble(n)}function a9(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 vle(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(ls(e[r],vle(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=a9(t[0],t[1]),i=r9(s,r),o=a9(-t[0],-t[1]);return r9(i,o)}function s9(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 i9(e,t){return[ls(e,t[0]),ls(e,t[1])]}function o9(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 l=0;l<s;l++){let u=r*(l+.5);for(let d=0;d<i;d++){let p=r*(d+.5);for(let c=0;c<o;c++)n.push([p,u])}}}return n}var l9=6;function wle(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),l=me(i,2),u=ye(o,l),d=ie(o,l),p=B(u,n),c=B(d,n);return Tl([p,c],1)}var u9=class{constructor(t,n){this.model=t,this.anchorsData=o9(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 u=De.resizeBilinear(t,[this.inputSize,this.inputSize]).div(127.5).sub(.5),d=this.model.execute(u),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=wle(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 l=0;l<i.length;l++){let u=r[i[l]];if(u>this.config.face.detector.minConfidence){let d=Re(a,[i[l],0],[1,-1]),p=n9(d);d.dispose();let c=this.anchorsData[i[l]],h=V(()=>Re(n,[i[l],l9-1],[1,-1]).squeeze().reshape([l9,-1]));o.push({box:p,landmarks:h,anchor:c,confidence:u})}}return n.dispose(),a.dispose(),{boxes:o,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function d9(e){let t=await ct(ft(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new u9(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 kle=[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],Ile=[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],Sle=[33,133,362,263,1,78,308],fue=kle.map(e=>cp[e]),mue=Ile.map(e=>cp[e]),gue=Sle.map(e=>cp[e]);var c2=rr.leftEyeLower0,h2=rr.rightEyeLower0,du={leftBounds:[c2[0],c2[c2.length-1]],rightBounds:[h2[0],h2[h2.length-1]]},U0={count:468,mouth:13,symmetryLine:[13,rr.midwayBetweenEyes[0]]},p9={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},pu={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}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var f2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=pp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?j0(a,[0,0]):V0,l=a!==0?i.map(p=>[...i9(p,o),p[2]]):i,u=a!==0?s9(r):V0,d=[...lu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+ls(d,u[0])),Math.round(p[1]+ls(d,u[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[du.leftBounds[0]][2],a=t[du.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=B0(W0(u2([t[a],t[r]]),this.irisEnlarge)),o=pp(i),l=De.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&sa.flags.IS_BROWSER&&(l=De.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<pu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],l/this.irisSize*a[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(pu.index)}}getAdjustedIrisCoords(t,n,a){let r=t[rr[`${a}EyeUpper0`][pu.upperCenter]][2],s=t[rr[`${a}EyeLower0`][pu.lowerCenter]][2],i=(r+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=r:l===4&&(u=s),[o[0],o[1],u]})}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=t9({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),l=W0(o),u=B0(l),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,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 l,u=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:p9.symmetryLine;u=d2(i.landmarks[x],i.landmarks[v]);let b=lu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t,u,0,w);d=j0(-u,b),n.face.mesh.enabled?l=uu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):l=uu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=V0;let x=t.clone();n.face.mesh.enabled?l=uu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=uu({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:l};let[,p,c]=this.meshDetector.execute(l),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,l,du.leftBounds[0],du.leftBounds[1],!0),{box:w,boxSize:N,crop:C}=this.getEyeBox(f,l,du.rightBounds[0],du.rightBounds[1]),_=this.irisModel.predict(lt([b,C])).dataSync(),$=_.slice(0,pu.numCoordinates*3),{rawCoords:S,iris:z}=this.getEyeCoords($,x,v,!0),O=_.slice(pu.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,u,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:p9.symmetryLine;u=d2(i.landmarks[x],i.landmarks[v]);let b=lu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t.toFloat(),u,0,w);d=j0(-u,b),l=uu({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:l};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 c9(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 l=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],u=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:l,boxRaw:u,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?d9(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 h9=Zi,f9=cp;var Nle=["angry","disgust","fear","happy","sad","surprise","neutral"],Pa,G0=[],m9=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&&m9===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,l]=Zt(s,3,3);s.dispose();let u=B(i,A2[0]),d=B(o,A2[1]),p=B(l,A2[2]);i.dispose(),o.dispose(),l.dispose();let c=jc([u,d,p]);u.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:Nle[y]});m.sort((y,A)=>A.score-y.score)}h.dispose(),G0[n]=m,m9=a,r(m)})):null}var La,q0=[],g9=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 y9(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&&g9===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),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(l=await La.predict(o)),he(o),l&&(V(()=>{let d=l.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(u.gender=d[0]<=.5?"female":"male",u.genderScore=Math.min(.99,p));let c=l.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=l.find(f=>f.shape[1]===100).dataSync();u.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=l.find(f=>f.shape[1]===1024);u.descriptor=[...m.dataSync()]}),l.forEach(d=>he(d))),q0[n]=u,g9=a,i(u)})):null}var Tle=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]],l=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Cle=(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 l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[o[10],o[152],o[234],o[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),d=n(a(u[1],u[0])),p=n(a(u[3],u[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?Tle(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,l=[];e.state="run:face",n=Ke();let u=await c9(t,e.config);if(e.performance.face=Math.trunc(Ke()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let g=0;g<u.length;g++){if(e.analyze("Get Face"),!u[g].image||u[g].image.isDisposedInternal){de("Face object is disposed:",u[g].image);continue}let y=Cle(u[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?b2(u[g].image||ln([]),e.config,g,u.length):{}:(e.state="run:emotion",n=Ke(),s=e.config.face.emotion.enabled?await b2(u[g].image||ln([]),e.config,g,u.length):{},e.performance.emotion=Math.trunc(Ke()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?S2(u[g].image||ln([]),e.config,g,u.length):[]:(e.state="run:description",n=Ke(),o=e.config.face.description.enabled?await S2(u[g].image||ln([]),e.config,g,u.length):[],e.performance.embedding=Math.trunc(Ke()-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=u[g])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((h=(c=u[g])==null?void 0:c.annotations)==null?void 0:h.rightEyeIris)&&(delete u[g].annotations.leftEyeIris,delete u[g].annotations.rightEyeIris);let A=((m=u[g].annotations)==null?void 0:m.leftEyeIris)&&((f=u[g].annotations)==null?void 0:f.rightEyeIris)?Math.max(Math.abs(u[g].annotations.leftEyeIris[3][0]-u[g].annotations.leftEyeIris[1][0]),Math.abs(u[g].annotations.rightEyeIris[4][1]-u[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0;l.push({...u[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(u[g].image):null}),he(u[g].image),u[g].image&&delete u[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),l};var hp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A9=hp.length,fp=hp.reduce((e,t,n)=>(e[t]=n,e),{}),Ele=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rle=Ele.map(([e,t])=>[fp[e],fp[t]]),x9=[["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 b9(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 v9(e,[t,n],[a,r]){let s=t/a,i=n/r,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/a,u.box[2]/r,u.box[3]/a],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.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((u,d)=>o(u,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+A9)}}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 w9(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,cu=16,Mle=50**2;function k9(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)}),l=(y,A,x)=>({y:R2(Math.round(y.y/cu),0,A-1),x:R2(Math.round(y.x/cu),0,x-1)}),[u,d]=a.shape,p=l(t.position,u,d),c=o(p),m=M2(t.position,c);for(let y=0;y<i;y++){let A=l(m,u,d),x=C2(A.y,A.x,n,r);m=M2({x:A.x*cu,y:A.y*cu},{x:x.x,y:x.y})}let f=l(m,u,d),g=a.get(f.y,f.x,n);return{position:m,part:hp[n],score:g}}function Fle(e,t,n,a,r){let s=x9.map(([c,h])=>[fp[c],fp[h]]),i=s.map(([,c])=>c),o=s.map(([c])=>c),l=t.shape[2],u=i.length,d=new Array(l),p=E2(e.part,cu,n);d[e.part.id]={score:e.score,part:hp[e.part.id],position:p};for(let c=u-1;c>=0;--c){let h=i[c],m=o[c];d[h]&&!d[m]&&(d[m]=k9(c,d[h],m,t,n,r))}for(let c=0;c<u;++c){let h=o[c],m=i[c];d[h]&&!d[m]&&(d[m]=k9(c,d[h],m,t,n,a))}return d}function $le(e,t,n,a,r){let[s,i]=r.shape,o=!0,l=Math.max(n-X0,0),u=Math.min(n+X0+1,s);for(let d=l;d<u;++d){let p=Math.max(a-X0,0),c=Math.min(a+X0+1,i);for(let h=p;h<c;++h)if(r.get(d,h,e)>t){o=!1;break}if(!o)break}return o}function Dle(e,t){let[n,a,r]=t.shape,s=new T2(n*a*r,({score:i})=>i);for(let i=0;i<n;++i)for(let o=0;o<a;++o)for(let l=0;l<r;++l){let u=t.get(i,o,l);u<e||$le(l,u,i,o,t)&&s.enqueue({score:u,part:{heatmapY:i,heatmapX:o,id:l}})}return s}function I9(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?w9(n,t,s.y,s.x)<=Mle:!1})}function Ole(e,t){return t.reduce((a,{position:r,score:s},i)=>(I9(e,r,i)||(a+=s),a),0)/t.length}function S9(e,t,n,a,r,s){let i=[],o=Dle(s,t);for(;i.length<r&&!o.empty();){let l=o.dequeue(),u=E2(l.part,cu,e);if(I9(i,u,l.part.id))continue;let d=Fle(l,t,e,n,a);d=d.filter(h=>h.score>s);let p=Ole(i,d),c=b9(d);p>s&&i.push({keypoints:d,box:c,score:Math.round(100*p)/100})}return i}var Jn,zle=["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),u=Jn.execute(o,zle).map(d=>Vt(d,[0]));return u[1]=u[1].sigmoid(),u}),a=await Promise.all(n.map(i=>i.buffer()));for(let i of n)i.dispose();let r=await S9(a[0],a[1],a[2],a[3],t.body.maxDetected,t.body.minConfidence);return Jn.inputs[0].shape?v9(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 N9(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 T9(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 C9=[{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=C9.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 Tl([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]),l=this.normalizeBoxes(o);o.dispose();let u=await De.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=u.arraySync();s.dispose(),u.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(l,[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(),l.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 l of i){let u=l.box.dataSync(),d=u.slice(0,2),p=u.slice(2,4),c=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(T9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function _le(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function E9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return _le(n)}var R9=(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 Ple(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function M9(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],Ple(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=R9(t[0],t[1]),i=M9(s,r),o=R9(-t[0],-t[1]);return M9(i,o)}function F9(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 Lle=5,$9=1.65,D9=[0,5,9,13,17,1,2],Wle=0,Ble=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),Lle)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=Z0(Y0(n),$9);a.palmLandmarks=[];for(let r=0;r<D9.length;r++)a.palmLandmarks.push(t[D9[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]]),l=O2(a,[0,0]),u=o.map(h=>[...z2(h,l),h[2]]),d=F9(r),p=[...mp(n),1],c=[us(p,d[0]),us(p,d[1])];return u.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 l=n.hand.rotation?E9(o.palmLandmarks[Wle],o.palmLandmarks[Ble]):0,u=mp(o),d=[u[0]/t.shape[2],u[1]/t.shape[1]],p=n.hand.rotation&&sa.flags.IS_BROWSER?De.rotateWithOffset(t,l,0,d):t.clone(),c=O2(-l,u),h=a?this.getBoxForPalmLandmarks(o.palmLandmarks,c):o,m=N9(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,l,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 l=Z0(Y0(o),$9),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var O9={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,z9;async function P2(e,t){let n=await z9.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let u of Object.keys(O9))s[u]=O9[u].map(d=>n[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let u of i)u[0]<o[0]&&(o[0]=u[0]),u[1]<o[1]&&(o[1]=u[1]),u[0]>o[2]&&(o[2]=u[0]),u[1]>o[3]&&(o[3]=u[1]);o[2]-=o[0],o[3]-=o[1],l=[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],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:l,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 z9=new _2(t,ps),[ds,ps]}var _9=["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"],P9=["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=[],l=(i==null?void 0:i.length)===195?_9:P9,u=5;for(let g=0;g<i.length/u;g++)o.push({id:g,part:l[g],position:[Math.trunc(n.width*i[u*g+0]/255),Math.trunc(n.height*i[u*g+1]/255),Math.trunc(i[u*g+2])+0],positionRaw:[i[u*g+0]/255,i[u*g+1]/255,i[u*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[u*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[u*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,Vle=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function L9(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 jle(e,t){let[n,a]=e.shape;return V(()=>{let r=(o,l)=>ye(o,B(me(o,ke(l,"int32")),ke(l,"int32"))),s=q(e,[a*n]),i=Vn(s,0).dataSync()[0];if(i>t){let o=ki(s,0),l=r(o,n).dataSync()[0],u=me(o,ke(n,"int32")).dataSync()[0];return[l,u,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 u=De.resizeBilinear(e,[_n.inputs[0].shape[2],_n.inputs[0].shape[1]],!1);return B(u,2).sub(1)}),r;if(t.body.enabled&&(r=await _n.predict(a)),a.dispose(),r){sr.length=0;let u=r.squeeze();he(r);let d=u.unstack(2);he(u);for(let p=0;p<d.length;p++){let[c,h,m]=jle(d[p],t.body.minConfidence);Q0>t.body.minConfidence&&sr.push({score:Math.round(100*m)/100,part:Vle[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((u,d)=>d.score>u?d.score:u,0);let s=sr.map(u=>u.position[0]),i=sr.map(u=>u.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(u=>u.positionRaw[0]),l=sr.map(u=>u.positionRaw[1]);V2=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:Q0,box:B2,boxRaw:V2,keypoints:sr}])}))}var Wa,ir=[],H2=[0,0,0,0],G2=[0,0,0,0],hu=0,q2=Number.MAX_SAFE_INTEGER,Ule=["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:hu,box:H2,boxRaw:G2,keypoints:ir}]):(q2=0,new Promise(async n=>{let a=V(()=>{if(!Wa.inputs[0].shape)return null;let u=De.resizeBilinear(e,[Wa.inputs[0].shape[2],Wa.inputs[0].shape[1]],!1);return ge(u,"int32")}),r;if(t.body.enabled&&(r=await Wa.predict(a)),a.dispose(),r){ir.length=0;let u=r.arraySync();he(r);let d=u[0][0];for(let p=0;p<d.length;p++)hu=d[p][2],hu>t.body.minConfidence&&ir.push({score:Math.round(100*hu)/100,part:Ule[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])]})}hu=ir.reduce((u,d)=>d.score>u?d.score:u,0);let s=ir.map(u=>u.position[0]),i=ir.map(u=>u.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(u=>u.positionRaw[0]),l=ir.map(u=>u.positionRaw[1]);G2=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:hu,box:H2,boxRaw:G2,keypoints:ir}])}))}var fu=[{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: ${e.object.modelPath}`);!Qn||!Qn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",Qn.modelUrl)}return Qn}async function Hle(e,t,n,a){let r=0,s=[];for(let u of[1,2,4])V(()=>{var g,y;let d=u*13,p=(g=e.find(A=>A.shape[1]===d**2&&A.shape[2]===fu.length))==null?void 0:g.squeeze(),c=(y=e.find(A=>A.shape[1]===d**2&&A.shape[2]<fu.length))==null?void 0:y.squeeze(),m=c.reshape([-1,4,c.shape[1]/4]).argMax(2).arraySync(),f=p.arraySync();for(let A=0;A<p.shape[0];A++)for(let x=0;x<p.shape[1];x++){let v=f[A][x];if(v>a.object.minConfidence&&x!==61){let b=(.5+Math.trunc(A%d))/d,w=(.5+Math.trunc(A/d))/d,N=m[A].map(W=>W*(d/u/t)),[C,E]=[b-ef/u*N[0],w-ef/u*N[1]],[_,$]=[b+ef/u*N[2]-C,w+ef/u*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:fu[x].label,box:z.map(W=>Math.trunc(W)),boxRaw:S};s.push(O)}}});e.forEach(u=>he(u));let i=s.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),o=s.map(u=>u.score),l=[];if(i&&i.length>0){let u=await De.nonMaxSuppressionAsync(i,o,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);l=u.dataSync(),he(u)}return s=s.filter((u,d)=>l.includes(d)).sort((u,d)=>d.score-u.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 l=await Hle(o,Qn.inputSize,a,t);Z2=l,n(l)}))}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 Gle(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 u=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(u,d,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);u.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=fu[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 Gle(s,ea.inputSize,a,t);e5=i,n(i)}))}var W9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.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(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},B9=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 ${o<0?"up":"down"}`})}return t},V9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let a=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(a*r),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],c=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(c>.06||p>.06)&&(u=!1),c>.06&&t.push({iris:n,gesture:"looking right"}),p>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],m=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(m<.01||h<.01||m>.022||h>.022)&&(u=!1),(m<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(m>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},j9=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=[];for(let[r,s]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(s)&&a.push({name:r.toLowerCase(),position:s[0]});if(a&&a.length>0){let r=a.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=a.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${r.name} forward ${s.name} up`})}}return t};function qle(e,t,n){let a=function(o,l,u){let d=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(d,(p,c)=>(u[c]=0,p))},r=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let 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 U9(e){e||(e={});let t=0,n=null,a=!1,r=-1,s=[null,null],i=[],o=-1,l=-1,u=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===l)){if(c.width=b,o=b,c.height=w,l=w,!u){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]);u=f.createBuffer(),f.bindBuffer(f.ARRAY_BUFFER,u),f.bufferData(f.ARRAY_BUFFER,N,f.STATIC_DRAW),f.pixelStorei(f.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}f.viewport(0,0,o,l),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,l),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 qle(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/l,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/l,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/l,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 Yi(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 l=Ee.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),l.setTransform(1,0,0,1,0,0)):l.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 U9({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 u;if(wt.data){let d=[wt.height,wt.width,3];u=Lc(wt.data,d,"int32")}else if(wt instanceof ImageData)u=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),u=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);u=oa?oa.fromPixels(c):null}if(u){let d=u.toFloat();n=d.expandDims(0),u.dispose(),d.dispose()}}let a=t.filter.return?wt:null;return{tensor:n,canvas:a}}var i5={};v5(i5,{all:()=>Zle,body:()=>q9,canvas:()=>Kle,face:()=>G9,gesture:()=>H9,hand:()=>X9,object:()=>K9,options:()=>cs,person:()=>Xle});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}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function yp(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){s5(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function H9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[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 G9(e,t,n){var s,i,o,l;let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let u of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&gp(r,u.box[0],u.box[1],u.box[2],u.box[3],a);let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${nf(u.rotation.angle.roll)}\xB0 yaw:${nf(u.rotation.angle.yaw)}\xB0 pitch:${nf(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${nf(u.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(u.box[0],0),h=p*a.lineHeight+u.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,u.mesh&&u.mesh.length>0){if(a.drawPoints)for(let p of u.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=>u.mesh[h]);s5(r,c,a)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,c=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],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(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,c=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],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=u.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((l=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let p=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(p[0],p[1]);let c=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(c[0],c[1]),r.stroke()}}}}}async function q9(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,l=[];l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),yp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),l.length===4&&s5(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftFoot"),o&&l.push([o.position[0],o.position[1]]),yp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightFoot"),o&&l.push([o.position[0],o.position[1]]),yp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftPalm"),o&&l.push([o.position[0],o.position[1]]),yp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightPalm"),o&&l.push([o.position[0],o.position[1]]),yp(r,l,a)}}}}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&&(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]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,r5(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)r.beginPath(),r.strokeStyle=a.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:a.color,r.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),r.lineTo(o[l][0],o[l][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function K9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function Xle(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${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 Kle(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 Zle(e,t,n){let a=Ke(),r=Ln(cs,n);!t||!e||e instanceof HTMLCanvasElement&&(G9(e,t.face,r),q9(e,t.body,r),X9(e,t.hand,r),K9(e,t.object,r),H9(e,t.gesture,r),t.performance.draw=Math.trunc(Ke()-a))}function Z9(e,t,n,a,r){var o,l,u,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?(l=C.gestures)==null||l.push(O):O.body!==void 0&&O.body===((u=C.body)==null?void 0:u.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 $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Y9(e){var r,s,i,o,l,u,d,p,c,h,m,f,g,y,A,x,v,b,w,N,C;let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if($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=$e.face[E].rotation)==null?void 0:s.angle)==null?void 0:i.roll)||0)+(((l=(o=e.face[E].rotation)==null?void 0:o.angle)==null?void 0:l.roll)||0))/n,yaw:((n-1)*(((d=(u=$e.face[E].rotation)==null?void 0:u.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 l5(e){var m,f;let t=((m=e.tensor)==null?void 0:m.shape[1])||0,n=((f=e.tensor)==null?void 0:f.shape[2])||0;if(!e.tensor||!ha||!ha.inputs[0].shape)return null;let a=De.resizeBilinear(e.tensor,[ha.inputs[0].shape[1],ha.inputs[0].shape[2]],!1),r=a.div(255),s=ha.predict(r);he(a),he(r);let i=Vt(s,0),o;if(i.shape[2]===2){let g=i.softmax(),[y,A]=Gn(g,2),x=A.expandDims(2),v=x.expandDims(0);he(g),he(y),he(A);let b=De.cropAndResize(v,[[0,0,.5,.5]],[0],[t,n]);o=b.squeeze(0),he(b),he(x),he(v)}else o=De.resizeBilinear(i,[t,n]);if(typeof document=="undefined")return o.dataSync();let l=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");l.width=t,l.height=n,oa&&await oa.toPixels(o,l),he(o),he(i),he(s);let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");u.width=t,u.height=n;let d=u.getContext("2d");d.filter="blur(8px",await d.drawImage(l,0,0);let p=d.getImageData(0,0,t,n).data,c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");c.width=t,c.height=n;let h=c.getContext("2d");return e.canvas&&await h.drawImage(e.canvas,0,0),h.globalCompositeOperation="darken",h.filter="blur(8px)",await h.drawImage(l,0,0),h.globalCompositeOperation="source-over",h.filter="none",e.canvas=c,p}async function J9(e,t,n){var s;if(o5)return null;o5=!0,ha||await af(n);let a=Yi(e,n),r=await l5(a);if(he(a.tensor),t&&r){let i=Yi(t,n),o=i.canvas;he(i.tensor);let l=a.canvas,u=(s=l.getContext("2d"))==null?void 0:s.getImageData(0,0,l.width,l.height).data,d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(l.width,l.height):document.createElement("canvas");d.width=l.width,d.height=l.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*u[4*h+0],c.data[4*h+1]=(255-r[4*h+1])/255*c.data[4*h+1]+r[4*h+1]/255*u[4*h+1],c.data[4*h+2]=(255-r[4*h+2])/255*c.data[4*h+2]+r[4*h+2]/255*u[4*h+2],c.data[4*h+3]=(255-r[4*h+3])/255*c.data[4*h+3]+r[4*h+3]/255*u[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 Q9="2.0.0";var mu,Ap,xp,Ji,Qi,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,Ji,void 0);ra(this,Qi,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=Ke();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"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&de("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&e9();try{await this.tf.setBackend(this.config.backend)}catch(r){de("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(de("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&de(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(Ke()-a)}});this.next=t=>Y9(t||this.result);ra(this,lf,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let l=0;l<r.length/3;l++)s+=r[3*l+2];a.dispose();let i=100*(Math.max(s,pn(this,Qi))/Math.min(s,pn(this,Qi))-1);Ia(this,Qi,s);let o=i<Math.max(this.config.cacheSensitivity,pn(this,gu));return Ia(this,gu,i>10*this.config.cacheSensitivity?0:i),o});ra(this,uf,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${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(k5,t||{}),this.tf=dp,this.draw=i5,this.version=Q9,this.state="idle",Ia(this,mu,0),Ia(this,Ap,!1),Ia(this,xp,!1),Ia(this,Ji,!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=>Yi(n,this.config),this.faceTriangulation=h9,this.faceUVMap=f9,this.sysinfo=I5(),Ia(this,Qi,1)}similarity(t,n){return k2(t,n)}segmentation(t,n){return J9(t,n,this.config)}enhance(t){return I2(t)}match(t,n,a=0){return y9(t,n,a)}async load(t){this.state="load";let n=Ke();t&&(this.config=Ln(this.config,t)),pn(this,Ji)&&(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")?L9(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,Ji)&&(this.config.debug&&de("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ia(this,Ji,!1));let a=Math.trunc(Ke()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r,s;this.config=Ln(this.config,n),this.state="check";let i=pn(this,of).call(this,t);i&&(de(i,t),a({error:i}));let o=Ke();await pn(this,bp).call(this),await this.load(),r=Ke();let l=Yi(t,this.config);if(this.performance.image=Math.trunc(Ke()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ke(),await l5(l),s=Math.trunc(Ke()-r),s>0&&(this.performance.segmentation=s),l.canvas&&(l.tensor.dispose(),l=Yi(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){de("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}r=Ke(),this.config.skipFrame=await pn(this,lf).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ke()-r),this.analyze("Check Changed:");let u,d,p,c;this.config.async?(u=this.config.face.enabled?N2(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ke(),u=this.config.face.enabled?await N2(this,l.tensor):[],s=Math.trunc(Ke()-r),s>0&&(this.performance.face=s)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?U2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?K2(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ke(),this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?await F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?await W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?await U2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?await K2(l.tensor,this.config):[]),s=Math.trunc(Ke()-r),s>0&&(this.performance.body=s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(p=this.config.hand.enabled?P2(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ke(),p=this.config.hand.enabled?await P2(l.tensor,this.config):[],s=Math.trunc(Ke()-r),s>0&&(this.performance.hand=s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?Q2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?a5(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ke(),this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?await Q2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?await a5(l.tensor,this.config):[]),s=Math.trunc(Ke()-r),s>0&&(this.performance.object=s)),this.analyze("End Object:"),this.config.async&&([u,d,p,c]=await Promise.all([u,d,p,c]));let h=[];this.config.gesture.enabled&&(r=Ke(),h=[...B9(u),...W9(d),...j9(p),...V9(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ke()-r)),this.performance.total=Math.trunc(Ke()-o),this.state="idle",this.result={face:u,body:d,hand:p,gesture:h,object:c,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var m;return Z9(u,d,p,h,(m=l==null?void 0:l.tensor)==null?void 0:m.shape)}},he(l.tensor),a(this.result)})}async warmup(t){let n=Ke();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=Ke();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,Ji=new WeakMap,Qi=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
|