/*
  Human
  homepage: <https://github.com/vladmandic/human>
  author: <https://github.com/vladmandic>'
*/

var Human=(()=>{var Mc=Object.defineProperty;var AE=Object.getOwnPropertyDescriptor;var xE=Object.getOwnPropertyNames;var bE=Object.prototype.hasOwnProperty;var vE=(e,t,r)=>t in e?Mc(e,t,{enumerable:!0,configurable:!0,writable:!0,value:r}):e[t]=r;var wE=e=>Mc(e,"__esModule",{value:!0});var Qd=(e,t)=>{for(var r in t)Mc(e,r,{get:t[r],enumerable:!0})},kE=(e,t,r,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of xE(t))!bE.call(e,a)&&(r||a!=="default")&&Mc(e,a,{get:()=>t[a],enumerable:!(n=AE(t,a))||n.enumerable});return e};var IE=(e=>(t,r)=>e&&e.get(t)||(r=kE(wE({}),t,1),e&&e.set(t,r),r))(typeof WeakMap!="undefined"?new WeakMap:0);var fe=(e,t,r)=>(vE(e,typeof t!="symbol"?t+"":t,r),r),i3=(e,t,r)=>{if(!t.has(e))throw TypeError("Cannot "+r)};var ep=(e,t,r)=>(i3(e,t,"read from private field"),r?r.call(e):t.get(e)),tp=(e,t,r)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,r)},rp=(e,t,r,n)=>(i3(e,t,"write to private field"),n?n.call(e,r):t.set(e,r),r);var gAe={};Qd(gAe,{Human:()=>IN,default:()=>IN,defaults:()=>gs,env:()=>ce});function ie(...e){let t=new Date,r=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(r,"Human:",...e)}function o3(e,t){let r=e.endsWith("/")?"":"/",a=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${r}${t}`;if(!a.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${a}`);return a}var oe=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function T1(e,t,r="config",n=[]){for(let a of Object.keys(t))if(typeof t[a]=="object")T1(e[a],t[a],a,n);else{let s=e&&typeof e[a]!="undefined";s||n.push({reason:"unknown property",where:`${r}.${a} = ${t[a]}`});let i=e&&typeof e[a]==typeof t[a];s&&!i&&n.push({reason:"property type mismatch",where:`${r}.${a} = ${t[a]}`,expected:typeof e[a]})}return t.debug&&r==="config"&&n.length>0&&ie("invalid configuration",n),n}function vr(...e){let t=r=>r&&typeof r=="object";return e.reduce((r,n)=>(Object.keys(n||{}).forEach(a=>{let s=r[a],i=n[a];Array.isArray(s)&&Array.isArray(i)?r[a]=s.concat(...i):t(s)&&t(i)?r[a]=vr(s,i):r[a]=i}),r),{})}var gs={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var Ue={};Qd(Ue,{Abs:()=>Mo,Acos:()=>Tu,Acosh:()=>Nu,AdadeltaOptimizer:()=>Tm,AdagradOptimizer:()=>Nm,AdamOptimizer:()=>Em,AdamaxOptimizer:()=>Rm,Add:()=>Ha,AddN:()=>Vs,All:()=>Eu,Any:()=>Ru,ArgMax:()=>Us,ArgMin:()=>Mu,Asin:()=>Fu,Asinh:()=>$u,Atan:()=>Pu,Atan2:()=>zu,Atanh:()=>_u,AvgPool:()=>Gs,AvgPool3D:()=>Op,AvgPool3DGrad:()=>_f,AvgPoolGrad:()=>Pf,BackendWasm:()=>MC,BatchMatMul:()=>js,BatchToSpaceND:()=>Fo,Bincount:()=>zf,BroadcastArgs:()=>Of,BroadcastTo:()=>nw,Callback:()=>M4,CallbackList:()=>M7,Cast:()=>Hs,Ceil:()=>qs,ClipByValue:()=>qa,Complex:()=>Dp,ComplexAbs:()=>Lp,Concat:()=>$o,Conv2D:()=>Ks,Conv2DBackpropFilter:()=>Df,Conv2DBackpropInput:()=>Xs,Conv3D:()=>Bp,Conv3DBackpropFilterV2:()=>Lf,Conv3DBackpropInputV2:()=>Bf,Cos:()=>Zs,Cosh:()=>Ys,CropAndResize:()=>_o,Cumsum:()=>Po,CustomCallback:()=>$7,DataStorage:()=>zp,DenseBincount:()=>Wf,DepthToSpace:()=>zo,DepthwiseConv2dNative:()=>Js,DepthwiseConv2dNativeBackpropFilter:()=>Vf,DepthwiseConv2dNativeBackpropInput:()=>Uf,Diag:()=>Gf,Dilation2D:()=>Wp,Dilation2DBackpropFilter:()=>af,Dilation2DBackpropInput:()=>nf,ENV:()=>ma,EarlyStopping:()=>F4,Einsum:()=>Vp,Elu:()=>ei,EluGrad:()=>jf,Environment:()=>tw,Equal:()=>Oo,Erf:()=>Ou,Exp:()=>ti,ExpandDims:()=>Do,Expm1:()=>Lo,FFT:()=>Hf,Fill:()=>Du,FlipLeftRight:()=>Bo,Floor:()=>ri,FloorDiv:()=>ni,FromPixels:()=>kp,FusedBatchNorm:()=>ai,FusedConv2D:()=>Ns,FusedDepthwiseConv2D:()=>Es,GPGPUContext:()=>du,GatherNd:()=>Vo,GatherV2:()=>Wo,GraphModel:()=>t0,Greater:()=>Uo,GreaterEqual:()=>si,History:()=>F7,IFFT:()=>qf,Identity:()=>ii,Imag:()=>Up,InputSpec:()=>qt,IsFinite:()=>Lu,IsInf:()=>Bu,IsNan:()=>Wu,KernelBackend:()=>Su,LRN:()=>jp,LRNGrad:()=>Xf,LayerVariable:()=>T7,LayersModel:()=>Ga,LeakyRelu:()=>oi,Less:()=>Go,LessEqual:()=>jo,LinSpace:()=>Kf,Log:()=>li,Log1p:()=>Vu,LogSoftmax:()=>aw,LogicalAnd:()=>Ho,LogicalNot:()=>Uu,LogicalOr:()=>Gp,MathBackendCPU:()=>Tx,MathBackendWebGL:()=>Th,Max:()=>ui,MaxPool:()=>pi,MaxPool3D:()=>Hp,MaxPool3DGrad:()=>Yf,MaxPoolGrad:()=>Zf,MaxPoolWithArgmax:()=>Jf,Maximum:()=>di,Mean:()=>hi,Min:()=>ci,Minimum:()=>fi,MirrorPad:()=>mi,Mod:()=>Gu,MomentumOptimizer:()=>Mm,Multinomial:()=>Qf,Multiply:()=>gi,Neg:()=>qo,NonMaxSuppressionV3:()=>Xo,NonMaxSuppressionV4:()=>ju,NonMaxSuppressionV5:()=>Zo,NotEqual:()=>Ko,OP_SCOPE_SUFFIX:()=>xw,OneHot:()=>Jo,OnesLike:()=>Yo,Optimizer:()=>Ya,OptimizerConstructors:()=>As,Pack:()=>Qo,PadV2:()=>yi,Pool:()=>mR,Pow:()=>Ai,Prelu:()=>xi,Prod:()=>el,RMSPropOptimizer:()=>Fm,RNN:()=>Ja,Range:()=>Hu,Rank:()=>uw,Real:()=>qp,RealDiv:()=>Qs,Reciprocal:()=>qu,Reduction:()=>i7,Relu:()=>bi,Relu6:()=>wi,Reshape:()=>tl,ResizeBilinear:()=>vi,ResizeBilinearGrad:()=>tm,ResizeNearestNeighbor:()=>Ku,ResizeNearestNeighborGrad:()=>em,Reverse:()=>rl,RotateWithOffset:()=>gl,Round:()=>nl,Rsqrt:()=>ki,SGDOptimizer:()=>hh,ScatterNd:()=>al,Select:()=>sl,Selu:()=>Xu,Sequential:()=>Hm,Sigmoid:()=>Si,Sign:()=>Zu,Sin:()=>Ii,Sinh:()=>ol,Slice:()=>il,Softmax:()=>Ni,Softplus:()=>Yu,SpaceToBatchND:()=>ll,SparseFillEmptyRows:()=>Kp,SparseReshape:()=>Ju,SparseSegmentMean:()=>Xp,SparseSegmentSum:()=>Zp,SparseToDense:()=>Yp,SplitV:()=>ul,Sqrt:()=>Ci,Square:()=>Qu,SquaredDifference:()=>Ei,Step:()=>$i,StridedSlice:()=>dl,StringNGrams:()=>Jp,StringSplit:()=>rm,StringToHashBucketFast:()=>nm,Sub:()=>Ri,Sum:()=>Ti,SymbolicTensor:()=>sa,Tan:()=>pl,Tanh:()=>Mi,Tensor:()=>rt,TensorBuffer:()=>rr,Tile:()=>Ka,TopK:()=>hl,Transform:()=>cl,Transpose:()=>Fi,Unique:()=>am,Unpack:()=>fl,UnsortedSegmentSum:()=>Qp,Variable:()=>Cp,ZerosLike:()=>ml,_FusedMatMul:()=>Ts,abs:()=>er,acos:()=>Jw,acosh:()=>Qw,add:()=>le,addN:()=>im,all:()=>f2,any:()=>pf,argMax:()=>Cn,argMin:()=>ek,asin:()=>tk,asinh:()=>rk,atan:()=>nk,atan2:()=>ak,atanh:()=>sk,avgPool:()=>om,avgPool3d:()=>g2,backend:()=>ga,backend_util:()=>N,basicLSTMCell:()=>r$,batchNorm:()=>fu,batchNorm2d:()=>uk,batchNorm3d:()=>dk,batchNorm4d:()=>pk,batchToSpaceND:()=>lm,bincount:()=>y2,booleanMaskAsync:()=>fz,broadcastArgs:()=>hk,broadcastTo:()=>Ap,broadcast_util:()=>yl,browser:()=>$n,buffer:()=>We,callbacks:()=>WG,cast:()=>me,ceil:()=>ck,clipByValue:()=>un,clone:()=>Or,complex:()=>Rs,concat:()=>kt,concat1d:()=>fk,concat2d:()=>td,concat3d:()=>mk,concat4d:()=>gk,constraints:()=>f7,conv1d:()=>A2,conv2d:()=>Fs,conv2dTranspose:()=>b2,conv3d:()=>v2,conv3dTranspose:()=>Ak,copyRegisteredKernels:()=>xR,cos:()=>um,cosh:()=>w2,cosineWindow:()=>H2,cumsum:()=>k2,customGrad:()=>Ra,data:()=>n6,denseBincount:()=>xk,deprecationWarn:()=>p2,depthToSpace:()=>bk,depthwiseConv2d:()=>oh,deregisterOp:()=>GG,device_util:()=>nh,diag:()=>F$,dilation2d:()=>vk,disableDeprecationWarnings:()=>AF,dispose:()=>re,disposeVariables:()=>xF,div:()=>pe,divNoNan:()=>wk,dot:()=>L$,dropout:()=>Yk,einsum:()=>kk,elu:()=>lh,enableDebugMode:()=>yF,enableProdMode:()=>d2,enclosingPowerOfTwo:()=>Jk,engine:()=>kr,env:()=>J,equal:()=>Tn,erf:()=>Ik,exp:()=>Nn,expandDims:()=>Ht,expm1:()=>Sk,eye:()=>I2,fft:()=>bm,fill:()=>rd,findBackend:()=>c2,findBackendFactory:()=>kF,floor:()=>uh,floorDiv:()=>sh,forceHalfFloat:()=>I8,fused:()=>_s,gather:()=>mu,gatherND:()=>Zk,gather_util:()=>n2,getBackend:()=>pn,getGradient:()=>G1,getKernel:()=>sf,getKernelsForBackend:()=>Na,getThreadsCount:()=>zye,gpgpu_util:()=>t8,grad:()=>dP,grads:()=>pP,greater:()=>hn,greaterEqual:()=>xl,ifft:()=>Rp,imag:()=>dm,image:()=>Ie,inTopKAsync:()=>Sz,initializers:()=>A7,input:()=>q7,io:()=>Ir,irfft:()=>W2,isFinite:()=>eP,isInf:()=>rP,isNaN:()=>Ck,keep:()=>hr,kernel_impls:()=>Un,layers:()=>S7,leakyRelu:()=>pm,less:()=>S2,lessEqual:()=>bl,linalg:()=>o7,linspace:()=>Tk,loadGraphModel:()=>qj,loadLayersModel:()=>JV,localResponseNormalization:()=>Nk,log:()=>En,log1p:()=>hm,logSigmoid:()=>yP,logSoftmax:()=>C2,logSumExp:()=>$k,logicalAnd:()=>pa,logicalNot:()=>fm,logicalOr:()=>E2,logicalXor:()=>EP,losses:()=>uD,matMul:()=>Je,math:()=>$w,max:()=>fr,maxPool:()=>mm,maxPool3d:()=>R2,maxPoolWithArgmax:()=>Pk,maximum:()=>Xa,mean:()=>Bt,memory:()=>df,meshgrid:()=>_P,metrics:()=>N4,min:()=>$s,minimum:()=>dh,mirrorPad:()=>_k,mod:()=>ad,model:()=>ZV,models:()=>E4,moments:()=>gm,movingAverage:()=>yz,mul:()=>L,multiRNNCell:()=>UP,multinomial:()=>zk,neg:()=>zt,nextFrame:()=>l7,norm:()=>G2,notEqual:()=>gu,oneHot:()=>Np,ones:()=>ln,onesLike:()=>Rn,op:()=>W,outerProduct:()=>KP,pad:()=>Vn,pad1d:()=>YP,pad2d:()=>QP,pad3d:()=>t_,pad4d:()=>n_,pool:()=>l_,pow:()=>Ps,prelu:()=>Am,print:()=>Ew,prod:()=>M2,profile:()=>bF,rand:()=>c_,randomGamma:()=>y_,randomNormal:()=>Ok,randomUniform:()=>sd,range:()=>yu,ready:()=>ed,real:()=>Ep,reciprocal:()=>Dk,registerBackend:()=>Al,registerCallbackConstructor:()=>QV,registerGradient:()=>sw,registerKernel:()=>Wn,registerOp:()=>UG,regularizers:()=>R4,relu:()=>$a,relu6:()=>P2,removeBackend:()=>wF,reshape:()=>G,reverse:()=>Mn,reverse1d:()=>C_,reverse2d:()=>N_,reverse3d:()=>R_,reverse4d:()=>F_,rfft:()=>vm,round:()=>_2,rsqrt:()=>z2,scalar:()=>Se,scatterND:()=>Xk,scatter_util:()=>a2,selu:()=>O2,separableConv2d:()=>Lk,sequential:()=>YV,serialization:()=>ue,setBackend:()=>h2,setPlatform:()=>IF,setThreadsCount:()=>_ye,setWasmPath:()=>Pye,setWasmPaths:()=>lb,setWebGLContext:()=>a0,setdiff1dAsync:()=>Bk,shared:()=>r0,sigmoid:()=>Sr,sign:()=>Wk,signal:()=>lD,sin:()=>D2,sinh:()=>L2,slice:()=>Pe,slice1d:()=>xm,slice2d:()=>B2,slice3d:()=>vl,slice4d:()=>wo,slice_util:()=>_t,softmax:()=>id,softplus:()=>nd,spaceToBatchND:()=>ym,sparse:()=>up,sparseToDense:()=>j2,spectral:()=>oD,split:()=>Kt,sqrt:()=>Cr,square:()=>yt,squaredDifference:()=>V2,squeeze:()=>et,stack:()=>sr,step:()=>ph,stridedSlice:()=>Vk,string:()=>jc,sub:()=>he,sum:()=>ke,sumOutType:()=>rh,tan:()=>Uk,tanh:()=>cu,tensor:()=>ct,tensor1d:()=>St,tensor2d:()=>oa,tensor3d:()=>_w,tensor4d:()=>az,tensor5d:()=>sz,tensor6d:()=>iz,tensor_util:()=>ia,test_util:()=>Xw,tidy:()=>K,tile:()=>Dn,time:()=>vF,topk:()=>Gk,train:()=>so,transpose:()=>nt,truncatedNormal:()=>wm,unique:()=>ny,unregisterGradient:()=>AR,unregisterKernel:()=>yR,unsortedSegmentSum:()=>jk,unstack:()=>en,upcastType:()=>Dr,util:()=>w,valueAndGrad:()=>hP,valueAndGrads:()=>cP,variable:()=>Hk,variableGrads:()=>Ek,version:()=>zh,version_converter:()=>Kj,version_core:()=>u2,version_cpu:()=>Pq,version_layers:()=>yA,version_wasm:()=>Oye,version_webgl:()=>nte,webgl:()=>ate,webgl_util:()=>CI,webgpu:()=>TS,where:()=>Lr,whereAsync:()=>U2,zeros:()=>Wt,zerosLike:()=>at});var SE=Object.create,Rf=Object.defineProperty,CE=Object.getOwnPropertyDescriptor,Uv=Object.getOwnPropertyNames,TE=Object.getPrototypeOf,NE=Object.prototype.hasOwnProperty,EE=e=>Rf(e,"__esModule",{value:!0}),ir=(e,t)=>function(){return t||(0,e[Uv(e)[0]])((t={exports:{}}).exports,t),t.exports},Le=(e,t)=>{for(var r in t)Rf(e,r,{get:t[r],enumerable:!0})},RE=(e,t,r,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of Uv(t))!NE.call(e,a)&&(r||a!=="default")&&Rf(e,a,{get:()=>t[a],enumerable:!(n=CE(t,a))||n.enumerable});return e},Eo=(e,t)=>RE(EE(Rf(e!=null?SE(TE(e)):{},"default",!t&&e&&e.__esModule?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),ME=ir({"src/node_modules/long/src/long.js"(e,t){t.exports=n;var r=null;try{r=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(I){}function n(I,O,z){this.low=I|0,this.high=O|0,this.unsigned=!!z}n.prototype.__isLong__,Object.defineProperty(n.prototype,"__isLong__",{value:!0});function a(I){return(I&&I.__isLong__)===!0}n.isLong=a;var s={},i={};function o(I,O){var z,j,X;return O?(I>>>=0,(X=0<=I&&I<256)&&(j=i[I],j)?j:(z=u(I,(I|0)<0?-1:0,!0),X&&(i[I]=z),z)):(I|=0,(X=-128<=I&&I<128)&&(j=s[I],j)?j:(z=u(I,I<0?-1:0,!1),X&&(s[I]=z),z))}n.fromInt=o;function l(I,O){if(isNaN(I))return O?b:x;if(O){if(I<0)return b;if(I>=g)return R}else{if(I<=-y)return _;if(I+1>=y)return E}return I<0?l(-I,O).neg():u(I%m|0,I/m|0,O)}n.fromNumber=l;function u(I,O,z){return new n(I,O,z)}n.fromBits=u;var d=Math.pow;function h(I,O,z){if(I.length===0)throw Error("empty string");if(I==="NaN"||I==="Infinity"||I==="+Infinity"||I==="-Infinity")return x;if(typeof O=="number"?(z=O,O=!1):O=!!O,z=z||10,z<2||36<z)throw RangeError("radix");var j;if((j=I.indexOf("-"))>0)throw Error("interior hyphen");if(j===0)return h(I.substring(1),O,z).neg();for(var X=l(d(z,8)),D=x,Q=0;Q<I.length;Q+=8){var V=Math.min(8,I.length-Q),ee=parseInt(I.substring(Q,Q+V),z);if(V<8){var Y=l(d(z,V));D=D.mul(Y).add(l(ee))}else D=D.mul(X),D=D.add(l(ee))}return D.unsigned=O,D}n.fromString=h;function p(I,O){return typeof I=="number"?l(I,O):typeof I=="string"?h(I,O):u(I.low,I.high,typeof O=="boolean"?O:I.unsigned)}n.fromValue=p;var c=1<<16,f=1<<24,m=c*c,g=m*m,y=g/2,A=o(f),x=o(0);n.ZERO=x;var b=o(0,!0);n.UZERO=b;var v=o(1);n.ONE=v;var C=o(1,!0);n.UONE=C;var S=o(-1);n.NEG_ONE=S;var E=u(-1,2147483647,!1);n.MAX_VALUE=E;var R=u(-1,-1,!0);n.MAX_UNSIGNED_VALUE=R;var _=u(0,-2147483648,!1);n.MIN_VALUE=_;var M=n.prototype;M.toInt=function(){return this.unsigned?this.low>>>0:this.low},M.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},M.toString=function(I){if(I=I||10,I<2||36<I)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(_)){var O=l(I),z=this.div(O),j=z.mul(O).sub(this);return z.toString(I)+j.toInt().toString(I)}else return"-"+this.neg().toString(I);for(var X=l(d(I,6),this.unsigned),D=this,Q="";;){var V=D.div(X),ee=D.sub(V.mul(X)).toInt()>>>0,Y=ee.toString(I);if(D=V,D.isZero())return Y+Q;for(;Y.length<6;)Y="0"+Y;Q=""+Y+Q}},M.getHighBits=function(){return this.high},M.getHighBitsUnsigned=function(){return this.high>>>0},M.getLowBits=function(){return this.low},M.getLowBitsUnsigned=function(){return this.low>>>0},M.getNumBitsAbs=function(){if(this.isNegative())return this.eq(_)?64:this.neg().getNumBitsAbs();for(var I=this.high!=0?this.high:this.low,O=31;O>0&&(I&1<<O)==0;O--);return this.high!=0?O+33:O+1},M.isZero=function(){return this.high===0&&this.low===0},M.eqz=M.isZero,M.isNegative=function(){return!this.unsigned&&this.high<0},M.isPositive=function(){return this.unsigned||this.high>=0},M.isOdd=function(){return(this.low&1)===1},M.isEven=function(){return(this.low&1)===0},M.equals=function(I){return a(I)||(I=p(I)),this.unsigned!==I.unsigned&&this.high>>>31===1&&I.high>>>31===1?!1:this.high===I.high&&this.low===I.low},M.eq=M.equals,M.notEquals=function(I){return!this.eq(I)},M.neq=M.notEquals,M.ne=M.notEquals,M.lessThan=function(I){return this.comp(I)<0},M.lt=M.lessThan,M.lessThanOrEqual=function(I){return this.comp(I)<=0},M.lte=M.lessThanOrEqual,M.le=M.lessThanOrEqual,M.greaterThan=function(I){return this.comp(I)>0},M.gt=M.greaterThan,M.greaterThanOrEqual=function(I){return this.comp(I)>=0},M.gte=M.greaterThanOrEqual,M.ge=M.greaterThanOrEqual,M.compare=function(I){if(a(I)||(I=p(I)),this.eq(I))return 0;var O=this.isNegative(),z=I.isNegative();return O&&!z?-1:!O&&z?1:this.unsigned?I.high>>>0>this.high>>>0||I.high===this.high&&I.low>>>0>this.low>>>0?-1:1:this.sub(I).isNegative()?-1:1},M.comp=M.compare,M.negate=function(){return!this.unsigned&&this.eq(_)?_:this.not().add(v)},M.neg=M.negate,M.add=function(I){a(I)||(I=p(I));var O=this.high>>>16,z=this.high&65535,j=this.low>>>16,X=this.low&65535,D=I.high>>>16,Q=I.high&65535,V=I.low>>>16,ee=I.low&65535,Y=0,se=0,Z=0,ae=0;return ae+=X+ee,Z+=ae>>>16,ae&=65535,Z+=j+V,se+=Z>>>16,Z&=65535,se+=z+Q,Y+=se>>>16,se&=65535,Y+=O+D,Y&=65535,u(Z<<16|ae,Y<<16|se,this.unsigned)},M.subtract=function(I){return a(I)||(I=p(I)),this.add(I.neg())},M.sub=M.subtract,M.multiply=function(I){if(this.isZero())return x;if(a(I)||(I=p(I)),r){var O=r.mul(this.low,this.high,I.low,I.high);return u(O,r.get_high(),this.unsigned)}if(I.isZero())return x;if(this.eq(_))return I.isOdd()?_:x;if(I.eq(_))return this.isOdd()?_:x;if(this.isNegative())return I.isNegative()?this.neg().mul(I.neg()):this.neg().mul(I).neg();if(I.isNegative())return this.mul(I.neg()).neg();if(this.lt(A)&&I.lt(A))return l(this.toNumber()*I.toNumber(),this.unsigned);var z=this.high>>>16,j=this.high&65535,X=this.low>>>16,D=this.low&65535,Q=I.high>>>16,V=I.high&65535,ee=I.low>>>16,Y=I.low&65535,se=0,Z=0,ae=0,de=0;return de+=D*Y,ae+=de>>>16,de&=65535,ae+=X*Y,Z+=ae>>>16,ae&=65535,ae+=D*ee,Z+=ae>>>16,ae&=65535,Z+=j*Y,se+=Z>>>16,Z&=65535,Z+=X*ee,se+=Z>>>16,Z&=65535,Z+=D*V,se+=Z>>>16,Z&=65535,se+=z*Y+j*ee+X*V+D*Q,se&=65535,u(ae<<16|de,se<<16|Z,this.unsigned)},M.mul=M.multiply,M.divide=function(I){if(a(I)||(I=p(I)),I.isZero())throw Error("division by zero");if(r){if(!this.unsigned&&this.high===-2147483648&&I.low===-1&&I.high===-1)return this;var O=(this.unsigned?r.div_u:r.div_s)(this.low,this.high,I.low,I.high);return u(O,r.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:x;var z,j,X;if(this.unsigned){if(I.unsigned||(I=I.toUnsigned()),I.gt(this))return b;if(I.gt(this.shru(1)))return C;X=b}else{if(this.eq(_)){if(I.eq(v)||I.eq(S))return _;if(I.eq(_))return v;var D=this.shr(1);return z=D.div(I).shl(1),z.eq(x)?I.isNegative()?v:S:(j=this.sub(I.mul(z)),X=z.add(j.div(I)),X)}else if(I.eq(_))return this.unsigned?b:x;if(this.isNegative())return I.isNegative()?this.neg().div(I.neg()):this.neg().div(I).neg();if(I.isNegative())return this.div(I.neg()).neg();X=x}for(j=this;j.gte(I);){z=Math.max(1,Math.floor(j.toNumber()/I.toNumber()));for(var Q=Math.ceil(Math.log(z)/Math.LN2),V=Q<=48?1:d(2,Q-48),ee=l(z),Y=ee.mul(I);Y.isNegative()||Y.gt(j);)z-=V,ee=l(z,this.unsigned),Y=ee.mul(I);ee.isZero()&&(ee=v),X=X.add(ee),j=j.sub(Y)}return X},M.div=M.divide,M.modulo=function(I){if(a(I)||(I=p(I)),r){var O=(this.unsigned?r.rem_u:r.rem_s)(this.low,this.high,I.low,I.high);return u(O,r.get_high(),this.unsigned)}return this.sub(this.div(I).mul(I))},M.mod=M.modulo,M.rem=M.modulo,M.not=function(){return u(~this.low,~this.high,this.unsigned)},M.and=function(I){return a(I)||(I=p(I)),u(this.low&I.low,this.high&I.high,this.unsigned)},M.or=function(I){return a(I)||(I=p(I)),u(this.low|I.low,this.high|I.high,this.unsigned)},M.xor=function(I){return a(I)||(I=p(I)),u(this.low^I.low,this.high^I.high,this.unsigned)},M.shiftLeft=function(I){return a(I)&&(I=I.toInt()),(I&=63)===0?this:I<32?u(this.low<<I,this.high<<I|this.low>>>32-I,this.unsigned):u(0,this.low<<I-32,this.unsigned)},M.shl=M.shiftLeft,M.shiftRight=function(I){return a(I)&&(I=I.toInt()),(I&=63)===0?this:I<32?u(this.low>>>I|this.high<<32-I,this.high>>I,this.unsigned):u(this.high>>I-32,this.high>=0?0:-1,this.unsigned)},M.shr=M.shiftRight,M.shiftRightUnsigned=function(I){if(a(I)&&(I=I.toInt()),I&=63,I===0)return this;var O=this.high;if(I<32){var z=this.low;return u(z>>>I|O<<32-I,O>>>I,this.unsigned)}else return I===32?u(O,0,this.unsigned):u(O>>>I-32,0,this.unsigned)},M.shru=M.shiftRightUnsigned,M.shr_u=M.shiftRightUnsigned,M.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},M.toUnsigned=function(){return this.unsigned?this:u(this.low,this.high,!0)},M.toBytes=function(I){return I?this.toBytesLE():this.toBytesBE()},M.toBytesLE=function(){var I=this.high,O=this.low;return[O&255,O>>>8&255,O>>>16&255,O>>>24,I&255,I>>>8&255,I>>>16&255,I>>>24]},M.toBytesBE=function(){var I=this.high,O=this.low;return[I>>>24,I>>>16&255,I>>>8&255,I&255,O>>>24,O>>>16&255,O>>>8&255,O&255]},n.fromBytes=function(I,O,z){return z?n.fromBytesLE(I,O):n.fromBytesBE(I,O)},n.fromBytesLE=function(I,O){return new n(I[0]|I[1]<<8|I[2]<<16|I[3]<<24,I[4]|I[5]<<8|I[6]<<16|I[7]<<24,O)},n.fromBytesBE=function(I,O){return new n(I[4]<<24|I[5]<<16|I[6]<<8|I[7],I[0]<<24|I[1]<<16|I[2]<<8|I[3],O)}}}),FE=ir({"(disabled):src/node_modules/node-fetch/browser.js"(){}}),$E=ir({"(disabled):util"(){}}),PE=ir({"src/node_modules/seedrandom/lib/alea.js"(e,t){(function(r,n,a){function s(u){var d=this,h=l();d.next=function(){var p=2091639*d.s0+d.c*23283064365386963e-26;return d.s0=d.s1,d.s1=d.s2,d.s2=p-(d.c=p|0)},d.c=1,d.s0=h(" "),d.s1=h(" "),d.s2=h(" "),d.s0-=h(u),d.s0<0&&(d.s0+=1),d.s1-=h(u),d.s1<0&&(d.s1+=1),d.s2-=h(u),d.s2<0&&(d.s2+=1),h=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 h=new s(u),p=d&&d.state,c=h.next;return c.int32=function(){return h.next()*4294967296|0},c.double=function(){return c()+(c()*2097152|0)*11102230246251565e-32},c.quick=c,p&&(typeof p=="object"&&i(p,h),c.state=function(){return i(h,{})}),c}function l(){var u=4022871197,d=function(h){h=String(h);for(var p=0;p<h.length;p++){u+=h.charCodeAt(p);var c=.02519603282416938*u;u=c>>>0,c-=u,c*=u,u=c>>>0,c-=u,u+=c*4294967296}return(u>>>0)*23283064365386963e-26};return d}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),_E=ir({"src/node_modules/seedrandom/lib/xor128.js"(e,t){(function(r,n,a){function s(l){var u=this,d="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var p=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^p^p>>>8},l===(l|0)?u.x=l:d+=l;for(var h=0;h<d.length+64;h++)u.x^=d.charCodeAt(h)|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),h=u&&u.state,p=function(){return(d.next()>>>0)/4294967296};return p.double=function(){do var c=d.next()>>>11,f=(d.next()>>>0)/4294967296,m=(c+f)/(1<<21);while(m===0);return m},p.int32=d.next,p.quick=p,h&&(typeof h=="object"&&i(h,d),p.state=function(){return i(d,{})}),p}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),zE=ir({"src/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(r,n,a){function s(l){var u=this,d="";u.next=function(){var p=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(p^p<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:d+=l;for(var h=0;h<d.length+64;h++)u.x^=d.charCodeAt(h)|0,h==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),h=u&&u.state,p=function(){return(d.next()>>>0)/4294967296};return p.double=function(){do var c=d.next()>>>11,f=(d.next()>>>0)/4294967296,m=(c+f)/(1<<21);while(m===0);return m},p.int32=d.next,p.quick=p,h&&(typeof h=="object"&&i(h,d),p.state=function(){return i(d,{})}),p}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),OE=ir({"src/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(r,n,a){function s(l){var u=this;u.next=function(){var h=u.x,p=u.i,c,f,m;return c=h[p],c^=c>>>7,f=c^c<<24,c=h[p+1&7],f^=c^c>>>10,c=h[p+3&7],f^=c^c>>>3,c=h[p+4&7],f^=c^c<<7,c=h[p+7&7],c=c^c<<13,f^=c^c<<9,h[p]=f,u.i=p+1&7,f};function d(h,p){var c,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,c=0;c<p.length;++c)m[c&7]=m[c&7]<<15^p.charCodeAt(c)+m[c+1&7]<<13;for(;m.length<8;)m.push(0);for(c=0;c<8&&m[c]===0;++c);for(c==8?f=m[7]=-1:f=m[c],h.x=m,h.i=0,c=256;c>0;--c)h.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),h=u&&u.state,p=function(){return(d.next()>>>0)/4294967296};return p.double=function(){do var c=d.next()>>>11,f=(d.next()>>>0)/4294967296,m=(c+f)/(1<<21);while(m===0);return m},p.int32=d.next,p.quick=p,h&&(h.x&&i(h,d),p.state=function(){return i(d,{})}),p}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),DE=ir({"src/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(r,n,a){function s(l){var u=this;u.next=function(){var h=u.w,p=u.X,c=u.i,f,m;return u.w=h=h+1640531527|0,m=p[c+34&127],f=p[c=c+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[c]=m^f,u.i=c,m+(h^h>>>16)|0};function d(h,p){var c,f,m,g,y,A=[],x=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,x=Math.max(x,p.length)),m=0,g=-32;g<x;++g)p&&(f^=p.charCodeAt((g+32)%p.length)),g===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(y=y+1640531527|0,c=A[g&127]^=f+y,m=c==0?m+1:0);for(m>=128&&(A[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=A[m+34&127],c=A[m=m+1&127],f^=f<<13,c^=c<<17,f^=f>>>15,c^=c>>>12,A[m]=f^c;h.w=y,h.X=A,h.i=m}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),h=u&&u.state,p=function(){return(d.next()>>>0)/4294967296};return p.double=function(){do var c=d.next()>>>11,f=(d.next()>>>0)/4294967296,m=(c+f)/(1<<21);while(m===0);return m},p.int32=d.next,p.quick=p,h&&(h.X&&i(h,d),p.state=function(){return i(d,{})}),p}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),LE=ir({"src/node_modules/seedrandom/lib/tychei.js"(e,t){(function(r,n,a){function s(l){var u=this,d="";u.next=function(){var p=u.b,c=u.c,f=u.d,m=u.a;return p=p<<25^p>>>7^c,c=c-f|0,f=f<<24^f>>>8^m,m=m-p|0,u.b=p=p<<20^p>>>12^c,u.c=c=c-f|0,u.d=f<<16^c>>>16^m,u.a=m-p|0},u.a=0,u.b=0,u.c=-1640531527,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):d+=l;for(var h=0;h<d.length+20;h++)u.b^=d.charCodeAt(h)|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),h=u&&u.state,p=function(){return(d.next()>>>0)/4294967296};return p.double=function(){do var c=d.next()>>>11,f=(d.next()>>>0)/4294967296,m=(c+f)/(1<<21);while(m===0);return m},p.int32=d.next,p.quick=p,h&&(typeof h=="object"&&i(h,d),p.state=function(){return i(d,{})}),p}n&&n.exports?n.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),BE=ir({"(disabled):crypto"(){}}),WE=ir({"src/node_modules/seedrandom/seedrandom.js"(e,t){(function(r,n,a){var s=256,i=6,o=52,l="random",u=a.pow(s,i),d=a.pow(2,o),h=d*2,p=s-1,c;function f(v,C,S){var E=[];C=C==!0?{entropy:!0}:C||{};var R=A(y(C.entropy?[v,b(n)]:v==null?x():v,3),E),_=new m(E),M=function(){for(var I=_.g(i),O=u,z=0;I<d;)I=(I+z)*s,O*=s,z=_.g(1);for(;I>=h;)I/=2,O/=2,z>>>=1;return(I+z)/O};return M.int32=function(){return _.g(4)|0},M.quick=function(){return _.g(4)/4294967296},M.double=M,A(b(_.S),n),(C.pass||S||function(I,O,z,j){return j&&(j.S&&g(j,_),I.state=function(){return g(_,{})}),z?(a[l]=I,O):I})(M,R,"global"in C?C.global:this==a,C.state)}function m(v){var C,S=v.length,E=this,R=0,_=E.i=E.j=0,M=E.S=[];for(S||(v=[S++]);R<s;)M[R]=R++;for(R=0;R<s;R++)M[R]=M[_=p&_+v[R%S]+(C=M[R])],M[_]=C;(E.g=function(I){for(var O,z=0,j=E.i,X=E.j,D=E.S;I--;)O=D[j=p&j+1],z=z*s+D[p&(D[j]=D[X=p&X+O])+(D[X]=O)];return E.i=j,E.j=X,z})(s)}function g(v,C){return C.i=v.i,C.j=v.j,C.S=v.S.slice(),C}function y(v,C){var S=[],E=typeof v,R;if(C&&E=="object")for(R in v)try{S.push(y(v[R],C-1))}catch(_){}return S.length?S:E=="string"?v:v+"\0"}function A(v,C){for(var S=v+"",E,R=0;R<S.length;)C[p&R]=p&(E^=C[p&R]*19)+S.charCodeAt(R++);return b(C)}function x(){try{var v;return c&&(v=c.randomBytes)?v=v(s):(v=new Uint8Array(s),(r.crypto||r.msCrypto).getRandomValues(v)),b(v)}catch(E){var C=r.navigator,S=C&&C.plugins;return[+new Date,r,S,r.screen,b(n)]}}function b(v){return String.fromCharCode.apply(0,v)}if(A(a.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{c=BE()}catch(v){}}else typeof define=="function"&&define.amd?define(function(){return f}):a["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),Mf=ir({"src/node_modules/seedrandom/index.js"(e,t){var r=PE(),n=_E(),a=zE(),s=OE(),i=DE(),o=LE(),l=WE();l.alea=r,l.xor128=n,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}}),Gv=ir({"(disabled):src/node_modules/string_decoder/index.js"(){}}),Gy=ir({"(disabled):fs"(){}}),ef=ir({"(disabled):path"(){}}),VE=ir({"(disabled):worker_threads"(){}}),UE=ir({"(disabled):perf_hooks"(){}}),GE=ir({"(disabled):os"(){}}),jE=ir({"src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e,t){var r=(()=>{var n=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(n=n||__filename),function(a){a=a||{};function s(){return $e.buffer!=Mr&&Zn($e.buffer),Zh}function i(){return $e.buffer!=Mr&&Zn($e.buffer),Yh}function o(){return $e.buffer!=Mr&&Zn($e.buffer),Vd}function l(){return $e.buffer!=Mr&&Zn($e.buffer),Jh}function u(){return $e.buffer!=Mr&&Zn($e.buffer),Qh}function d(){return $e.buffer!=Mr&&Zn($e.buffer),ec}function h(){return $e.buffer!=Mr&&Zn($e.buffer),tc}var p=typeof a!="undefined"?a:{},c,f;p.ready=new Promise(function(T,$){c=T,f=$});var m;typeof process!="undefined"&&process.listeners&&(m={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var g=Object.assign({},p),y=[],A="./this.program",x=(T,$)=>{throw $},b=typeof window=="object",v=typeof importScripts=="function",C=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",S=p.ENVIRONMENT_IS_PTHREAD||!1,E="";function R(T){return p.locateFile?p.locateFile(T,E):E+T}var _,M,I,O;function z(T){T instanceof Yd||Y("exiting due to exception: "+T)}var j,X,D;if(C){v?E=ef().dirname(E)+"/":E=__dirname+"/",D=()=>{X||(j=Gy(),X=ef())},_=function($,U){return D(),$=X.normalize($),j.readFileSync($,U?void 0:"utf8")},I=$=>{var U=_($,!0);return U.buffer||(U=new Uint8Array(U)),U},M=($,U,te)=>{D(),$=X.normalize($),j.readFile($,function(ge,xe){ge?te(ge):U(xe.buffer)})},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),y=process.argv.slice(2),process.on("uncaughtException",function($){if(!($ instanceof Yd))throw $}),process.on("unhandledRejection",function($){throw $}),x=($,U)=>{if(Yi())throw process.exitCode=$,U;z(U),process.exit($)},p.inspect=function(){return"[Emscripten Module object]"};let T;try{T=VE()}catch($){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),$}global.Worker=T.Worker}else(b||v)&&(v?E=self.location.href:typeof document!="undefined"&&document.currentScript&&(E=document.currentScript.src),typeof n!="undefined"&&n&&(E=n),E.indexOf("blob:")!==0?E=E.substr(0,E.replace(/[?#].*/,"").lastIndexOf("/")+1):E="",C||(_=T=>{var $=new XMLHttpRequest;return $.open("GET",T,!1),$.send(null),$.responseText},v&&(I=T=>{var $=new XMLHttpRequest;return $.open("GET",T,!1),$.responseType="arraybuffer",$.send(null),new Uint8Array($.response)}),M=(T,$,U)=>{var te=new XMLHttpRequest;te.open("GET",T,!0),te.responseType="arraybuffer",te.onload=()=>{if(te.status==200||te.status==0&&te.response){$(te.response);return}U()},te.onerror=U,te.send(null)}),O=T=>document.title=T);C&&typeof performance=="undefined"&&(global.performance=UE().performance);var Q=console.log.bind(console),V=console.warn.bind(console);C&&(D(),Q=T=>j.writeSync(1,T+`
`),V=T=>j.writeSync(2,T+`
`));var ee=p.print||Q,Y=p.printErr||V;Object.assign(p,g),g=null,p.arguments&&(y=p.arguments),p.thisProgram&&(A=p.thisProgram),p.quit&&(x=p.quit);var se=4;function Z(T){Z.shown||(Z.shown={}),Z.shown[T]||(Z.shown[T]=1,Y(T))}function ae(T,$){if(typeof WebAssembly.Function=="function"){for(var U={i:"i32",j:"i64",f:"f32",d:"f64"},te={parameters:[],results:$[0]=="v"?[]:[U[$[0]]]},ge=1;ge<$.length;++ge)te.parameters.push(U[$[ge]]);return new WebAssembly.Function(te,T)}var xe=[1,0,1,96],Ce=$.slice(0,1),_e=$.slice(1),$t={i:127,j:126,f:125,d:124};xe.push(_e.length);for(var ge=0;ge<_e.length;++ge)xe.push($t[_e[ge]]);Ce=="v"?xe.push(0):xe=xe.concat([1,$t[Ce]]),xe[1]=xe.length-2;var ea=new Uint8Array([0,97,115,109,1,0,0,0].concat(xe,[2,7,1,1,101,1,102,0,0,7,5,1,1,102,0,0])),ta=new WebAssembly.Module(ea),Rc=new WebAssembly.Instance(ta,{e:{f:T}}),Jd=Rc.exports.f;return Jd}var de=[],Ae;function be(){if(de.length)return de.pop();try{vn.grow(1)}catch(T){throw T instanceof RangeError?"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.":T}return vn.length-1}function Ee(T,$){for(var U=T;U<T+$;U++){var te=Ul(U);te&&Ae.set(te,U)}}var Me=0,De=T=>{Me=T},Be=Atomics.load,Ze=Atomics.store,ot=Atomics.compareExchange,dt;p.wasmBinary&&(dt=p.wasmBinary);var pt=p.noExitRuntime||!0;typeof WebAssembly!="object"&&Bl("no native wasm support detected");var $e,vt,gt=!1,Rr;function ur(T,$){T||Bl($)}function Xr(T){var $=p["_"+T];return $}function Jt(T,$,U,te,ge){var xe={string:function(wn){var Zl=0;if(wn!=null&&wn!==0){var s3=(wn.length<<2)+1;Zl=Xl(s3),Xi(wn,Zl,s3)}return Zl},array:function(wn){var Zl=Xl(wn.length);return Da(wn,Zl),Zl}};function Ce(wn){return $==="string"?bn(wn):$==="boolean"?Boolean(wn):wn}var _e=Xr(T),$t=[],ea=0;if(te)for(var ta=0;ta<te.length;ta++){var Rc=xe[U[ta]];Rc?(ea===0&&(ea=S1()),$t[ta]=Rc(te[ta])):$t[ta]=te[ta]}var Jd=_e.apply(null,$t);function yE(wn){return ea!==0&&Cc(ea),Ce(wn)}return Jd=yE(Jd),Jd}function dr(T,$,U,te){U=U||[];var ge=U.every(function(Ce){return Ce==="number"}),xe=$!=="string";return xe&&ge&&!te?Xr(T):function(){return Jt(T,$,U,arguments,te)}}var Xn=1;function Zr(T){var $=new TextDecoder(T);this.decode=U=>(U.buffer instanceof SharedArrayBuffer&&(U=new Uint8Array(U)),$.decode.call($,U))}var Qt=typeof TextDecoder!="undefined"?new Zr("utf8"):void 0;function xn(T,$,U){for(var te=$+U,ge=$;T[ge]&&!(ge>=te);)++ge;if(ge-$>16&&T.subarray&&Qt)return Qt.decode(T.subarray($,ge));for(var xe="";$<ge;){var Ce=T[$++];if(!(Ce&128)){xe+=String.fromCharCode(Ce);continue}var _e=T[$++]&63;if((Ce&224)==192){xe+=String.fromCharCode((Ce&31)<<6|_e);continue}var $t=T[$++]&63;if((Ce&240)==224?Ce=(Ce&15)<<12|_e<<6|$t:Ce=(Ce&7)<<18|_e<<12|$t<<6|T[$++]&63,Ce<65536)xe+=String.fromCharCode(Ce);else{var ea=Ce-65536;xe+=String.fromCharCode(55296|ea>>10,56320|ea&1023)}}return xe}function bn(T,$){return T?xn(i(),T,$):""}function ps(T,$,U,te){if(!(te>0))return 0;for(var ge=U,xe=U+te-1,Ce=0;Ce<T.length;++Ce){var _e=T.charCodeAt(Ce);if(_e>=55296&&_e<=57343){var $t=T.charCodeAt(++Ce);_e=65536+((_e&1023)<<10)|$t&1023}if(_e<=127){if(U>=xe)break;$[U++]=_e}else if(_e<=2047){if(U+1>=xe)break;$[U++]=192|_e>>6,$[U++]=128|_e&63}else if(_e<=65535){if(U+2>=xe)break;$[U++]=224|_e>>12,$[U++]=128|_e>>6&63,$[U++]=128|_e&63}else{if(U+3>=xe)break;$[U++]=240|_e>>18,$[U++]=128|_e>>12&63,$[U++]=128|_e>>6&63,$[U++]=128|_e&63}}return $[U]=0,U-ge}function Xi(T,$,U){return ps(T,i(),$,U)}function Xh(T){for(var $=0,U=0;U<T.length;++U){var te=T.charCodeAt(U);te>=55296&&te<=57343&&(te=65536+((te&1023)<<10)|T.charCodeAt(++U)&1023),te<=127?++$:te<=2047?$+=2:te<=65535?$+=3:$+=4}return $}var hs=typeof TextDecoder!="undefined"?new Zr("utf-16le"):void 0;function Da(T,$){s().set(T,$)}function Wd(T,$,U){for(var te=0;te<T.length;++te)s()[$++>>0]=T.charCodeAt(te);U||(s()[$>>0]=0)}function Dl(T,$){return T%$>0&&(T+=$-T%$),T}var Mr,Zh,Yh,Vd,Jh,Qh,L5,ec,tc;S&&(Mr=p.buffer);function Zn(T){Mr=T,p.HEAP8=Zh=new Int8Array(T),p.HEAP16=Vd=new Int16Array(T),p.HEAP32=Qh=new Int32Array(T),p.HEAPU8=Yh=new Uint8Array(T),p.HEAPU16=Jh=new Uint16Array(T),p.HEAPU32=L5=new Uint32Array(T),p.HEAPF32=ec=new Float32Array(T),p.HEAPF64=tc=new Float64Array(T)}var rc=p.INITIAL_MEMORY||16777216;if(S)$e=p.wasmMemory,Mr=p.buffer;else if(p.wasmMemory)$e=p.wasmMemory;else if($e=new WebAssembly.Memory({initial:rc/65536,maximum:32768,shared:!0}),!($e.buffer instanceof SharedArrayBuffer))throw Y("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"),C&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");$e&&(Mr=$e.buffer),rc=Mr.byteLength,Zn(Mr);var vn,Ll=[],cs=[],Y0=[],nc=[],Zi=!1,J0=!1,ac=0;function Yi(){return pt||ac>0}function Fr(){if(p.preRun)for(typeof p.preRun=="function"&&(p.preRun=[p.preRun]);p.preRun.length;)B5(p.preRun.shift());uc(Ll)}function Ud(){Zi=!0,!S&&uc(cs)}function Q0(){S||(ze.terminateAllThreads(),J0=!0)}function eg(){if(!S){if(p.postRun)for(typeof p.postRun=="function"&&(p.postRun=[p.postRun]);p.postRun.length;)Gd(p.postRun.shift());uc(nc)}}function B5(T){Ll.unshift(T)}function W5(T){cs.unshift(T)}function Gd(T){nc.unshift(T)}var fs=0,sc=null,Yn=null;function jd(T){fs++,p.monitorRunDependencies&&p.monitorRunDependencies(fs)}function V5(T){if(fs--,p.monitorRunDependencies&&p.monitorRunDependencies(fs),fs==0&&(sc!==null&&(clearInterval(sc),sc=null),Yn)){var $=Yn;Yn=null,$()}}p.preloadedImages={},p.preloadedAudios={};function Bl(T){S?postMessage({cmd:"onAbort",arg:T}):p.onAbort&&p.onAbort(T),T="Aborted("+T+")",Y(T),gt=!0,Rr=1,T+=". Build with -s ASSERTIONS=1 for more info.";var $=new WebAssembly.RuntimeError(T);throw f($),$}var tg="data:application/octet-stream;base64,";function ic(T){return T.startsWith(tg)}function oc(T){return T.startsWith("file://")}var $r;$r="tfjs-backend-wasm-threaded-simd.wasm",ic($r)||($r=R($r));function lc(T){try{if(T==$r&&dt)return new Uint8Array(dt);if(I)return I(T);throw"both async and sync fetching of the wasm failed"}catch($){Bl($)}}function Wl(){if(!dt&&(b||v)){if(typeof fetch=="function"&&!oc($r))return fetch($r,{credentials:"same-origin"}).then(function(T){if(!T.ok)throw"failed to load wasm binary file at '"+$r+"'";return T.arrayBuffer()}).catch(function(){return lc($r)});if(M)return new Promise(function(T,$){M($r,function(U){T(new Uint8Array(U))},$)})}return Promise.resolve().then(function(){return lc($r)})}function rg(){var T={env:vc,wasi_snapshot_preview1:vc};function $(Ce,_e){var $t=Ce.exports;if(p.asm=$t,ug(p.asm.emscripten_tls_init),vn=p.asm.__indirect_function_table,W5(p.asm.__wasm_call_ctors),vt=_e,!S){var ea=ze.unusedWorkers.length;ze.unusedWorkers.forEach(function(ta){ze.loadWasmModuleToWorker(ta,function(){--ea||V5("wasm-instantiate")})})}}S||jd("wasm-instantiate");function U(Ce){$(Ce.instance,Ce.module)}function te(Ce){return Wl().then(function(_e){return WebAssembly.instantiate(_e,T)}).then(function(_e){return _e}).then(Ce,function(_e){Y("failed to asynchronously prepare wasm: "+_e),Bl(_e)})}function ge(){return!dt&&typeof WebAssembly.instantiateStreaming=="function"&&!ic($r)&&!oc($r)&&typeof fetch=="function"?fetch($r,{credentials:"same-origin"}).then(function(Ce){var _e=WebAssembly.instantiateStreaming(Ce,T);return _e.then(U,function($t){return Y("wasm streaming compile failed: "+$t),Y("falling back to ArrayBuffer instantiation"),te(U)})}):te(U)}if(p.instantiateWasm)try{var xe=p.instantiateWasm(T,$);return xe}catch(Ce){return Y("Module.instantiateWasm callback failed with error: "+Ce),!1}return ge().catch(f),{}}var U5,G5,ng={};function uc(T){for(;T.length>0;){var $=T.shift();if(typeof $=="function"){$(p);continue}var U=$.func;typeof U=="number"?$.arg===void 0?Ul(U)():Ul(U)($.arg):U($.arg===void 0?null:$.arg)}}function Vl(T){var $=S1(),U=T();return Cc($),U}function SN(T){return T}function j5(T){var $=/\b_Z[\w\d_]+/g;return T.replace($,function(U){var te=U;return U===te?U:te+" ["+U+"]"})}function ag(T){u()[T>>2]=0;var $=ze.pthreads[T];delete ze.pthreads[T],$.worker.terminate(),I1(T),ze.runningWorkers.splice(ze.runningWorkers.indexOf($.worker),1),$.worker.pthread=void 0}function sg(T){var $=ze.pthreads[T];$.worker.postMessage({cmd:"cancel"})}function dc(T){var $=ze.pthreads[T];if($){u()[T>>2]=0;var U=$.worker;ze.returnWorkerToPool(U)}}function pc(T){fE(T)}function ig(T){if(T instanceof Yd||T=="unwind")return Rr;x(1,T)}var ze={unusedWorkers:[],runningWorkers:[],tlsInitFunctions:[],init:function(){S?ze.initWorker():ze.initMainThread()},initMainThread:function(){for(var T=8,$=0;$<T;++$)ze.allocateUnusedWorker()},initWorker:function(){pt=!1},pthreads:{},setExitStatus:function(T){Rr=T},terminateAllThreads:function(){for(var T in ze.pthreads){var $=ze.pthreads[T];$&&$.worker&&ze.returnWorkerToPool($.worker)}for(var U=0;U<ze.unusedWorkers.length;++U){var te=ze.unusedWorkers[U];te.terminate()}ze.unusedWorkers=[]},returnWorkerToPool:function(T){ze.runWithoutMainThreadQueuedCalls(function(){delete ze.pthreads[T.pthread.threadInfoStruct],ze.unusedWorkers.push(T),ze.runningWorkers.splice(ze.runningWorkers.indexOf(T),1),I1(T.pthread.threadInfoStruct),T.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(T){u()[a3>>2]=0;try{T()}finally{u()[a3>>2]=1}},receiveObjectTransfer:function(T){},threadInit:function(){for(var T in ze.tlsInitFunctions)ze.tlsInitFunctions[T]()},loadWasmModuleToWorker:function(T,$){T.onmessage=U=>{var te=U.data,ge=te.cmd;if(T.pthread&&(ze.currentProxiedOperationCallerThread=T.pthread.threadInfoStruct),te.targetThread&&te.targetThread!=Sc()){var xe=ze.pthreads[te.targetThread];xe?xe.worker.postMessage(te,te.transferList):Y('Internal error! Worker sent a message "'+ge+'" to target pthread '+te.targetThread+", but that thread no longer exists!"),ze.currentProxiedOperationCallerThread=void 0;return}ge==="processQueuedMainThreadWork"?Q5():ge==="spawnThread"?cc(te):ge==="cleanupThread"?dc(te.thread):ge==="killThread"?ag(te.thread):ge==="cancelThread"?sg(te.thread):ge==="loaded"?(T.loaded=!0,$&&$(T),T.runPthread&&(T.runPthread(),delete T.runPthread)):ge==="print"?ee("Thread "+te.threadId+": "+te.text):ge==="printErr"?Y("Thread "+te.threadId+": "+te.text):ge==="alert"?alert("Thread "+te.threadId+": "+te.text):te.target==="setimmediate"?T.postMessage(te):ge==="onAbort"?p.onAbort&&p.onAbort(te.arg):Y("worker sent an unknown command "+ge),ze.currentProxiedOperationCallerThread=void 0},T.onerror=U=>{var te="worker sent an error!";throw Y(te+" "+U.filename+":"+U.lineno+": "+U.message),U},C&&(T.on("message",function(U){T.onmessage({data:U})}),T.on("error",function(U){T.onerror(U)}),T.on("detachedExit",function(){})),T.postMessage({cmd:"load",urlOrBlob:p.mainScriptUrlOrBlob||n,wasmMemory:$e,wasmModule:vt})},allocateUnusedWorker:function(){var T=R("tfjs-backend-wasm-threaded-simd.worker.js");ze.unusedWorkers.push(new Worker(T))},getNewWorker:function(){return ze.unusedWorkers.length==0&&(ze.allocateUnusedWorker(),ze.loadWasmModuleToWorker(ze.unusedWorkers[0])),ze.unusedWorkers.pop()}};function og(){var T=Sc(),$=u()[T+44>>2],U=u()[T+48>>2],te=$-U;n3($,te),Cc($)}p.establishStackSpace=og;function hc(T){if(S)return eo(1,0,T);try{pc(T)}catch($){ig($)}}var Ji=[];function Ul(T){var $=Ji[T];return $||(T>=Ji.length&&(Ji.length=T+1),Ji[T]=$=vn.get(T)),$}function lg(T,$){return Ul(T)($)}p.invokeEntryPoint=lg;function H5(){var T=new Error;if(!T.stack){try{throw new Error}catch($){T=$}if(!T.stack)return"(no stack trace available)"}return T.stack.toString()}function ug(T,$,U){ze.tlsInitFunctions.push(T)}function q5(T,$){vn.set(T,$),Ji[T]=$}var Qi;C?Qi=()=>{var T=process.hrtime();return T[0]*1e3+T[1]/1e6}:S?Qi=()=>performance.now()-p.__performance_now_clock_drift:Qi=()=>performance.now();var dg=!0;function pg(T){return u()[J5()>>2]=T,T}function hg(T,$){var U;if(T===0)U=Date.now();else if((T===1||T===4)&&dg)U=Qi();else return pg(28),-1;return u()[$>>2]=U/1e3|0,u()[$+4>>2]=U%1e3*1e3*1e3|0,0}function cg(T,$){return hg(T,$)}function fg(T){e3(T,!v,1,!b),ze.threadInit()}function mg(T){S?postMessage({cmd:"cleanupThread",thread:T}):dc(T)}function cc(T){var $=ze.getNewWorker();if(!$)return 6;ze.runningWorkers.push($);var U=ze.pthreads[T.pthread_ptr]={worker:$,threadInfoStruct:T.pthread_ptr};$.pthread=U;var te={cmd:"run",start_routine:T.startRoutine,arg:T.arg,threadInfoStruct:T.pthread_ptr};return $.runPthread=()=>{te.time=performance.now(),$.postMessage(te,T.transferList)},$.loaded&&($.runPthread(),delete $.runPthread),0}function gg(T,$,U,te){if(typeof SharedArrayBuffer=="undefined")return Y("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;var ge=[],xe=0;if(S&&(ge.length===0||xe))return t3(687865856,T,$,U,te);if(xe)return xe;var Ce={startRoutine:U,pthread_ptr:T,arg:te,transferList:ge};return S?(Ce.cmd="spawnThread",postMessage(Ce,ge),0):cc(Ce)}function yg(){return 2097152}function Ag(T,$){if(T==$)postMessage({cmd:"processQueuedMainThreadWork"});else if(S)postMessage({targetThread:T,cmd:"processThreadQueue"});else{var U=ze.pthreads[T],te=U&&U.worker;if(!te)return;te.postMessage({cmd:"processThreadQueue"})}return 1}function xg(){Bl("")}function bg(){C||v||Z("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread")}function fc(){return 2147483648}function vg(T,$,U){i().copyWithin(T,$,$+U)}function wg(){return C?GE().cpus().length:navigator.hardwareConcurrency}function eo(T,$){var U=arguments.length-2,te=arguments;return Vl(function(){for(var ge=U,xe=Xl(ge*8),Ce=xe>>3,_e=0;_e<U;_e++){var $t=te[2+_e];h()[Ce+_e]=$t}return r3(T,ge,xe,$)})}var Hd=[];function kg(T,$,U){Hd.length=$;for(var te=U>>3,ge=0;ge<$;ge++)Hd[ge]=h()[te+ge];var xe=T<0,Ce=xe?ng[-T-1]:Vg[T];return Ce.apply(null,Hd)}function Ig(T){try{return $e.grow(T-Mr.byteLength+65535>>>16),Zn($e.buffer),1}catch($){}}function Sg(T){var $=i().length;if(T=T>>>0,T<=$)return!1;var U=fc();if(T>U)return!1;for(var te=1;te<=4;te*=2){var ge=$*(1+.2/te);ge=Math.min(ge,T+100663296);var xe=Math.min(U,Dl(Math.max(T,ge),65536)),Ce=Ig(xe);if(Ce)return!0}return!1}var Ke={inEventHandler:0,removeAllEventListeners:function(){for(var T=Ke.eventHandlers.length-1;T>=0;--T)Ke._removeHandler(T);Ke.eventHandlers=[],Ke.deferredCalls=[]},registerRemoveEventListeners:function(){Ke.removeEventListenersRegistered||(Y0.push(Ke.removeAllEventListeners),Ke.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(T,$,U){function te(Ce,_e){if(Ce.length!=_e.length)return!1;for(var $t in Ce)if(Ce[$t]!=_e[$t])return!1;return!0}for(var ge in Ke.deferredCalls){var xe=Ke.deferredCalls[ge];if(xe.targetFunction==T&&te(xe.argsList,U))return}Ke.deferredCalls.push({targetFunction:T,precedence:$,argsList:U}),Ke.deferredCalls.sort(function(Ce,_e){return Ce.precedence<_e.precedence})},removeDeferredCalls:function(T){for(var $=0;$<Ke.deferredCalls.length;++$)Ke.deferredCalls[$].targetFunction==T&&(Ke.deferredCalls.splice($,1),--$)},canPerformEventHandlerRequests:function(){return Ke.inEventHandler&&Ke.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Ke.canPerformEventHandlerRequests())for(var T=0;T<Ke.deferredCalls.length;++T){var $=Ke.deferredCalls[T];Ke.deferredCalls.splice(T,1),--T,$.targetFunction.apply(null,$.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(T,$){for(var U=0;U<Ke.eventHandlers.length;++U)Ke.eventHandlers[U].target==T&&(!$||$==Ke.eventHandlers[U].eventTypeString)&&Ke._removeHandler(U--)},_removeHandler:function(T){var $=Ke.eventHandlers[T];$.target.removeEventListener($.eventTypeString,$.eventListenerFunc,$.useCapture),Ke.eventHandlers.splice(T,1)},registerOrRemoveHandler:function(T){var $=function(te){++Ke.inEventHandler,Ke.currentEventHandler=T,Ke.runDeferredCalls(),T.handlerFunc(te),Ke.runDeferredCalls(),--Ke.inEventHandler};if(T.callbackfunc)T.eventListenerFunc=$,T.target.addEventListener(T.eventTypeString,$,T.useCapture),Ke.eventHandlers.push(T),Ke.registerRemoveEventListeners();else for(var U=0;U<Ke.eventHandlers.length;++U)Ke.eventHandlers[U].target==T.target&&Ke.eventHandlers[U].eventTypeString==T.eventTypeString&&Ke._removeHandler(U--)},queueEventHandlerOnThread_iiii:function(T,$,U,te,ge){Vl(function(){var xe=Xl(12);u()[xe>>2]=U,u()[xe+4>>2]=te,u()[xe+8>>2]=ge,k1(T,637534208,$,te,xe)})},getTargetThreadForEventCallback:function(T){switch(T){case 1:return 0;case 2:return ze.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 Cg(T){var $=Xh(T)+1,U=w1($);return Xi(T,U,$),U}function Tg(T,$,U,te){Vl(function(){var ge=Xl(12),xe=0;$&&(xe=Cg($)),u()[ge>>2]=xe,u()[ge+4>>2]=U,u()[ge+8>>2]=te,k1(T,657457152,0,xe,ge)})}function Ng(T,$,U,te){$=$?bn($):"",Tg(T,$,U,te)}function Eg(T){return T>2?bn(T):T}var Rg=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function Mg(T){T=Eg(T);var $=Rg[T]||(typeof document!="undefined"?document.querySelector(T):void 0);return $}function qd(T){return Mg(T)}function mc(T,$,U){var te=qd(T);if(!te)return-4;if(te.canvasSharedPtr&&(u()[te.canvasSharedPtr>>2]=$,u()[te.canvasSharedPtr+4>>2]=U),te.offscreenCanvas||!te.controlTransferredOffscreen){te.offscreenCanvas&&(te=te.offscreenCanvas);var ge=!1;if(te.GLctxObject&&te.GLctxObject.GLctx){var xe=te.GLctxObject.GLctx.getParameter(2978);ge=xe[0]===0&&xe[1]===0&&xe[2]===te.width&&xe[3]===te.height}te.width=$,te.height=U,ge&&te.GLctxObject.GLctx.viewport(0,0,$,U)}else if(te.canvasSharedPtr){var Ce=u()[te.canvasSharedPtr+8>>2];return Ng(Ce,T,$,U),1}else return-4;return 0}function gc(T,$,U){return S?eo(2,1,T,$,U):mc(T,$,U)}function Fg(T,$,U){var te=qd(T);return te?mc(T,$,U):gc(T,$,U)}function $g(){throw"unwind"}function Pg(T){var $=T.getExtension("ANGLE_instanced_arrays");if($)return T.vertexAttribDivisor=function(U,te){$.vertexAttribDivisorANGLE(U,te)},T.drawArraysInstanced=function(U,te,ge,xe){$.drawArraysInstancedANGLE(U,te,ge,xe)},T.drawElementsInstanced=function(U,te,ge,xe,Ce){$.drawElementsInstancedANGLE(U,te,ge,xe,Ce)},1}function _g(T){var $=T.getExtension("OES_vertex_array_object");if($)return T.createVertexArray=function(){return $.createVertexArrayOES()},T.deleteVertexArray=function(U){$.deleteVertexArrayOES(U)},T.bindVertexArray=function(U){$.bindVertexArrayOES(U)},T.isVertexArray=function(U){return $.isVertexArrayOES(U)},1}function zg(T){var $=T.getExtension("WEBGL_draw_buffers");if($)return T.drawBuffers=function(U,te){$.drawBuffersWEBGL(U,te)},1}function Og(T){return!!(T.multiDrawWebgl=T.getExtension("WEBGL_multi_draw"))}var Ft={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},queries:[],stringCache:{},unpackAlignment:4,recordError:function(T){Ft.lastError||(Ft.lastError=T)},getNewId:function(T){for(var $=Ft.counter++,U=T.length;U<$;U++)T[U]=null;return $},getSource:function(T,$,U,te){for(var ge="",xe=0;xe<$;++xe){var Ce=te?u()[te+xe*4>>2]:-1;ge+=bn(u()[U+xe*4>>2],Ce<0?void 0:Ce)}return ge},createContext:function(T,$){T.getContextSafariWebGL2Fixed||(T.getContextSafariWebGL2Fixed=T.getContext,T.getContext=function(ge,xe){var Ce=T.getContextSafariWebGL2Fixed(ge,xe);return ge=="webgl"==Ce instanceof WebGLRenderingContext?Ce:null});var U=T.getContext("webgl",$);if(!U)return 0;var te=Ft.registerContext(U,$);return te},registerContext:function(T,$){var U=w1(8);u()[U+4>>2]=Sc();var te={handle:U,attributes:$,version:$.majorVersion,GLctx:T};return T.canvas&&(T.canvas.GLctxObject=te),Ft.contexts[U]=te,(typeof $.enableExtensionsByDefault=="undefined"||$.enableExtensionsByDefault)&&Ft.initExtensions(te),U},makeContextCurrent:function(T){return Ft.currentContext=Ft.contexts[T],p.ctx=bc=Ft.currentContext&&Ft.currentContext.GLctx,!(T&&!bc)},getContext:function(T){return Ft.contexts[T]},deleteContext:function(T){Ft.currentContext===Ft.contexts[T]&&(Ft.currentContext=null),typeof Ke=="object"&&Ke.removeAllHandlersOnTarget(Ft.contexts[T].GLctx.canvas),Ft.contexts[T]&&Ft.contexts[T].GLctx.canvas&&(Ft.contexts[T].GLctx.canvas.GLctxObject=void 0),Y5(Ft.contexts[T].handle),Ft.contexts[T]=null},initExtensions:function(T){if(T||(T=Ft.currentContext),!T.initExtensionsDone){T.initExtensionsDone=!0;var $=T.GLctx;Pg($),_g($),zg($),$.disjointTimerQueryExt=$.getExtension("EXT_disjoint_timer_query"),Og($);var U=$.getSupportedExtensions()||[];U.forEach(function(te){!te.includes("lose_context")&&!te.includes("debug")&&$.getExtension(te)})}}},Dg=["default","low-power","high-performance"];function Lg(T,$){var U=$>>2,te=u()[U+6],ge={alpha:!!u()[U+0],depth:!!u()[U+1],stencil:!!u()[U+2],antialias:!!u()[U+3],premultipliedAlpha:!!u()[U+4],preserveDrawingBuffer:!!u()[U+5],powerPreference:Dg[te],failIfMajorPerformanceCaveat:!!u()[U+7],majorVersion:u()[U+8],minorVersion:u()[U+9],enableExtensionsByDefault:u()[U+10],explicitSwapControl:u()[U+11],proxyContextToMainThread:u()[U+12],renderViaOffscreenBackBuffer:u()[U+13]},xe=qd(T);if(!xe||ge.explicitSwapControl)return 0;var Ce=Ft.createContext(xe,ge);return Ce}function Bg(T,$){return Lg(T,$)}var Gl={mappings:{},buffers:[null,[],[]],printChar:function(T,$){var U=Gl.buffers[T];$===0||$===10?((T===1?ee:Y)(xn(U,0)),U.length=0):U.push($)},varargs:void 0,get:function(){Gl.varargs+=4;var T=u()[Gl.varargs-4>>2];return T},getStr:function(T){var $=bn(T);return $},get64:function(T,$){return T}};function yc(T){return S?eo(3,1,T):0}function Ac(T,$,U,te,ge){if(S)return eo(4,1,T,$,U,te,ge)}function xc(T,$,U,te){if(S)return eo(5,1,T,$,U,te);for(var ge=0,xe=0;xe<U;xe++){var Ce=u()[$>>2],_e=u()[$+4>>2];$+=8;for(var $t=0;$t<_e;$t++)Gl.printChar(T,i()[Ce+$t]);ge+=_e}return u()[te>>2]=ge,0}function Wg(T){De(T)}ze.init();var bc,Vg=[null,hc,gc,yc,Ac,xc],K5=!1,vc={__clock_gettime:cg,__emscripten_init_main_thread_js:fg,__emscripten_thread_cleanup:mg,__pthread_create_js:gg,_emscripten_default_pthread_stack_size:yg,_emscripten_notify_thread_queue:Ag,abort:xg,emscripten_check_blocking_allowed:bg,emscripten_get_heap_max:fc,emscripten_get_now:Qi,emscripten_memcpy_big:vg,emscripten_num_logical_cores:wg,emscripten_receive_on_main_thread_js:kg,emscripten_resize_heap:Sg,emscripten_set_canvas_element_size:Fg,emscripten_unwind_to_js_event_loop:$g,emscripten_webgl_create_context:Bg,exit:pc,fd_close:yc,fd_seek:Ac,fd_write:xc,memory:$e||p.wasmMemory,setTempRet0:Wg},X5=rg(),Ug=p.___wasm_call_ctors=function(){return(Ug=p.___wasm_call_ctors=p.asm.__wasm_call_ctors).apply(null,arguments)},Gg=p._init=function(){return(Gg=p._init=p.asm.init).apply(null,arguments)},jg=p._init_with_threads_count=function(){return(jg=p._init_with_threads_count=p.asm.init_with_threads_count).apply(null,arguments)},Hg=p._get_threads_count=function(){return(Hg=p._get_threads_count=p.asm.get_threads_count).apply(null,arguments)},qg=p._register_tensor=function(){return(qg=p._register_tensor=p.asm.register_tensor).apply(null,arguments)},Kg=p._dispose_data=function(){return(Kg=p._dispose_data=p.asm.dispose_data).apply(null,arguments)},Xg=p._dispose=function(){return(Xg=p._dispose=p.asm.dispose).apply(null,arguments)},Zg=p._Abs=function(){return(Zg=p._Abs=p.asm.Abs).apply(null,arguments)},Yg=p._Add=function(){return(Yg=p._Add=p.asm.Add).apply(null,arguments)},Jg=p._AddN=function(){return(Jg=p._AddN=p.asm.AddN).apply(null,arguments)},Qg=p._All=function(){return(Qg=p._All=p.asm.All).apply(null,arguments)},e1=p._Any=function(){return(e1=p._Any=p.asm.Any).apply(null,arguments)},t1=p._ArgMax=function(){return(t1=p._ArgMax=p.asm.ArgMax).apply(null,arguments)},r1=p._AvgPool=function(){return(r1=p._AvgPool=p.asm.AvgPool).apply(null,arguments)},n1=p._BatchMatMul=function(){return(n1=p._BatchMatMul=p.asm.BatchMatMul).apply(null,arguments)},a1=p._Ceil=function(){return(a1=p._Ceil=p.asm.Ceil).apply(null,arguments)},s1=p._ClipByValue=function(){return(s1=p._ClipByValue=p.asm.ClipByValue).apply(null,arguments)},i1=p._Conv2D=function(){return(i1=p._Conv2D=p.asm.Conv2D).apply(null,arguments)},o1=p._Conv2DBackpropInput=function(){return(o1=p._Conv2DBackpropInput=p.asm.Conv2DBackpropInput).apply(null,arguments)},l1=p._Cos=function(){return(l1=p._Cos=p.asm.Cos).apply(null,arguments)},u1=p._Cosh=function(){return(u1=p._Cosh=p.asm.Cosh).apply(null,arguments)},d1=p._CropAndResize=function(){return(d1=p._CropAndResize=p.asm.CropAndResize).apply(null,arguments)},p1=p._Cumsum=function(){return(p1=p._Cumsum=p.asm.Cumsum).apply(null,arguments)},h1=p._DepthToSpace=function(){return(h1=p._DepthToSpace=p.asm.DepthToSpace).apply(null,arguments)},c1=p._DepthwiseConv2dNative=function(){return(c1=p._DepthwiseConv2dNative=p.asm.DepthwiseConv2dNative).apply(null,arguments)},f1=p._Elu=function(){return(f1=p._Elu=p.asm.Elu).apply(null,arguments)},m1=p._Equal=function(){return(m1=p._Equal=p.asm.Equal).apply(null,arguments)},g1=p._Exp=function(){return(g1=p._Exp=p.asm.Exp).apply(null,arguments)},y1=p._FlipLeftRight=function(){return(y1=p._FlipLeftRight=p.asm.FlipLeftRight).apply(null,arguments)},wc=p._Floor=function(){return(wc=p._Floor=p.asm.Floor).apply(null,arguments)},kc=p._FloorDiv=function(){return(kc=p._FloorDiv=p.asm.FloorDiv).apply(null,arguments)},Kd=p._FusedBatchNorm=function(){return(Kd=p._FusedBatchNorm=p.asm.FusedBatchNorm).apply(null,arguments)},A1=p._FusedConv2D=function(){return(A1=p._FusedConv2D=p.asm.FusedConv2D).apply(null,arguments)},x1=p._FusedDepthwiseConv2D=function(){return(x1=p._FusedDepthwiseConv2D=p.asm.FusedDepthwiseConv2D).apply(null,arguments)},jl=p._Gather=function(){return(jl=p._Gather=p.asm.Gather).apply(null,arguments)},Xd=p._GatherNd=function(){return(Xd=p._GatherNd=p.asm.GatherNd).apply(null,arguments)},Zd=p._Greater=function(){return(Zd=p._Greater=p.asm.Greater).apply(null,arguments)},Z5=p._GreaterEqual=function(){return(Z5=p._GreaterEqual=p.asm.GreaterEqual).apply(null,arguments)},Hl=p._LeakyRelu=function(){return(Hl=p._LeakyRelu=p.asm.LeakyRelu).apply(null,arguments)},ql=p._Less=function(){return(ql=p._Less=p.asm.Less).apply(null,arguments)},b1=p._LessEqual=function(){return(b1=p._LessEqual=p.asm.LessEqual).apply(null,arguments)},H=p._Log=function(){return(H=p._Log=p.asm.Log).apply(null,arguments)},ne=p._LogicalAnd=function(){return(ne=p._LogicalAnd=p.asm.LogicalAnd).apply(null,arguments)},ye=p._Max=function(){return(ye=p._Max=p.asm.Max).apply(null,arguments)},Re=p._MaxPool=function(){return(Re=p._MaxPool=p.asm.MaxPool).apply(null,arguments)},lt=p._Maximum=function(){return(lt=p._Maximum=p.asm.Maximum).apply(null,arguments)},ht=p._Mean=function(){return(ht=p._Mean=p.asm.Mean).apply(null,arguments)},Ye=p._Min=function(){return(Ye=p._Min=p.asm.Min).apply(null,arguments)},He=p._Minimum=function(){return(He=p._Minimum=p.asm.Minimum).apply(null,arguments)},jt=p._MirrorPad=function(){return(jt=p._MirrorPad=p.asm.MirrorPad).apply(null,arguments)},Jn=p._Multiply=function(){return(Jn=p._Multiply=p.asm.Multiply).apply(null,arguments)},Qn=p._Neg=function(){return(Qn=p._Neg=p.asm.Neg).apply(null,arguments)},Kl=p._NonMaxSuppressionV3=function(){return(Kl=p._NonMaxSuppressionV3=p.asm.NonMaxSuppressionV3).apply(null,arguments)},to=p._NonMaxSuppressionV4=function(){return(to=p._NonMaxSuppressionV4=p.asm.NonMaxSuppressionV4).apply(null,arguments)},v1=p._NonMaxSuppressionV5=function(){return(v1=p._NonMaxSuppressionV5=p.asm.NonMaxSuppressionV5).apply(null,arguments)},Yr=p._NotEqual=function(){return(Yr=p._NotEqual=p.asm.NotEqual).apply(null,arguments)},ms=p._OneHot=function(){return(ms=p._OneHot=p.asm.OneHot).apply(null,arguments)},Ic=p._PadV2=function(){return(Ic=p._PadV2=p.asm.PadV2).apply(null,arguments)},CN=p._Pow=function(){return(CN=p._Pow=p.asm.Pow).apply(null,arguments)},TN=p._Prelu=function(){return(TN=p._Prelu=p.asm.Prelu).apply(null,arguments)},NN=p._Prod=function(){return(NN=p._Prod=p.asm.Prod).apply(null,arguments)},EN=p._RealDiv=function(){return(EN=p._RealDiv=p.asm.RealDiv).apply(null,arguments)},RN=p._Relu=function(){return(RN=p._Relu=p.asm.Relu).apply(null,arguments)},MN=p._Relu6=function(){return(MN=p._Relu6=p.asm.Relu6).apply(null,arguments)},FN=p._ResizeBilinear=function(){return(FN=p._ResizeBilinear=p.asm.ResizeBilinear).apply(null,arguments)},$N=p._Reverse=function(){return($N=p._Reverse=p.asm.Reverse).apply(null,arguments)},PN=p._RotateWithOffset=function(){return(PN=p._RotateWithOffset=p.asm.RotateWithOffset).apply(null,arguments)},_N=p._Round=function(){return(_N=p._Round=p.asm.Round).apply(null,arguments)},zN=p._Rsqrt=function(){return(zN=p._Rsqrt=p.asm.Rsqrt).apply(null,arguments)},ON=p._ScatterNd=function(){return(ON=p._ScatterNd=p.asm.ScatterNd).apply(null,arguments)},DN=p._SelectV2=function(){return(DN=p._SelectV2=p.asm.SelectV2).apply(null,arguments)},LN=p._Sigmoid=function(){return(LN=p._Sigmoid=p.asm.Sigmoid).apply(null,arguments)},BN=p._Sin=function(){return(BN=p._Sin=p.asm.Sin).apply(null,arguments)},WN=p._Softmax=function(){return(WN=p._Softmax=p.asm.Softmax).apply(null,arguments)},VN=p._SparseFillEmptyRows=function(){return(VN=p._SparseFillEmptyRows=p.asm.SparseFillEmptyRows).apply(null,arguments)},UN=p._SparseReshape=function(){return(UN=p._SparseReshape=p.asm.SparseReshape).apply(null,arguments)},GN=p._SparseSegmentReduction=function(){return(GN=p._SparseSegmentReduction=p.asm.SparseSegmentReduction).apply(null,arguments)},jN=p._Sqrt=function(){return(jN=p._Sqrt=p.asm.Sqrt).apply(null,arguments)},HN=p._Square=function(){return(HN=p._Square=p.asm.Square).apply(null,arguments)},qN=p._SquaredDifference=function(){return(qN=p._SquaredDifference=p.asm.SquaredDifference).apply(null,arguments)},KN=p._Step=function(){return(KN=p._Step=p.asm.Step).apply(null,arguments)},XN=p._StridedSlice=function(){return(XN=p._StridedSlice=p.asm.StridedSlice).apply(null,arguments)},ZN=p._Sub=function(){return(ZN=p._Sub=p.asm.Sub).apply(null,arguments)},YN=p._Sum=function(){return(YN=p._Sum=p.asm.Sum).apply(null,arguments)},JN=p._Tan=function(){return(JN=p._Tan=p.asm.Tan).apply(null,arguments)},QN=p._Tanh=function(){return(QN=p._Tanh=p.asm.Tanh).apply(null,arguments)},eE=p._Tile=function(){return(eE=p._Tile=p.asm.Tile).apply(null,arguments)},tE=p._TopK=function(){return(tE=p._TopK=p.asm.TopK).apply(null,arguments)},rE=p._Transform=function(){return(rE=p._Transform=p.asm.Transform).apply(null,arguments)},nE=p._Transpose=function(){return(nE=p._Transpose=p.asm.Transpose).apply(null,arguments)},aE=p.__FusedMatMul=function(){return(aE=p.__FusedMatMul=p.asm._FusedMatMul).apply(null,arguments)},w1=p._malloc=function(){return(w1=p._malloc=p.asm.malloc).apply(null,arguments)},Y5=p._free=function(){return(Y5=p._free=p.asm.free).apply(null,arguments)},sE=p._emscripten_tls_init=function(){return(sE=p._emscripten_tls_init=p.asm.emscripten_tls_init).apply(null,arguments)},J5=p.___errno_location=function(){return(J5=p.___errno_location=p.asm.__errno_location).apply(null,arguments)},Sc=p._pthread_self=function(){return(Sc=p._pthread_self=p.asm.pthread_self).apply(null,arguments)},Q5=p._emscripten_main_thread_process_queued_calls=function(){return(Q5=p._emscripten_main_thread_process_queued_calls=p.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},iE=p.__emscripten_thread_crashed=function(){return(iE=p.__emscripten_thread_crashed=p.asm._emscripten_thread_crashed).apply(null,arguments)},e3=p.__emscripten_thread_init=function(){return(e3=p.__emscripten_thread_init=p.asm._emscripten_thread_init).apply(null,arguments)},oE=p._emscripten_current_thread_process_queued_calls=function(){return(oE=p._emscripten_current_thread_process_queued_calls=p.asm.emscripten_current_thread_process_queued_calls).apply(null,arguments)},lE=p._emscripten_main_browser_thread_id=function(){return(lE=p._emscripten_main_browser_thread_id=p.asm.emscripten_main_browser_thread_id).apply(null,arguments)},uE=p._emscripten_sync_run_in_main_thread_2=function(){return(uE=p._emscripten_sync_run_in_main_thread_2=p.asm.emscripten_sync_run_in_main_thread_2).apply(null,arguments)},t3=p._emscripten_sync_run_in_main_thread_4=function(){return(t3=p._emscripten_sync_run_in_main_thread_4=p.asm.emscripten_sync_run_in_main_thread_4).apply(null,arguments)},r3=p._emscripten_run_in_main_runtime_thread_js=function(){return(r3=p._emscripten_run_in_main_runtime_thread_js=p.asm.emscripten_run_in_main_runtime_thread_js).apply(null,arguments)},k1=p._emscripten_dispatch_to_thread_=function(){return(k1=p._emscripten_dispatch_to_thread_=p.asm.emscripten_dispatch_to_thread_).apply(null,arguments)},I1=p.__emscripten_thread_free_data=function(){return(I1=p.__emscripten_thread_free_data=p.asm._emscripten_thread_free_data).apply(null,arguments)},dE=p.__emscripten_thread_exit=function(){return(dE=p.__emscripten_thread_exit=p.asm._emscripten_thread_exit).apply(null,arguments)},pE=p._memalign=function(){return(pE=p._memalign=p.asm.memalign).apply(null,arguments)},n3=p._emscripten_stack_set_limits=function(){return(n3=p._emscripten_stack_set_limits=p.asm.emscripten_stack_set_limits).apply(null,arguments)},S1=p.stackSave=function(){return(S1=p.stackSave=p.asm.stackSave).apply(null,arguments)},Cc=p.stackRestore=function(){return(Cc=p.stackRestore=p.asm.stackRestore).apply(null,arguments)},Xl=p.stackAlloc=function(){return(Xl=p.stackAlloc=p.asm.stackAlloc).apply(null,arguments)},hE=p.dynCall_iijjiiii=function(){return(hE=p.dynCall_iijjiiii=p.asm.dynCall_iijjiiii).apply(null,arguments)},cE=p.dynCall_jiji=function(){return(cE=p.dynCall_jiji=p.asm.dynCall_jiji).apply(null,arguments)},a3=p.__emscripten_allow_main_runtime_queued_calls=21408;p.cwrap=dr,p.keepRuntimeAlive=Yi,p.PThread=ze,p.PThread=ze,p.wasmMemory=$e,p.ExitStatus=Yd;var Tc;function Yd(T){this.name="ExitStatus",this.message="Program terminated with exit("+T+")",this.status=T}Yn=function T(){Tc||C1(),Tc||(Yn=T)};function C1(T){if(T=T||y,fs>0)return;if(S){c(p),Ud(),postMessage({cmd:"loaded"});return}if(Fr(),fs>0)return;function $(){Tc||(Tc=!0,p.calledRun=!0,!gt&&(Ud(),c(p),p.onRuntimeInitialized&&p.onRuntimeInitialized(),eg()))}p.setStatus?(p.setStatus("Running..."),setTimeout(function(){setTimeout(function(){p.setStatus("")},1),$()},1)):$()}p.run=C1;function fE(T,$){if(Rr=T,!$&&S)throw hc(T),"unwind";Yi()||Q0(),mE(T)}function mE(T){Rr=T,Yi()||(ze.terminateAllThreads(),p.onExit&&p.onExit(T),gt=!0),x(T,new Yd(T))}if(p.preInit)for(typeof p.preInit=="function"&&(p.preInit=[p.preInit]);p.preInit.length>0;)p.preInit.pop()();C1();var Nc;m&&(Nc={uncaughtException:process.listeners("uncaughtException").filter(function(T){return!m.uncaughtException.indexOf(T)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(T){return!m.unhandledRejection.indexOf(T)>-1})});var Ec;if(typeof WasmBackendModule!="undefined")Ec=WasmBackendModule;else if(typeof a!="undefined")Ec=a;else throw new Error("Could not find wasm module in post.js");if(Nc){var gE=Ec._dispose;Ec._dispose=function(){gE(),Nc.uncaughtException.forEach(function(T){process.removeListener("uncaughtException",T)}),Nc.unhandledRejection.forEach(function(T){process.removeListener("unhandledRejection",T)})}}return a.ready}})();typeof e=="object"&&typeof t=="object"?t.exports=r:typeof define=="function"&&define.amd?define([],function(){return r}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=r)}}),HE=ir({"src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e,t){var r=(()=>{var n=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(n=n||__filename),function(a){a=a||{};var s=typeof a!="undefined"?a:{},i,o;s.ready=new Promise(function(H,ne){i=H,o=ne});var l;typeof process!="undefined"&&process.listeners&&(l={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var u=Object.assign({},s),d=[],h="./this.program",p=(H,ne)=>{throw ne},c=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g="";function y(H){return s.locateFile?s.locateFile(H,g):g+H}var A,x,b,v;function C(H){H instanceof Xd||M("exiting due to exception: "+H)}var S,E,R;m?(f?g=ef().dirname(g)+"/":g=__dirname+"/",R=()=>{E||(S=Gy(),E=ef())},A=function(H,ne){return R(),H=E.normalize(H),S.readFileSync(H,ne?void 0:"utf8")},b=H=>{var ne=A(H,!0);return ne.buffer||(ne=new Uint8Array(ne)),ne},x=(H,ne,ye)=>{R(),H=E.normalize(H),S.readFile(H,function(Re,lt){Re?ye(Re):ne(lt.buffer)})},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),d=process.argv.slice(2),process.on("uncaughtException",function(H){if(!(H instanceof Xd))throw H}),process.on("unhandledRejection",function(H){throw H}),p=(H,ne)=>{if(Vd())throw process.exitCode=H,ne;C(ne),process.exit(H)},s.inspect=function(){return"[Emscripten Module object]"}):(c||f)&&(f?g=self.location.href:typeof document!="undefined"&&document.currentScript&&(g=document.currentScript.src),n&&(g=n),g.indexOf("blob:")!==0?g=g.substr(0,g.replace(/[?#].*/,"").lastIndexOf("/")+1):g="",A=H=>{var ne=new XMLHttpRequest;return ne.open("GET",H,!1),ne.send(null),ne.responseText},f&&(b=H=>{var ne=new XMLHttpRequest;return ne.open("GET",H,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),x=(H,ne,ye)=>{var Re=new XMLHttpRequest;Re.open("GET",H,!0),Re.responseType="arraybuffer",Re.onload=()=>{if(Re.status==200||Re.status==0&&Re.response){ne(Re.response);return}ye()},Re.onerror=ye,Re.send(null)},v=H=>document.title=H);var _=s.print||console.log.bind(console),M=s.printErr||console.warn.bind(console);Object.assign(s,u),u=null,s.arguments&&(d=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(p=s.quit);var I=4;function O(H){O.shown||(O.shown={}),O.shown[H]||(O.shown[H]=1,M(H))}function z(H,ne){if(typeof WebAssembly.Function=="function"){for(var ye={i:"i32",j:"i64",f:"f32",d:"f64"},Re={parameters:[],results:ne[0]=="v"?[]:[ye[ne[0]]]},lt=1;lt<ne.length;++lt)Re.parameters.push(ye[ne[lt]]);return new WebAssembly.Function(Re,H)}var ht=[1,0,1,96],Ye=ne.slice(0,1),He=ne.slice(1),jt={i:127,j:126,f:125,d:124};ht.push(He.length);for(var lt=0;lt<He.length;++lt)ht.push(jt[He[lt]]);Ye=="v"?ht.push(0):ht=ht.concat([1,jt[Ye]]),ht[1]=ht.length-2;var Jn=new Uint8Array([0,97,115,109,1,0,0,0].concat(ht,[2,7,1,1,101,1,102,0,0,7,5,1,1,102,0,0])),Qn=new WebAssembly.Module(Jn),Kl=new WebAssembly.Instance(Qn,{e:{f:H}}),to=Kl.exports.f;return to}var j=[],X;function D(){if(j.length)return j.pop();try{hs.grow(1)}catch(H){throw H instanceof RangeError?"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.":H}return hs.length-1}function Q(H,ne){for(var ye=H;ye<H+ne;ye++){var Re=jd(ye);Re&&X.set(Re,ye)}}var V=0,ee=H=>{V=H},Y;s.wasmBinary&&(Y=s.wasmBinary);var se=s.noExitRuntime||!0;typeof WebAssembly!="object"&&Zi("no native wasm support detected");var Z,ae=!1,de;function Ae(H,ne){H||Zi(ne)}function be(H){var ne=s["_"+H];return ne}function Ee(H,ne,ye,Re,lt){var ht={string:function(Yr){var ms=0;if(Yr!=null&&Yr!==0){var Ic=(Yr.length<<2)+1;ms=Kd(Ic),pt(Yr,ms,Ic)}return ms},array:function(Yr){var ms=Kd(Yr.length);return gt(Yr,ms),ms}};function Ye(Yr){return ne==="string"?ot(Yr):ne==="boolean"?Boolean(Yr):Yr}var He=be(H),jt=[],Jn=0;if(Re)for(var Qn=0;Qn<Re.length;Qn++){var Kl=ht[ye[Qn]];Kl?(Jn===0&&(Jn=wc()),jt[Qn]=Kl(Re[Qn])):jt[Qn]=Re[Qn]}var to=He.apply(null,jt);function v1(Yr){return Jn!==0&&kc(Jn),Ye(Yr)}return to=v1(to),to}function Me(H,ne,ye,Re){ye=ye||[];var lt=ye.every(function(Ye){return Ye==="number"}),ht=ne!=="string";return ht&&lt&&!Re?be(H):function(){return Ee(H,ne,ye,arguments,Re)}}var De=1,Be=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function Ze(H,ne,ye){for(var Re=ne+ye,lt=ne;H[lt]&&!(lt>=Re);)++lt;if(lt-ne>16&&H.subarray&&Be)return Be.decode(H.subarray(ne,lt));for(var ht="";ne<lt;){var Ye=H[ne++];if(!(Ye&128)){ht+=String.fromCharCode(Ye);continue}var He=H[ne++]&63;if((Ye&224)==192){ht+=String.fromCharCode((Ye&31)<<6|He);continue}var jt=H[ne++]&63;if((Ye&240)==224?Ye=(Ye&15)<<12|He<<6|jt:Ye=(Ye&7)<<18|He<<12|jt<<6|H[ne++]&63,Ye<65536)ht+=String.fromCharCode(Ye);else{var Jn=Ye-65536;ht+=String.fromCharCode(55296|Jn>>10,56320|Jn&1023)}}return ht}function ot(H,ne){return H?Ze(dr,H,ne):""}function dt(H,ne,ye,Re){if(!(Re>0))return 0;for(var lt=ye,ht=ye+Re-1,Ye=0;Ye<H.length;++Ye){var He=H.charCodeAt(Ye);if(He>=55296&&He<=57343){var jt=H.charCodeAt(++Ye);He=65536+((He&1023)<<10)|jt&1023}if(He<=127){if(ye>=ht)break;ne[ye++]=He}else if(He<=2047){if(ye+1>=ht)break;ne[ye++]=192|He>>6,ne[ye++]=128|He&63}else if(He<=65535){if(ye+2>=ht)break;ne[ye++]=224|He>>12,ne[ye++]=128|He>>6&63,ne[ye++]=128|He&63}else{if(ye+3>=ht)break;ne[ye++]=240|He>>18,ne[ye++]=128|He>>12&63,ne[ye++]=128|He>>6&63,ne[ye++]=128|He&63}}return ne[ye]=0,ye-lt}function pt(H,ne,ye){return dt(H,dr,ne,ye)}function $e(H){for(var ne=0,ye=0;ye<H.length;++ye){var Re=H.charCodeAt(ye);Re>=55296&&Re<=57343&&(Re=65536+((Re&1023)<<10)|H.charCodeAt(++ye)&1023),Re<=127?++ne:Re<=2047?ne+=2:Re<=65535?ne+=3:ne+=4}return ne}var vt=typeof TextDecoder!="undefined"?new TextDecoder("utf-16le"):void 0;function gt(H,ne){Jt.set(H,ne)}function Rr(H,ne,ye){for(var Re=0;Re<H.length;++Re)Jt[ne++>>0]=H.charCodeAt(Re);ye||(Jt[ne>>0]=0)}function ur(H,ne){return H%ne>0&&(H+=ne-H%ne),H}var Xr,Jt,dr,Xn,Zr,Qt,xn,bn,ps;function Xi(H){Xr=H,s.HEAP8=Jt=new Int8Array(H),s.HEAP16=Xn=new Int16Array(H),s.HEAP32=Qt=new Int32Array(H),s.HEAPU8=dr=new Uint8Array(H),s.HEAPU16=Zr=new Uint16Array(H),s.HEAPU32=xn=new Uint32Array(H),s.HEAPF32=bn=new Float32Array(H),s.HEAPF64=ps=new Float64Array(H)}var Xh=s.INITIAL_MEMORY||16777216,hs,Da=[],Wd=[],Dl=[],Mr=!1,Zh=!1,Yh=0;function Vd(){return se||Yh>0}function Jh(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)tc(s.preRun.shift());Gd(Da)}function Qh(){Mr=!0,Gd(Wd)}function L5(){Zh=!0}function ec(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)rc(s.postRun.shift());Gd(Dl)}function tc(H){Da.unshift(H)}function Zn(H){Wd.unshift(H)}function rc(H){Dl.unshift(H)}var vn=0,Ll=null,cs=null;function Y0(H){vn++,s.monitorRunDependencies&&s.monitorRunDependencies(vn)}function nc(H){if(vn--,s.monitorRunDependencies&&s.monitorRunDependencies(vn),vn==0&&(Ll!==null&&(clearInterval(Ll),Ll=null),cs)){var ne=cs;cs=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function Zi(H){s.onAbort&&s.onAbort(H),H="Aborted("+H+")",M(H),ae=!0,de=1,H+=". Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(H);throw o(ne),ne}var J0="data:application/octet-stream;base64,";function ac(H){return H.startsWith(J0)}function Yi(H){return H.startsWith("file://")}var Fr;Fr="tfjs-backend-wasm.wasm",ac(Fr)||(Fr=y(Fr));function Ud(H){try{if(H==Fr&&Y)return new Uint8Array(Y);if(b)return b(H);throw"both async and sync fetching of the wasm failed"}catch(ne){Zi(ne)}}function Q0(){if(!Y&&(c||f)){if(typeof fetch=="function"&&!Yi(Fr))return fetch(Fr,{credentials:"same-origin"}).then(function(H){if(!H.ok)throw"failed to load wasm binary file at '"+Fr+"'";return H.arrayBuffer()}).catch(function(){return Ud(Fr)});if(x)return new Promise(function(H,ne){x(Fr,function(ye){H(new Uint8Array(ye))},ne)})}return Promise.resolve().then(function(){return Ud(Fr)})}function eg(){var H={env:Vl,wasi_snapshot_preview1:Vl};function ne(Ye,He){var jt=Ye.exports;s.asm=jt,Z=s.asm.memory,Xi(Z.buffer),hs=s.asm.__indirect_function_table,Zn(s.asm.__wasm_call_ctors),nc("wasm-instantiate")}Y0("wasm-instantiate");function ye(Ye){ne(Ye.instance)}function Re(Ye){return Q0().then(function(He){return WebAssembly.instantiate(He,H)}).then(function(He){return He}).then(Ye,function(He){M("failed to asynchronously prepare wasm: "+He),Zi(He)})}function lt(){return!Y&&typeof WebAssembly.instantiateStreaming=="function"&&!ac(Fr)&&!Yi(Fr)&&typeof fetch=="function"?fetch(Fr,{credentials:"same-origin"}).then(function(Ye){var He=WebAssembly.instantiateStreaming(Ye,H);return He.then(ye,function(jt){return M("wasm streaming compile failed: "+jt),M("falling back to ArrayBuffer instantiation"),Re(ye)})}):Re(ye)}if(s.instantiateWasm)try{var ht=s.instantiateWasm(H,ne);return ht}catch(Ye){return M("Module.instantiateWasm callback failed with error: "+Ye),!1}return lt().catch(o),{}}var B5,W5;function Gd(H){for(;H.length>0;){var ne=H.shift();if(typeof ne=="function"){ne(s);continue}var ye=ne.func;typeof ye=="number"?ne.arg===void 0?jd(ye)():jd(ye)(ne.arg):ye(ne.arg===void 0?null:ne.arg)}}function fs(H){return H}function sc(H){var ne=/\b_Z[\w\d_]+/g;return H.replace(ne,function(ye){var Re=ye;return ye===Re?ye:Re+" ["+ye+"]"})}var Yn=[];function jd(H){var ne=Yn[H];return ne||(H>=Yn.length&&(Yn.length=H+1),Yn[H]=ne=hs.get(H)),ne}function V5(){var H=new Error;if(!H.stack){try{throw new Error}catch(ne){H=ne}if(!H.stack)return"(no stack trace available)"}return H.stack.toString()}function Bl(H,ne){hs.set(H,ne),Yn[H]=ne}function tg(){Zi("")}function ic(H,ne,ye){dr.copyWithin(H,ne,ne+ye)}function oc(){return 2147483648}function $r(H){try{return Z.grow(H-Xr.byteLength+65535>>>16),Xi(Z.buffer),1}catch(ne){}}function lc(H){var ne=dr.length;H=H>>>0;var ye=oc();if(H>ye)return!1;for(var Re=1;Re<=4;Re*=2){var lt=ne*(1+.2/Re);lt=Math.min(lt,H+100663296);var ht=Math.min(ye,ur(Math.max(H,lt),65536)),Ye=$r(ht);if(Ye)return!0}return!1}var Wl={mappings:{},buffers:[null,[],[]],printChar:function(H,ne){var ye=Wl.buffers[H];ne===0||ne===10?((H===1?_:M)(Ze(ye,0)),ye.length=0):ye.push(ne)},varargs:void 0,get:function(){Wl.varargs+=4;var H=Qt[Wl.varargs-4>>2];return H},getStr:function(H){var ne=ot(H);return ne},get64:function(H,ne){return H}};function rg(H){return 0}function U5(H,ne,ye,Re,lt){}function G5(H,ne,ye,Re){for(var lt=0,ht=0;ht<ye;ht++){var Ye=Qt[ne>>2],He=Qt[ne+4>>2];ne+=8;for(var jt=0;jt<He;jt++)Wl.printChar(H,dr[Ye+jt]);lt+=He}return Qt[Re>>2]=lt,0}function ng(H){ee(H)}var uc=!1,Vl={abort:tg,emscripten_memcpy_big:ic,emscripten_resize_heap:lc,fd_close:rg,fd_seek:U5,fd_write:G5,setTempRet0:ng},SN=eg(),j5=s.___wasm_call_ctors=function(){return(j5=s.___wasm_call_ctors=s.asm.__wasm_call_ctors).apply(null,arguments)},ag=s._init=function(){return(ag=s._init=s.asm.init).apply(null,arguments)},sg=s._init_with_threads_count=function(){return(sg=s._init_with_threads_count=s.asm.init_with_threads_count).apply(null,arguments)},dc=s._get_threads_count=function(){return(dc=s._get_threads_count=s.asm.get_threads_count).apply(null,arguments)},pc=s._register_tensor=function(){return(pc=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},ig=s._dispose_data=function(){return(ig=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},ze=s._dispose=function(){return(ze=s._dispose=s.asm.dispose).apply(null,arguments)},og=s._Abs=function(){return(og=s._Abs=s.asm.Abs).apply(null,arguments)},hc=s._Add=function(){return(hc=s._Add=s.asm.Add).apply(null,arguments)},Ji=s._AddN=function(){return(Ji=s._AddN=s.asm.AddN).apply(null,arguments)},Ul=s._All=function(){return(Ul=s._All=s.asm.All).apply(null,arguments)},lg=s._Any=function(){return(lg=s._Any=s.asm.Any).apply(null,arguments)},H5=s._ArgMax=function(){return(H5=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},ug=s._AvgPool=function(){return(ug=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},q5=s._BatchMatMul=function(){return(q5=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},Qi=s._Ceil=function(){return(Qi=s._Ceil=s.asm.Ceil).apply(null,arguments)},dg=s._ClipByValue=function(){return(dg=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},pg=s._Conv2D=function(){return(pg=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},hg=s._Conv2DBackpropInput=function(){return(hg=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},cg=s._Cos=function(){return(cg=s._Cos=s.asm.Cos).apply(null,arguments)},fg=s._Cosh=function(){return(fg=s._Cosh=s.asm.Cosh).apply(null,arguments)},mg=s._CropAndResize=function(){return(mg=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},cc=s._Cumsum=function(){return(cc=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},gg=s._DepthToSpace=function(){return(gg=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},yg=s._DepthwiseConv2dNative=function(){return(yg=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},Ag=s._Elu=function(){return(Ag=s._Elu=s.asm.Elu).apply(null,arguments)},xg=s._Equal=function(){return(xg=s._Equal=s.asm.Equal).apply(null,arguments)},bg=s._Exp=function(){return(bg=s._Exp=s.asm.Exp).apply(null,arguments)},fc=s._FlipLeftRight=function(){return(fc=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},vg=s._Floor=function(){return(vg=s._Floor=s.asm.Floor).apply(null,arguments)},wg=s._FloorDiv=function(){return(wg=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},eo=s._FusedBatchNorm=function(){return(eo=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},Hd=s._FusedConv2D=function(){return(Hd=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},kg=s._FusedDepthwiseConv2D=function(){return(kg=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Ig=s._Gather=function(){return(Ig=s._Gather=s.asm.Gather).apply(null,arguments)},Sg=s._GatherNd=function(){return(Sg=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},Ke=s._Greater=function(){return(Ke=s._Greater=s.asm.Greater).apply(null,arguments)},Cg=s._GreaterEqual=function(){return(Cg=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},Tg=s._LeakyRelu=function(){return(Tg=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},Ng=s._Less=function(){return(Ng=s._Less=s.asm.Less).apply(null,arguments)},Eg=s._LessEqual=function(){return(Eg=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},Rg=s._Log=function(){return(Rg=s._Log=s.asm.Log).apply(null,arguments)},Mg=s._LogicalAnd=function(){return(Mg=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},qd=s._Max=function(){return(qd=s._Max=s.asm.Max).apply(null,arguments)},mc=s._MaxPool=function(){return(mc=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},gc=s._Maximum=function(){return(gc=s._Maximum=s.asm.Maximum).apply(null,arguments)},Fg=s._Mean=function(){return(Fg=s._Mean=s.asm.Mean).apply(null,arguments)},$g=s._Min=function(){return($g=s._Min=s.asm.Min).apply(null,arguments)},Pg=s._Minimum=function(){return(Pg=s._Minimum=s.asm.Minimum).apply(null,arguments)},_g=s._MirrorPad=function(){return(_g=s._MirrorPad=s.asm.MirrorPad).apply(null,arguments)},zg=s._Multiply=function(){return(zg=s._Multiply=s.asm.Multiply).apply(null,arguments)},Og=s._Neg=function(){return(Og=s._Neg=s.asm.Neg).apply(null,arguments)},Ft=s._NonMaxSuppressionV3=function(){return(Ft=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},Dg=s._NonMaxSuppressionV4=function(){return(Dg=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},Lg=s._NonMaxSuppressionV5=function(){return(Lg=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},Bg=s._NotEqual=function(){return(Bg=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},Gl=s._OneHot=function(){return(Gl=s._OneHot=s.asm.OneHot).apply(null,arguments)},yc=s._PadV2=function(){return(yc=s._PadV2=s.asm.PadV2).apply(null,arguments)},Ac=s._Pow=function(){return(Ac=s._Pow=s.asm.Pow).apply(null,arguments)},xc=s._Prelu=function(){return(xc=s._Prelu=s.asm.Prelu).apply(null,arguments)},Wg=s._Prod=function(){return(Wg=s._Prod=s.asm.Prod).apply(null,arguments)},bc=s._RealDiv=function(){return(bc=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},Vg=s._Relu=function(){return(Vg=s._Relu=s.asm.Relu).apply(null,arguments)},K5=s._Relu6=function(){return(K5=s._Relu6=s.asm.Relu6).apply(null,arguments)},vc=s._ResizeBilinear=function(){return(vc=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},X5=s._Reverse=function(){return(X5=s._Reverse=s.asm.Reverse).apply(null,arguments)},Ug=s._RotateWithOffset=function(){return(Ug=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},Gg=s._Round=function(){return(Gg=s._Round=s.asm.Round).apply(null,arguments)},jg=s._Rsqrt=function(){return(jg=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},Hg=s._ScatterNd=function(){return(Hg=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},qg=s._SelectV2=function(){return(qg=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},Kg=s._Sigmoid=function(){return(Kg=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},Xg=s._Sin=function(){return(Xg=s._Sin=s.asm.Sin).apply(null,arguments)},Zg=s._Softmax=function(){return(Zg=s._Softmax=s.asm.Softmax).apply(null,arguments)},Yg=s._SparseFillEmptyRows=function(){return(Yg=s._SparseFillEmptyRows=s.asm.SparseFillEmptyRows).apply(null,arguments)},Jg=s._SparseReshape=function(){return(Jg=s._SparseReshape=s.asm.SparseReshape).apply(null,arguments)},Qg=s._SparseSegmentReduction=function(){return(Qg=s._SparseSegmentReduction=s.asm.SparseSegmentReduction).apply(null,arguments)},e1=s._Sqrt=function(){return(e1=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},t1=s._Square=function(){return(t1=s._Square=s.asm.Square).apply(null,arguments)},r1=s._SquaredDifference=function(){return(r1=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},n1=s._Step=function(){return(n1=s._Step=s.asm.Step).apply(null,arguments)},a1=s._StridedSlice=function(){return(a1=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},s1=s._Sub=function(){return(s1=s._Sub=s.asm.Sub).apply(null,arguments)},i1=s._Sum=function(){return(i1=s._Sum=s.asm.Sum).apply(null,arguments)},o1=s._Tan=function(){return(o1=s._Tan=s.asm.Tan).apply(null,arguments)},l1=s._Tanh=function(){return(l1=s._Tanh=s.asm.Tanh).apply(null,arguments)},u1=s._Tile=function(){return(u1=s._Tile=s.asm.Tile).apply(null,arguments)},d1=s._TopK=function(){return(d1=s._TopK=s.asm.TopK).apply(null,arguments)},p1=s._Transform=function(){return(p1=s._Transform=s.asm.Transform).apply(null,arguments)},h1=s._Transpose=function(){return(h1=s._Transpose=s.asm.Transpose).apply(null,arguments)},c1=s.__FusedMatMul=function(){return(c1=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},f1=s._malloc=function(){return(f1=s._malloc=s.asm.malloc).apply(null,arguments)},m1=s._free=function(){return(m1=s._free=s.asm.free).apply(null,arguments)},g1=s.___errno_location=function(){return(g1=s.___errno_location=s.asm.__errno_location).apply(null,arguments)},y1=s._emscripten_main_thread_process_queued_calls=function(){return(y1=s._emscripten_main_thread_process_queued_calls=s.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},wc=s.stackSave=function(){return(wc=s.stackSave=s.asm.stackSave).apply(null,arguments)},kc=s.stackRestore=function(){return(kc=s.stackRestore=s.asm.stackRestore).apply(null,arguments)},Kd=s.stackAlloc=function(){return(Kd=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},A1=s.dynCall_iijjiiii=function(){return(A1=s.dynCall_iijjiiii=s.asm.dynCall_iijjiiii).apply(null,arguments)},x1=s.dynCall_jiji=function(){return(x1=s.dynCall_jiji=s.asm.dynCall_jiji).apply(null,arguments)};s.cwrap=Me;var jl;function Xd(H){this.name="ExitStatus",this.message="Program terminated with exit("+H+")",this.status=H}cs=function H(){jl||Zd(),jl||(cs=H)};function Zd(H){if(H=H||d,vn>0||(Jh(),vn>0))return;function ne(){jl||(jl=!0,s.calledRun=!0,!ae&&(Qh(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),ec()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}s.run=Zd;function Z5(H){de=H,Vd()||(s.onExit&&s.onExit(H),ae=!0),p(H,new Xd(H))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();Zd();var Hl;l&&(Hl={uncaughtException:process.listeners("uncaughtException").filter(function(H){return!l.uncaughtException.indexOf(H)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(H){return!l.unhandledRejection.indexOf(H)>-1})});var ql;if(typeof a!="undefined")ql=a;else if(typeof WasmBackendModuleThreadedSimd!="undefined")ql=WasmBackendModuleThreadedSimd;else throw new Error("Could not find wasm module in post.js");if(Hl){var b1=ql._dispose;ql._dispose=function(){b1(),Hl.uncaughtException.forEach(function(H){process.removeListener("uncaughtException",H)}),Hl.unhandledRejection.forEach(function(H){process.removeListener("unhandledRejection",H)})}}return a.ready}})();typeof e=="object"&&typeof t=="object"?t.exports=r:typeof define=="function"&&define.amd?define([],function(){return r}):typeof e=="object"&&(e.WasmBackendModule=r)}}),qE=1e-7,KE=1e-4,zp=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},Su=class{refCount(e){return kn("refCount")}incRef(e){return kn("incRef")}timerAvailable(){return!0}time(e){return kn("time")}read(e){return kn("read")}readSync(e){return kn("readSync")}readToGPU(e,t){return kn("readToGPU")}numDataIds(){return kn("numDataIds")}disposeData(e,t){return kn("disposeData")}write(e,t,r){return kn("write")}move(e,t,r,n,a){return kn("move")}memory(){return kn("memory")}floatPrecision(){return kn("floatPrecision")}epsilon(){return this.floatPrecision()===32?qE:KE}dispose(){return kn("dispose")}};function kn(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 jv(e){let t=e.length,r=0;for(;t>0;)r=Math.random()*t|0,t--,tf(e,t,r)}function XE(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 r=e.length,n=0;for(;r>0;)n=Math.random()*r|0,r--,tf(e,r,n),tf(t,r,n)}function wp(e,t,r){return Math.max(e,Math.min(t,r))}function ZE(e){return e%2===0?e:e+1}function tf(e,t,r){let n=e[t];e[t]=e[r],e[r]=n}function YE(e){let t=0;for(let r=0;r<e.length;r++)t+=e[r];return t}function JE(e,t){let r=Math.random();return t*r+(1-r)*e}function QE(e,t){let r=0;for(let n=0;n<e.length;n++){let a=Number(e[n])-Number(t[n]);r+=a*a}return r}function P(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function Wr(e,t,r=""){P(Ws(e,t),()=>r+` Shapes ${e} and ${t} must match`)}function Ro(e){P(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Ao(e,t=[],r=!1){if(t==null&&(t=[]),Array.isArray(e)||wr(e)&&!r)for(let n=0;n<e.length;++n)Ao(e[n],t,r);else t.push(e);return t}function Ct(e){if(e.length===0)return 1;let t=e[0];for(let r=1;r<e.length;r++)t*=e[r];return t}function eR(e){return e.length===0}function Ws(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let r=0;r<e.length;r++)if(e[r]!==t[r])return!1;return!0}function pu(e){return e%1===0}function tR(e){if(Math.tanh!=null)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function rR(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function nR(e){let t=new Uint32Array(e);for(let r=0;r<e;++r)t[r]=r;return jv(t),t}function yp(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function aR(e,t=n=>0,r){return new Promise((n,a)=>{let s=0,i=()=>{if(e()){n();return}s++;let o=t(s);if(r!=null&&s>=r){a();return}setTimeout(i,o)};i()})}function sR(e,t){let r=1,n=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)r*=e[s];else if(e[s]===-1){if(n!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${n} and dim ${s}`);n=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(n===-1){if(t>0&&t!==r)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(r===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%r!==0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${r}`);let a=e.slice();return a[n]=t/r,a}function Bn(e,t){let r=t.length;return e=e==null?t.map((n,a)=>a):[].concat(e),P(e.every(n=>n>=-r&&n<r),()=>`All values in axis param must be in range [-${r}, ${r}) but got axis ${e}`),P(e.every(n=>pu(n)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(n=>n<0?r+n:n)}function Hv(e,t){let r=[],n=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:Bn(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&&(r.push(e[o]),n.push(o)),s[i]<=o&&i++}e[o]!==1&&(r.push(e[o]),n.push(o))}return{newShape:r,keptDims:n}}function qv(e,t){let r=null;if(e==null||e==="float32")r=new Float32Array(t);else if(e==="int32")r=new Int32Array(t);else if(e==="bool")r=new Uint8Array(t);else throw new Error(`Unknown data type ${e}`);return r}function Kv(e,t){let r=null;if(e==null||e==="float32")r=new Float32Array(t);else if(e==="int32")r=new Int32Array(t);else if(e==="bool")r=new Uint8Array(t);else if(e==="string")r=new Array(t);else throw new Error(`Unknown data type ${e}`);return r}function Xv(e,t){for(let r=0;r<e.length;r++){let n=e[r];if(isNaN(n)||!isFinite(n))throw Error(`A tensor of type ${t} being uploaded contains ${n}.`)}}function Zv(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function iR(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function wr(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray}function U1(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Yv(e){if(e==null)return 0;let t=0;return e.forEach(r=>t+=r.length),t}function vs(e){return typeof e=="string"||e instanceof String}function Jv(e){return typeof e=="boolean"}function Qv(e){return typeof e=="number"}function Ff(e){return Array.isArray(e)?Ff(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray?"int32":Qv(e)?"float32":vs(e)?"string":Jv(e)?"bool":"float32"}function Cs(e){return!!(e&&e.constructor&&e.call&&e.apply)}function rf(e,t){for(let r=t;r<e;++r)if(e%r===0)return r;return e}function Cu(e){let t=e.length;if(t<2)return[];let r=new Array(t-1);r[t-2]=e[t-1];for(let n=t-3;n>=0;--n)r[n]=r[n+1]*e[n+1];return r}function ew(e,t,r,n=!1){let a=new Array;if(t.length===1){let s=t[0]*(n?2:1);for(let i=0;i<s;i++)a[i]=r[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((l,u)=>l*u)*(n?2:1);for(let l=0;l<s;l++)a[l]=ew(e+l*o,i,r,n)}return a}function iu(e,t,r=!1){if(e.length===0)return t[0];let n=e.reduce((a,s)=>a*s)*(r?2:1);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${r?" for a complex tensor":""}.`);return ew(0,e,t,r)}function jy(e,t){let r=$f(e,t);for(let n=0;n<r.length;n++)r[n]=1;return r}function $f(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 oR(e,t){let r=e.reduce((n,a)=>n*a,1);if(t==null||t==="float32")return iu(e,new Float32Array(r));if(t==="int32")return iu(e,new Int32Array(r));if(t==="bool")return iu(e,new Uint8Array(r));throw new Error(`Unknown data type ${t}`)}function Hy(e){e.forEach(t=>{P(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function lR(e,t,r){if(t===0)return 0;if(t===1)return e[0];let n=e[e.length-1];for(let a=0;a<e.length-1;++a)n+=r[a]*e[a];return n}function uR(e,t,r){if(t===0)return[];if(t===1)return[e];let n=new Array(t);for(let a=0;a<n.length-1;++a)n[a]=Math.floor(e/r[a]),e-=n[a]*r[a];return n[n.length-1]=e,n}function qy(e){return e&&e.then&&typeof e.then=="function"}var l3="tfjsflags",tw=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=dR,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&(J().getBool("IS_TEST")||J().getBool("PROD")||console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)),this.platformName=e,this.platform=t}registerFlag(e,t,r){if(this.flagRegistry[e]={evaluationFn:t,setHook:r},this.urlFlags[e]!=null){let n=this.urlFlags[e];J().getBool("IS_TEST")||J().getBool("PROD")||console.warn(`Setting feature override from URL ${e}: ${n}.`),this.set(e,n)}}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(qy(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);l3 in e&&e[l3].split(",").forEach(t=>{let[r,n]=t.split(":");this.urlFlags[r]=hR(r,n)})}};function dR(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(r,...n)=>(pR(t,n[0],n[1]),n.join("="))),t}function pR(e,t,r){e[decodeURIComponent(t)]=decodeURIComponent(r||"")}function hR(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 ${e}.`)}function J(){return ma}var ma=null;function cR(e){ma=e}var N1;function rw(){if(N1==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");N1=e}return N1}function fR(){let e=rw();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function Ky(e,t){let r=fR();if(r.has(e))return r.get(e);{let n=t();return r.set(e,n),r.get(e)}}var Mo="Abs",Tu="Acos",Nu="Acosh",Ha="Add",Vs="AddN",Eu="All",Ru="Any",Us="ArgMax",Mu="ArgMin",Fu="Asin",$u="Asinh",Pu="Atan",_u="Atanh",zu="Atan2",Gs="AvgPool",Pf="AvgPoolGrad",Op="AvgPool3D",_f="AvgPool3DGrad",js="BatchMatMul",Fo="BatchToSpaceND",zf="Bincount",nw="BroadcastTo",Of="BroadcastArgs",Hs="Cast",qs="Ceil",qa="ClipByValue",Dp="Complex",Lp="ComplexAbs",$o="Concat",Ks="Conv2D",Df="Conv2DBackpropFilter",Xs="Conv2DBackpropInput",Bp="Conv3D",Lf="Conv3DBackpropFilterV2",Bf="Conv3DBackpropInputV2",Zs="Cos",Ys="Cosh",Po="Cumsum",_o="CropAndResize",Wf="DenseBincount",zo="DepthToSpace",Js="DepthwiseConv2dNative",Vf="DepthwiseConv2dNativeBackpropFilter",Uf="DepthwiseConv2dNativeBackpropInput",Gf="Diag",Wp="Dilation2D",nf="Dilation2DBackpropInput",af="Dilation2DBackpropFilter",Qs="RealDiv",Vp="Einsum",ei="Elu",jf="EluGrad",Ou="Erf",Oo="Equal",ti="Exp",Do="ExpandDims",Lo="Expm1",Hf="FFT",Du="Fill",Bo="FlipLeftRight",ri="Floor",ni="FloorDiv",ai="FusedBatchNorm",Wo="GatherV2",Vo="GatherNd",Uo="Greater",si="GreaterEqual",ii="Identity",qf="IFFT",Up="Imag",Lu="IsFinite",Bu="IsInf",Wu="IsNan",oi="LeakyRelu",Go="Less",jo="LessEqual",Kf="LinSpace",li="Log",Vu="Log1p",Ho="LogicalAnd",Uu="LogicalNot",Gp="LogicalOr",aw="LogSoftmax",jp="LRN",Xf="LRNGrad",ui="Max",di="Maximum",pi="MaxPool",Zf="MaxPoolGrad",Hp="MaxPool3D",Yf="MaxPool3DGrad",Jf="MaxPoolWithArgmax",hi="Mean",ci="Min",fi="Minimum",mi="MirrorPad",Gu="Mod",Qf="Multinomial",gi="Multiply",qo="Neg",Ko="NotEqual",Xo="NonMaxSuppressionV3",ju="NonMaxSuppressionV4",Zo="NonMaxSuppressionV5",Yo="OnesLike",Jo="OneHot",Qo="Pack",yi="PadV2",mR="Pool",Ai="Pow",xi="Prelu",el="Prod",Hu="Range",qp="Real",qu="Reciprocal",bi="Relu",tl="Reshape",Ku="ResizeNearestNeighbor",em="ResizeNearestNeighborGrad",vi="ResizeBilinear",tm="ResizeBilinearGrad",wi="Relu6",rl="Reverse",nl="Round",ki="Rsqrt",al="ScatterNd",sl="Select",Xu="Selu",il="Slice",Ii="Sin",ol="Sinh",Zu="Sign",Si="Sigmoid",Yu="Softplus",Ci="Sqrt",Ti="Sum",ll="SpaceToBatchND",ul="SplitV",Ni="Softmax",Kp="SparseFillEmptyRows",Ju="SparseReshape",Xp="SparseSegmentMean",Zp="SparseSegmentSum",Yp="SparseToDense",Ei="SquaredDifference",Qu="Square",dl="StridedSlice",Jp="StringNGrams",rm="StringSplit",nm="StringToHashBucketFast",Ri="Sub",pl="Tan",Mi="Tanh",Ka="Tile",hl="TopK",cl="Transform",Fi="Transpose",am="Unique",fl="Unpack",Qp="UnsortedSegmentSum",ml="ZerosLike",$i="Step",kp="FromPixels",gl="RotateWithOffset",Ts="_FusedMatMul",Ns="FusedConv2D",Es="FusedDepthwiseConv2D";function bs(...e){J().getBool("IS_TEST")||J().getBool("PROD")||console.warn(...e)}function gR(...e){J().getBool("IS_TEST")||J().getBool("PROD")||console.log(...e)}var hu=Ky("kernelRegistry",()=>new Map),Ip=Ky("gradRegistry",()=>new Map);function sf(e,t){let r=Xy(e,t);return hu.get(r)}function G1(e){return Ip.get(e)}function Na(e){let t=hu.entries(),r=[];for(;;){let{done:n,value:a}=t.next();if(n)break;let[s,i]=a,[o]=s.split("_");o===e&&r.push(i)}return r}function Wn(e){let{kernelName:t,backendName:r}=e,n=Xy(t,r);hu.has(n)&&bs(`The kernel '${t}' for backend '${r}' is already registered`),hu.set(n,e)}function sw(e){let{kernelName:t}=e;Ip.has(t)&&J().getBool("DEBUG")&&bs(`Overriding the gradient for '${t}'`),Ip.set(t,e)}function yR(e,t){let r=Xy(e,t);if(!hu.has(r))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);hu.delete(r)}function AR(e){if(!Ip.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Ip.delete(e)}function xR(e,t){Na(e).forEach(r=>{let n=Object.assign({},r,{backendName:t});Wn(n)})}function Xy(e,t){return`${t}_${e}`}var w={};Le(w,{arraysEqual:()=>Ws,assert:()=>P,assertNonNegativeIntegerDimensions:()=>Hy,assertNonNull:()=>Ro,assertShapesMatch:()=>Wr,bytesFromStringArray:()=>Yv,bytesPerElement:()=>U1,checkConversionForErrors:()=>Xv,clamp:()=>wp,computeStrides:()=>Cu,createScalarValue:()=>SR,createShuffledIndices:()=>nR,decodeString:()=>of,distSquared:()=>QE,encodeString:()=>th,fetch:()=>TR,fingerPrint64:()=>IR,flatten:()=>Ao,getArrayFromDType:()=>Kv,getTypedArrayFromDType:()=>qv,hasEncodingLoss:()=>iR,hexToLong:()=>eh,indexToLoc:()=>uR,inferDtype:()=>Ff,inferFromImplicitShape:()=>sR,isBoolean:()=>Jv,isFunction:()=>Cs,isInt:()=>pu,isNumber:()=>Qv,isPromise:()=>qy,isScalarShape:()=>eR,isString:()=>vs,isTypedArray:()=>wr,isValidDtype:()=>Zv,locToIndex:()=>lR,makeOnesTypedArray:()=>jy,makeZerosNestedTypedArray:()=>oR,makeZerosTypedArray:()=>$f,nearestDivisor:()=>rf,nearestLargerEven:()=>ZE,now:()=>Sp,parseAxisParam:()=>Bn,randUniform:()=>JE,repeatedTry:()=>aR,rightPad:()=>yp,shuffle:()=>jv,shuffleCombo:()=>XE,sizeFromShape:()=>Ct,sizeToSquarishShape:()=>rR,squeezeShape:()=>Hv,sum:()=>YE,swap:()=>tf,tanh:()=>tR,toNestedArray:()=>iu,toTypedArray:()=>sm});var u3=Eo(ME()),io=u3.default||u3;function eh(e){return io.fromString(e,!0,16)}var iw=eh("c3a5c85c97cb3127"),ao=eh("b492b66fbe98f273"),Pr=eh("9ae16a3b2f90404f");function j1(e){return e.xor(e.shru(47))}function ow(e,t,r){let n=e.slice(t,t+r);return io.fromBytes(Array.from(n),!0,!0)}function wt(e,t){return ow(e,t,8)}function d3(e,t){return ow(e,t,4)}function pr(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function ks(e,t,r=eh("9ddfea08eb382d69")){let n=e.xor(t).mul(r);n=n.xor(n.shru(47));let a=t.xor(n).mul(r);return a=a.xor(a.shru(47)),a=a.mul(r),a}function bR(e,t,r,n,a,s){a=a.add(e),s=pr(s.add(a).add(n),21);let i=a;return a=a.add(t),a=a.add(r),s=s.add(pr(a,44)),[a.add(n),s.add(i)]}function Fc(e,t,r,n){return bR(wt(e,t),wt(e,t+8),wt(e,t+16),wt(e,t+24),r,n)}function vR(e,t=e.length){if(t>=8){let r=Pr.add(t*2),n=wt(e,0).add(Pr),a=wt(e,t-8),s=pr(a,37).mul(r).add(n),i=pr(n,25).add(a).mul(r);return ks(s,i,r)}if(t>=4){let r=Pr.add(t*2),n=d3(e,0);return ks(n.shl(3).add(t),d3(e,t-4),r)}if(t>0){let r=e[0],n=e[t>>1],a=e[t-1],s=r+(n<<8),i=t+(a<<2);return j1(Pr.mul(s).xor(iw.mul(i))).mul(Pr)}return Pr}function wR(e,t=e.length){let r=Pr.add(t*2),n=wt(e,0).mul(ao),a=wt(e,8),s=wt(e,t-8).mul(r),i=wt(e,t-16).mul(Pr);return ks(pr(n.add(a),43).add(pr(s,30)).add(i),n.add(pr(a.add(Pr),18)).add(s),r)}function kR(e,t=e.length){let r=Pr.add(t*2),n=wt(e,0).mul(Pr),a=wt(e,8),s=wt(e,t-8).mul(r),i=wt(e,t-16).mul(Pr),o=pr(n.add(a),43).add(pr(s,30)).add(i),l=ks(o,n.add(pr(a.add(Pr),18)).add(s),r),u=wt(e,16).mul(r),d=wt(e,24),h=o.add(wt(e,t-32)).mul(r),p=l.add(wt(e,t-24)).mul(r);return ks(pr(u.add(d),43).add(pr(h,30)).add(p),u.add(pr(d.add(n),18)).add(h),r)}function IR(e,t=e.length){let r=io.fromNumber(81,!0);if(t<=32)return t<=16?vR(e,t):wR(e,t);if(t<=64)return kR(e,t);let n=r,a=r.mul(ao).add(113),s=j1(a.mul(Pr).add(113)).mul(Pr),i=[io.UZERO,io.UZERO],o=[io.UZERO,io.UZERO];n=n.mul(Pr).add(wt(e,0));let l=0,u=(t-1>>6)*64,d=u+(t-1&63)-63;do n=pr(n.add(a).add(i[0]).add(wt(e,l+8)),37).mul(ao),a=pr(a.add(i[1]).add(wt(e,l+48)),42).mul(ao),n=n.xor(o[1]),a=a.add(i[0]).add(wt(e,l+40)),s=pr(s.add(o[0]),33).mul(ao),i=Fc(e,l,i[1].mul(ao),n.add(o[0])),o=Fc(e,l+32,s.add(o[1]),a.add(wt(e,l+16))),[s,n]=[n,s],l+=64;while(l!==u);let h=ao.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]),n=pr(n.add(a).add(i[0]).add(wt(e,l+8)),37).mul(h),a=pr(a.add(i[1]).add(wt(e,l+48)),42).mul(h),n=n.xor(o[1].mul(9)),a=a.add(i[0].mul(9).add(wt(e,l+40))),s=pr(s.add(o[0]),33).mul(h),i=Fc(e,l,i[1].mul(h),n.add(o[0])),o=Fc(e,l+32,s.add(o[1]),a.add(wt(e,l+16))),[s,n]=[n,s],ks(ks(i[0],o[0],h).add(j1(a).mul(iw)).add(s),ks(i[1],o[1],h).add(n),h)}function SR(e,t){return t==="string"?th(e):sm([e],t)}function CR(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function sm(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Ao(e)),J().getBool("DEBUG")&&Xv(e,t),CR(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 r=new Uint8Array(e.length);for(let n=0;n<r.length;++n)Math.round(e[n])!==0&&(r[n]=1);return r}else throw new Error(`Unknown data type ${t}`)}function Sp(){return J().platform.now()}function TR(e,t){return J().platform.fetch(e,t)}function th(e,t="utf-8"){return t=t||"utf-8",J().platform.encode(e,t)}function of(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var NR=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new RR)}profileKernel(e,t,r){let n,a=()=>{n=r()},s,i=Sp();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of n)o.dataSync();s=Promise.resolve({kernelMs:Sp()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<n.length;o++){let l=n[o];l.data().then(u=>{ER(u,l.dtype,e)})}return{kernelName:e,outputs:n,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:r,timeMs:n,inputs:a,extraInfo:s}=e;r.forEach(i=>{Promise.all([i.data(),n,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],a,o[2])})})}};function ER(e,t,r){if(t!=="float32")return!1;for(let n=0;n<e.length;n++){let a=e[n];if(isNaN(a)||!isFinite(a))return console.warn(`Found ${a} in the result of '${r}'`),!0}return!1}var RR=class{logKernelProfile(e,t,r,n,a,s){let i=typeof n=="number"?yp(`${n}ms`,9):n.error,o=yp(e,25),l=t.rank,u=t.size,d=yp(t.shape.toString(),14),h="";for(let p in a){let c=a[p];if(c!=null){let f=c.shape||t.shape,m=f.length;h+=`${p}: ${m}D ${m>0?f:""} `}}console.log(`%c${o}	%c${i}	%c${l}D ${d}	%c${u}	%c${h}	%c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function MR(e,t,r){let n={},a={};for(let l=0;l<t.length;l++)n[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],d=u.inputs;for(let h in d){let p=d[h],c=!1;for(let f=0;f<t.length;f++)if(n[p.id]){u.outputs.forEach(m=>n[m.id]=!0),c=!0,a[u.id]=!0;break}if(c)break}}let s={};s[r.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],d=u.inputs;for(let h=0;h<u.outputs.length;h++)if(s[u.outputs[h].id]){for(let p in d)s[d[p].id]=!0,i[u.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let u=e[l];if(a[u.id]&&i[u.id]){let d={};for(let p in u.inputs){let c=u.inputs[p];n[c.id]&&(d[p]=c)}let h=Object.assign({},u);h.inputs=d,h.outputs=u.outputs,o.push(h)}}return o}function FR(e,t,r,n){for(let a=t.length-1;a>=0;a--){let s=t[a],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=r(()=>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(!Ws(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 h=e[d.id];e[d.id]=n(h,u),h.dispose()}}}}var p3=20,np=3,E1=7;function $R(e,t,r,n){let a=Cu(t),s=PR(e,t,r,a),i=t.length,o=Uc(e,t,r,a,s),l=["Tensor"];return n&&(l.push(`  dtype: ${r}`),l.push(`  rank: ${i}`),l.push(`  shape: [${t}]`),l.push("  values:")),l.push(o.map(u=>"    "+u).join(`
`)),l.join(`
`)}function PR(e,t,r,n){let a=Ct(t),s=n[n.length-1],i=new Array(s).fill(0),o=t.length,l=r==="complex64"?lp(e):e;if(o>1)for(let u=0;u<a/s;u++){let d=u*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],op(l[d+h],0,r).length)}return i}function op(e,t,r){let n;return Array.isArray(e)?n=`${parseFloat(e[0].toFixed(E1))} + ${parseFloat(e[1].toFixed(E1))}j`:vs(e)?n=`'${e}'`:r==="bool"?n=lw(e):n=parseFloat(e.toFixed(E1)).toString(),yp(n,t)}function lw(e){return e===0?"false":"true"}function Uc(e,t,r,n,a,s=!0){let i=r==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(r==="complex64"){let m=lp(e);return[op(m[0],0,r)]}return r==="bool"?[lw(e[0])]:[e[0].toString()]}if(l===1){if(o>p3){let g=np*i,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((o-np)*i,o*i));return r==="complex64"&&(y=lp(y),A=lp(A)),["["+y.map((x,b)=>op(x,a[b],r)).join(", ")+", ..., "+A.map((x,b)=>op(x,a[o-np+b],r)).join(", ")+"]"]}let m=r==="complex64"?lp(e):Array.from(e);return["["+m.map((g,y)=>op(g,a[y],r)).join(", ")+"]"]}let u=t.slice(1),d=n.slice(1),h=n[0]*i,p=[];if(o>p3){for(let m=0;m<np;m++){let g=m*h,y=g+h;p.push(...Uc(e.slice(g,y),u,r,d,a,!1))}p.push("...");for(let m=o-np;m<o;m++){let g=m*h,y=g+h;p.push(...Uc(e.slice(g,y),u,r,d,a,m===o-1))}}else for(let m=0;m<o;m++){let g=m*h,y=g+h;p.push(...Uc(e.slice(g,y),u,r,d,a,m===o-1))}let c=l===2?",":"";p[0]="["+p[0]+c;for(let m=1;m<p.length-1;m++)p[m]=" "+p[m]+c;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return p[p.length-1]=" "+p[p.length-1]+"]"+(s?"":f),p}function lp(e){let t=[];for(let r=0;r<e.length;r+=2)t.push([e[r],e[r+1]]);return t}var rr=class{constructor(e,t,r){if(this.dtype=t,this.shape=e.slice(),this.size=Ct(e),r!=null){let n=r.length;P(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${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=r||Kv(t,this.size),this.strides=Cu(e)}set(e,...t){t.length===0&&(t=[0]),P(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let r=this.locToIndex(t);this.values[r]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}t++}let r=e[e.length-1];for(let n=0;n<e.length-1;++n)r+=this.strides[n]*e[n];return this.values[r]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let r=0;r<e.length-1;++r)t+=this.strides[r]*e[r];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let r=0;r<t.length-1;++r)t[r]=Math.floor(e/this.strides[r]),e-=t[r]*this.strides[r];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return ra().makeTensor(this.values,this.shape,this.dtype)}},ra=null,ru=null,_R=null;function zR(e){ra=e}function OR(e){ru=e}function DR(e){_R=e}var rt=class{constructor(e,t,r,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ct(e),this.strides=Cu(e),this.dataId=r,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ru.buffer(this.shape,this.dtype,e)}bufferSync(){return ru.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return iu(this.shape,e,this.dtype==="complex64")}arraySync(){return iu(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=ra().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(r=>of(r))}catch(r){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),ra().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=ra().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>of(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 ra().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ra().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ru.print(this,e)}clone(){return this.throwIfDisposed(),ru.clone(this)}toString(e=!1){let t=this.dataSync();return $R(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ru.cast(this,e)}variable(e=!0,t,r){return this.throwIfDisposed(),ra().makeVariable(this,e,t,r)}};Object.defineProperty(rt,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function LR(){return Ky("Tensor",()=>rt)}LR();var Cp=class extends rt{constructor(e,t,r,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=r}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Ws(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ra().disposeTensor(this),this.dataId=e.dataId,ra().incRef(this,null)}dispose(){ra().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Cp,Symbol.hasInstance,{value:e=>e instanceof rt&&e.assign!=null&&e.assign instanceof Function});var ia={};Le(ia,{assertTypesMatch:()=>fw,getTensorsInContainer:()=>Zy,isTensorInList:()=>WR,makeTypesMatch:()=>Ot});var uw=(e=>(e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6",e))(uw||{}),dw=(e=>(e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64",e))(dw||{}),pw=(e=>(e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64",e))(pw||{}),hw=(e=>(e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64",e))(hw||{}),cw=(e=>(e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64",e))(cw||{}),BR={float32:hw,int32:dw,bool:pw,complex64:cw};function Dr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return BR[e][t]}function rh(e){return Dr(e,"int32")}function Ot(e,t){if(e.dtype===t.dtype)return[e,t];let r=Dr(e.dtype,t.dtype);return[e.cast(r),t.cast(r)]}function fw(e,t){P(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function WR(e,t){return t.some(r=>r.id===e.id)}function Zy(e){let t=[];return mw(e,t,new Set),t}function mw(e,t,r){if(e==null)return;if(e instanceof rt){t.push(e);return}if(!VR(e))return;let n=e;for(let a in n){let s=n[a];r.has(s)||(r.add(s),mw(s,t,r))}}function VR(e){return Array.isArray(e)||typeof e=="object"}function R1(e){return e.kernelName!=null}var h3=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},H1=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new h3}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t];if(await this.initializeBackend(r).success){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,r=1){return e in this.registryFactory?(bs(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:r},!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:r}=this.initializeBackend(e);if(!(r?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new NR(this.backendInstance),!0}setupRegisteredKernels(){Na(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Na(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 r=t.factory();if(r&&!(r instanceof Su)&&typeof r.then=="function"){let n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,bs(`Initialization of backend ${e} failed`),bs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return bs(`Initialization of backend ${e} failed`),bs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=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");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return H1.nextTensorId++}nextVariableId(){return H1.nextVariableId++}clone(e){let t=B.runKernel(ii,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(Hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,sf(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=R1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(R1(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=sf(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:C,dtype:S}=b;return this.makeTensorFromDataId(v,C,S)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=R1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=G1(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&vs(e[0])&&(a=e.map(o=>th(o)));let s=n.write(a,t,r),i=new rt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Yv(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new rt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Cp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*U1(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof Cp||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 r=e.size*U1(e.dtype);this.state.numBytes-=r}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,r=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=G1(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=$f(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,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=Zy(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof rt,()=>"The result y returned by f() must be a tensor.");let s=MR(this.state.activeTape,t,a);if(!n&&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[a.id]=r==null?UR(a.shape):r,FR(i,s,l=>this.tidy(l),GR);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:a,grads:o}})}customGrad(e){return P(Cs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(i=>i instanceof rt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,n={};t.forEach((i,o)=>{n[o]=i});let a=(i,o)=>(r=e(...t,o),P(r.value instanceof rt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(Cs(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),s=(i,o)=>{let l=r.gradFunc(i,o),u=Array.isArray(l)?l:[l];P(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(...)."),P(u.every(h=>h instanceof rt),()=>"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((h,p)=>{d[p]=()=>h}),d};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Sp(),r=await this.backend.time(e);return r.wallMs=Sp()-t,r}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 h3;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}},Yy=H1;Yy.nextTensorId=0;Yy.nextVariableId=0;function UR(e){let t=jy(Ct(e),"float32");return B.makeTensor(t,e,"float32")}function gw(){let e=rw();if(e._tfengine==null){let t=new tw(e);e._tfengine=new Yy(t)}return cR(e._tfengine.ENV),zR(()=>e._tfengine),e._tfengine}var B=gw();function GR(e,t){let r={a:e,b:t};return B.runKernel(Ha,r)}var nh={};Le(nh,{isBrowser:()=>yw,isMobile:()=>qR,mockIsMobile:()=>HR});function jR(){return typeof navigator!="undefined"&&navigator!=null}var q1;function HR(e){q1=e}function qR(e){if(q1!==void 0)return q1;if(e||jR()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let r=e;return r.userAgentData&&r.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function yw(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var da=J();da.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.")});da.registerFlag("IS_BROWSER",()=>yw());da.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");da.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));da.registerFlag("PROD",()=>!1);da.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>da.getBool("DEBUG"));da.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);da.registerFlag("IS_TEST",()=>!1);da.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);da.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Ea(e,t){let r=e;if(wr(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let n=[];for(;Array.isArray(r)||wr(r)&&t!=="string";)n.push(r.length),r=r[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Aw(e,n,[]),n}function Aw(e,t,r){if(r=r||[],!Array.isArray(e)&&!wr(e)){P(t.length===0,()=>`Element arr[${r.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}P(t.length>0,()=>`Element arr[${r.join("][")}] should be a primitive, but is an array of ${e.length} elements`),P(e.length===t[0],()=>`Element arr[${r.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let n=t.slice(1);for(let a=0;a<e.length;++a)Aw(e[a],n,r.concat(a))}function c3(e,t,r,n){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${r}' passed to '${n}' must be ${e} tensor, but got ${t} tensor`)}}function F(e,t,r,n="numeric"){if(e instanceof rt)return c3(n,e.dtype,t,r),e;let a=Ff(e);if(a!=="string"&&["bool","int32","float32"].indexOf(n)>=0&&(a=n),c3(n,a,t,r),e==null||!wr(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${r}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Ea(e,a);!wr(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?sm(e,a):Ao(e,[],!0);return B.makeTensor(i,s,a)}function Tp(e,t,r,n="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${r} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>F(a,`${t}[${s}]`,r,n))}var xw="__op";function W(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let r=t[0],n=e[r];r.endsWith("_")&&(r=r.substring(0,r.length-1)),r=r+xw;let a=(...s)=>{B.startScope(r);try{let i=n(...s);return qy(i)&&console.error("Cannot return a Promise inside of tidy."),B.endScope(i),i}catch(i){throw B.endScope(null),i}};return Object.defineProperty(a,"name",{value:r,configurable:!0}),a}function KR(e,t){let r=F(e,"real","complex"),n=F(t,"imag","complex");Wr(r.shape,n.shape,`real and imag shapes, ${r.shape} and ${n.shape}, must match in call to tf.complex().`);let a={real:r,imag:n};return B.runKernel(Dp,a)}var Rs=W({complex_:KR});function Pi(e,t,r,n){if(n==null&&(n=Ff(e)),n==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!wr(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Hy(t);let a=Ct(t),s=Ct(r);P(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<r.length;++i){let o=r[i],l=i===r.length-1?o!==Ct(t.slice(i)):!0;P(r[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${r}) does not match the provided shape (${t}). `)}}return!wr(e)&&!Array.isArray(e)&&(e=[e]),t=t||r,e=n!=="string"?sm(e,n):Ao(e,[],!0),B.makeTensor(e,t,n)}function ct(e,t,r){let n=Ea(e,r);return Pi(e,t,n,r)}var K1={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},lf=4;async function XR(e,t){let r=[],n=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let u={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let d=new Promise(async h=>{let p=await l.bytes(),c=p.reduce((g,y)=>g+y.length,0)+lf*p.length,f=new Uint8Array(c),m=0;for(let g=0;g<p.length;g++){let y=p[g],A=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(A,m),m+=lf,f.set(y,m),m+=y.length}h(f)});n.push(d)}else n.push(l.data());t!=null&&(u.group=t),r.push(u)}let s=await Promise.all(n);return{data:ZR(s),specs:r}}function bw(e,t){let r={},n,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=Ct(l),d;if("quantization"in s){let h=s.quantization;if(h.dtype==="uint8"||h.dtype==="uint16"){if(!("min"in h&&"scale"in h))throw new Error(`Weight ${s.name} with quantization ${h.dtype} doesn't have corresponding metadata min and scale.`)}else if(h.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${h.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${h.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let p=K1[h.dtype],c=e.slice(a,a+u*p),f=h.dtype==="uint8"?new Uint8Array(c):new Uint16Array(c);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){d=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];d[m]=g*h.scale+h.min}}else if(h.dtype==="float16")n===void 0&&(n=rM()),d=n(f);else throw new Error(`Unsupported quantization type ${h.dtype} for weight type float32.`);else if(o==="int32"){if(h.dtype!=="uint8"&&h.dtype!=="uint16")throw new Error(`Unsupported quantization type ${h.dtype} for weight type int32.`);d=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];d[m]=Math.round(g*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*p}else if(o==="string"){let h=Ct(s.shape);d=[];for(let p=0;p<h;p++){let c=new Uint32Array(e.slice(a,a+lf))[0];a+=lf;let f=new Uint8Array(e.slice(a,a+c));d.push(f),a+=c}}else{let h=K1[o],p=e.slice(a,a+u*h);if(o==="float32")d=new Float32Array(p);else if(o==="int32")d=new Int32Array(p);else if(o==="bool")d=new Uint8Array(p);else if(o==="complex64"){d=new Float32Array(p);let c=new Float32Array(d.length/2),f=new Float32Array(d.length/2);for(let y=0;y<c.length;y++)c[y]=d[y*2],f[y]=d[y*2+1];let m=ct(c,l,"float32"),g=ct(f,l,"float32");r[i]=Rs(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*h}o!=="complex64"&&(r[i]=ct(d,l,o))}return r}function ZR(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,r=[];e.forEach(s=>{if(t+=s.byteLength,r.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 n=new Uint8Array(t),a=0;return r.forEach(s=>{n.set(new Uint8Array(s.buffer),a),a+=s.byteLength}),n.buffer}var Jy=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function f3(e){return Jy?Buffer.byteLength(e):new Blob([e]).size}function YR(e){if(Jy)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),r="";for(let n=0,a=t.length;n<a;n++)r+=String.fromCharCode(t[n]);return btoa(r)}function JR(e){if(Jy){let n=Buffer.from(e,"base64");return n.buffer.slice(n.byteOffset,n.byteOffset+n.byteLength)}let t=atob(e),r=new Uint8Array(t.length);for(let n=0;n<t.length;++n)r.set([t.charCodeAt(n)],n);return r.buffer}function Qy(e){if(e.length===1)return e[0];let t=0;e.forEach(a=>{t+=a.byteLength});let r=new Uint8Array(t),n=0;return e.forEach(a=>{r.set(new Uint8Array(a),n),n+=a.byteLength}),r.buffer}function m3(e){let t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);let r=e.split(t);return r[r.length-1]}function vw(e,t){let r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:t};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),e.trainingConfig!=null&&(r.trainingConfig=e.trainingConfig),r}async function e2(e,t){let r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};if(e.trainingConfig!=null&&(r.trainingConfig=e.trainingConfig),e.weightsManifest!=null){let[n,a]=await t(e.weightsManifest);r.weightSpecs=n,r.weightData=a}return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),r}function ah(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:f3(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:f3(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function QR(){let e=r=>{let n=r<<13,a=0;for(;(n&8388608)===0;)a-=8388608,n<<=1;return n&=-8388609,a+=947912704,n|a},t=new Uint32Array(2048);t[0]=0;for(let r=1;r<1024;r++)t[r]=e(r);for(let r=1024;r<2048;r++)t[r]=939524096+(r-1024<<13);return t}function eM(){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 tM(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function rM(){let e=QR(),t=eM(),r=tM();return n=>{let a=new ArrayBuffer(4*n.length),s=new Uint32Array(a);for(let i=0;i<n.length;i++){let o=n[i],l=e[r[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(a)}}var Lt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Lt.instance==null&&(Lt.instance=new Lt),Lt.instance}static registerSaveRouter(e){Lt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Lt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Lt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Lt.getHandlers(e,"load",t)}static getHandlers(e,t,r){let n=[];return(t==="load"?Lt.getInstance().loadRouters:Lt.getInstance().saveRouters).forEach(a=>{let s=a(e,r);s!==null&&n.push(s)}),n}},nM=e=>Lt.registerSaveRouter(e),aM=e=>Lt.registerLoadRouter(e),sM=e=>Lt.getSaveHandlers(e),iM=(e,t)=>Lt.getLoadHandlers(e,t),X1="tensorflowjs",Z1=1,po="models_store",ws="model_info_store";function ww(){if(!J().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 Y1(e){let t=e.result;t.createObjectStore(po,{keyPath:"modelPath"}),t.createObjectStore(ws,{keyPath:"modelPath"})}var xo=class{constructor(e){if(this.indexedDB=ww(),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((r,n)=>{let a=this.indexedDB.open(X1,Z1);a.onupgradeneeded=()=>Y1(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(po,"readonly"),o=i.objectStore(po).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));r(o.result.modelArtifacts)},o.onerror=l=>(s.close(),n(o.error)),i.oncomplete=()=>s.close()}else{let i=ah(t),o=s.transaction(ws,"readwrite"),l=o.objectStore(ws),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),d;u.onsuccess=()=>{d=s.transaction(po,"readwrite");let h=d.objectStore(po).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>r({modelArtifactsInfo:i}),h.onerror=p=>{l=o.objectStore(ws);let c=l.delete(this.modelPath);c.onsuccess=()=>(s.close(),n(h.error)),c.onerror=f=>(s.close(),n(h.error))}},u.onerror=h=>(s.close(),n(u.error)),o.oncomplete=()=>{d==null?s.close():d.oncomplete=()=>s.close()}}},a.onerror=s=>n(a.error)})}};xo.URL_SCHEME="indexeddb://";var kw=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME)?oM(e.slice(xo.URL_SCHEME.length)):null;Lt.registerSaveRouter(kw);Lt.registerLoadRouter(kw);function oM(e){return new xo(e)}function lM(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}var uM=class{constructor(){this.indexedDB=ww()}async listModels(){return new Promise((e,t)=>{let r=this.indexedDB.open(X1,Z1);r.onupgradeneeded=()=>Y1(r),r.onsuccess=()=>{let n=r.result,a=n.transaction(ws,"readonly"),s=a.objectStore(ws).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(n.close(),t(s.error)),a.oncomplete=()=>n.close()},r.onerror=n=>t(r.error)})}async removeModel(e){return e=lM(e),new Promise((t,r)=>{let n=this.indexedDB.open(X1,Z1);n.onupgradeneeded=()=>Y1(n),n.onsuccess=()=>{let a=n.result,s=a.transaction(ws,"readwrite"),i=s.objectStore(ws),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return a.close(),r(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),d=()=>{l=a.transaction(po,"readwrite");let h=l.objectStore(po).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=p=>r(o.error)};u.onsuccess=d,u.onerror=h=>(d(),a.close(),r(o.error))}},o.onerror=u=>(a.close(),r(o.error)),s.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}},n.onerror=a=>r(n.error)})}},Va="/",nu="tensorflowjs_models",Iw="info",dM="model_topology",pM="weight_specs",hM="weight_data",cM="model_metadata";function Sw(e){return{info:[nu,e,Iw].join(Va),topology:[nu,e,dM].join(Va),weightSpecs:[nu,e,pM].join(Va),weightData:[nu,e,hM].join(Va),modelMetadata:[nu,e,cM].join(Va)}}function Cw(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function fM(e){let t=e.split(Va);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Va)}function mM(e){return e.startsWith(bo.URL_SCHEME)?e.slice(bo.URL_SCHEME.length):e}var bo=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Sw(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),r=JSON.stringify(e.weightSpecs),n=ah(e);try{this.LS.setItem(this.keys.info,JSON.stringify(n)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,r),this.LS.setItem(this.keys.weightData,YR(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:n}}catch(a){throw Cw(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},r=JSON.parse(this.LS.getItem(this.keys.topology));if(r==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=r;let n=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(n==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=n;let a=this.LS.getItem(this.keys.modelMetadata);if(a!=null){let i=JSON.parse(a);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),i.trainingConfig!=null&&(t.trainingConfig=i.trainingConfig)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=JR(s),t}};bo.URL_SCHEME="localstorage://";var Tw=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bo.URL_SCHEME)?gM(e.slice(bo.URL_SCHEME.length)):null;Lt.registerSaveRouter(Tw);Lt.registerLoadRouter(Tw);function gM(e){return new bo(e)}var yM=class{constructor(){P(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),P(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=nu+Va,r=Va+Iw;for(let n=0;n<this.LS.length;++n){let a=this.LS.key(n);if(a.startsWith(t)&&a.endsWith(r)){let s=fM(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=mM(e);let t=Sw(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let r=JSON.parse(this.LS.getItem(t.info));return Cw(t),r}},ou="://",In=class{constructor(){this.managers={}}static getInstance(){return In.instance==null&&(In.instance=new In),In.instance}static registerManager(e,t){P(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(ou)&&(e=e.slice(0,e.indexOf(ou))),P(e.length>0,()=>"scheme must not be an empty string.");let r=In.getInstance();P(r.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),r.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function Gc(e){if(e.indexOf(ou)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${In.getSchemes().join(",")}`);return{scheme:e.split(ou)[0],path:e.split(ou)[1]}}async function Nw(e,t,r=!1){P(e!==t,()=>`Old path and new path are the same: '${e}'`);let n=Lt.getLoadHandlers(e);P(n.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),P(n.length<2,()=>`Copying failed because more than one (${n.length}) load handlers for source URL ${e}.`);let a=n[0],s=Lt.getSaveHandlers(t);P(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),P(s.length<2,()=>`Copying failed because more than one (${n.length}) save handlers for destination URL ${t}.`);let i=s[0],o=Gc(e).scheme,l=Gc(e).path,u=o===Gc(e).scheme,d=await a.load();r&&u&&await In.getManager(o).removeModel(l);let h=await i.save(d);return r&&!u&&await In.getManager(o).removeModel(l),h.modelArtifactsInfo}async function AM(){let e=In.getSchemes(),t={};for(let r of e){let n=await In.getManager(r).listModels();for(let a in n){let s=r+ou+a;t[s]=n[a]}}return t}async function xM(e){let t=Gc(e);return In.getManager(t.scheme).removeModel(t.path)}async function bM(e,t){return Nw(e,t,!1)}async function vM(e,t){return Nw(e,t,!0)}var wM=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 ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(J().get("IS_BROWSER")){J().setPlatform("browser",new wM);try{In.registerManager(bo.URL_SCHEME,new yM)}catch(e){}try{In.registerManager(xo.URL_SCHEME,new uM)}catch(e){}}var kM={importFetch:()=>FE()},M1,IM=class{constructor(){this.util=$E(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return J().global.fetch!=null?J().global.fetch(e,t):(M1==null&&(M1=kM.importFetch()),M1(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)}};J().get("IS_NODE")&&!J().get("IS_BROWSER")&&J().setPlatform("node",new IM);function We(e,t="float32",r){return t=t||"float32",Hy(e),new rr(e,t,r)}function SM(e,t){let r=F(e,"x","cast");if(!Zv(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&r.dtype!=="string"||t!=="string"&&r.dtype==="string")throw new Error("Only strings can be casted to strings");let n={x:r},a={dtype:t};return B.runKernel(Hs,n,a)}var me=W({cast_:SM});function CM(e){let t={x:F(e,"x","clone","string_or_numeric")};return B.runKernel(ii,t)}var Or=W({clone_:CM});function Ew(e,t=!1){console.log(e.toString(t))}gw();var TM={buffer:We,cast:me,clone:Or,print:Ew};OR(TM);var Ir={};Le(Ir,{browserFiles:()=>PM,browserHTTPRequest:()=>LM,concatenateArrayBuffers:()=>Qy,copyModel:()=>bM,decodeWeights:()=>bw,encodeWeights:()=>XR,fromMemory:()=>WM,getLoadHandlers:()=>iM,getModelArtifactsForJSON:()=>e2,getModelArtifactsInfoForJSON:()=>ah,getSaveHandlers:()=>sM,http:()=>r2,isHTTPScheme:()=>Q1,listModels:()=>AM,loadWeights:()=>_M,moveModel:()=>vM,registerLoadRouter:()=>aM,registerSaveRouter:()=>nM,removeModel:()=>xM,weightsLoaderFactory:()=>Mw,withSaveHandler:()=>VM});var NM="model",EM=".json",RM=".weights.bin";function g3(e){return new Promise(t=>setTimeout(t)).then(e)}var J1=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(J1.URL_SCHEME)&&(e=e.slice(J1.URL_SCHEME.length)),(e==null||e.length===0)&&(e=NM),this.modelJsonFileName=e+EM,this.weightDataFileName=e+RM}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 r=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],n=vw(e,r),a=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:"application/json"})),s=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=a,await g3(()=>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 g3(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:ah(e)}}}},uf=J1;uf.URL_SCHEME="downloads://";var MM=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let r=new FileReader;r.onload=n=>{let a=JSON.parse(n.target.result),s=a.modelTopology;if(s==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(a.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:s});return}let i=e2(a,o=>this.loadWeights(o));e(i)},r.onerror=n=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(this.jsonFile)})}loadWeights(e){let t=[],r=[];for(let s of e)t.push(...s.weights),r.push(...s.paths);let n=this.checkManifestAndWeightFiles(e),a=r.map(s=>this.loadWeightsFile(s,n[s]));return Promise.all(a).then(s=>[t,Qy(s)])}loadWeightsFile(e,t){return new Promise((r,n)=>{let a=new FileReader;a.onload=s=>{let i=s.target.result;r(i)},a.onerror=s=>n(`Failed to weights data from file of path '${e}'.`),a.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],r=this.weightsFiles.map(a=>m3(a.name)),n={};for(let a of e)a.paths.forEach(s=>{let i=m3(s);if(t.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);if(t.push(i),r.indexOf(i)===-1)throw new Error(`Weight file with basename '${i}' is not provided.`);n[s]=this.weightsFiles[r.indexOf(i)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return n}},FM=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(uf.URL_SCHEME)?$M(e.slice(uf.URL_SCHEME.length)):null;Lt.registerSaveRouter(FM);function $M(e="model"){return new uf(e)}function PM(e){return new MM(e)}function y3(e,t,r,n){i(e),r=r==null?0:r,n=n==null?1:n,o(r,n);let a=0,s=l=>(l.then(u=>{let d=r+ ++a/e.length*(n-r);return t(d),u}),l);function i(l){P(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){P(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),P(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),P(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function Rw(e,t){t==null&&(t={});let r=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,n=e.map(u=>r(u,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(n):await y3(n,t.onProgress,a,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await y3(i,t.onProgress,o,l)}async function _M(e,t="",r,n){return Mw(a=>Rw(a,{requestInit:n}))(e,t,r)}function Mw(e){return async(t,r="",n)=>{let a=t.map(()=>!1),s={},i=n!=null?n.map(()=>!1):[],o=[];if(t.forEach((c,f)=>{let m=0;c.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,A=K1[y]*Ct(g.shape),x=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:g,groupOffset:m,sizeBytes:A})};n!=null?n.forEach((b,v)=>{b===g.name&&(x(),i[v]=!0)}):x(),o.push(g.name),m+=A})}),!i.every(c=>c)){let c=n.filter((f,m)=>!i[m]);throw new Error(`Could not find weights in manifest with names: ${c.join(", ")}. 
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=a.reduce((c,f,m)=>(f&&c.push(m),c),[]),u=[];l.forEach(c=>{t[c].paths.forEach(f=>{let m=r+(r.endsWith("/")?"":"/")+f;u.push(m)})});let d=await e(u),h={},p=0;return l.forEach(c=>{let f=t[c].paths.length,m=0;for(let x=0;x<f;x++)m+=d[p+x].byteLength;let g=new ArrayBuffer(m),y=new Uint8Array(g),A=0;for(let x=0;x<f;x++){let b=new Uint8Array(d[p+x]);y.set(b,A),A+=b.byteLength}s[c].forEach(x=>{let b=g.slice(x.groupOffset,x.groupOffset+x.sizeBytes),v=bw(b,[x.manifestEntry]);for(let C in v)h[C]=v[C]}),p+=f}),h}}var zM="application/octet-stream",OM="application/json",t2=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?(P(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=J().platform.fetch,P(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&P(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 r=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],n=vw(e,r);t.body.append("model.json",new Blob([JSON.stringify(n)],{type:OM}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:zM}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:ah(e),responses:[a]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${a.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(a){let s=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?s+=" 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.":s+=" Please make sure the server is serving valid JSON for this request.",new Error(s)}let r=t.modelTopology,n=t.weightsManifest;if(r==null&&n==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return e2(t,a=>this.loadWeights(a))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[r,n]=DM(t),a=this.weightPathPrefix||r,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(a+d+n);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await Rw(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Qy(l)]}};t2.URL_SCHEME_REGEX=/^https?:\/\//;function DM(e){let t=e.lastIndexOf("/"),r=e.lastIndexOf("?"),n=e.substring(0,t),a=r>t?e.substring(r):"";return[n+"/",a]}function Q1(e){return e.match(t2.URL_SCHEME_REGEX)!=null}var Fw=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let r=!0;if(Array.isArray(e)?r=e.every(n=>Q1(n)):r=Q1(e),r)return r2(e,t)}return null};Lt.registerSaveRouter(Fw);Lt.registerLoadRouter(Fw);function r2(e,t){return new t2(e,t)}function LM(e,t){return r2(e,t)}var F1=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},BM=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function WM(e,t,r,n){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new F1(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 F1({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 F1({modelTopology:e,weightSpecs:t,weightData:r,trainingConfig:n}))}function VM(e){return new BM(e)}var $w={};Le($w,{confusionMatrix:()=>qM});function UM(e,t,r=!1,n=!1){let a=F(e,"a","matMul"),s=F(t,"b","matMul");[a,s]=Ot(a,s);let i={a,b:s},o={transposeA:r,transposeB:n};return B.runKernel(js,i,o)}var Je=W({matMul_:UM});function GM(e,t,r=1,n=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:F(e,"indices","oneHot","int32")},s={depth:t,onValue:r,offValue:n};return B.runKernel(Jo,a,s)}var Np=W({oneHot_:GM});function jM(e,t){let r=F(e,"x","transpose");if(t==null&&(t=r.shape.map((s,i)=>i).reverse()),P(r.rank===t.length,()=>`Error in transpose: rank of input ${r.rank} must match length of perm ${t}.`),t.forEach(s=>{P(s>=0&&s<r.rank,()=>`All entries in 'perm' must be between 0 and ${r.rank-1} but got ${t}`)}),r.rank<=1)return r.clone();let n={x:r},a={perm:t};return B.runKernel(Fi,n,a)}var nt=W({transpose_:jM});function HM(e,t,r){let n=F(e,"labels","confusionMatrix"),a=F(t,"predictions","confusionMatrix");P(r==null||r>0&&Number.isInteger(r),()=>`If provided, numClasses must be a positive integer, but got ${r}`),P(n.rank===1,()=>`Expected the rank of labels to be 1, but got ${n.rank}`),P(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),P(n.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${n.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),P(r>0&&Number.isInteger(r),()=>`numClasses is required to be a positive integer, but got ${r}`);let s=Np(me(n,"int32"),r),i=Np(me(a,"int32"),r),o=nt(s),l=Je(o,i);return me(l,"int32")}var qM=W({confusionMatrix_:HM}),yl={};Le(yl,{assertAndGetBroadcastShape:()=>xt,getBroadcastDims:()=>Pw,getReductionAxes:()=>Xt});function Pw(e,t){let r=e.length,n=[];for(let a=0;a<r;a++){let s=r-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&n.unshift(s)}return n}function Xt(e,t){let r=[];for(let n=0;n<t.length;n++){let a=e[e.length-n-1],s=t.length-n-1,i=t[s];(a==null||a===1&&i>1)&&r.unshift(s)}return r}function xt(e,t){let r=[],n=Math.max(e.length,t.length);for(let a=0;a<n;a++){let s=e[e.length-a-1];s==null&&(s=1);let i=t[t.length-a-1];if(i==null&&(i=1),s===1)r.unshift(i);else if(i===1)r.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else r.unshift(s)}return r}var $n={};Le($n,{fromPixels:()=>eF,fromPixelsAsync:()=>JM,toPixels:()=>QM});function _w(e,t,r){if(Ro(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let n=Ea(e,r);if(n.length!==3&&n.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Pi(e,t,n,r)}var ro;function zw(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 r=!1,n=!1,a=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)r=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)n=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)a=!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(a&&a&&e.readyState<2)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");if(sf(kp,B.backendName)!=null){let p={pixels:e},c={numChannels:t};return B.runKernel(kp,p,c)}let[l,u]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;if(i)d=e.getContext("2d").getImageData(0,0,l,u).data;else if(n||r)d=e.data;else if(s||a||o){if(ro==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")ro=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else ro=document.createElement("canvas").getContext("2d");ro.canvas.width=l,ro.canvas.height=u,ro.drawImage(e,0,0,l,u),d=ro.getImageData(0,0,l,u).data}let h;if(t===4)h=new Int32Array(d);else{let p=l*u;h=new Int32Array(p*t);for(let c=0;c<p;c++)for(let f=0;f<t;++f)h[c*t+f]=d[c*4+f]}return _w(h,[u,l,t],"int32")}function KM(e){return e!=null&&e.data instanceof Uint8Array}function XM(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function ZM(e){return e!=null&&e.width!==0&&e.height!==0}function YM(e){return XM()&&!(e instanceof ImageBitmap)&&ZM(e)&&!KM(e)}async function JM(e,t=3){let r=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&YM(e)){let n;try{n=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(a){n=null}n!=null&&n.width===e.width&&n.height===e.height?r=n:r=e}else r=e;return zw(r,t)}async function QM(e,t){let r=F(e,"img","toPixels");if(!(e instanceof rt)){let u=r;r=me(u,"int32"),u.dispose()}if(r.rank!==2&&r.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`);let[n,a]=r.shape.slice(0,2),s=r.rank===2?1:r.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(r.dtype!=="float32"&&r.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`);let i=await r.data(),o=r.dtype==="float32"?255:1,l=new Uint8ClampedArray(a*n*4);for(let u=0;u<n*a;++u){let d=[0,0,0,255];for(let p=0;p<s;p++){let c=i[u*s+p];if(r.dtype==="float32"){if(c<0||c>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${c}.`)}else if(r.dtype==="int32"&&(c<0||c>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${c}.`);s===1?(d[0]=c*o,d[1]=c*o,d[2]=c*o):d[p]=c*o}let h=u*4;l[h+0]=Math.round(d[0]),l[h+1]=Math.round(d[1]),l[h+2]=Math.round(d[2]),l[h+3]=Math.round(d[3])}if(t!=null){t.width=a,t.height=n;let u=t.getContext("2d"),d=new ImageData(l,a,n);u.putImageData(d,0,0)}return r!==e&&r.dispose(),l}var eF=W({fromPixels_:zw}),n2={};Le(n2,{prepareAndValidate:()=>Ow});function Ow(e,t){let r=e.shape.length,n=t.shape.length;if(r<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${r}.`);if(n<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${n}.`);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[n-1]>r)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[n-1]} vs. ${r}`);if(Ct(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let a=t.shape,s=a[a.length-1],i=1;for(let h=0;h<a.length-1;++h)i*=a[h];let o=e.shape,l=a.slice();l.pop();let u=1;for(let h=s;h<r;++h)u*=o[h],l.push(o[h]);let d=[...Cu(e.shape).map(h=>h/u),1].slice(0,s);return[l,i,u,d]}var a2={};Le(a2,{calculateShapes:()=>Dw,validateInput:()=>i2,validateUpdateShape:()=>s2});function s2(e,t,r){let n=t.rank>1?t.shape[t.rank-1]:1,a=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${r.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${n}, and batchDim: ${a}.`;if(r.rank<a)throw new Error(s+` update.rank < ${a}. `);if(e.length<n+(r.rank-a))throw new Error(s+` Output shape length < ${n+(r.rank-a)}`);if(r.rank!==a+e.length-n)throw new Error(s+` update.rank != ${a+e.length-n}`);for(let i=0;i<a;++i)if(r.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${r.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<r.rank-a;++i)if(r.shape[i+a]!==e[i+n])throw new Error(s+` updates.shape[${i+a}] (${r.shape[i+a]}) != shape[${i+a}] (${e[i+a]})`)}function i2(e,t,r){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(r.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${r}`);if(r.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: ${e.shape}`)}s2(r,t,e)}function Dw(e,t,r){let n=t.shape.length,a=n>1?t.shape[n-1]:1,s=r.length,i=1;for(let h=a;h<s;++h)i*=r[h];let o=a<1?1:a,l=Ct(t.shape)/o,u=[...Cu(r.slice(0,a)),1],d=Ct(r);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:u,outputSize:d}}var _t={};Le(_t,{assertParamsValid:()=>rF,computeFlatOffset:()=>oF,computeOutShape:()=>aF,getNormalizedAxes:()=>sF,isSliceContinous:()=>iF,maskToAxes:()=>nF,parseSliceParams:()=>qw,sliceInfo:()=>lF,startForAxis:()=>jw,startIndicesWithElidedDims:()=>Vw,stopForAxis:()=>Hw,stopIndicesWithElidedDims:()=>Uw,stridesForAxis:()=>Gw,stridesWithElidedDims:()=>Lw});var ey=-2,tF=-1;function rF(e,t,r){let n=e.shape.length;P(n===t.length,()=>`Error in slice${n}D: Length of begin ${t} must match the rank of the array (${n}).`),P(n===r.length,()=>`Error in slice${n}D: Length of size ${r} must match the rank of the array (${n}).`);for(let a=0;a<n;++a)P(t[a]+r[a]<=e.shape[a],()=>`Error in slice${n}D: begin[${a}] + size[${a}] (${t[a]+r[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function nF(e){let t=[],r=0;for(;e>0;)e&1&&t.push(r),e/=2,r++;return t}function aF(e,t,r){let n=[];for(let a=0;a<e.length;a++)n[a]=Math.ceil((t[a]-e[a])/r[a]);return n}function Lw(e,t,r,n){let a=[...e];for(let s=a.length;s<n.length;s++)a.push(1);for(let s=0;s<r;s++)s===0?a[t]=1:(a.splice(t,0,1),a.pop());return a}function Bw(e,t,r){return r<=e?r:r-(t-1)}function Ww(e,t){let r=[];for(let n=0;n<e;n++)r.push(t+n);return r}function sF(e,t,r,n,a,s,i,o,l){let u=e.length,d=new Array(u),h=new Array(u),p=new Array(u);if(t.length&&r>0){let c=t[0],f=r+1;d=Vw(i,c,f,n,e),h=Uw(o,c,f,a,e),p=Lw(s,c,f,e)}else for(let c=0;c<u;c++)d[c]=jw(i,n,s,e,c,l),h[c]=Hw(o,a,s,e,c,l),p[c]=Gw(s,c,l);return{begin:d,end:h,strides:p}}function Vw(e,t,r,n,a){let s=[...a],i=Ww(r,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=Bw(t,r,o),u=n[l];e&1<<l&&(u=0),s[o]=u}return s}function Uw(e,t,r,n,a){let s=[...a],i=Ww(r,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=Bw(t,r,o),u=n[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),s[o]=u}for(let o=0;o<s.length;o++){let l=a[o];s[o]<0&&(s[o]+=l),s[o]=wp(0,s[o],a[o])}return s}function Gw(e,t,r){let n=e[t];return(r&1<<t||n==null)&&(n=1),n}function jw(e,t,r,n,a,s){let i=t[a],o=r[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=n[a];return i<0&&(i+=l),i=wp(0,i,l-1),i}function Hw(e,t,r,n,a,s){let i=t[a],o=r[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=n[a];return i<0&&(i+=l),o>0?i=wp(0,i,l):i=wp(-1,i,l-1),i}function iF(e,t,r){let n=r.length;for(let a=0;a<r.length;a++)if(r[a]>1){n=a;break}for(let a=n+1;a<r.length;a++)if(t[a]>0||r[a]!==e[a])return!1;return!0}function oF(e,t){let r=e.length>0?e[e.length-1]:1;for(let n=0;n<e.length-1;n++)r+=e[n]*t[n];return r}function qw(e,t,r){let n,a=e.shape.length;typeof t=="number"?n=[t,...new Array(a-1).fill(0)]:t.length<a?n=t.concat(new Array(a-t.length).fill(0)):n=t.slice(),n.forEach(i=>{P(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return r==null?s=new Array(a).fill(-1):typeof r=="number"?s=[r,...new Array(a-1).fill(-1)]:r.length<a?s=r.concat(new Array(a-r.length).fill(-1)):s=r,s=s.map((i,o)=>i>=0?i:(P(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-n[o])),[n,s]}function lF(e,t,r,n,a,s,i,o,l){let u;if(n==null?(u=new Array(t.length),u.fill(1)):u=n,i!=null&&(i&i-1)!==0)throw new Error("Multiple ellipses in slice is not allowed.");let d=!1,h={dims:u.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:r.slice(),strides:u.slice(),beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};for(let A=0;A<h.dims;A++)d&&(1<<A&o)!==0&&h.numAddAxisAfterEllipsis++,1<<A&i&&(d=!0);d||(h.ellipsisMask|=1<<h.dims,h.dims++);let p={dims:e.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};uF(h,p);let c=!0,f=!0,m=!0,g=[],y=[];for(let A=0;A<e.length;++A){if(p.strides[A]===0)throw Error(`strides[${A}] must be non-zero`);let x=!!(p.shrinkAxisMask&1<<A),b=e[A];if(b===-1){g.push(x?1:-1);continue}let v=[p.beginMask&1<<A,p.endMask&1<<A],C=[p.strides[A]>0?0:-1,p.strides[A]>0?b:b-1];if(x&&p.strides[A]<=0)throw Error("only stride 1 allowed on non-range indexing.");m=m&&p.strides[A]===1;let S=!!(p.beginMask&1<<A&&p.endMask&1<<A);if(p.beginValid&&p.endValid){if(x){let M=p.begin[A]<0?b+p.begin[A]:p.begin[A];if(p.begin[A]=M,p.end[A]=p.begin[A]+1,M<0||M>=b)throw Error(`slice index ${p.begin[A]} of dimension ${A} out of bounds.`)}else p.begin[A]=A3(p.begin[A],0,p.strides[A],b,v,C),p.end[A]=A3(p.end[A],1,p.strides[A],b,v,C);let _=p.strides[A]===1&&p.begin[A]===0&&p.end[A]===b;c=c&&_,f=f&&(A===0&&p.strides[A]===1||_)}else c=c&&p.strides[A]===1&&S,f=f&&(A===0&&p.strides[A]===1||S);let E,R=!1;if(p.beginValid&&p.endValid?(E=p.end[A]-p.begin[A],R=!0):x?(E=1,R=!0):S&&b>=0&&(p.strides[A]<0?E=-b:E=b,R=!0),R){let _;E===0||E<0!=p.strides[A]<0?_=0:_=Math.trunc(E/p.strides[A])+(E%p.strides[A]!==0?1:0),g.push(_)}else g.push(-1)}for(let A=0;A<p.finalShapeGatherIndices.length;++A){let x=p.finalShapeGatherIndices[A];x>=0?y.push(g[x]):x===ey&&y.push(1)}return{finalShapeSparse:y.filter((A,x)=>p.finalShapeGatherIndices[x]!==ey),finalShape:y,isIdentity:c,sliceDim0:f,isSimpleSlice:m,begin:p.begin,end:p.end,strides:p.strides}}function uF(e,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let r=0;t.beginValid=e.begin!=null,t.endValid=e.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let n=0;n<e.dims;n++)if(1<<n&e.ellipsisMask){let a=Math.min(t.dims-(e.dims-n)+1+e.numAddAxisAfterEllipsis,t.dims);for(;r<a;r++)t.begin[r]=0,t.end[r]=0,t.strides[r]=1,t.beginMask|=1<<r,t.endMask|=1<<r,t.finalShapeGatherIndices.push(r),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[r]=n}else if(1<<n&e.newAxisMask)t.finalShapeGatherIndices.push(ey),t.finalShapeGatherIndicesSparse.push(-1);else{if(r===t.begin.length)throw Error(`Index out of range using input dim ${r}; input has only ${t.dims} dims, ${t.begin.length}.`);e.begin!=null&&(t.begin[r]=e.begin[n]),e.end!=null&&(t.end[r]=e.end[n]),t.strides[r]=e.strides[n],e.beginMask&1<<n&&(t.beginMask|=1<<r),e.endMask&1<<n&&(t.endMask|=1<<r),e.shrinkAxisMask&1<<n?(t.finalShapeGatherIndices.push(tF),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<r):(t.finalShapeGatherIndices.push(r),t.finalShapeGatherIndicesSparse.push(n)),t.inputShapeGatherIndicesSparse[r]=n,r++}}function A3(e,t,r,n,a,s){if(a[t])return r>0?s[t]:s[t+1&1];{let i=e<0?n+e:e;return i<s[0]?s[0]:i>s[1]?s[1]:i}}var ue={};Le(ue,{Serializable:()=>Kw,SerializationMap:()=>oo,registerClass:()=>_i});var Kw=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},oo=class{constructor(){this.classNameMap={}}static getMap(){return oo.instance==null&&(oo.instance=new oo),oo.instance}static register(e){oo.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function _i(e){P(e.className!=null,()=>"Class being registered does not have the static className property defined."),P(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),P(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),oo.register(e)}var Xw={};Le(Xw,{TEST_EPSILON_FLOAT16:()=>Zw,encodeStrings:()=>Yw,expectArrayBuffersEqual:()=>gF,expectArraysClose:()=>pF,expectArraysEqual:()=>cF,expectNumbersClose:()=>fF,expectPromiseToFail:()=>hF,expectValuesInRange:()=>mF,testEpsilon:()=>o2});var dF=.001,Zw=.1;function pF(e,t,r){return r==null&&(r=o2()),ty(e,t,(n,a)=>l2(n,a,r))}function o2(){return B.backend.floatPrecision()===32?dF:Zw}function ty(e,t,r){let n=!0;if((wr(e)||wr(t))&&(n=!1),wr(e)&&wr(t)&&(n=!0),n){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Ea(e),o=Ea(t);if(!Ws(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=wr(e)?e:Ao(e),s=wr(t)?t:Ao(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.
Actual:   ${a}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=a[i],l=s[i];if(!r(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual:   ${a}.
Expected: ${s}.`)}}function hF(e,t){e().then(()=>t.fail(),()=>t())}function cF(e,t){let r=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return vs(e)||vs(e[0])||vs(t)||vs(t[0])?ty(e,r,(n,a)=>n==a):ty(e,t,(n,a)=>l2(n,a,0))}function fF(e,t,r){if(r==null&&(r=o2()),!l2(e,t,r))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function l2(e,t,r){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>r)}function mF(e,t,r){for(let n=0;n<e.length;n++)if(e[n]<t||e[n]>r)throw new Error(`Value out of range:${e[n]} low: ${t}, high: ${r}`)}function gF(e,t){let r=new Float32Array(e),n=new Float32Array(t);if(r.length!==n.length)throw new Error(`Expected ArrayBuffer to be of length ${n.length}, but it was ${r.length}`);for(let a=0;a<n.length;a++)if(r[a]!==n[a])throw new Error(`Expected ArrayBuffer value at ${a} to be ${n[a]} but got ${r[a]} instead`)}function Yw(e){for(let t=0;t<e.length;t++){let r=e[t];Array.isArray(r)?Yw(r):e[t]=th(r)}return e}var u2="0.0.0";function d2(){J().set("PROD",!0)}function yF(){J().set("DEBUG",!0)}function AF(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function p2(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}DR(p2);function xF(){B.disposeVariables()}function kr(){return B}function df(){return B.memory()}function bF(e){return B.profile(e)}function K(e,t){return B.tidy(e,t)}function re(e){Zy(e).forEach(t=>t.dispose())}function hr(e){return B.keep(e)}function vF(e){return B.time(e)}function h2(e){return B.setBackend(e)}function ed(){return B.ready()}function pn(){return B.backendName}function wF(e){B.removeBackend(e)}function c2(e){return B.findBackend(e)}function kF(e){return B.findBackendFactory(e)}function Al(e,t,r=1){return B.registerBackend(e,t,r)}function ga(){return B.backend}function IF(e,t){J().setPlatform(e,t)}function SF(e,t){let r=F(e,"a","add"),n=F(t,"b","add");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(Ha,a)}var le=W({add_:SF});function CF(e,t){let r=F(e,"a","floorDiv"),n=F(t,"b","floorDiv");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(ni,a)}var sh=W({floorDiv_:CF});function TF(e,t){let r=F(e,"a","div"),n=F(t,"b","div");if([r,n]=Ot(r,n),r.dtype==="int32"&&n.dtype==="int32")return sh(r,n);let a={a:r,b:n},s={};return B.runKernel(Qs,a,s)}var pe=W({div_:TF});function NF(e,t){let r=F(e,"a","mul"),n=F(t,"b","mul");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(gi,a)}var L=W({mul_:NF});function EF(e){let t=F(e,"x","abs");if(t.dtype==="complex64"){let r={x:t};return B.runKernel(Lp,r)}else{let r={x:t};return B.runKernel(Mo,r)}}var er=W({abs_:EF});function RF(e){let t={x:F(e,"x","acos")};return B.runKernel(Tu,t)}var Jw=W({acos_:RF});function MF(e){let t={x:F(e,"x","acosh")};return B.runKernel(Nu,t)}var Qw=W({acosh_:MF});function FF(e){P(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),P(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>F(a,`tensors${s}`,"addN")),r=t[0];t.forEach(a=>{if(a.dtype!==r.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!Ws(a.shape,r.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let n=t;return B.runKernel(Vs,n)}var im=W({addN_:FF});function $F(e,t=null,r=!1){let n={x:F(e,"x","all","bool")},a={axis:t,keepDims:r};return B.runKernel(Eu,n,a)}var f2=W({all_:$F});function PF(e,t=null,r=!1){let n={x:F(e,"x","any","bool")},a={axis:t,keepDims:r};return B.runKernel(Ru,n,a)}var pf=W({any_:PF});function _F(e,t=0){let r={x:F(e,"x","argMax")},n={axis:t};return B.runKernel(Us,r,n)}var Cn=W({argMax_:_F});function zF(e,t=0){let r={x:F(e,"x","argMin")},n={axis:t};return B.runKernel(Mu,r,n)}var ek=W({argMin_:zF});function OF(e){let t={x:F(e,"x","asin")};return B.runKernel(Fu,t)}var tk=W({asin_:OF});function DF(e){let t={x:F(e,"x","asinh")};return B.runKernel($u,t)}var rk=W({asinh_:DF});function LF(e){let t={x:F(e,"x","atan")};return B.runKernel(Pu,t)}var nk=W({atan_:LF});function BF(e,t){let r=F(e,"a","atan2"),n=F(t,"b","atan2");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(zu,a)}var ak=W({atan2_:BF});function WF(e){let t={x:F(e,"x","atanh")};return B.runKernel(_u,t)}var sk=W({atanh_:WF});function VF(e,t,r,n,a="NHWC",s){let i=e[3],o=[...t,i],l=lk(a);return ih(e,o,r,s,n,null,null,l)}function ik(e,t,r,n,a,s,i="channelsLast"){let[o,l]=hf(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 ih(e,u,r,n,a,s,!1,i)}function UF(e,t,r,n,a,s,i="NDHWC"){let[o,l,u]=ry(t),d,h;if(i==="NDHWC")h="channelsLast",d=[o,l,u,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",d=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return ok(e,d,r,n,a,!1,h,s)}function ih(e,t,r,n,a,s,i=!1,o="channelsLast"){let[l,u,d,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,d,h]=e;else if(o==="channelsFirst")[l,h,u,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[p,c,,f]=t,[m,g]=hf(r),[y,A]=hf(n),x=lu(p,y),b=lu(c,A),{padInfo:v,outHeight:C,outWidth:S}=HF(a,u,d,m,g,x,b,s,o),E=i?f*h:f,R;return o==="channelsFirst"?R=[l,E,C,S]:o==="channelsLast"&&(R=[l,C,S,E]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:d,inChannels:h,outHeight:C,outWidth:S,outChannels:E,padInfo:v,strideHeight:m,strideWidth:g,filterHeight:p,filterWidth:c,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:A,inShape:e,outShape:R,filterShape:t}}function ok(e,t,r,n,a,s=!1,i="channelsLast",o){let[l,u,d,h,p]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,d,h,p]=e;else if(i==="channelsFirst")[l,p,u,d,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[c,f,m,,g]=t,[y,A,x]=ry(r),[b,v,C]=ry(n),S=lu(c,b),E=lu(f,v),R=lu(m,C),{padInfo:_,outDepth:M,outHeight:I,outWidth:O}=qF(a,u,d,h,y,A,x,S,E,R,o),z=s?g*p:g,j;return i==="channelsFirst"?j=[l,z,M,I,O]:i==="channelsLast"&&(j=[l,M,I,O,z]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:d,inWidth:h,inChannels:p,outDepth:M,outHeight:I,outWidth:O,outChannels:z,padInfo:_,strideDepth:y,strideHeight:A,strideWidth:x,filterDepth:c,filterHeight:f,filterWidth:m,effectiveFilterDepth:S,effectiveFilterHeight:E,effectiveFilterWidth:R,dilationDepth:b,dilationHeight:v,dilationWidth:C,inShape:e,outShape:j,filterShape:t}}function GF(e,t,r,n,a){n==null&&(n=m2(e,t,r));let s=e[0],i=e[1],o=fo((s-t+2*n)/r+1,a),l=fo((i-t+2*n)/r+1,a);return[o,l]}function jF(e,t,r,n,a,s){a==null&&(a=m2(e,t,n));let i=e[0],o=e[1],l=e[2],u=fo((i-t+2*a)/n+1,s),d=fo((o-t+2*a)/n+1,s),h=fo((l-t+2*a)/n+1,s);return[u,d,h,r]}function m2(e,t,r,n=1){let a=lu(t,n);return Math.floor((e[0]*(r-1)-r+a)/2)}function hf(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function ry(e){return typeof e=="number"?[e,e,e]:e}function lu(e,t){return t<=1?e:e+(e-1)*(t-1)}function HF(e,t,r,n,a,s,i,o,l){let u,d,h;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let p=GF([t,r],s,n,e,o);d=p[0],h=p[1]}else if(e==="same"){d=Math.ceil(t/n),h=Math.ceil(r/a);let p=Math.max(0,(d-1)*n+s-t),c=Math.max(0,(h-1)*a+i-r),f=Math.floor(p/2),m=p-f,g=Math.floor(c/2),y=c-g;u={top:f,bottom:m,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)/n),h=Math.ceil((r-i+1)/a);else if(typeof e=="object"){let p=l==="channelsLast"?e[1][0]:e[2][0],c=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];u={top:p,bottom:c,left:f,right:m,type:p===0&&c===0&&f===0&&m===0?"VALID":"EXPLICIT"},d=fo((t-s+p+c)/n+1,o),h=fo((r-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:d,outWidth:h}}function qF(e,t,r,n,a,s,i,o,l,u,d){let h,p,c,f;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let m=jF([t,r,n,1],o,1,a,e,d);p=m[0],c=m[1],f=m[2]}else if(e==="same"){p=Math.ceil(t/a),c=Math.ceil(r/s),f=Math.ceil(n/i);let m=(p-1)*a+o-t,g=(c-1)*s+l-r,y=(f-1)*i+u-n,A=Math.floor(m/2),x=m-A,b=Math.floor(g/2),v=g-b,C=Math.floor(y/2),S=y-C;h={top:b,bottom:v,left:C,right:S,front:A,back:x,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-o+1)/a),c=Math.ceil((r-l+1)/s),f=Math.ceil((n-u+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:p,outHeight:c,outWidth:f}}function fo(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 Ms(e){let[t,r,n]=hf(e);return t===1&&r===1&&n===1}function Fa(e,t){return Ms(e)||Ms(t)}function lk(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function Vr(e,t,r){if(r!=null){if(typeof t=="string")throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${r} but got pad ${t}.`);if(typeof t=="number")P(pu(t),()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${r} but got pad ${t}.`);else if(typeof t=="object")t.forEach(n=>{n.forEach(a=>{P(pu(a),()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${r} but got pad ${a}.`)})});else throw Error(`Error in ${e}: Unknown padding parameter: ${t}`)}}function KF(e,t){let r={x:F(e,"x","reshape","string_or_numeric")},n={shape:t};return B.runKernel(tl,r,n)}var G=W({reshape_:KF});function XF(e,t,r,n,a){let s=F(e,"x","avgPool","float32"),i=1;P(Fa(r,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${r} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),P(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),Vr("avgPool",n,a);let u={x:o},d={filterSize:t,strides:r,pad:n,dimRoundingMode:a},h=B.runKernel(Gs,u,d);return h=me(h,s.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var om=W({avgPool_:XF});function ZF(e,t,r,n,a,s="NDHWC"){let i=F(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),P(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),P(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Vr("avgPool3d",n,a);let u={x:o},d={filterSize:t,strides:r,pad:n,dimRoundingMode:a,dataFormat:s},h=B.runKernel(Op,u,d);return h=me(h,o.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var g2=W({avgPool3d_:ZF});function YF(e,t=0){P(e.length>=1,()=>"Pass at least one tensor to concat");let r=Tp(e,"tensors","concat","string_or_numeric");if(r[0].dtype==="complex64"&&r.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${s.dtype}. `)}),r.length===1)return Or(r[0]);let n=r,a={axis:t};return B.runKernel($o,n,a)}var kt=W({concat_:YF});function JF(e){let t={x:F(e,"x","sigmoid","float32")};return B.runKernel(Si,t)}var Sr=W({sigmoid_:JF});function QF(e,t,r){let n=F(e,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let a={x:n},s={begin:t,size:r};return B.runKernel(il,a,s)}var Pe=W({slice_:QF});function e$(e){let t={x:F(e,"x","tanh","float32")};return B.runKernel(Mi,t)}var cu=W({tanh_:e$});function t$(e,t,r,n,a,s){let i=F(e,"forgetBias","basicLSTMCell"),o=F(t,"lstmKernel","basicLSTMCell"),l=F(r,"lstmBias","basicLSTMCell"),u=F(n,"data","basicLSTMCell"),d=F(a,"c","basicLSTMCell"),h=F(s,"h","basicLSTMCell"),p=kt([u,h],1),c=Je(p,o),f=le(c,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],A=Pe(f,[0,0],y),x=Pe(f,[0,g],y),b=Pe(f,[0,g*2],y),v=Pe(f,[0,g*3],y),C=le(L(Sr(A),cu(x)),L(d,Sr(le(i,b)))),S=L(cu(C),Sr(v));return[C,S]}var r$=W({basicLSTMCell_:t$});function n$(e,t,r){let n=F(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);P(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),P(r.length===t.length,()=>`crops.length is ${r.length} but should be equal to blockShape.length  ${t.length}`),P(n.shape[0]%a===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:n},i={blockShape:t,crops:r};return B.runKernel(Fo,s,i)}var lm=W({batchToSpaceND_:n$});function a$(e){let t;return e.rank===0||e.rank===1?t=G(e,[1,1,1,e.size]):e.rank===2?t=G(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function s$(e,t,r,n,a,s){s==null&&(s=.001);let i=F(e,"x","batchNorm"),o=F(t,"mean","batchNorm"),l=F(r,"variance","batchNorm"),u;a!=null&&(u=F(a,"scale","batchNorm"));let d;n!=null&&(d=F(n,"offset","batchNorm")),P(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),P(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),P(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:a$(i),scale:u,offset:d,mean:o,variance:l},p={varianceEpsilon:s},c=B.runKernel(ai,h,p);return G(c,i.shape)}var fu=W({batchNorm_:s$});function i$(e,t,r,n,a,s){let i=F(e,"x","batchNorm"),o=F(t,"mean","batchNorm"),l=F(r,"variance","batchNorm"),u;a!=null&&(u=F(a,"scale","batchNorm"));let d;return n!=null&&(d=F(n,"offset","batchNorm")),P(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),P(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),P(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),d!=null&&P(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),fu(i,o,l,d,u,s)}var uk=W({batchNorm2d_:i$});function o$(e,t,r,n,a,s){let i=F(e,"x","batchNorm"),o=F(t,"mean","batchNorm"),l=F(r,"variance","batchNorm"),u;a!=null&&(u=F(a,"scale","batchNorm"));let d;return n!=null&&(d=F(n,"offset","batchNorm")),P(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),P(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),P(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),d!=null&&P(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),fu(i,o,l,d,u,s)}var dk=W({batchNorm3d_:o$});function l$(e,t,r,n,a,s){let i=F(e,"x","batchNorm"),o=F(t,"mean","batchNorm"),l=F(r,"variance","batchNorm"),u;a!=null&&(u=F(a,"scale","batchNorm"));let d;return n!=null&&(d=F(n,"offset","batchNorm")),P(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),P(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),P(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&P(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),fu(i,o,l,d,u,s)}var pk=W({batchNorm4d_:l$});function u$(e,t,r){let n=F(e,"x","bincount"),a=F(t,"weights","bincount");P(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),P(r>=0,()=>`size must be non-negative, but got ${r}.`),P(a.size===n.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${a.shape}.`);let s={x:n,weights:a},i={size:r};return B.runKernel(zf,s,i)}var y2=W({bincount_:u$});function d$(e,t){let r=F(e,"s0","broadcastArgs","int32"),n=F(t,"s1","broadcastArgs","int32");if(r.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${r.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let a={s0:r,s1:n};return B.runKernel(Of,a)}var hk=W({broadcastArgs_:d$});function p$(e,t){let r=F(e,"broadcastTo","x"),n=r.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<r.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${r.rank}.`);if(t.length>r.rank){let l=r.shape.slice();for(;l.length<t.length;)l.unshift(1);r=G(r,l)}let a=r.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(r.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Or(r);let i={x:r},o={reps:s};return B.runKernel(Ka,i,o)}var Ap=W({broadcastTo_:p$});function h$(e){let t={x:F(e,"x","ceil","float32")};return B.runKernel(qs,t)}var ck=W({ceil_:h$});function c$(e,t,r){let n=F(e,"x","clipByValue");P(t<=r,()=>`Error in clip: min (${t}) must be less than or equal to max (${r}).`);let a={x:n},s={clipValueMin:t,clipValueMax:r};return B.runKernel(qa,a,s)}var un=W({clipByValue_:c$});function f$(e){return kt(e,0)}var fk=W({concat1d_:f$});function m$(e,t){return kt(e,t)}var td=W({concat2d_:m$});function g$(e,t){return kt(e,t)}var mk=W({concat3d_:g$});function y$(e,t){return kt(e,t)}var gk=W({concat4d_:y$});function A$(e,t,r,n,a="NHWC",s=[1,1],i){let o=F(e,"x","conv2d","float32"),l=F(t,"filter","conv2d","float32"),u=o,d=!1;o.rank===3&&(d=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Vr("conv2d",n,i);let h=a==="NHWC"?u.shape[3]:u.shape[1];P(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),P(Fa(r,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`);let p={x:u,filter:l},c={strides:r,pad:n,dataFormat:a,dilations:s,dimRoundingMode:i},f=B.runKernel(Ks,p,c);return d?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Fs=W({conv2d_:A$});function x$(e,t,r,n,a="NWC",s=1,i){let o=F(e,"x","conv1d"),l=F(t,"filter","conv1d"),u=o,d=!1;o.rank===2&&(d=!0,u=G(o,[1,o.shape[0],o.shape[1]])),P(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),P(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Vr("conv1d",n,i),P(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),P(Fa(r,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${r} and dilation '${s}'`),P(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=G(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=G(u,[u.shape[0],1,u.shape[1],u.shape[2]]),c=Fs(p,h,[1,r],n,"NHWC",[1,s],i);return d?G(c,[c.shape[2],c.shape[3]]):G(c,[c.shape[0],c.shape[2],c.shape[3]])}var A2=W({conv1d_:x$});function b$(e,t,r,n,a,s="NHWC",i){P(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),P(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),P(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),P(r.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${r.rank}`);let d=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];P(d===r.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${r.shape[2]}.`),P(h===r.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${r.shape[3]}.`),Vr("conv2dDerInput",a,i);let p={dy:l,filter:r},c={strides:n,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=B.runKernel(Xs,p,c);return u?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var x2=W({conv2DBackpropInput_:b$});function v$(e,t,r,n,a,s){let i=F(e,"x","conv2dTranspose"),o=F(t,"filter","conv2dTranspose");return x2(r,i,o,n,a,"NHWC",s)}var b2=W({conv2dTranspose_:v$});function w$(e,t,r,n,a="NDHWC",s=[1,1,1]){let i=F(e,"x","conv3d"),o=F(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),P(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),P(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),P(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),P(Fa(r,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`),P(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let d={x:l,filter:o},h={strides:r,pad:n,dataFormat:a,dilations:s},p=B.runKernel(Bp,d,h);return u?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var v2=W({conv3d_:w$});function k$(e,t,r,n,a){P(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=G(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];P(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),P(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),P(r.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${r.rank}`),P(l===r.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${r.shape[3]}.`),P(u===r.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${r.shape[4]}.`);let d={dy:i,filter:r},h={pad:a,strides:n,inputShape:s},p=B.runKernel(Bf,d,h);return o?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var yk=W({conv3DBackpropInput_:k$});function I$(e,t,r,n,a){let s=F(e,"x","conv3dTranspose"),i=F(t,"filter","conv3dTranspose");return yk(r,s,i,n,a)}var Ak=W({conv3dTranspose_:I$});function S$(e){let t={x:F(e,"x","cos","float32")};return B.runKernel(Zs,t)}var um=W({cos_:S$});function C$(e){let t={x:F(e,"x","cosh","float32")};return B.runKernel(Ys,t)}var w2=W({cosh_:C$});function T$(e,t=0,r=!1,n=!1){let a={x:F(e,"x","cumsum")},s={axis:t,exclusive:r,reverse:n};return B.runKernel(Po,a,s)}var k2=W({cumsum_:T$});function N$(e,t,r,n=!1){let a=F(e,"x","denseBincount"),s=F(t,"weights","denseBincount");P(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),P(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),P(r>=0,()=>`size must be non-negative, but got ${r}.`),P(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:r,binaryOutput:n};return B.runKernel(Wf,i,o)}var xk=W({denseBincount_:N$});function E$(e,t,r="NHWC"){let n=F(e,"x","depthToSpace","float32"),a=r==="NHWC"?n.shape[1]:n.shape[2],s=r==="NHWC"?n.shape[2]:n.shape[3],i=r==="NHWC"?n.shape[3]:n.shape[1];P(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),P(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${a} and ${t}  for depthToSpace with input shape
    ${n.shape}`),P(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t} for depthToSpace with input shape
        ${n.shape}`),P(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let o={x:n},l={blockSize:t,dataFormat:r};return B.runKernel(zo,o,l)}var bk=W({depthToSpace_:E$});function R$(e,t,r,n,a="NHWC",s=[1,1],i){let o=F(e,"x","depthwiseConv2d","float32"),l=F(t,"filter","depthwiseConv2d","float32"),u=o,d=!1;o.rank===3&&(d=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),P(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),Vr("depthwiseConv2d",n,i);let h={x:u,filter:l},p={strides:r,pad:n,dataFormat:a,dilations:s,dimRoundingMode:i},c=B.runKernel(Js,h,p);return d?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var oh=W({depthwiseConv2d_:R$});function M$(e){let t={x:F(e,"x","diag")};return B.runKernel(Gf,t)}var F$=W({diag_:M$});function $$(e,t,r,n,a=[1,1],s="NHWC"){let i=F(e,"x","dilation2d"),o=F(t,"filter","dilation2d");P(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),P(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),P(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let d={x:l,filter:o},h={strides:r,pad:n,dilations:a},p=B.runKernel(Wp,d,h);return u?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var vk=W({dilation2d_:$$});function P$(e,t){let r=F(e,"a","equal","string_or_numeric"),n=F(t,"b","equal","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Oo,a)}var Tn=W({equal_:P$});function _$(e,t,r){let n=F(t,"a","where"),a=F(r,"b","where"),s=F(e,"condition","where","bool"),i=xt(xt(s.shape,n.shape),a.shape),o=Ap(s,i),l=Ap(n,i),u=Ap(a,i),d={condition:o,t:l,e:u};return B.runKernel(sl,d)}var Lr=W({where_:_$});function z$(e){let t={x:F(e,"x","zerosLike")};return B.runKernel(ml,t)}var at=W({zerosLike_:z$});function O$(e,t){let r=F(e,"a","div"),n=F(t,"b","div");[r,n]=Ot(r,n);let a=pe(r,n),s=at(a),i=Tn(n,s);return Lr(i,s,a)}var wk=W({divNoNan_:O$});function D$(e,t){let r=F(e,"t1","dot"),n=F(t,"t2","dot");P((r.rank===1||r.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${r.rank} and ${n.rank}.`);let a=r.rank===1?r.size:r.shape[1],s=n.rank===1?n.size:n.shape[0];if(P(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),r.rank===1&&n.rank===1){let i=G(r,[1,-1]),o=G(n,[-1,1]),l=Je(i,o);return G(l,[])}else if(r.rank===1&&n.rank===2){let i=G(r,[1,-1]),o=G(n,[n.shape[0],n.shape[1]]),l=Je(i,o);return G(l,[l.size])}else if(r.rank===2&&n.rank===1){let i=G(n,[-1,1]),o=Je(r,i);return G(o,[o.size])}else{let i=G(n,[n.shape[0],n.shape[1]]);return Je(r,i)}}var L$=W({dot_:D$});function B$(e,...t){let r=t.map((a,s)=>F(a,`tensors${s}`,"einsum")),n={equation:e};return B.runKernel(Vp,r,n)}var kk=W({einsum_:B$});function W$(e){let t={x:F(e,"x","elu","float32")};return B.runKernel(ei,t)}var lh=W({elu_:W$});function V$(e){let t=F(e,"x","erf");P(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let r={x:t};return B.runKernel(Ou,r)}var Ik=W({erf_:V$});function U$(e){let t={x:F(e,"x","exp")};return B.runKernel(ti,t)}var Nn=W({exp_:U$});function G$(e,t=0){let r=F(e,"x","expandDims","string_or_numeric");P(t<=r.rank,()=>"Axis must be <= rank of the tensor");let n={input:r},a={dim:t};return B.runKernel(Do,n,a)}var Ht=W({expandDims_:G$});function j$(e){let t={x:F(e,"x","expm1")};return B.runKernel(Lo,t)}var Sk=W({expm1_:j$});function H$(e,t){let r=F(e,"x","tile","string_or_numeric");P(r.rank===t.length,()=>`Error in transpose: rank of input ${r.rank} must match length of reps ${t}.`);let n={x:r},a={reps:t};return B.runKernel(Ka,n,a)}var Dn=W({tile_:H$});function q$(e,t,r,n="float32"){t==null&&(t=e);let a=We([e,t],n),s=e<=t?e:t;for(let o=0;o<s;++o)a.set(1,o,o);let i=G(a.toTensor(),[e,t]);if(r==null)return i;if(r.length===1)return Dn(Ht(i,0),[r[0],1,1]);if(r.length===2)return Dn(Ht(Ht(i,0),0),[r[0],r[1],1,1]);if(r.length===3)return Dn(Ht(Ht(Ht(i,0),0),0),[r[0],r[1],r[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${r.length}D.`)}var I2=W({eye_:q$});function rd(e,t,r){let n={shape:e,value:t,dtype:r};return B.runKernel(Du,{},n)}function K$(e){let t={x:F(e,"x","floor","float32")};return B.runKernel(ri,t)}var uh=W({floor_:K$});function X$(e,t,r=0,n=0){let a=F(e,"x","gather"),s=F(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:r,batchDims:n};return B.runKernel(Wo,i,o)}var mu=W({gather_:X$});function Z$(e,t){let r=F(e,"a","greater","string_or_numeric"),n=F(t,"b","greater","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Uo,a)}var hn=W({greater_:Z$});function Y$(e,t){let r=F(e,"a","greaterEqual","string_or_numeric"),n=F(t,"b","greaterEqual","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(si,a)}var xl=W({greaterEqual_:Y$});function J$(e){let t={input:F(e,"input","imag")};return B.runKernel(Up,t)}var dm=W({imag_:J$});function Q$(e){let t={x:F(e,"x","isFinite")};return B.runKernel(Lu,t)}var eP=W({isFinite_:Q$});function tP(e){let t={x:F(e,"x","isInf")};return B.runKernel(Bu,t)}var rP=W({isInf_:tP});function nP(e){let t={x:F(e,"x","isNaN")};return B.runKernel(Wu,t)}var Ck=W({isNaN_:nP});function aP(e,t=.2){let r={x:F(e,"x","leakyRelu")},n={alpha:t};return B.runKernel(oi,r,n)}var pm=W({leakyRelu_:aP});function sP(e,t){let r=F(e,"a","less","string_or_numeric"),n=F(t,"b","less","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Go,a)}var S2=W({less_:sP});function iP(e,t){let r=F(e,"a","lessEqual","string_or_numeric"),n=F(t,"b","lessEqual","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(jo,a)}var bl=W({lessEqual_:iP});function Tk(e,t,r){if(r<=0)throw new Error("The number of values should be positive.");let n={start:e,stop:t,num:r};return B.runKernel(Kf,{},n)}function oP(e,t=5,r=1,n=1,a=.5){let s=F(e,"x","localResponseNormalization");P(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${s.rank}.`),P(pu(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=G(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:r,alpha:n,beta:a},d=B.runKernel(jp,l,u);return o?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Nk=W({localResponseNormalization_:oP});function lP(e){let t={x:F(e,"x","log","float32")};return B.runKernel(li,t)}var En=W({log_:lP});function uP(e){let t={x:F(e,"x","log1p")};return B.runKernel(Vu,t)}var hm=W({log1p_:uP});function dP(e){return P(Cs(e),()=>"The f passed in grad(f) must be a function"),(t,r)=>{let n=F(t,"x","tf.grad","string_or_numeric"),a=r!=null?F(r,"dy","tf.grad"):null;return B.tidy(()=>{let{value:s,grads:i}=B.gradients(()=>e(n),[n],a);return a!=null&&Wr(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),cm(i),i[0]})}}function pP(e){return P(Cs(e),()=>"The f passed in grads(f) must be a function"),(t,r)=>{P(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let n=Tp(t,"args","tf.grads","string_or_numeric"),a=r!=null?F(r,"dy","tf.grads"):null;return B.tidy(()=>{let{value:s,grads:i}=B.gradients(()=>e(...n),n,a);return a!=null&&Wr(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cm(i),i})}}function hP(e){return P(Cs(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,r)=>{P(t instanceof rt,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),P(r==null||r instanceof rt,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:n,value:a}=B.gradients(()=>e(t),[t],r);return cm(n),{grad:n[0],value:a}}}function cP(e){return P(Cs(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,r)=>{P(Array.isArray(t)&&t.every(a=>a instanceof rt),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),P(r==null||r instanceof rt,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let n=B.gradients(()=>e(...t),t,r);return r!=null&&Wr(n.value.shape,r.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cm(n.grads),n}}function Ek(e,t){P(Cs(e),()=>"The f passed in variableGrads(f) must be a function"),P(t==null||Array.isArray(t)&&t.every(u=>u instanceof Cp),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let r=t!=null;if(!r){t=[];for(let u in B.registeredVariables)t.push(B.registeredVariables[u])}let n=r?t.filter(u=>!u.trainable):null,a=t.length;t=t.filter(u=>u.trainable),P(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=B.gradients(e,t,null,s);P(o.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),P(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((u,d)=>{o[d]!=null&&(l[u.name]=o[d])}),n!=null&&n.forEach(u=>l[u.name]=null),{value:i,grads:l}}function Ra(e){return B.customGrad(e)}function cm(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 fP(e){let t={x:F(e,"x","neg")};return B.runKernel(qo,t)}var zt=W({neg_:fP});function mP(e){let t={x:F(e,"x","softplus")};return B.runKernel(Yu,t)}var nd=W({softplus_:mP});function gP(e){let t=F(e,"x","logSigmoid");return Ra(r=>({value:zt(nd(zt(r))),gradFunc:n=>L(n,Sr(zt(r)))}))(t)}var yP=W({logSigmoid_:gP});function AP(e,t=null,r=!1){let n={x:F(e,"x","max")},a={reductionIndices:t,keepDims:r};return B.runKernel(ui,n,a)}var fr=W({max_:AP});function xP(e,t){let r=F(e,"a","sub"),n=F(t,"b","sub");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(Ri,a)}var he=W({sub_:xP});function bP(e,t=null,r=!1){let n=F(e,"x","sum");n.dtype==="bool"&&(n=me(n,"int32"));let a={x:n},s={axis:t,keepDims:r};return B.runKernel(Ti,a,s)}var ke=W({sum_:bP});function vP(e,t=-1){let r=F(e,"logits","logSoftmax");if(t===-1&&(t=r.rank-1),t!==r.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${r.rank} and axis was ${t}`);return Ra((n,a)=>{let s=fr(n,t,!0),i=he(n,s),o=he(me(i,"float32"),En(ke(Nn(i),t,!0)));return a([o]),{value:o,gradFunc:(l,u)=>{let[d]=u,h=!0,p=Nn(d);return he(l,L(ke(l,t,h),p))}}})(r)}var C2=W({logSoftmax_:vP});function T2(e,t){for(let r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return!1;return!0}function Rk(e,t,r){let n=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<n;o++)r.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function Mk(e,t){let r=[],n=e.length;for(let s=0;s<n;s++)t.indexOf(s)===-1&&r.push(e[s]);let a=t.map(s=>e[s]);return[r,a]}function vo(e,t){let r=t.map(n=>1);return Rk(e,r,t)}function wP(e,t,r){P(T2(t,r),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${r} input.`)}function Fk(e,t){if(T2(e,t))return null;let r=[];for(let n=0;n<t;++n)e.indexOf(n)===-1&&r.push(n);return e.forEach(n=>r.push(n)),r}function N2(e){return e.map((t,r)=>[r,t]).sort((t,r)=>t[1]-r[1]).map(t=>t[0])}function kP(e,t){let r=[];for(let n=t-e;n<t;++n)r.push(n);return r}function IP(e,t=null,r=!1){let n=F(e,"x","logSumExp"),a=Bn(t,n.shape),s=fr(n,a,!0),i=he(n,s),o=Nn(i),l=ke(o,a),u=En(l),d=le(G(s,u.shape),u);if(r){let h=vo(d.shape,a);return G(d,h)}return d}var $k=W({logSumExp_:IP});function SP(e,t){let r=F(e,"a","logicalAnd","bool"),n=F(t,"b","logicalAnd","bool");xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Ho,a)}var pa=W({logicalAnd_:SP});function CP(e){let t={x:F(e,"x","logicalNot","bool")};return B.runKernel(Uu,t)}var fm=W({logicalNot_:CP});function TP(e,t){let r=F(e,"a","logicalOr","bool"),n=F(t,"b","logicalOr","bool");xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Gp,a)}var E2=W({logicalOr_:TP});function NP(e,t){let r=F(e,"a","logicalXor","bool"),n=F(t,"b","logicalXor","bool");return xt(r.shape,n.shape),pa(E2(e,t),fm(pa(e,t)))}var EP=W({logicalXor_:NP});function RP(e,t,r,n,a){let s=F(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),P(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),P(Fa(r,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${r} and dilations '${i}'`),Vr("maxPool",n,a);let u={x:o},d={filterSize:t,strides:r,pad:n,dimRoundingMode:a},h=B.runKernel(pi,u,d);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var mm=W({maxPool_:RP});function MP(e,t=[1,1,1],r,n,a,s="NDHWC"){let i=F(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),P(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),P(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Vr("maxPool3d",n,a);let u={x:o},d={filterSize:t,strides:r,pad:n,dimRoundingMode:a,dataFormat:s},h=B.runKernel(Hp,u,d);return l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var R2=W({maxPool3d_:MP});function FP(e,t,r,n,a=!1){let s={x:F(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:r,pad:n,includeBatchInIndex:a},o=B.runKernel(Jf,s,i);return{result:o[0],indexes:o[1]}}var Pk=W({maxPoolWithArgmax_:FP});function $P(e,t){let r=F(e,"a","maximum"),n=F(t,"b","maximum");[r,n]=Ot(r,n),r.dtype==="bool"&&(r=me(r,"int32"),n=me(n,"int32")),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(di,a)}var Xa=W({maximum_:$P});function PP(e,t=null,r=!1){let n={x:F(e,"x","mean")},a={axis:t,keepDims:r};return B.runKernel(hi,n,a)}var Bt=W({mean_:PP});function Wt(e,t="float32"){if(t==="complex64"){let n=Wt(e,"float32"),a=Wt(e,"float32");return Rs(n,a)}let r=$f(Ct(e),t);return B.makeTensor(r,e,t)}function ln(e,t="float32"){if(t==="complex64"){let n=ln(e,"float32"),a=Wt(e,"float32");return Rs(n,a)}let r=jy(Ct(e),t);return B.makeTensor(r,e,t)}function _P(e,t,{indexing:r="xy"}={}){if(r!=="xy"&&r!=="ij")throw new TypeError(`${r} is not a valid third argument to meshgrid`);if(e===void 0)return[];let n=F(e,"x","meshgrid",e instanceof rt?e.dtype:"float32");if(t===void 0)return[n];let a=F(t,"y","meshgrid",t instanceof rt?t.dtype:"float32"),s=Ct(n.shape),i=Ct(a.shape);return r==="xy"?(n=G(n,[1,-1]),a=G(a,[-1,1]),[Je(ln([i,1],n.dtype),n),Je(a,ln([1,s],a.dtype))]):(n=G(n,[-1,1]),a=G(a,[1,-1]),[Je(n,ln([1,i],n.dtype)),Je(ln([s,1],a.dtype),a)])}function zP(e,t=null,r=!1){let n={x:F(e,"x","min")},a={axis:t,keepDims:r};return B.runKernel(ci,n,a)}var $s=W({min_:zP});function OP(e,t){let r=F(e,"a","minimum"),n=F(t,"b","minimum");[r,n]=Ot(r,n),r.dtype==="bool"&&(r=me(r,"int32"),n=me(n,"int32")),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(fi,a)}var dh=W({minimum_:OP});function DP(e,t,r){P(r==="reflect"||r==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${r}.`);let n=F(e,"x","mirrorPad");if(n.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");P(t.length===n.rank,()=>`Padding doesn't match input. Must be ${n.rank}. Got ${t.length}.`);let a=r==="reflect"?1:0;for(let o=0;o<n.rank;o++)P(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),P(t[o][0]>=0&&t[o][0]<=n.shape[o]-a&&t[o][1]>=0&&t[o][1]<=n.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${n.shape[o]-a} or less than 0 for input of shape ${n.shape}`);let s={paddings:t,mode:r},i={x:n};return B.runKernel(mi,i,s)}var _k=W({mirrorPad_:DP});function LP(e,t){let r=F(e,"a","mod"),n=F(t,"b","mod");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(Gu,a)}var ad=W({mod_:LP});function BP(e){let t=F(e,"x","square"),r={};return B.runKernel("Square",{x:t},r)}var yt=W({square_:BP});function WP(e,t=null,r=!1){e=F(e,"x","moments");let n=Bn(t,e.shape),a=Bt(e,n,r),s=a.shape;r||(s=vo(a.shape,n));let i=yt(he(me(e,"float32"),G(a,s))),o=Bt(i,n,r);return{mean:a,variance:o}}var gm=W({moments_:WP});function VP(e,t,r,n){let a=F(t,"data","multiRNNCell"),s=Tp(r,"c","multiRNNCell"),i=Tp(n,"h","multiRNNCell"),o=a,l=[];for(let h=0;h<e.length;h++){let p=e[h](o,s[h],i[h]);l.push(p[0]),l.push(p[1]),o=p[1]}let u=[],d=[];for(let h=0;h<l.length;h+=2)u.push(l[h]),d.push(l[h+1]);return[u,d]}var UP=W({multiRNNCell_:VP});function GP(e,t,r,n=!1){let a=F(e,"logits","multinomial"),s=a.size,i=a.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}`);r=r||Math.random();let o={logits:i===1?G(a,[1,-1]):a},l={numSamples:t,seed:r,normalized:n},u=B.runKernel(Qf,o,l);return i===1?G(u,[u.size]):u}var zk=W({multinomial_:GP});function jP(e,t){let r=F(e,"a","notEqual","string_or_numeric"),n=F(t,"b","notEqual","string_or_numeric");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n};return B.runKernel(Ko,a)}var gu=W({notEqual_:jP});function HP(e){let t={x:F(e,"x","onesLike")};return B.runKernel(Yo,t)}var Rn=W({onesLike_:HP});function qP(e,t){let r=F(e,"v1","outerProduct"),n=F(t,"v2","outerProduct");P(r.rank===1&&n.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${r.rank} and ${n.rank}.`);let a=G(r,[-1,1]),s=G(n,[1,-1]);return Je(a,s)}var KP=W({outerProduct_:qP});function XP(e,t,r=0){let n=F(e,"x","pad");if(n.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:r},s={x:n};return B.runKernel(yi,s,a)}var Vn=W({pad_:XP});function ZP(e,t,r=0){return P(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Vn(e,[t],r)}var YP=W({pad1d_:ZP});function JP(e,t,r=0){return P(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Vn(e,t,r)}var QP=W({pad2d_:JP});function e_(e,t,r=0){return P(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."),Vn(e,t,r)}var t_=W({pad3d_:e_});function r_(e,t,r=0){return P(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."),Vn(e,t,r)}var n_=W({pad4d_:r_});function a_(e,t,r){let n=F(e,"x","spaceToBatchND");P(n.rank>=1+t.length,()=>`input rank ${n.rank} should be > than [blockShape] ${t.length}`),P(r.length===t.length,()=>`paddings.shape[0] ${r.length} must be equal to [blockShape] ${t.length}`),P(n.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+r[l-1][0]+r[l-1][1])%t[l-1]===0:i,!0),()=>`input spatial dimensions ${n.shape.slice(1)} with paddings ${r.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:n},s={blockShape:t,paddings:r};return B.runKernel(ll,a,s)}var ym=W({spaceToBatchND_:a_});function s_(e,t,r,n,a,s,i){a==null&&(a=[1,1]),s==null&&(s=1),n===0&&(n="valid");let o=F(e,"x","maxPool"),l=o,u=!1;o.rank===3&&(u=!0,l=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(Fa(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let d=ik(l.shape,t,s,a,n),h=[d.dilationHeight,d.dilationWidth],p;n==="same"?p=o_([d.filterHeight,d.filterWidth],h):p=[[0,0],[0,0]];let c=h[0]===1&&h[1]===1,[f,m]=i_([d.inHeight,d.inWidth],h,p),g=c?n:"valid",y=c?l:ym(l,h,f),A=(r==="avg"?()=>om(y,t,s,g,i):()=>mm(y,t,s,g,i))(),x=c?A:lm(A,h,m);return u?G(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function i_(e,t,r){let n=r.map(d=>d[0]),a=r.map(d=>d[1]),s=e.concat(n,a),i=t.map((d,h)=>(d-s[h]%d)%d),o=a.map((d,h)=>d+i[h]),l=t.map((d,h)=>[n[h],o[h]]),u=t.map((d,h)=>[0,i[h]]);return[l,u]}function o_(e,t){let r=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),n=r.map(s=>Math.floor(s/2)),a=r.map((s,i)=>s-n[i]);return r.map((s,i)=>[n[i],a[i]])}var l_=W({pool_:s_});function u_(e,t){let r=F(e,"base","pow"),n=F(t,"exp","pow");[r,n]=Ot(r,n);let a={a:r,b:n};return B.runKernel(Ai,a)}var Ps=W({pow_:u_});function d_(e,t){let r=F(e,"x","prelu"),n=F(t,"alpha","prelu"),a={x:r,alpha:n};return B.runKernel(xi,a)}var Am=W({prelu_:d_});function p_(e,t=null,r=!1){let n=F(e,"x","prod");n.dtype==="bool"&&(n=me(n,"int32"));let a={x:n},s={axis:t,keepDims:r};return B.runKernel(el,a,s)}var M2=W({prod_:p_});function h_(e,t,r){let n=Ct(e),a=null;if(r==null||r==="float32")a=new Float32Array(n);else if(r==="int32")a=new Int32Array(n);else if(r==="bool")a=new Uint8Array(n);else throw new Error(`Unknown data type ${r}`);for(let s=0;s<n;s++)a[s]=t();return B.makeTensor(a,e,r)}var c_=W({rand_:h_}),F2=Eo(Mf()),$2=class{constructor(e,t,r,n,a){this.mean=e,this.stdDev=t,this.dtype=r,this.nextVal=NaN,this.truncated=n,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=a||Math.random();this.random=F2.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let n=this.nextVal;return this.nextVal=NaN,n}let e,t,r=!1;for(;!r;){let n,a,s;do n=2*this.random()-1,a=2*this.random()-1,s=n*n+a*a;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*n*i,t=this.mean+this.stdDev*a*i,(!this.truncated||this.isValidTruncated(e))&&(r=!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}},f_=class{constructor(e,t,r,n){this.alpha=e,this.beta=1/t,this.dtype=r;let a=n||Math.random();this.randu=F2.alea(a.toString()),this.randn=new $2(0,1,r,!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,r,n,a,s;for(;;){do n=this.randn.nextValue(),s=1+this.c*n;while(s<=0);if(s*=s*s,e=n*n,t=1-.331*e*e,r=.5*e+this.d*(1-s+Math.log(s)),a=this.randu(),a<t||Math.log(a)<r)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)}},m_=class{constructor(e=0,t=1,r,n){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=r,n==null&&(n=Math.random()),typeof n=="number"&&(n=n.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=F2.alea(n)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function g_(e,t,r=1,n="float32",a){if(r==null&&(r=1),n==null&&(n="float32"),n!=="float32"&&n!=="int32")throw new Error(`Unsupported data type ${n}`);let s=new f_(t,r,n,a),i=We(e,n);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var y_=W({randomGamma_:g_});function A_(e,t=0,r=1,n,a){if(n!=null&&n==="bool")throw new Error(`Unsupported data type ${n}`);let s=new $2(t,r,n,!1,a),i=We(e,n);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Ok=W({randomNormal_:A_});function x_(e,t=0,r=1,n="float32",a){let s=We(e,n),i=new m_(t,r,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var sd=W({randomUniform_:x_});function yu(e,t,r=1,n="float32"){if(r===0)throw new Error("Cannot have a step of zero");let a={start:e,stop:t,step:r,dtype:n};return B.runKernel(Hu,{},a)}function b_(e){let t={input:F(e,"input","real")};return B.runKernel(qp,t)}var Ep=W({real_:b_});function v_(e){let t={x:F(e,"x","reciprocal")};return B.runKernel(qu,t)}var Dk=W({reciprocal_:v_});function w_(e){let t={x:F(e,"x","relu")};return B.runKernel(bi,t)}var $a=W({relu_:w_});function k_(e){let t={x:F(e,"x","relu6")};return B.runKernel(wi,t)}var P2=W({relu6_:k_});function I_(e,t){let r={x:F(e,"x","reverse")},n={dims:t};return B.runKernel(rl,r,n)}var Mn=W({reverse_:I_});function S_(e){let t=F(e,"x","reverse");return P(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Mn(t,0)}var C_=W({reverse1d_:S_});function T_(e,t){let r=F(e,"x","reverse");return P(r.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${r.rank}.`),Mn(r,t)}var N_=W({reverse2d_:T_});function E_(e,t){let r=F(e,"x","reverse");return P(r.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${r.rank}.`),Mn(r,t)}var R_=W({reverse3d_:E_});function M_(e,t){let r=F(e,"x","reverse");return P(r.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${r.rank}.`),Mn(r,t)}var F_=W({reverse4d_:M_});function $_(e){let t={x:F(e,"x","round")};return B.runKernel(nl,t)}var _2=W({round_:$_});function P_(e){let t={x:F(e,"x","rsqrt","float32")};return B.runKernel(ki,t)}var z2=W({rsqrt_:P_});function Se(e,t){if((wr(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"&&wr(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Pi(e,[],[],t)}function __(e){let t={x:F(e,"x","selu")};return B.runKernel(Xu,t)}var O2=W({selu_:__});function z_(e,t,r,n,a,s=[1,1],i="NHWC"){let o=F(e,"x","separableConv2d"),l=F(t,"depthwiseFilter","separableConv2d"),u=F(r,"pointwiseFilter","separableConv2d"),d=o,h=!1;if(o.rank===3&&(h=!0,d=G(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");P(d.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${d.rank}.`),P(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),P(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),P(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${u.shape[0]}.`),P(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let p=l.shape[2],c=l.shape[3];P(u.shape[2]===p*c,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${p*c}, but got ${u.shape[2]}.`);let f=oh(d,l,n,a,i,s),m=Fs(f,u,1,"valid",i);return h?G(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Lk=W({separableConv2d_:z_});async function O_(e,t){let r=F(e,"x","setdiff1d"),n=F(t,"y","setdiff1d");P(r.dtype===n.dtype,()=>`x and y should have the same dtype, but got x (${r.dtype}) and y (${n.dtype}).`),P(r.rank===1,()=>`x should be 1D tensor, but got x (${r.shape}).`),P(n.rank===1,()=>`y should be 1D tensor, but got y (${n.shape}).`);let a=await r.data(),s=await n.data(),i=new Set(s),o=0;for(let d=0;d<a.length;d++)i.has(a[d])||o++;let l=new rr([o],r.dtype),u=new rr([o],"int32");for(let d=0,h=0;d<a.length;d++)i.has(a[d])||(l.values[h]=a[d],u.values[h]=d,h++);return[l.toTensor(),u.toTensor()]}var Bk=O_;function D_(e){let t={x:F(e,"x","sign")};return B.runKernel(Zu,t)}var Wk=W({sign_:D_});function L_(e){let t={x:F(e,"x","sin","float32")};return B.runKernel(Ii,t)}var D2=W({sin_:L_});function B_(e){let t={x:F(e,"x","sinh")};return B.runKernel(ol,t)}var L2=W({sinh_:B_});function W_(e,t,r){let n=F(e,"x","slice1d");return P(n.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${n.rank} tensor`),Pe(n,[t],[r])}var xm=W({slice1d_:W_});function V_(e,t,r){let n=F(e,"x","slice2d");return P(n.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${n.rank} tensor`),Pe(n,t,r)}var B2=W({slice2d_:V_});function U_(e,t,r){let n=F(e,"x","slice3d");return P(n.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${n.rank} tensor`),Pe(n,t,r)}var vl=W({slice3d_:U_});function G_(e,t,r){let n=F(e,"x","slice4d");return P(n.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${n.rank} tensor`),Pe(n,t,r)}var wo=W({slice4d_:G_});function j_(e,t=-1){let r=F(e,"logits","softmax","float32");if(t===-1&&(t=r.rank-1),t!==r.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${r.rank} and dim was ${t}`);let n={logits:r},a={dim:t};return B.runKernel(Ni,n,a)}var id=W({softmax_:j_});function H_(e){P(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return B.runKernel(Hf,t)}var bm=W({fft_:H_});function q_(e){P(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return B.runKernel(qf,t)}var Rp=W({ifft_:q_});function K_(e){let t=e.shape[e.shape.length-1],r=e.size/t,n;if(t<=2){let a=G(e,[r,t]);n=Rp(a)}else{let a=[r,2*(t-1)],s=G(Ep(e),[r,t]),i=G(dm(e),[r,t]),o=Mn(Pe(s,[0,1],[r,t-2]),1),l=L(Mn(Pe(i,[0,1],[r,t-2]),1),Se(-1)),u=kt([s,o],1),d=kt([i,l],1),h=G(Rs(u,d),[a[0],a[1]]);n=Rp(h)}if(n=Ep(n),e.rank===3&&e.shape[0]!==0){let a=n,s=e.shape[0];n=G(n,[s,n.shape[0]/s,n.shape[1]]),a.dispose()}return n}var W2=W({irfft_:K_});function X_(e,t,r=0){let n={x:F(e,"x","split")},a={numOrSizeSplits:t,axis:r};return B.runKernel(ul,n,a)}var Kt=W({split_:X_});function Z_(e,t){P(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let r=e.shape[e.shape.length-1],n=e.size/r,a;if(t!=null&&t<r){let f=e.shape.map(g=>0),m=e.shape.map(g=>g);m[e.shape.length-1]=t,a=Pe(e,f,m),r=t}else if(t!=null&&t>r){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-r,a=kt([e,Wt(f)],e.shape.length-1),r=t}else a=e;let s=at(a),i=G(Rs(a,s),[n,r]),o=bm(i),l=Math.floor(r/2)+1,u=Ep(o),d=dm(o),h=Kt(u,[l,r-l],u.shape.length-1),p=Kt(d,[l,r-l],d.shape.length-1),c=a.shape.slice();return c[a.shape.length-1]=l,G(Rs(h[0],p[0]),c)}var vm=W({rfft_:Z_});function Y_(e){let t={x:F(e,"x","sqrt","float32")};return B.runKernel(Ci,t)}var Cr=W({sqrt_:Y_});function J_(e,t){let r=F(e,"a","squaredDifference"),n=F(t,"b","squaredDifference");[r,n]=Ot(r,n),xt(r.shape,n.shape);let a={a:r,b:n},s={};return B.runKernel(Ei,a,s)}var V2=W({squaredDifference_:J_});function Q_(e,t){let r=F(e,"x","squeeze");return G(r,Hv(r.shape,t).newShape)}var et=W({squeeze_:Q_});function ez(e,t=0){let r=Tp(e,"tensors","stack","string_or_numeric");P(r.length>=1,()=>"Pass at least one tensor to tf.stack"),r.length>0&&P(t<=r[0].rank,()=>"Axis must be <= rank of the tensor");let n=r,a={axis:t};return B.runKernel(Qo,n,a)}var sr=W({stack_:ez});function tz(e,t=0){let r={x:F(e,"x","step")},n={alpha:t};return B.runKernel($i,r,n)}var ph=W({step_:tz});function rz(e,t,r,n,a=0,s=0,i=0,o=0,l=0){let u={x:F(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:r,strides:n,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return B.runKernel(dl,u,d)}var Vk=W({stridedSlice_:rz});function nz(e){let t={x:F(e,"x","tan","float32")};return B.runKernel(pl,t)}var Uk=W({tan_:nz});function St(e,t){Ro(e);let r=Ea(e,t);if(r.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Pi(e,null,r,t)}function oa(e,t,r){if(Ro(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let n=Ea(e,r);if(n.length!==2&&n.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Pi(e,t,n,r)}function az(e,t,r){if(Ro(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let n=Ea(e,r);if(n.length!==4&&n.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Pi(e,t,n,r)}function sz(e,t,r){if(Ro(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let n=Ea(e,r);if(n.length!==5&&n.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Pi(e,t,n,r)}function iz(e,t,r){if(Ro(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let n=Ea(e,r);if(n.length!==6&&n.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||n,Pi(e,t,n,r)}function oz(e,t=1,r=!0){let n=F(e,"x","topk");if(n.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=n.shape[n.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:n},i={k:t,sorted:r},[o,l]=B.runKernel(hl,s,i);return{values:o,indices:l}}var Gk=W({topk_:oz});function lz(e,t=0,r=1,n,a){if(n!=null&&n==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new $2(t,r,n,!0,a),i=We(e,n);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var wm=W({truncatedNormal_:lz});function uz(e,t=0){let r=F(e,"x","unique","string_or_numeric");P(r.rank>0,()=>"The input tensor must be at least 1D");let n={x:r},a={axis:t},[s,i]=B.runKernel(am,n,a);return{values:s,indices:i}}var ny=W({unique_:uz});function dz(e,t,r){let n=F(e,"x","unsortedSegmentSum"),a=F(t,"segmentIds","unsortedSegmentSum","int32");P(pu(r),()=>"numSegments must be of dtype int");let s={x:n,segmentIds:a},i={numSegments:r};return B.runKernel(Qp,s,i)}var jk=W({unsortedSegmentSum_:dz});function pz(e,t=0){let r=F(e,"x","unstack","string_or_numeric");P(t>=-r.shape.length&&t<r.shape.length,()=>`Axis = ${t} is not in [-${r.shape.length}, ${r.shape.length})`);let n={value:r},a={axis:t};return B.runKernel(fl,n,a)}var en=W({unstack_:pz});function Hk(e,t=!0,r,n){return B.makeVariable(e,t,r,n)}function qk(e,t){let r=[];for(let s=0;s<t.length;s++)t[s]&&r.push(s);let n=We(e,"int32"),a=We([r.length,e.length],"int32");for(let s=0;s<r.length;s++){let i=n.indexToLoc(r[s]),o=s*e.length;a.values.set(i,o)}return a.toTensor()}async function hz(e){let t=F(e,"condition","whereAsync","bool"),r=await t.data(),n=qk(t.shape,r);return e!==t&&t.dispose(),n}var U2=hz;async function cz(e,t,r){let n=F(e,"tensor","boolMask"),a=F(t,"mask","boolMask","bool"),s=r==null?0:r,i=a.rank,o=n.shape;P(i>0,()=>"mask cannot be scalar"),Wr(o.slice(s,s+i),a.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=s;m<s+i;m++)l*=o[m];let u=o.slice(0,s).concat([l],o.slice(s+i)),d=G(n,u),h=G(a,[-1]),p=await U2(h),c=et(p,[1]),f=mu(d,c,s);return e!==n&&n.dispose(),t!==a&&a.dispose(),c.dispose(),d.dispose(),h.dispose(),p.dispose(),f}var fz=cz;function mz(e,t="euclidean",r=null,n=!1){e=F(e,"x","norm");let a=Kk(e,t,r),s=a.shape;if(n){let i=Bn(r,e.shape);s=vo(a.shape,i)}return G(a,s)}function Kk(e,t,r=null){if(e.rank===0)return er(e);if(e.rank!==1&&r===null)return Kk(G(e,[-1]),t,r);if(e.rank===1||typeof r=="number"||Array.isArray(r)&&r.length===1){if(t===1)return ke(er(e),r);if(t===1/0)return fr(er(e),r);if(t===-1/0)return $s(er(e),r);if(t==="euclidean"||t===2)return Cr(ke(Ps(er(e),Se(2,"int32")),r));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(r)&&r.length===2){if(t===1)return fr(ke(er(e),r[0]),r[1]-1);if(t===1/0)return fr(ke(er(e),r[1]),r[0]);if(t===-1/0)return $s(ke(er(e),r[1]),r[0]);if(t==="fro"||t==="euclidean")return Cr(ke(yt(e),r));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${r}`)}var G2=W({norm_:mz});function gz(e,t,r,n,a=!0){let s=F(e,"v","movingAverage"),i=F(t,"x","movingAverage"),o=F(r,"decay","movingAverage");fw(s,i),P(Ws(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=Se(1),u=he(l,o),d=L(he(i,s),u);if(a){P(n!=null,()=>"When using zeroDebias: true, step is required.");let h=F(n,"step","movingAverage");d=pe(d,he(l,Ps(o,h)))}return le(s,d)}var yz=W({movingAverage_:gz});function Az(e,t,r){let n=F(e,"indices","scatterND","int32"),a=F(t,"updates","scatterND");i2(a,n,r);let s={indices:n,updates:a},i={shape:r};return B.runKernel(al,s,i)}var Xk=W({scatterND_:Az});function xz(e,t,r,n){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 a=e.rank>0?e.shape[0]:1,s=e.rank>1?e.shape[1]:1;if(r.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${r.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===a))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${a}]`);if(t.dtype!==n.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function bz(e,t,r,n=0){let a=F(e,"sparseIndices","sparseToDense","int32"),s=F(t,"sparseValues","sparseToDense"),i=F(n,"defaultValue","sparseToDense",s.dtype);xz(a,s,r,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:r};return B.runKernel(Yp,o,l)}var j2=W({sparseToDense_:bz});function vz(e,t){let r=F(t,"indices","gatherND","int32"),n={params:F(e,"x","gatherND","string_or_numeric"),indices:r};return B.runKernel(Vo,n)}var Zk=W({gatherND_:vz});function wz(e,t){if(t==null)return e.shape.slice();if(Ws(e.shape,t))return t;if(e.shape.length===t.length){let r=[];for(let n=0;n<e.shape.length;n++)t[n]==null&&e.shape[n]!=null?r.push(e.shape[n]):r.push(t[n]);return r}return t}function kz(e,t,r,n){let a=F(e,"x","dropout");if(P(a.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),P(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof rt?a.clone():a;let s=wz(a,r),i=1-t,o=pe(uh(le(sd(s,0,1,"float32",n),i)),i);return L(a,o)}var Yk=W({dropout_:kz});function Jk(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function H2(e,t,r){let n=1-e%2,a=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+n-1);a[s]=t-r*Math.cos(i)}return St(a,"float32")}async function Iz(e,t,r=1){let n=F(e,"predictions","inTopK"),a=F(t,"targets","inTopK");P(n.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${n.rank}`),P(n.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${n.rank} and targets rank ${a.rank}`),Wr(n.shape.slice(0,n.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=n.shape[n.shape.length-1];P(r>0&&r<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${r}`);let i=await n.data(),o=await a.data(),[l,u]=[i.length/s,s],d=qv("bool",l);for(let h=0;h<l;h++){let p=h*u,c=i.subarray(p,p+u),f=[];for(let m=0;m<c.length;m++)f.push({value:c[m],index:m});f.sort((m,g)=>g.value-m.value),d[h]=0;for(let m=0;m<r;m++)if(f[m].index===o[h]){d[h]=1;break}}return e!==n&&n.dispose(),t!==a&&a.dispose(),ct(d,a.shape,"bool")}var Sz=Iz,_s={};Le(_s,{conv2d:()=>Nz,depthwiseConv2d:()=>Fz,matMul:()=>Pz});function Cz(e,t,r,n,a,s="NHWC",i){let o=e;e.rank===3&&(o=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]])),P(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),P(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),P(r.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${r}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],d=s==="NHWC"?l.shape[3]:l.shape[1];P(u===r[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${r[2]}.`),P(d===r[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${r[3]}).`),Vr("conv2dDerFilter",a,i);let h={x:o,dy:l},p={strides:n,pad:a,dataFormat:s,dimRoundingMode:i,filterShape:r};return B.runKernel(Df,h,p)}var q2=W({conv2DBackpropFilter_:Cz});function km(e,t,r){if(r==null||r==="linear")return e;if(r==="relu")return L(e,ph(t));throw new Error(`Cannot compute gradient for fused activation ${r}.`)}function Im(e,t){let r=t,n=Xt(e.shape,t.shape);return n.length>0&&(r=ke(r,n)),G(r,e.shape)}function Sm(e,t,r,n){if(t==="linear")return e;if(t==="relu")return $a(e);if(t==="elu")return lh(e);if(t==="relu6")return P2(e);if(t==="prelu")return Am(e,r);if(t==="leakyrelu")return pm(e,n);if(t==="sigmoid")return Sr(e);throw new Error(`Unknown fused activation ${t}.`)}var Cm=(e,t)=>!(e>0)||t==="linear";function Tz({x:e,filter:t,strides:r,pad:n,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(l=l||"linear",Cm(B.state.gradientDepth,l)===!1){let v=Fs(e,t,r,n,a,s,i);return o!=null&&(v=le(v,o)),Sm(v,l,u,d)}let h=F(e,"x","conv2d","float32"),p=F(t,"filter","conv2d","float32"),c=h,f=!1;h.rank===3&&(f=!0,c=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),P(c.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${c.rank}.`),P(p.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${p.rank}.`),Vr("fused conv2d",n,i),P(c.shape[3]===p.shape[2],()=>`Error in conv2d: depth of input (${c.shape[3]}) must match input depth for filter ${p.shape[2]}.`),P(Fa(r,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`),P(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=ih(c.shape,p.shape,r,s,n,i),g;o!=null&&(g=F(o,"bias","fused conv2d"),[g]=Ot(g,h),xt(m.outShape,g.shape));let y;u!=null&&(y=F(u,"prelu weights","fused conv2d"));let A=(v,C)=>{let[S,E,R,_]=C,M=km(v,R,l);P(Ms(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let I=x2(E.shape,M,S,r,n),O=q2(E,M,S.shape,r,n),z=[I,O];if(_!=null){let j=Im(_,M);z.push(j)}return z},x={x:c,filter:p,bias:g,preluActivationWeights:y},b={strides:r,pad:n,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return o==null?Ra((v,C,S)=>{let E=B.runKernel(Ns,x,b);return S([C,v,E]),f&&(E=G(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(c,p):Ra((v,C,S,E)=>{let R=B.runKernel(Ns,x,b);return E([C,v,R,S]),f&&(R=G(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:A}})(c,p,g)}var Nz=W({fusedConv2d_:Tz});function Ez(e,t,r,n,a,s=[1,1],i){let o=e;e.rank===3&&(o=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},d={strides:n,pad:a,dimRoundingMode:i,dilations:s,filterShape:r};return B.runKernel(Vf,u,d)}var Qk=W({depthwiseConv2dNativeBackpropFilter_:Ez});function Rz(e,t,r,n,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:r},d={strides:n,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=B.runKernel(Uf,u,d);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var e7=W({depthwiseConv2dNativeBackpropInput_:Rz});function Mz({x:e,filter:t,strides:r,pad:n,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(Cm(B.state.gradientDepth,l)===!1){let v=oh(e,t,r,n,a,s,i);return o!=null&&(v=le(v,o)),Sm(v,l,u,d)}let h=F(e,"x","depthwiseConv2d","float32"),p=F(t,"filter","depthwiseConv2d","float32"),c=h,f=!1;h.rank===3&&(f=!0,c=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),P(c.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),P(p.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`),P(c.shape[3]===p.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${p.shape[2]}.`),s==null&&(s=[1,1]),P(Fa(r,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`),Vr("fused depthwiseConv2d",n,i);let m=ih(c.shape,p.shape,r,s,n,i,!0),g;o!=null&&(g=F(o,"bias","fused conv2d"),[g]=Ot(g,h),xt(m.outShape,g.shape));let y;u!=null&&(y=F(u,"prelu weights","fused depthwiseConv2d"));let A=(v,C)=>{P(Ms(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[S,E,R,_]=C,M=km(v,R,l),I=e7(E.shape,M,S,r,n,s,i),O=Qk(E,M,S.shape,r,n,s,i);if(_!=null){let z=Im(g,M);return[I,O,z]}return[I,O]},x={x:c,filter:p,bias:g,preluActivationWeights:y},b={strides:r,pad:n,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return o==null?Ra((v,C,S)=>{let E=B.runKernel(Es,x,b);return S([C,v,E]),f&&(E=G(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(c,p):Ra((v,C,S,E)=>{let R=B.runKernel(Es,x,b);return E([C,v,R,S]),f&&(R=G(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:A}})(c,p,g)}var Fz=W({fusedDepthwiseConv2d_:Mz});function $z({a:e,b:t,transposeA:r=!1,transposeB:n=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Cm(B.state.gradientDepth,s)===!1){let _=Je(e,t,r,n);return a!=null&&(_=le(_,a)),Sm(_,s,i,o)}let l=F(e,"a","fused matMul"),u=F(t,"b","fused matMul");[l,u]=Ot(l,u);let d=r?l.shape[l.rank-2]:l.shape[l.rank-1],h=n?u.shape[u.rank-1]:u.shape[u.rank-2],p=r?l.shape[l.rank-1]:l.shape[l.rank-2],c=n?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),g=Ct(f),y=Ct(m);P(d===h,()=>`Error in fused matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${r} and transposeB=${n} must match.`);let A=xt(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([p,c]),x=r?G(l,[g,d,p]):G(l,[g,p,d]),b=n?G(u,[y,c,h]):G(u,[y,h,c]),v;a!=null&&(v=F(a,"bias","fused matMul"),[v]=Ot(v,l),xt(A,v.shape));let C;i!=null&&(C=F(i,"prelu weights","fused matMul"));let S=(_,M)=>{let[I,O,z,j]=M,X=km(G(_,z.shape),z,s),D,Q;if(!r&&!n?(D=Je(X,O,!1,!0),Q=Je(I,X,!0,!1)):!r&&n?(D=Je(X,O,!1,!1),Q=Je(X,I,!0,!1)):r&&!n?(D=Je(O,X,!1,!0),Q=Je(I,X,!1,!1)):(D=Je(O,X,!0,!0),Q=Je(X,I,!0,!0)),a!=null){let V=Im(j,X);return[D,Q,V]}else return[D,Q]},E={a:x,b,bias:v,preluActivationWeights:C},R={transposeA:r,transposeB:n,activation:s,leakyreluAlpha:o};return a==null?Ra((_,M,I)=>{let O=B.runKernel(Ts,E,R);return I([_,M,O]),{value:G(O,A),gradFunc:S}})(x,b):Ra((_,M,I,O)=>{let z=B.runKernel(Ts,E,R);return O([_,M,z,I]),{value:G(z,A),gradFunc:S}})(x,b,v)}var Pz=W({fusedMatMul_:$z});function _z(e){return H2(e,.54,.46)}var zz=W({hammingWindow_:_z});function Oz(e){return H2(e,.5,.5)}var t7=W({hannWindow_:Oz});function Dz(e,t,r,n=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Pe(e,s,t)),s+=r;if(n)for(;s<e.size;){let o=s+t-e.size,l=kt([Pe(e,s,t-o),rd([o],a)]);i.push(l),s+=r}return i.length===0?oa([],[0,t]):G(kt(i),[i.length,t])}var r7=W({frame_:Dz});function Lz(e,t,r,n,a=t7){n==null&&(n=Jk(t));let s=r7(e,t,r),i=L(s,a(t));return vm(i,n)}var Bz=W({stft_:Lz});function Wz(e,t,r,n,a="bilinear",s=0){let i=F(e,"image","cropAndResize"),o=F(t,"boxes","cropAndResize","float32"),l=F(r,"boxInd","cropAndResize","int32"),u=o.shape[0];P(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),P(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),P(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),P(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),P(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),P(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let d={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:n};return B.runKernel(_o,d,h)}var Vz=W({cropAndResize_:Wz});function Uz(e){let t=F(e,"image","flipLeftRight","float32");P(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let r={image:t};return B.runKernel(Bo,r,{})}var Gz=W({flipLeftRight_:Uz});function jz(e){let t=F(e,"image","grayscaleToRGB"),r=t.rank-1,n=t.shape[r];P(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),P(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let a=new Array(t.rank);return a.fill(1,0,r),a[r]=3,Dn(t,a)}var Hz=W({grayscaleToRGB_:jz});function qz(e,t,r=0,n=.5){let a=F(e,"image","rotateWithOffset","float32");P(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:r,center:n};return B.runKernel(gl,s,i)}var Kz=W({rotateWithOffset_:qz});function od(e,t,r,n,a,s){n==null&&(n=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return r=Math.min(r,i),P(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),P(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),P(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),P(t.rank===1,()=>"scores must be a 1D tensor"),P(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),P(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:r,iouThreshold:n,scoreThreshold:a,softNmsSigma:s}}function Xz(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY){let s=F(e,"boxes","nonMaxSuppression","float32"),i=F(t,"scores","nonMaxSuppression","float32"),o=od(s,i,r,n,a);r=o.maxOutputSize,n=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:r,iouThreshold:n,scoreThreshold:a};return B.runKernel(Xo,{boxes:s,scores:i},l)}var Zz=W({nonMaxSuppression_:Xz});function Yz(e,t,r){let n=Jz(e,t,r),a=n<0?-(n+1):n;e.splice(a,0,t)}function Jz(e,t,r){return eO(e,t,r||Qz)}function Qz(e,t){return e>t?1:e<t?-1:0}function eO(e,t,r){let n=0,a=e.length,s=0,i=!1;for(;n<a;){s=n+(a-n>>>1);let o=r(t,e[s]);o>0?n=s+1:(a=s,i=!o)}return i?n:-n-1}function n7(e,t,r,n,a){return K2(e,t,r,n,a,0)}function a7(e,t,r,n,a,s){return K2(e,t,r,n,a,0,!1,s,!0)}function s7(e,t,r,n,a,s){return K2(e,t,r,n,a,s,!0)}function K2(e,t,r,n,a,s,i=!1,o=!1,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>a&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(x3);let d=s>0?-.5/s:0,h=[],p=[];for(;h.length<r&&u.length>0;){let g=u.pop(),{score:y,boxIndex:A,suppressBeginIndex:x}=g;if(y<a)break;let b=!1;for(let v=h.length-1;v>=x;--v){let C=tO(e,A,h[v]);if(C>=n){b=!0;break}if(g.score=g.score*rO(n,d,C),g.score<=a)break}g.suppressBeginIndex=h.length,b||(g.score===y?(h.push(A),p.push(g.score)):g.score>a&&Yz(u,g,x3))}let c=h.length,f=r-c;o&&f>0&&(h.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));let m={selectedIndices:h};return i&&(m.selectedScores=p),l&&(m.validOutputs=c),m}function tO(e,t,r){let n=e.subarray(t*4,t*4+4),a=e.subarray(r*4,r*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),o=Math.max(n[0],n[2]),l=Math.max(n[1],n[3]),u=Math.min(a[0],a[2]),d=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),p=Math.max(a[1],a[3]),c=(o-s)*(l-i),f=(h-u)*(p-d);if(c<=0||f<=0)return 0;let m=Math.max(s,u),g=Math.max(i,d),y=Math.min(o,h),A=Math.min(l,p),x=Math.max(y-m,0)*Math.max(A-g,0);return x/(c+f-x)}function rO(e,t,r){let n=Math.exp(t*r*r);return r<=e?n:0}function x3(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function nO(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY){let s=F(e,"boxes","nonMaxSuppressionAsync"),i=F(t,"scores","nonMaxSuppressionAsync"),o=od(s,i,r,n,a);r=o.maxOutputSize,n=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],d=l[1],{selectedIndices:h}=n7(u,d,r,n,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),St(h,"int32")}var aO=nO;function sO(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=F(e,"boxes","nonMaxSuppression"),o=F(t,"scores","nonMaxSuppression"),l=od(i,o,r,n,a,s);r=l.maxOutputSize,n=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},d={maxOutputSize:r,iouThreshold:n,scoreThreshold:a,softNmsSigma:s},h=B.runKernel(Zo,u,d);return{selectedIndices:h[0],selectedScores:h[1]}}var iO=W({nonMaxSuppressionWithScore_:sO});async function oO(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=F(e,"boxes","nonMaxSuppressionAsync"),o=F(t,"scores","nonMaxSuppressionAsync"),l=od(i,o,r,n,a,s);r=l.maxOutputSize,n=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),d=u[0],h=u[1],{selectedIndices:p,selectedScores:c}=s7(d,h,r,n,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:St(p,"int32"),selectedScores:St(c)}}var lO=oO;function uO(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=F(e,"boxes","nonMaxSuppression"),o=F(t,"scores","nonMaxSuppression"),l=od(i,o,r,n,a,null),u=l.maxOutputSize,d=l.iouThreshold,h=l.scoreThreshold,p={boxes:i,scores:o},c={maxOutputSize:u,iouThreshold:d,scoreThreshold:h,padToMaxOutputSize:s},f=B.runKernel(ju,p,c);return{selectedIndices:f[0],validOutputs:f[1]}}var dO=W({nonMaxSuppressionPadded_:uO});async function pO(e,t,r,n=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=F(e,"boxes","nonMaxSuppressionAsync"),o=F(t,"scores","nonMaxSuppressionAsync"),l=od(i,o,r,n,a,null),u=l.maxOutputSize,d=l.iouThreshold,h=l.scoreThreshold,[p,c]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=a7(p,c,u,d,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:St(f,"int32"),validOutputs:Se(m,"int32")}}var hO=pO;function cO(e,t,r=!1,n=!1){let a=F(e,"images","resizeBilinear");P(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),P(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),P(n===!1||r===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:r,halfPixelCenters:n,size:t},u=B.runKernel(vi,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var fO=W({resizeBilinear_:cO});function mO(e,t,r=!1,n=!1){let a=F(e,"images","resizeNearestNeighbor");P(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),P(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),P(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),P(n===!1||r===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:r,halfPixelCenters:n,size:t},u=B.runKernel(Ku,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var gO=W({resizeNearestNeighbor_:mO});function yO(e,t="binary",r=!1,n=.5){let a=F(e,"image","threshold"),s=.2989,i=.587,o=.114,l=a.shape[0]*a.shape[1],u=L(St([n]),255),d,h,p,c;if(P(a.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${a.rank}.`),P(a.shape[2]===3||a.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${a.shape[2]}.`),P(a.dtype==="int32"||a.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${a.dtype}.`),P(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),a.shape[2]===3){[d,h,p]=Kt(a,[1,1,1],-1);let m=L(d,s),g=L(h,i),y=L(p,o);c=le(le(m,g),y)}else c=e;if(t==="otsu"){let m=y2(me(_2(c),"int32"),ct([]),256);u=AO(m,l)}let f=r?bl(c,u):hn(c,u);return me(L(f,255),"int32")}function AO(e,t){let r=St([-1]),n=St([0]),a=St([0]),s,i,o,l,u,d;for(let h=0;h<e.size-1;h++){s=Pe(e,0,h+1),i=Pe(e,h+1),u=pe(ke(s),t),d=pe(ke(i),t);let p=ke(L(s,yu(0,s.size)));o=pe(p,ke(s));let c=rd(i.shape,s.size),f=le(yu(0,i.size),c),m=L(i,f);l=pe(ke(m),ke(i));let g=he(o,l),y=he(o,l),A=L(u,d);a=L(L(A,g),y);let x=hn(a,n);n=Lr(x,a,n),r=Lr(x,St([h]),r)}return r}var xO=W({threshold_:yO});function bO(e,t,r="nearest",n="constant",a=0,s){let i=F(e,"image","transform","float32"),o=F(t,"transforms","transform","float32");P(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),P(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"),P(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:r,fillMode:n,fillValue:a,outputShape:s};return B.runKernel(cl,l,u)}var vO=W({transform_:bO});function wO(e,t,r){P(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),P(r%1===0,()=>`bandPart(): numUpper must be an integer, got ${r}.`);let n=F(e,"a","bandPart");P(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let a=n.shape,[s,i]=n.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(r<=i))throw new Error(`bandPart(): numUpper (${r}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),r<0&&(r=i);let o=G(yu(0,s,1,"int32"),[-1,1]),l=yu(0,i,1,"int32"),u=he(o,l),d=pa(bl(u,Se(+t,"int32")),xl(u,Se(-r,"int32"))),h=Wt([s,i],n.dtype);return G(sr(en(G(n,[-1,s,i])).map(p=>Lr(d,p,h))),a)}var kO=W({bandPart_:wO});function IO(e){let t;if(Array.isArray(e)){t=!1,P(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)P(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Kt(e,e.shape[0],0).map(a=>et(a,[0]));P(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let r=[],n=e;for(let a=0;a<e.length;++a)r.push(B.tidy(()=>{let s=n[a];if(a>0)for(let i=0;i<a;++i){let o=L(ke(L(r[i],s)),r[i]);s=he(s,o)}return pe(s,G2(s,"euclidean"))}));return t?sr(r,0):r}var SO=W({gramSchmidt_:IO});function CO(e,t=!1){if(P(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return b3(e,t);{let r=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),n=en(G(e,[r,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];n.forEach(l=>{let[u,d]=b3(l,t);a.push(u),s.push(d)});let i=G(sr(a,0),e.shape),o=G(sr(s,0),e.shape);return[i,o]}}function b3(e,t=!1){return B.tidy(()=>{P(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let r=e.shape[0],n=e.shape[1],a=I2(r),s=Or(e),i=oa([[1]],[1,1]),o=Or(i),l=r>=n?n:r;for(let u=0;u<l;++u){let d=s,h=o,p=a;[o,s,a]=B.tidy(()=>{let c=Pe(s,[u,u],[r-u,1]),f=G2(c),m=Pe(s,[u,u],[1,1]),g=Lr(hn(m,0),oa([[-1]]),oa([[1]])),y=he(m,L(g,f)),A=pe(c,y);A.shape[0]===1?o=Or(i):o=kt([i,Pe(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=zt(pe(Je(g,y),f)),b=Pe(s,[u,0],[r-u,n]),v=L(x,o),C=nt(o);if(u===0)s=he(b,Je(v,Je(C,b)));else{let R=he(b,Je(v,Je(C,b)));s=kt([Pe(s,[0,0],[u,n]),R],0)}let S=nt(v),E=Pe(a,[0,u],[r,a.shape[1]-u]);if(u===0)a=he(E,Je(Je(E,o),S));else{let R=he(E,Je(Je(E,o),S));a=kt([Pe(a,[0,0],[r,u]),R],1)}return[o,s,a]}),re([d,h,p])}return!t&&r>n&&(a=Pe(a,[0,0],[r,n]),s=Pe(s,[0,0],[n,n])),[a,s]})}var TO=W({qr_:CO}),i7=(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",e))(i7||{});function NO(e,t,r=3){let n=F(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=F(t,"weights","computeWeightedLoss"));let s=a==null?n:L(n,a);if(r===0)return s;if(r===2)return ke(s);if(r===1){if(a==null)return Bt(s);{let i=n.size/a.size,o=pe(ke(s),ke(a));return i>1?pe(o,Se(i)):o}}if(r===3){if(a==null)return pe(ke(s),Se(n.size));{let i=L(a,ln(n.shape)),o=me(ke(gu(i,Se(0))),"float32");return pe(ke(s),o)}}throw Error(`Unknown reduction: ${r}`)}var Za=W({computeWeightedLoss_:NO});function EO(e,t,r,n=3){let a=F(e,"labels","absoluteDifference"),s=F(t,"predictions","absoluteDifference"),i=null;r!=null&&(i=F(r,"weights","absoluteDifference")),Wr(a.shape,s.shape,"Error in absoluteDifference: ");let o=er(he(a,s));return Za(o,i,n)}var RO=W({absoluteDifference_:EO});function MO(e,t,r,n,a=3){let s=F(e,"labels","cosineDistance"),i=F(t,"predictions","cosineDistance"),o=null;n!=null&&(o=F(n,"weights","cosineDistance")),Wr(s.shape,i.shape,"Error in cosineDistance: ");let l=Se(1),u=he(l,ke(L(s,i),r,!0));return Za(u,o,a)}var FO=W({cosineDistance_:MO});function $O(e,t,r,n=3){let a=F(e,"labels","hingeLoss"),s=F(t,"predictions","hingeLoss"),i=null;r!=null&&(i=F(r,"weights","hingeLoss")),Wr(a.shape,s.shape,"Error in hingeLoss: ");let o=Se(1);a=he(L(Se(2),a),o);let l=$a(he(o,L(a,s)));return Za(l,i,n)}var PO=W({hingeLoss_:$O});function _O(e,t,r,n=1,a=3){let s=F(e,"labels","huberLoss"),i=F(t,"predictions","huberLoss"),o=null;r!=null&&(o=F(r,"weights","huberLoss")),Wr(s.shape,i.shape,"Error in huberLoss: ");let l=Se(n),u=er(he(i,s)),d=dh(u,l),h=he(u,d),p=le(L(Se(.5),yt(d)),L(l,h));return Za(p,o,a)}var zO=W({huberLoss_:_O});function OO(e,t,r,n=1e-7,a=3){let s=F(e,"labels","logLoss"),i=F(t,"predictions","logLoss"),o=null;r!=null&&(o=F(r,"weights","logLoss")),Wr(s.shape,i.shape,"Error in logLoss: ");let l=Se(1),u=Se(n),d=zt(L(s,En(le(i,u)))),h=L(he(l,s),En(le(he(l,i),u))),p=he(d,h);return Za(p,o,a)}var DO=W({logLoss_:OO});function LO(e,t,r,n=3){let a=F(e,"labels","meanSquaredError"),s=F(t,"predictions","meanSquaredError"),i=null;r!=null&&(i=F(r,"weights","meanSquaredError")),Wr(a.shape,s.shape,"Error in meanSquaredError: ");let o=V2(a,s);return Za(o,i,n)}var BO=W({meanSquaredError_:LO});function WO(e,t){let r=F(e,"labels","sigmoidCrossEntropyWithLogits"),n=F(t,"logits","sigmoidCrossEntropyWithLogits");Wr(r.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=$a(n),s=L(n,r),i=hm(Nn(zt(er(n))));return le(he(a,s),i)}function VO(e,t,r,n=0,a=3){let s=F(e,"multiClassLabels","sigmoidCrossEntropy"),i=F(t,"logits","sigmoidCrossEntropy"),o=null;if(r!=null&&(o=F(r,"weights","sigmoidCrossEntropy")),Wr(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=Se(n),d=Se(1),h=Se(.5);s=le(L(s,he(d,u)),L(h,u))}let l=WO(s,i);return Za(l,o,a)}var UO=W({sigmoidCrossEntropy_:VO});function GO(e,t,r=-1){if(r===-1&&(r=t.rank-1),r!==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 ${r}`);return Ra((n,a,s)=>{let i=$k(a,[r],!0),o=he(me(a,"float32"),i);s([n,o]);let l=zt(L(o,n));return{value:ke(l,[r]),gradFunc:(u,d)=>{let[h,p]=d,c=vo(u.shape,[r]);return[L(G(u,c),he(me(h,"float32"),Nn(p))),L(G(u,c),he(Nn(p),me(h,"float32")))]}}})(e,t)}function jO(e,t,r,n=0,a=3){let s=F(e,"onehotLabels","softmaxCrossEntropy"),i=F(t,"logits","softmaxCrossEntropy"),o=null;if(r!=null&&(o=F(r,"weights","softmaxCrossEntropy")),Wr(s.shape,i.shape,"Error in softmaxCrossEntropy: "),n>0){let u=Se(n),d=Se(1),h=Se(s.shape[1]);s=le(L(s,he(d,u)),pe(u,h))}let l=GO(s,i);return Za(l,o,a)}var HO=W({softmaxCrossEntropy_:jO});function qO(e,t,r,n){let a=F(e,"indices","sparseFillEmptyRows","int32"),s=F(t,"values","sparseFillEmptyRows"),i=F(r,"denseShape","sparseFillEmptyRows","int32"),o=F(n,"defaultValue","sparseFillEmptyRows",s.dtype);if(a.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${a.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:a,values:s,denseShape:i,defaultValue:o},u=B.runKernel(Kp,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var KO=W({sparseFillEmptyRows_:qO});function XO(e,t,r){let n=F(e,"inputIndices","sparseReshape","int32"),a=F(t,"inputShape","sparseReshape","int32"),s=F(r,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${n.shape}`);if(a.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${a.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:n,inputShape:a,newShape:s},o=B.runKernel(Ju,i);return{outputIndices:o[0],outputShape:o[1]}}var ZO=W({sparseReshape_:XO});function YO(e,t,r){let n=F(e,"data","sparseSegmentMean"),a=F(t,"indices","sparseSegmentMean","int32"),s=F(r,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${a.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Xp,i)}var JO=W({sparseSegmentMean_:YO});function QO(e,t,r){let n=F(e,"data","sparseSegmentSum"),a=F(t,"indices","sparseSegmentSum","int32"),s=F(r,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${a.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Zp,i)}var eD=W({sparseSegmentSum_:QO});function tD(e,t,r,n,a,s,i,o){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:r,nGramWidths:n,leftPad:a,rightPad:s,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=B.runKernel(Jp,h,d);return{nGrams:p[0],nGramsSplits:p[1]}}var rD=W({stringNGrams_:tD});function nD(e,t,r=!0){let n=F(e,"input","stringSplit","string"),a=F(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(a.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);let s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(rm,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var aD=W({stringSplit_:nD});function sD(e,t){let r=F(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel(nm,a,n)}var iD=W({stringToHashBucketFast_:sD}),oD={fft:bm,ifft:Rp,rfft:vm,irfft:W2},lD={hammingWindow:zz,hannWindow:t7,frame:r7,stft:Bz},Ie={flipLeftRight:Gz,grayscaleToRGB:Hz,resizeNearestNeighbor:gO,resizeBilinear:fO,rotateWithOffset:Kz,cropAndResize:Vz,nonMaxSuppression:Zz,nonMaxSuppressionAsync:aO,nonMaxSuppressionWithScore:iO,nonMaxSuppressionWithScoreAsync:lO,nonMaxSuppressionPadded:dO,nonMaxSuppressionPaddedAsync:hO,threshold:xO,transform:vO},o7={bandPart:kO,gramSchmidt:SO,qr:TO},uD={absoluteDifference:RO,computeWeightedLoss:Za,cosineDistance:FO,hingeLoss:PO,huberLoss:zO,logLoss:DO,meanSquaredError:BO,sigmoidCrossEntropy:UO,softmaxCrossEntropy:HO},up={sparseFillEmptyRows:KO,sparseReshape:ZO,sparseSegmentMean:JO,sparseSegmentSum:eD},jc={stringNGrams:rD,stringSplit:aD,stringToHashBucketFast:iD},Ya=class extends Kw{minimize(e,t=!1,r){let{value:n,grads:a}=this.computeGradients(e,r);if(r!=null){let s=r.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ek(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ya,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Tm=class extends Ya{constructor(e,t,r=null){super();this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(yt(s),1-this.rho)),u=L(pe(Cr(le(o,this.epsilon)),Cr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(yt(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(re(this.accumulatedGrads.map(e=>e.variable)),re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Tm.className="Adadelta";_i(Tm);var Nm=class extends Ya{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>rd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,yt(a));s.assign(i);let o=le(L(pe(a,Cr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(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(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Nm.className="Adagrad";_i(Nm);var Em=class extends Ya{constructor(e,t,r,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=he(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(yt(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Cr(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(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),K(()=>{this.accBeta1.assign(Ps(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ps(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}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)}};Em.className="Adam";_i(Em);var Rm=class extends Ya{constructor(e,t,r,n=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=L(d,this.beta2),c=er(l),f=Xa(p,c);u.assign(h),d.assign(f);let m=le(L(pe(n,r),pe(h,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(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)}};Rm.className="Adamax";_i(Rm);var hh=class extends Ya{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=hr(Se(-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)}};hh.className="SGD";_i(hh);var Mm=class extends hh{constructor(e,t,r=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=r,this.accumulations=[],this.m=Se(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulations[r]==null&&(this.accumulations[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(!1))});let a=this.accumulations[r].variable,s=Array.isArray(e)?e[r].tensor:e[t];s!=null&&K(()=>{let i,o=le(L(this.m,a),s);this.useNesterov?i=le(L(this.c,le(s,L(o,this.m))),n):i=le(L(this.c,o),n),a.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Mm.className="Momentum";_i(Mm);var Fm=class extends Ya{constructor(e,t=.9,r=0,n=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(yt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Cr(he(l,le(yt(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=he(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(yt(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Cr(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=he(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&re(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,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}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)}};Fm.className="RMSProp";_i(Fm);var As=class{static sgd(e){return new hh(e)}static momentum(e,t,r=!1){return new Mm(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new Fm(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new Em(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new Tm(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new Rm(e,t,r,n,a)}static adagrad(e,t=.1){return new Nm(e,t)}},so={sgd:As.sgd,momentum:As.momentum,adadelta:As.adadelta,adagrad:As.adagrad,rmsprop:As.rmsprop,adamax:As.adamax,adam:As.adam},dD=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function l7(){return new Promise(e=>dD(()=>e()))}var N={};Le(N,{ERF_A1:()=>vD,ERF_A2:()=>wD,ERF_A3:()=>kD,ERF_A4:()=>ID,ERF_A5:()=>SD,ERF_P:()=>bD,PARALLELIZE_THRESHOLD:()=>X2,SELU_SCALE:()=>d7,SELU_SCALEALPHA:()=>u7,applyActivation:()=>Sm,assertAndGetBroadcastShape:()=>xt,assertAxesAreInnerMostDims:()=>wP,assertParamsConsistent:()=>pD,assignToTypedArray:()=>MD,axesAreInnerMostDims:()=>T2,calculateShapes:()=>Dw,checkEinsumDimSizes:()=>OD,checkPadOnDimRoundingMode:()=>Vr,combineLocations:()=>Rk,complexWithEvenIndex:()=>ND,complexWithOddIndex:()=>ED,computeConv2DInfo:()=>ih,computeConv3DInfo:()=>ok,computeDefaultPad:()=>m2,computeDilation2DInfo:()=>VF,computeOptimalWindowSize:()=>cD,computeOutAndReduceShapes:()=>Mk,computeOutShape:()=>hD,computePool2DInfo:()=>ik,computePool3DInfo:()=>UF,convertConv2DDataFormat:()=>lk,decodeEinsumEquation:()=>_D,eitherStridesOrDilationsAreOne:()=>Fa,expandShapeToKeepDim:()=>vo,exponent:()=>$D,exponents:()=>FD,fromStringArrayToUint8:()=>aL,fromUint8ToStringArray:()=>nL,getAxesPermutation:()=>Fk,getBroadcastDims:()=>Pw,getComplexWithIndex:()=>RD,getEinsumComputePath:()=>DD,getEinsumPermutation:()=>zD,getFusedBiasGradient:()=>Im,getFusedDyActivation:()=>km,getImageCenter:()=>fD,getInnerMostAxes:()=>kP,getPermuted:()=>gD,getReductionAxes:()=>Xt,getReshaped:()=>mD,getReshapedPermuted:()=>yD,getSliceBeginCoords:()=>AD,getSliceSize:()=>xD,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>VD,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>UD,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>GD,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>qD,getSparseReshapeInputOutputMismatchErrorMessage:()=>XD,getSparseReshapeInputOutputMultipleErrorMessage:()=>KD,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>jD,getSparseReshapeNegativeOutputDimErrorMessage:()=>HD,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>QD,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>ZD,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>YD,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>JD,getUndoAxesPermutation:()=>N2,isIdentityPermutation:()=>LD,log:()=>gR,mergeRealAndImagArrays:()=>CD,prepareAndValidate:()=>Ow,prepareSplitSize:()=>WD,segment_util:()=>p7,shouldFuse:()=>Cm,slice_util:()=>_t,splitRealAndImagArrays:()=>TD,tupleValuesAreOne:()=>Ms,upcastType:()=>Dr,validateInput:()=>i2,validateUpdateShape:()=>s2,warn:()=>bs});function pD(e,t){let r=e[0].length;e.forEach((a,s)=>{P(a.length===r,()=>`Error in concat${r}D: rank of tensors[${s}] must be the same as the rank of the rest (${r})`)}),P(t>=0&&t<r,()=>`Error in concat${r}D: axis must be between 0 and ${r-1}.`);let n=e[0];e.forEach((a,s)=>{for(let i=0;i<r;i++)P(i===t||a[i]===n[i],()=>`Error in concat${r}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function hD(e,t){let r=e[0].slice();for(let n=1;n<e.length;n++)r[t]+=e[n][t];return r}var X2=30;function cD(e){return e<=X2?e:rf(e,Math.floor(Math.sqrt(e)))}function fD(e,t,r){let n=r*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[n,a]}function mD(e,t,r,n=!0){let a=[];if(n)a=a.concat(t.slice(0)),a.push(e[0]/r),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function gD(e,t,r=!0){let n=[];if(r){n.push(t);for(let a=t+1;a<e;++a)a<=2*t?(n.push(a),n.push(a-(t+1))):n.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?s.push(i):a.push(i);n.push(...a),n.push(0),n.push(...s)}return n}function yD(e,t,r,n=!0){let a=[];n?a.push(e[0]/r):a.push(e[0]*r);for(let s=1;s<e.length;++s)s<=t.length?n?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function AD(e,t){let r=[0];for(let n=0;n<t;++n)r.push(e[n][0]);return r}function xD(e,t,r){let n=e.slice(0,1);for(let a=0;a<r;++a)n.push(e[a+1]-t[a][0]-t[a][1]);return n}var u7=1.7580993408473768,d7=1.0507009873554805,bD=.3275911,vD=.254829592,wD=-.284496736,kD=1.421413741,ID=-1.453152027,SD=1.061405429;function CD(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let r=new Float32Array(e.length*2);for(let n=0;n<r.length;n+=2)r[n]=e[n/2],r[n+1]=t[n/2];return r}function TD(e){let t=new Float32Array(e.length/2),r=new Float32Array(e.length/2);for(let n=0;n<e.length;n+=2)t[n/2]=e[n],r[n/2]=e[n+1];return{real:t,imag:r}}function ND(e){let t=Math.ceil(e.length/4),r=new Float32Array(t),n=new Float32Array(t);for(let a=0;a<e.length;a+=4)r[Math.floor(a/4)]=e[a],n[Math.floor(a/4)]=e[a+1];return{real:r,imag:n}}function ED(e){let t=Math.floor(e.length/4),r=new Float32Array(t),n=new Float32Array(t);for(let a=2;a<e.length;a+=4)r[Math.floor(a/4)]=e[a],n[Math.floor(a/4)]=e[a+1];return{real:r,imag:n}}function RD(e,t){let r=e[t*2],n=e[t*2+1];return{real:r,imag:n}}function MD(e,t,r,n){e[n*2]=t,e[n*2+1]=r}function FD(e,t){let r=new Float32Array(e/2),n=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);r[a]=Math.cos(s),n[a]=Math.sin(s)}return{real:r,imag:n}}function $D(e,t,r){let n=(r?2:-2)*Math.PI*(e/t),a=Math.cos(n),s=Math.sin(n);return{real:a,imag:s}}var $1="->",PD=/->/g,v3=",",w3="...";function _D(e,t){e=e.replace(/\s/g,"");let r=(e.length-e.replace(PD,"").length)/$1.length;if(r<1)throw new Error("Equations without an arrow are not supported.");if(r>1)throw new Error(`Equation must contain exactly one arrow ("${$1}").`);let[n,a]=e.split($1);P(n.indexOf(w3)===-1,()=>`The ellipsis notation ("${w3}") is not supported yet.`);let s=n.split(v3),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 p=0;p<a.length;++p){let c=a[p];if(!s.some(f=>f.indexOf(c)!==-1))throw new Error(`Output subscripts contain the label ${c} not present in the input subscripts.`);o.indexOf(c)===-1&&o.push(c)}for(let p=0;p<n.length;++p){let c=n[p];o.indexOf(c)===-1&&c!==v3&&o.push(c)}let l=new Array(s.length);for(let p=0;p<i;++p){if(new Set(s[p].split("")).size!==s[p].length)throw new Error(`Found duplicate axes in input component ${s[p]}. Support for duplicate axes in input is not implemented yet.`);l[p]=[];for(let c=0;c<s[p].length;++c)l[p].push(o.indexOf(s[p][c]))}let u=o.length,d=a.length,h=[];for(let p=d;p<u;++p)h.push(p);return{allDims:o,summedDims:h,idDims:l}}function zD(e,t){let r=new Array(e);r.fill(-1);for(let a=0;a<t.length;++a)r[t[a]]=a;let n=[];for(let a=0;a<e;++a)r[a]===-1&&n.push(a);return r=r.filter(a=>a!==-1),{permutationIndices:r,expandDims:n}}function OD(e,t,r){let n=new Array(e);for(let a=0;a<r.length;++a){let s=r[a].shape;for(let i=0;i<t[a].length;++i)n[t[a][i]]===void 0?n[t[a][i]]=s[i]:P(n[t[a][i]]===s[i],()=>`Expected dimension ${n[t[a][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function DD(e,t){let r=e,n=[],a=0;e.length===0&&r.push(-1),a=e.length+1;for(let i=0;i<a;++i)n.push([]);let s=[];for(let i=0;i<r.length;++i){let o=r[i],l=BD(t,o);for(let u of l)s.indexOf(u)===-1&&(n[i].push(u),s.push(u))}return{path:r,steps:n}}function LD(e){return e.every((t,r)=>t===r)}function BD(e,t){let r=[];for(let n=0;n<e.length;++n)(e[n].length===0||e[n].indexOf(t)!==-1||t===-1)&&r.push(n);return r}function WD(e,t,r=0){let n=[];if(typeof t=="number")P(e.shape[r]%t===0,()=>"Number of splits must evenly divide the axis."),n=new Array(t).fill(e.shape[r]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);P(a<=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[r]-i}P(e.shape[r]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),n=t}return n}function VD(e){return`Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${e}`}function UD(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function GD(e,t,r){return`indices(${e}, 0) is invalid: ${t} >= ${r}`}function jD(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function HD(e,t){return`size ${e} must be non-negative, not ${t}`}function qD(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function KD(e,t){let r=Ct(e),n=Ct(t);return`Input to reshape is a SparseTensor with ${r}
  dense values, but the requested shape requires a multiple of ${n}. inputShape=${e} outputShape= ${t}`}function XD(e,t){let r=Ct(e),n=Ct(t);return`Input to reshape is a tensor with ${r} dense values, but the requested shape has ${n}. inputShape=${e} outputShape=${t}`}function ZD(){return"segment ids must be >= 0"}function YD(){return"segment ids are not increasing"}function JD(e,t){return`Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function QD(e,t,r){return`Bad: indices[${e}] == ${t} out of range [0, ${r})`}var p7={};Le(p7,{collectGatherOpShapeInfo:()=>rL,computeOutShape:()=>tL,segOpComputeOptimalWindowSize:()=>eL});function eL(e,t){let r=!1,n;for(e<=X2?(n=e,r=!0):n=rf(e,Math.floor(Math.sqrt(e)));!r;)n>t||n===e?r=!0:n=rf(e,n+1);return n}function tL(e,t,r){let n=[],a=e.length;for(let s=0;s<a;s++)s!==t?n.push(e[s]):n.push(r);return n}function rL(e,t,r,n){let a=t.shape.length,s=e.shape.length;if(n!==0&&(n<-a||n>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${n}`);if(n<0&&(n+=a),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) (
    ${s}).`);if(r<n)throw new Error(`batchDims (${n}) must be less than or equal to axis (${r}).`);for(let h=0;h<n;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[r],o=[],l=1,u=1,d=1;for(let h=0;h<n;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=n;h<r;h++)o.push(e.shape[h]),u*=e.shape[h];for(let h=n;h<a;h++)o.push(t.shape[h]);for(let h=r+1;h<s;h++)o.push(e.shape[h]),d*=e.shape[h];return{batchSize:l,sliceSize:d,outerSize:u,dimSize:i,outputShape:o}}function nL(e){try{return e.map(t=>of(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function aL(e){return e.map(t=>th(t))}var Un={};Le(Un,{nonMaxSuppressionV3Impl:()=>n7,nonMaxSuppressionV4Impl:()=>a7,nonMaxSuppressionV5Impl:()=>s7,whereImpl:()=>qk});var h7={kernelName:Mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,ph(me(r,"float32"),-1))}}},sL={kernelName:Tu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>{let n=yt(me(r,"float32")),a=Cr(he(Se(1),n));return zt(pe(e,a))}}}},iL={kernelName:Nu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>{let n=Cr(he(yt(me(r,"float32")),1));return pe(e,n)}}}},oL={kernelName:Ha,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=e,i=Xt(r.shape,a);return i.length>0&&(s=ke(s,i)),G(s,r.shape)},b:()=>{let s=e,i=Xt(n.shape,a);return i.length>0&&(s=ke(s,i)),G(s,n.shape)}}}},lL={kernelName:Vs,saveAllInputs:!0,gradFunc:(e,t)=>{let r={};return t.forEach((n,a)=>{r[a]=()=>e.clone()}),r}},uL={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>at(r)}}},dL={kernelName:Mu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>at(r)}}},pL={kernelName:Fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,Cr(he(Se(1),yt(me(r,"float32")))))}}},hL={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>{let n=Cr(le(Se(1),yt(me(r,"float32"))));return pe(e,n)}}}},cL={kernelName:zu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=le(yt(r),yt(n)),i=L(e,pe(n,s)),o=Xt(r.shape,a);return o.length>0&&(i=ke(i,o)),G(i,r.shape)},b:()=>{let s=le(yt(r),yt(n)),i=zt(L(e,pe(r,s))),o=Xt(n.shape,a);return o.length>0&&(i=ke(i,o)),G(i,n.shape)}}}},fL={kernelName:Pu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,le(yt(me(r,"float32")),1))}}},mL={kernelName:_u,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,he(Se(1),yt(me(r,"float32"))))}}};function gL(e,t,r,n,a,s){let i=F(e,"dy","avgPool3dGrad"),o=F(t,"input","avgPool3dGrad"),l=i,u=o,d=!1;o.rank===4&&(d=!0,l=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),u=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),P(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),P(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),Vr("avgPool3dGrad",a,s);let h={dy:l,input:u},p={filterSize:r,strides:n,pad:a,dimRoundingMode:s},c=B.runKernel(_f,h,p);return d?G(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var yL=W({avgPool3dGrad_:gL}),AL={kernelName:Op,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=r;return{x:()=>yL(e,n,a,s,i,o)}}};function xL(e,t,r,n,a){let s=F(e,"dy","avgPoolGrad"),i=F(t,"input","avgPoolGrad");P(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,u=!1;i.rank===3&&(u=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),P(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),P(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let d={dy:l,input:o},h={filterSize:r,strides:n,pad:a},p=B.runKernel(Pf,d,h);return u?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var bL=W({avgPoolGrad_:xL}),vL={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{filterSize:a,strides:s,pad:i}=r;return{x:()=>bL(e,n,a,s,i)}}},wL={kernelName:js,inputsToSave:["a","b"],gradFunc:(e,t,r)=>{let[n,a]=t,{transposeA:s,transposeB:i}=r;return!s&&!i?{a:()=>Je(e,a,!1,!0),b:()=>Je(n,e,!0,!1)}:!s&&i?{a:()=>Je(e,a,!1,!1),b:()=>Je(e,n,!0,!1)}:s&&!i?{a:()=>Je(a,e,!1,!0),b:()=>Je(n,e,!1,!1)}:{a:()=>Je(a,e,!0,!0),b:()=>Je(e,n,!0,!0)}}},kL={kernelName:Fo,gradFunc:(e,t,r)=>{let{blockShape:n,crops:a}=r;return{x:()=>ym(e,n,a)}}},IL={kernelName:nw,gradFunc:(e,t,r)=>{let n=r,a=n.inputShape,s=n.shape,i=Array.from(s);for(let l=a.length-1;l>=0;l--)if(a[l]===s[l])i[l]=1;else if(a[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>ke(e,o,!0)}}},SL={kernelName:Hs,gradFunc:e=>({x:()=>e.clone()})},CL={kernelName:qs,gradFunc:e=>({x:()=>at(e)})},TL={kernelName:qa,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{clipValueMin:a,clipValueMax:s}=r;return{x:()=>Lr(pa(xl(n,a),bl(n,s)),e,at(e))}}},NL={kernelName:Lp,inputsToSave:["x"],gradFunc:h7.gradFunc},EL={kernelName:$o,saveAllInputs:!0,gradFunc:(e,t,r)=>{let n=t.map(o=>o.shape),{axis:a}=r,s=Bn(a,t[0].shape)[0],i=n.map(o=>o[s]);return Kt(e,i,s).map(o=>()=>o)}},RL={kernelName:Ks,inputsToSave:["x","filter"],gradFunc:(e,t,r)=>{let[n,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=r;return P(Ms(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>x2(n.shape,e,a,i,o,l),filter:()=>q2(n,e,a.shape,i,o,l)}}},ML={kernelName:Xs,inputsToSave:["dy","filter"],gradFunc:(e,t,r)=>{let[n,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=r;return{dy:()=>Fs(e,a,s,i,o,1,l),filter:()=>q2(e,n,a.shape,s,i,o,l)}}};function FL(e,t,r,n,a){let s=e;e.rank===4&&(s=G(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=G(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),P(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),P(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),P(r.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${r}.`),P(s.shape[4]===r[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${r[3]}.`),P(i.shape[4]===r[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${r[4]}).`);let o={x:s,dy:i},l={strides:n,pad:a,filterShape:r};return B.runKernel(Lf,o,l)}var $L=W({conv3DBackpropFilter_:FL}),PL={kernelName:Bp,inputsToSave:["x","filter"],gradFunc:(e,t,r)=>{let{dilations:n,strides:a,pad:s}=r;P(Ms(n),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${n}'`);let[i,o]=t;return{x:()=>yk(i.shape,e,o,a,s),filter:()=>$L(i,e,o.shape,a,s)}}},_L={kernelName:Zs,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(zt(D2(me(r,"float32"))),e)}}},zL={kernelName:Ys,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(L2(me(r,"float32")),e)}}},OL={kernelName:Po,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{axis:a,exclusive:s,reverse:i}=r;return{x:()=>{let o=Fk([a],n.rank),l=k2(e,a,s,!i);return o!=null&&(l=nt(l,o)),l}}}},DL={kernelName:Js,inputsToSave:["x","filter"],gradFunc:(e,t,r)=>{let{dilations:n,strides:a,pad:s,dimRoundingMode:i}=r,o=n==null?[1,1]:n;P(Ms(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return P(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),P(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),P(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),P(Fa(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${a} and dilations '${o}'.`),Vr("depthwiseConv2d",s,i),{x:()=>e7(l.shape,e,u,a,s,o,i),filter:()=>Qk(l,e,u.shape,a,s,o,i)}}},LL={kernelName:Wp,inputsToSave:["x","filter"],gradFunc:(e,t,r)=>{let[n,a]=t,s={x:n,filter:a,dy:e},i={x:n,filter:a,dy:e};return{x:()=>B.runKernel(nf,s,r),filter:()=>B.runKernel(af,i,r)}}},BL={kernelName:ei,outputsToSave:[!0],gradFunc:(e,t)=>{let[r]=t,n={dy:e,y:r};return{x:()=>B.runKernel(jf,n)}}},WL={kernelName:Ou,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t,n=L(Nn(zt(yt(r))),2/Math.sqrt(Math.PI));return{x:()=>L(e,n)}}},VL={kernelName:ti,outputsToSave:[!0],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,r)}}},UL={kernelName:Do,inputsToSave:["input"],gradFunc:(e,t)=>{let[r]=t;return{input:()=>G(e,r.shape)}}},GL={kernelName:Lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,Nn(r))}}},jL={kernelName:ri,gradFunc:e=>({x:()=>at(e)})},HL={kernelName:ni,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=pe(e,me(n,"float32")),i=Xt(r.shape,a);return i.length>0?G(ke(s,i),r.shape):s},b:()=>{let s=L(e,me(r,"float32")),i=Xt(n.shape,a);i.length>0&&(s=G(ke(s,i),n.shape));let o=yt(n);return zt(pe(s,me(o,"float32")))}}}},qL={kernelName:ai,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,r)=>{let{varianceEpsilon:n}=r,[a,s,i,o]=t,l=o==null?Se(1):o,u=Xt(s.shape,a.shape),d=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)d.push(a.shape[m]);d.push(1)}let h=he(a,s),p=L(e,l),c=z2(le(i,Se(n))),f=L(L(L(c,c),c),Se(-.5));return{x:()=>s.rank===1?G(L(L(e,Dn(G(c,[1,1,1,s.shape[0]]),d)),l),a.shape):G(L(L(e,c),l),a.shape),mean:()=>{let m=L(L(c,Se(-1)),p);return s.rank===1&&(m=ke(m,u)),G(m,s.shape)},variance:()=>{let m=L(L(f,h),p);return s.rank===1&&(m=ke(m,u)),G(m,s.shape)},scale:()=>{let m=L(h,c),g=L(e,m);return s.rank===1&&(g=ke(g,u)),G(g,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=ke(m,u)),G(m,s.shape)}}}},KL={kernelName:Wo,inputsToSave:["x","indices"],gradFunc:(e,t,r)=>{let[n,a]=t,{axis:s}=r,i=Bn(s,n.shape)[0];return{x:()=>{let o=n.shape,l=a.size,u=o.slice(0,i),d=u.length,h=o.slice(s,o.length).slice(1),p=h.length,c=k3(0,d),f=k3(d+1,d+1+p),m=I3([u,[l],h]),g=G(e,m),y=G(a,[l]),A=I3([[d],c,f]),x=nt(g,A),b=jk(x,y,n.shape[i]),v=N2(A);return b=nt(b,v),b},indices:()=>a}}};function k3(e,t){let r=[];for(let n=e;n<t;++n)r.push(n);return r}function I3(e){let t=[];for(let r=0;r<e.length;++r)for(let n=0;n<e[r].length;++n)t.push(e[r][n]);return t}var XL={kernelName:si,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t;return{a:()=>at(r),b:()=>at(n)}}},ZL={kernelName:ii,gradFunc:e=>({x:()=>me(e,"float32")})},YL={kernelName:Lu,gradFunc:e=>({x:()=>at(e)})},JL={kernelName:Bu,gradFunc:e=>({x:()=>at(e)})},QL={kernelName:Wu,gradFunc:e=>({x:()=>at(e)})},eB={kernelName:oi,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{alpha:a}=r,s=hn(n,0);return{x:()=>Lr(s,e,L(e,a))}}},tB={kernelName:Vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,le(r,1))}}},rB={kernelName:li,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,me(r,"float32"))}}},nB={kernelName:aw,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,r)=>{let[n]=t,{axis:a}=r;return{logits:()=>{let s=Nn(n);return he(e,L(ke(e,a,!0),s))}}}};function aB(e,t,r,n=5,a=1,s=1,i=.5){let o={x:e,y:t,dy:r},l={depthRadius:n,bias:a,alpha:s,beta:i};return B.runKernel(Xf,o,l)}var sB=W({localResponseNormalizationBackprop_:aB}),iB={kernelName:jp,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,r)=>{let[n,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;return{x:()=>sB(n,a,e,s,i,o,l)}}};function c7(e,t,r,n){return t.rank<r.rank&&(t=G(t,vo(t.shape,n))),e.rank<r.rank&&(e=G(e,vo(e.shape,n))),{x:()=>L(e,me(Tn(r,t),e.dtype))}}var S3={kernelName:ui,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,r)=>{let n=r,{reductionIndices:a}=n,s=t[0],i=t[1],o=Bn(a,s.shape),l=c7(e,i,s,o);return{x:()=>l.x()}}},oB={kernelName:di,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t;return{a:()=>L(e,me(xl(r,n),"float32")),b:()=>L(e,me(S2(r,n),"float32"))}}};function lB(e,t,r,n,a,s,i){let o=F(e,"dy","maxPool3dGrad"),l=F(t,"input","maxPool3dGrad"),u=F(r,"output","maxPool3dGrad"),d=o,h=l,p=u,c=!1;l.rank===4&&(c=!0,d=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=G(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),p=G(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),P(d.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${d.rank}.`),P(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),P(p.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${p.rank}.`),Vr("maxPool3dGrad",s,i);let f={dy:d,input:h,output:p},m={filterSize:n,strides:a,pad:s,dimRoundingMode:i},g=B.runKernel(Yf,f,m);return c?G(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var uB=W({maxPool3dGrad_:lB}),dB={kernelName:Hp,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,r)=>{let[n,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r;return{x:()=>uB(e,n,a,s,i,o,l)}}};function pB(e,t,r,n,a,s,i){let o=F(e,"dy","maxPoolGrad"),l=F(t,"input","maxPoolGrad"),u=F(r,"output","maxPoolGrad");P(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),P(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),P(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),Vr("maxPoolGrad",s,i);let d={dy:o,input:l,output:u},h={filterSize:n,strides:a,pad:s,dimRoundingMode:i};return B.runKernel(Zf,d,h)}var hB=W({maxPoolGrad_:pB}),cB={kernelName:pi,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,r)=>{let[n,a]=t,{filterSize:s,strides:i,pad:o}=r;return{x:()=>hB(e,n,a,s,i,o)}}},fB={kernelName:hi,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{axis:a}=r,s=Bn(a,n.shape),i=Mk(n.shape,s)[1],o=Ct(i);return{x:()=>{let l=n.shape.slice();s.forEach(d=>{l[d]=1});let u=G(e,l);return pe(L(u,ln(n.shape,"float32")),o)}}}},mB={kernelName:ci,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,r)=>{let n=r,{axis:a}=n,[s,i]=t,o=Bn(a,s.shape),l=c7(e,i,s,o);return{x:()=>l.x()}}},gB={kernelName:fi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t;return{a:()=>L(e,me(bl(r,n),"float32")),b:()=>L(e,me(hn(r,n),"float32"))}}},yB={kernelName:mi,inputsToSave:["x"],gradFunc:(e,t,r)=>{let n=t[0],{paddings:a}=r,s=a.map(i=>i[0]);return{x:()=>Pe(e,s,n.shape)}}},AB={kernelName:Gu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=Xt(r.shape,a);return s.length>0?G(ke(e,s),r.shape):e},b:()=>{let s=L(e,zt(uh(pe(r,n)))),i=Xt(n.shape,a);return i.length>0?G(ke(s,i),n.shape):s}}}},xB={kernelName:gi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=L(e,me(n,"float32")),i=Xt(r.shape,a);return i.length>0?G(ke(s,i),r.shape):s},b:()=>{let s=L(e,me(r,"float32")),i=Xt(n.shape,a);return i.length>0?G(ke(s,i),n.shape):s}}}},bB={kernelName:qo,gradFunc:e=>({x:()=>zt(e)})},vB={kernelName:Jo,inputsToSave:["indices"],gradFunc:(e,t)=>{let r=t[0];return{indices:()=>Wt(r.shape,"float32")}}},wB={kernelName:Yo,gradFunc:e=>({x:()=>at(e)})},kB={kernelName:Qo,saveAllInputs:!0,gradFunc:(e,t,r)=>{let{axis:n}=r;return en(e,n).map(a=>()=>a)}},C3={kernelName:yi,inputsToSave:["x"],gradFunc:(e,t,r)=>{let n=t[0],{paddings:a}=r,s=a.map(i=>i[0]);return{x:()=>Pe(e,s,n.shape)}}},IB={kernelName:Ai,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[r,n,a]=t,s=r,i=n,o=xt(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),u=L(e,L(l,Ps(s,he(l,Se(1))))),d=Xt(s.shape,o);return d.length>0&&(u=ke(u,d)),G(u,s.shape)},b:()=>{let l=hn(s,0),u=Lr(l,En(s),at(s)),d=L(e,L(a,u)),h=Xt(i.shape,o);return h.length>0&&(d=ke(d,h)),G(d,i.shape)}}}},SB={kernelName:xi,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[r,n]=t,a=hn(r,0);return{x:()=>Lr(a,e,L(e,n)),alpha:()=>{let s=Lr(a,at(e),L(e,r)),i=Xt(n.shape,e.shape);return i.length>0&&(s=ke(s,i)),G(s,n.shape)}}}},CB={kernelName:Qs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=pe(e,me(n,"float32")),i=Xt(r.shape,a);return i.length>0?G(ke(s,i),r.shape):s},b:()=>{let s=L(e,me(r,"float32")),i=Xt(n.shape,a);i.length>0&&(s=G(ke(s,i),n.shape));let o=yt(n);return zt(pe(s,me(o,"float32")))}}}},TB={kernelName:qu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,zt(yt(r)))}}},NB={kernelName:wi,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t,n=L(bl(r,6),ph(r));return{x:()=>L(e,me(n,"float32"))}}},EB={kernelName:bi,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,me(ph(r),"float32"))}}},RB={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>G(e,r.shape)}}},MB={kernelName:vi,inputsToSave:["images"],gradFunc:(e,t,r)=>{let[n]=t,a={dy:e,images:n};return{images:()=>B.runKernel(tm,a,r)}}},FB={kernelName:Ku,inputsToSave:["images"],gradFunc:(e,t,r)=>{let[n]=t,a={dy:e,images:n};return{images:()=>B.runKernel(em,a,r)}}},$B={kernelName:rl,gradFunc:(e,t,r)=>{let{dims:n}=r,a=Bn(n,e.shape);return{x:()=>Mn(e,a)}}},PB={kernelName:nl,gradFunc:e=>({x:()=>at(e)})},_B={kernelName:ki,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>zt(pe(e,L(Ps(r,1.5),2)))}}},zB={kernelName:sl,inputsToSave:["condition"],gradFunc:(e,t)=>{let[r]=t;return{condition:()=>me(at(r),"float32"),t:()=>L(e,me(r,e.dtype)),e:()=>L(e,me(fm(r),e.dtype))}}},OB={kernelName:Xu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>{let n=hn(r,Se(0)),a=Se(u7),s=Se(d7),i=L(e,s),o=L(L(e,a),Nn(me(r,"float32")));return Lr(n,i,o)}}}},DB={kernelName:Si,outputsToSave:[!0],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,L(r,he(Se(1),r)))}}},LB={kernelName:Zu,gradFunc:e=>({x:()=>at(e)})},BB={kernelName:Ii,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(um(me(r,"float32")),e)}}},WB={kernelName:ol,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(w2(me(r,"float32")),e)}}},VB={kernelName:il,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{begin:a,size:s}=r,i=n.shape,[o,l]=qw(n,a,s),u=[];for(let d=0;d<e.rank;d++)u.push([o[d],i[d]-o[d]-l[d]]);return{x:()=>Vn(e,u)}}},UB={kernelName:Ni,outputsToSave:[!0],gradFunc:(e,t,r)=>{let[n]=t,{dim:a}=r,s=!0,i=L(e,n);return{logits:()=>he(i,L(ke(i,[a],s),n))}}},GB={kernelName:Yu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,Sr(r))}}},T3={kernelName:ll,gradFunc:(e,t,r)=>{let{blockShape:n,paddings:a}=r;return{x:()=>lm(e,n,a)}}},N3={kernelName:ul,gradFunc:(e,t,r)=>{let{axis:n}=r;return{x:()=>kt(e,n)}}},jB={kernelName:Ci,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,L(Cr(me(r,"float32")),2))}}},HB={kernelName:Qu,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(e,L(me(r,"float32"),2))}}},qB={kernelName:Ei,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=Se(2);return{a:()=>L(e,L(a,he(r,n))),b:()=>L(e,L(a,he(n,r)))}}},KB={kernelName:$i,gradFunc:e=>({x:()=>at(e)})},XB={kernelName:Ri,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[r,n]=t,a=xt(r.shape,n.shape);return{a:()=>{let s=e,i=Xt(r.shape,a);return i.length>0&&(s=ke(s,i)),G(s,r.shape)},b:()=>{let s=e,i=Xt(n.shape,a);return i.length>0&&(s=ke(s,i)),G(zt(s),n.shape)}}}},ZB={kernelName:Ti,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,a=n.shape.slice(),{axis:s}=r;Bn(s,n.shape).forEach(l=>{a[l]=1});let i=G(e,a),o=L(i,ln(n.shape,"float32"));return{x:()=>o}}},YB={kernelName:pl,inputsToSave:["x"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>pe(e,yt(um(r)))}}},JB={kernelName:Mi,outputsToSave:[!0],gradFunc:(e,t)=>{let[r]=t;return{x:()=>L(he(Se(1),yt(r)),e)}}},QB={kernelName:Ka,inputsToSave:["x"],gradFunc:(e,t,r)=>{let[n]=t,{reps:a}=r;return{x:()=>{let s=at(n);if(n.rank===1)for(let i=0;i<a[0];++i)s=le(s,Pe(e,[i*n.shape[0]],[n.shape[0]]));else if(n.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=le(s,Pe(e,[i*n.shape[0],o*n.shape[1]],[n.shape[0],n.shape[1]]));else if(n.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=le(s,Pe(e,[i*n.shape[0],o*n.shape[1],l*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else if(n.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let u=0;u<a[3];++u)s=le(s,Pe(e,[i*n.shape[0],o*n.shape[1],l*n.shape[2],u*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${n.rank} tensors yet.`);return s}}}},eW={kernelName:Fi,gradFunc:(e,t,r)=>{let n=r,{perm:a}=n,s=N2(a);return{x:()=>nt(e,s)}}},tW={kernelName:fl,gradFunc:(e,t,r)=>{let n=r,{axis:a}=n;return{value:()=>sr(e,a)}}},rW={kernelName:Qp,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[r]=t;return{x:()=>nW(e,r)}}};function nW(e,t){let r=Xa(t,at(t)),n=mu(e,r),a=xl(t,Se(0,"int32")),s=n.rank-a.rank;for(let o=0;o<s;++o)a=Ht(a,o+1);a=pa(a,ln(n.shape,"bool"));let i=at(n);return Lr(a,n,i)}var aW={kernelName:ml,gradFunc:e=>({x:()=>at(e)})},sW=[h7,sL,iL,oL,lL,uL,dL,pL,hL,cL,fL,mL,AL,vL,wL,kL,IL,SL,CL,TL,NL,EL,ML,RL,PL,_L,zL,OL,DL,LL,CB,BL,WL,VL,UL,GL,HL,jL,qL,KL,XL,ZL,YL,JL,QL,eB,tB,rB,nB,iB,S3,S3,oB,dB,cB,fB,mB,gB,yB,AB,xB,bB,vB,wB,kB,C3,C3,IB,SB,TB,NB,EB,RB,MB,FB,$B,PB,_B,zB,OB,DB,LB,BB,WB,VB,UB,GB,T3,T3,N3,N3,jB,qB,HB,KB,XB,ZB,YB,JB,QB,eW,tW,rW,aW];for(let e of sW)sw(e);var f7={};Le(f7,{maxNorm:()=>uW,minMaxNorm:()=>hW,nonNeg:()=>pW,unitNorm:()=>dW});var P1;function tr(){return P1==null&&(P1=ga().epsilon()),P1}function ha(){return"channelsLast"}var La=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,La.prototype)}},aa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,aa.prototype)}},q=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,q.prototype)}},Ve=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ve.prototype)}},m7=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,m7.prototype)}};function ko(e,t){if(Array.isArray(e)){let r=[];for(let n=0;n<t;n++)r=r.concat(e);return r}else{let r=new Array(t);return r.fill(e),r}}function Ia(e,t){if(!e)throw new m7(t)}function E3(e,t){let r=0;for(let n of e)n===t&&r++;return r}function Jr(e){return e.length===1?e[0]:e}function It(e){return Array.isArray(e)?e:[e]}function Ba(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function lo(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,r)=>r.toUpperCase())}var zn={};function Z2(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function ay(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>ay(t));else{let t=Object.keys(e);for(let r of t){let n=e[r];n!=null&&typeof n=="object"&&(!Array.isArray(n)&&n.type==="ndarray"&&typeof n.value=="number"?e[r]=n.value:ay(n))}}}function ch(e,t={},r={},n="object",a=!1){if(typeof e=="string"){let s=e,i;if(s in r)i=r[s];else if(s in zn)i=zn[s];else if(i=t[s],i==null)throw new q(`Unknown ${n}: ${e}. This may be due to one of the following reasons:
1. The ${n} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${n} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new q(`${n}: Improper config format: ${JSON.stringify(s)}.
'className' and 'config' must set.`);let i=s.className,o,l;if(i in r?[o,l]=r[i]:i in zn?[o,l]=zn.className:i in t&&([o,l]=t[i]),o==null)throw new q(`Unknown ${n}: ${i}. This may be due to one of the following reasons:
1. The ${n} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${n} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let c of Object.keys(zn))u[c]=zn[c];for(let c of Object.keys(r))u[c]=r[c];let d=s.config;d.customObjects=u;let h={...zn};for(let c of Object.keys(r))zn[c]=r[c];ay(s.config);let p=l(o,s.config,r,a);return zn={...h},p}else{let u={...zn};for(let h of Object.keys(r))zn[h]=r[h];let d=new o(s.config);return zn={...u},d}}}function iW(e,t){return e<t?-1:e>t?1:0}function $c(e,t){return-1*iW(e,t)}function Is(e){if(e==null)return e;let t=[];for(let r of e)t.indexOf(r)===-1&&t.push(r);return t}function oW(e){if(e==null)throw new q(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function wl(e,t,r){if(r!=null&&e.indexOf(r)<0)throw new q(`${r} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function Y2(e,t,r=0,n=1/0){return Ia(r>=0),Ia(n>=r),Array.isArray(e)&&e.length>=r&&e.length<=n&&e.every(a=>typeof a===t)}function cr(e,t){Array.isArray(e)?(w.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((r,n)=>cr(r,`element ${n+1} of ${t}`))):w.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${g7(e)}.`)}function g7(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>g7(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function lW(e,t,r){let n=r!=null?r():w.now(),a;return(...s)=>{let i=r!=null?r():w.now();return i-n<t||(n=i,a=e(...s)),a}}function y7(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function J2(e,t){return K(()=>Cr(ke(L(e,e),t,!0)))}var fh=class extends ue.Serializable{getConfig(){return{}}},Q2=class extends fh{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 K(()=>{let t=J2(e,this.axis),r=un(t,0,this.maxValue);return L(e,pe(r,le(tr(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};Q2.className="MaxNorm";ue.registerClass(Q2);var eA=class extends fh{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return K(()=>pe(e,le(tr(),J2(e,this.axis))))}getConfig(){return{axis:this.axis}}};eA.className="UnitNorm";ue.registerClass(eA);var tA=class extends fh{apply(e){return $a(e)}};tA.className="NonNeg";ue.registerClass(tA);var rA=class extends fh{constructor(e){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return K(()=>{let t=J2(e,this.axis),r=le(L(this.rate,un(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,pe(r,le(tr(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};rA.className="MinMaxNorm";ue.registerClass(rA);var R3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function nr(e){return Z2(e)}function M3(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"constraint")}function ar(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in R3?R3[e]:e,config:{}};return M3(t)}else return e instanceof fh?e:M3(e)}function uW(e){return new Q2(e)}function dW(e){return new eA(e)}function pW(){return new tA}function hW(e){return new rA(e)}var A7={};Le(A7,{constant:()=>_W,glorotNormal:()=>VW,glorotUniform:()=>WW,heNormal:()=>UW,heUniform:()=>GW,identity:()=>LW,leCunNormal:()=>jW,leCunUniform:()=>HW,ones:()=>PW,orthogonal:()=>qW,randomNormal:()=>OW,randomUniform:()=>zW,truncatedNormal:()=>DW,varianceScaling:()=>BW,zeros:()=>$W});var cW=["channelsFirst","channelsLast"],fW=["nearest","bilinear"],mW=["valid","same","causal"],gW=["max","avg"],yW=["sum","mul","concat","ave"],Yl=new Map;function Vt(e){wl(cW,"DataFormat",e)}function AW(e){wl(fW,"InterpolationFormat",e)}function Pn(e){wl(mW,"PaddingMode",e)}function x7(e){wl(gW,"PoolMode",e)}var xp=[],F3="/";function mo(e,t){xp.push(e);try{let r=t();return xp.pop(),r}catch(r){throw xp.pop(),r}}function xW(){return xp.length===0?"":xp.join(F3)+F3}function b7(e){if(!w7(e))throw new Error("Not a valid tensor name: '"+e+"'");return xW()+e}function v7(e){if(!w7(e))throw new Error("Not a valid tensor name: '"+e+"'");Yl.has(e)||Yl.set(e,0);let t=Yl.get(e);if(Yl.set(e,Yl.get(e)+1),t>0){let r=`${e}_${t}`;return Yl.set(r,1),r}else return e}var bW=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function w7(e){return!!e.match(bW)}function vW(e){return e===parseInt(e.toString(),10)}function Ss(e,t,r){t==null&&(t=0),r==null&&(r=e.length);let n=1;for(let a=t;a<r;++a)n*=e[a];return n}function Au(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let r=0;r<e.length;r++){let n=e[r];n<t&&(t=n)}return t}function zs(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let r=0;r<e.length;r++){let n=e[r];n>t&&(t=n)}return t}function ca(e,t){if(t<e)throw new q(`end (${t}) < begin (${e}) is forbidden.`);let r=[];for(let n=e;n<t;++n)r.push(n);return r}function $m(e,t){return me(e,t)}function mh(e,t=-1){let r=e.shape.slice();return t<0&&(t=r.length+t+1),r.splice(t,0,1),G(e,r)}function wW(e,t){return K(()=>{if(e.shape.length!==2)throw new q(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let r=mh(e,1);return sy(r,[1,t,1])})}function kW(e){let t=[Ss(e.shape)];return G(e,t)}function IW(e){if(e.rank<=1)throw new q(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Ss(e.shape,1)];return G(e,t)}function go(e,t,r){return K(()=>{switch(e.rank){case 1:return xm(e,t,r);case 2:return B2(e,[t,0],[r,e.shape[1]]);case 3:return vl(e,[t,0,0],[r,e.shape[1],e.shape[2]]);case 4:return wo(e,[t,0,0,0],[r,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Pe(e,[t,0,0,0,0],[r,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Pe(e,[t,0,0,0,0,0],[r,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new q(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function _1(e,t,r){return K(()=>{switch(e.rank){case 1:return xm(e,t,r);case 2:return B2(e,[0,t],[e.shape[0],r]);case 3:return vl(e,[0,0,t],[e.shape[0],e.shape[1],r]);case 4:return wo(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],r]);default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Pc(e,t,r,n){return K(()=>{switch(e.rank){case 1:return xm(e,t,r);case 2:switch(n){case 1:return go(e,t,r);case 2:return _1(e,t,r);default:throw new q(`The axis is not within the rank of the tensor ${n}`)}case 3:switch(n){case 1:return go(e,t,r);case 2:return vl(e,[0,t,0],[e.shape[0],r,e.shape[2]]);case 3:return _1(e,t,r);default:throw new q(`The axis is not within the rank of the tensor ${n}`)}case 4:switch(n){case 1:return go(e,t,r);case 2:return wo(e,[0,t,0,0],[e.shape[0],r,e.shape[2],e.shape[3]]);case 3:return wo(e,[0,0,t,0],[e.shape[0],e.shape[1],r,e.shape[3]]);case 4:return _1(e,t,r);default:throw new q(`The axis is not within the rank of the tensor ${n}`)}default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function nA(e,t=-1){let r;return t<0&&(r=e[0].rank,r!==0?t=r:t=0),t===e[0].rank&&(t=-1),kt(e,t)}function $3(e,t){switch(e.rank){case 1:return fk([e,t]);case 2:return td([e,t],0);case 3:return mk([e,t],0);case 4:return gk([e,t],0);default:throw new q(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function sy(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new q(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Dn(e,t)}function Pm(e,t=0,r=1,n,a){return Ok(e,t,r,n,a)}function Ta(e,t,r,n){if(e.rank<2||t.rank<2)throw new Ve(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let a=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(a!==s)throw new Ve(`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)return _s.matMul({a:e,b:t,transposeA:!1,transposeB:!1,bias:n?iy(e.rank,n,ha()):null,activation:r});{let a=e.shape.slice(),s=a.pop();e=G(e,[-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],d=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=G(nt(t,d),[l,-1]);let h=[...a,...u],p=!1,c=!1;return G(_s.matMul({a:e,b:t,transposeA:p,transposeB:c,bias:n?iy(e.rank,n,ha()):null,activation:r}),h)}}function k7(e,t,r){return K(()=>(Array.isArray(t)?t=St(t,"int32"):t=me(t,"int32"),mu(e,t,r)))}function gh(e){return L(e,e)}function iy(e,t,r){let n=t.shape;if(t.rank!==1&&t.rank!==e)throw new q(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(r==="channelsFirst")return n.length===1?G(t,[1,n[0],1,1,1]):G(t,[1,n[3],n[0],n[1],n[2]]);if(r==="channelsLast")return n.length===1?G(t,[1,1,1,1,n[0]]):G(t,[1].concat(n))}else if(e===4){if(r==="channelsFirst")return n.length===1?G(t,[1,n[0],1,1]):G(t,[1,n[2],n[0],n[1]]);if(r==="channelsLast")return n.length===1?G(t,[1,1,1,n[0]]):G(t,[1].concat(n))}else if(e===3){if(r==="channelsFirst")return n.length===1?G(t,[1,n[0],1]):G(t,[1,n[1],n[0]]);if(r==="channelsLast")return n.length===1?G(t,[1,1,n[0]]):G(t,[1].concat(n))}else if(e<3)return t;throw new q(`Unsupported input rank by biasAdd: ${t.rank}`)}function ya(e,t,r){return K(()=>(r==null&&(r=ha()),Vt(r),le(e,iy(e.rank,t,r))))}function SW(e,t=1){if(t!==1)throw new Ve(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return lh(e)}function CW(e){return K(()=>pe(e,le(er(e),1)))}function I7(e,t,r,n){return K(()=>Yk(e,t,r,n))}function TW(e){return K(()=>{let t=le(.5,L(.2,e));return un(t,0,1)})}function yh(e,t,r=!1){return r?e():t()}var NW=["fanIn","fanOut","fanAvg"],EW=["normal","uniform","truncatedNormal"];function RW(e){wl(NW,"FanMode",e)}function MW(e){wl(EW,"Distribution",e)}var Gn=class extends ue.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},aA=class extends Gn{apply(e,t){return Wt(e,t)}};aA.className="Zeros";ue.registerClass(aA);var _m=class extends Gn{apply(e,t){return ln(e,t)}};_m.className="Ones";ue.registerClass(_m);var sA=class extends Gn{constructor(e){super();if(typeof e!="object")throw new q(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new q(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return K(()=>L(Se(this.value),ln(e,t)))}getConfig(){return{value:this.value}}};sA.className="Constant";ue.registerClass(sA);var iA=class extends Gn{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 sd(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};iA.className="RandomUniform";ue.registerClass(iA);var oA=class extends Gn{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 Ve(`randomNormal does not support dType ${t}.`);return Pm(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};oA.className="RandomNormal";ue.registerClass(oA);var lA=class extends Gn{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 Ve(`truncatedNormal does not support dType ${t}.`);return wm(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};lA.className="TruncatedNormal";ue.registerClass(lA);var uA=class extends Gn{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return K(()=>{if(e.length!==2||e[0]!==e[1])throw new q("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,I2(e[0]))})}getConfig(){return{gain:this.gain}}};uA.className="Identity";ue.registerClass(uA);function FW(e,t="channelsLast"){let r,n;if(Vt(t),e.length===2)r=e[0],n=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Ss(e,2);r=e[1]*a,n=e[0]*a}else if(t==="channelsLast"){let a=Ss(e,0,e.length-2);r=e[e.length-2]*a,n=e[e.length-1]*a}}else{let a=Ss(e);r=Math.sqrt(a),n=Math.sqrt(a)}return[r,n]}var tn=class extends Gn{constructor(e){super();if(e.scale<0)throw new q(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,RW(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,MW(this.distribution),this.seed=e.seed}apply(e,t){let r=FW(e),n=r[0],a=r[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,n):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(n+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ve(`${this.getClassName()} does not support dType ${t}.`);return wm(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return sd(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};tn.className="VarianceScaling";ue.registerClass(tn);var zm=class extends tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return tn.className}};zm.className="GlorotUniform";ue.registerClass(zm);var Om=class extends tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return tn.className}};Om.className="GlorotNormal";ue.registerClass(Om);var Dm=class extends tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return tn.className}};Dm.className="HeNormal";ue.registerClass(Dm);var Lm=class extends tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return tn.className}};Lm.className="HeUniform";ue.registerClass(Lm);var Bm=class extends tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return tn.className}};Bm.className="LeCunNormal";ue.registerClass(Bm);var Wm=class extends tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return tn.className}};Wm.className="LeCunNormal";ue.registerClass(Wm);var dA=class extends Gn{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Ve("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return K(()=>{if(e.length<2)throw new Ve("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let r=e[0]>e[1]?[e[1],e[0]]:e,n=Pm(r,0,1,"float32"),a=o7.gramSchmidt(n);return e[0]>e[1]&&(a=nt(a)),L(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};dA.className="Orthogonal";ue.registerClass(dA);var P3={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 _3(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"initializer")}function Pt(e){return Z2(e)}function Et(e){if(typeof e=="string"){let t=e in P3?P3[e]:e;if(t==="GlorotNormal")return new Om;if(t==="GlorotUniform")return new zm;if(t==="HeNormal")return new Dm;if(t==="HeUniform")return new Lm;if(t==="LeCunNormal")return new Bm;if(t==="LeCunUniform")return new Wm;{let r={};return r.className=t,r.config={},_3(r)}}else return e instanceof Gn?e:_3(e)}function $W(){return new aA}function PW(){return new _m}function _W(e){return new sA(e)}function zW(e){return new iA(e)}function OW(e){return new oA(e)}function DW(e){return new lA(e)}function LW(e){return new uA(e)}function BW(e){return new tn(e)}function WW(e){return new zm(e)}function VW(e){return new Om(e)}function UW(e){return new Dm(e)}function GW(e){return new Lm(e)}function jW(e){return new Bm(e)}function HW(e){return new Wm(e)}function qW(e){return new dA(e)}var S7={};Le(S7,{Layer:()=>st,RNN:()=>Ja,RNNCell:()=>bh,activation:()=>NU,add:()=>OU,alphaDropout:()=>bG,average:()=>DU,averagePooling1d:()=>bx,averagePooling2d:()=>vx,averagePooling3d:()=>wx,avgPool1d:()=>qU,avgPool2d:()=>XU,avgPool3d:()=>YU,avgPooling1d:()=>KU,avgPooling2d:()=>ZU,avgPooling3d:()=>JU,batchNormalization:()=>GU,bidirectional:()=>hG,concatenate:()=>LU,conv1d:()=>xU,conv2d:()=>bU,conv2dTranspose:()=>vU,conv3d:()=>wU,conv3dTranspose:()=>kU,convLstm2d:()=>lG,convLstm2dCell:()=>uG,cropping2D:()=>SU,dense:()=>EU,depthwiseConv2d:()=>TU,dot:()=>UU,dropout:()=>RU,elu:()=>cU,embedding:()=>zU,flatten:()=>FU,gaussianDropout:()=>xG,gaussianNoise:()=>AG,globalAveragePooling1d:()=>QU,globalAveragePooling2d:()=>eG,globalMaxPool1d:()=>fG,globalMaxPool2d:()=>mG,globalMaxPooling1d:()=>I4,globalMaxPooling2d:()=>S4,gru:()=>rG,gruCell:()=>nG,input:()=>q7,inputLayer:()=>hU,layerNormalization:()=>jU,leakyReLU:()=>mU,lstm:()=>aG,lstmCell:()=>sG,masking:()=>vG,maxPool1d:()=>gG,maxPool2d:()=>yG,maxPooling1d:()=>C4,maxPooling2d:()=>T4,maxPooling3d:()=>tG,maximum:()=>BU,minimum:()=>WU,multiply:()=>VU,permute:()=>_U,prelu:()=>gU,reLU:()=>fU,repeatVector:()=>$U,reshape:()=>PU,rnn:()=>dG,separableConv2d:()=>IU,simpleRNN:()=>iG,simpleRNNCell:()=>oG,softmax:()=>yU,spatialDropout1d:()=>MU,stackedRNNCells:()=>pG,thresholdedReLU:()=>AU,timeDistributed:()=>cG,upSampling2d:()=>CU,zeroPadding2d:()=>HU});var KW=0;function C7(){return KW++}var _c={};function Vm(e=""){return e in _c||(_c[e]=0),_c[e]+=1,e+_c[e].toString()}function oy(e){return Array.isArray(e)&&Array.isArray(e[0])}function cf(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ge(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new q(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ft(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new q(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function ff(e){let t=0;for(let r of e)r.shape.length===0?t+=1:t+=r.shape.reduce((n,a)=>n*a);return t}var z3="Variable",T7=class{constructor(e,t="float32",r=z3,n=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=C7(),r=r==null?z3:r,this.originalName=b7(r),this.name=v7(this.originalName),this.trainable_=n,this.constraint=a,this.val=Hk(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),XW(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 XW(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function ly(e){return e.map(t=>t.read())}function pA(e){e.forEach(t=>{t[0].write(t[1])})}var qt=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||{}}},sa=class{constructor(e,t,r,n,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=r,this.inputs=n,this.callArgs=a,this.outputTensorIndex=i,this.id=C7(),s!=null&&(this.originalName=b7(s),this.name=v7(this.originalName)),this.rank=t.length}},ZW=0,Um=class{constructor(e,t){this.callArgs=t,this.id=ZW++,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 r of e.inboundLayers)r!=null&&r.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}}},YW=0,st=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=YW++,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 r=this.getClassName();t=Ba(r)+"_"+Vm(r)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let r;if(e.batchInputShape!=null)r=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),r=[a].concat(e.inputShape)}this.batchInputShape=r;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}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 aa(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jr(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jr(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new La(`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 La(`Layer ${this.name} is not connected, no input to return.`);return Jr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new La(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new La(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Jr(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=It(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=It(this.inputSpec);if(e.length!==t.length)throw new q(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.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 r=It(e),n=!0;for(let s of r)if(!(s instanceof sa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof sa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return mo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Jr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Jr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=JW(e),i=this.computeOutputShape(s),o,l=QW(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 sa(l,u,this,It(e),t,this.name,d)):o=new sa(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 La(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}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 La(`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 aa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return ff(this.weights)}build(e){this.built=!0}getWeights(e=!1){return ly(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${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 r=[],n=ly(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!w.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}pA(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():Et("zeros"));let l=n.apply(t,r),u=new T7(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=It(e);t=It(t),r=It(r),n=It(n),a=cf(a),s=cf(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new Um({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 JW(e){e=It(e);let t=[];for(let r of e)t.push(r.shape);return Jr(t)}function QW(e){return"float32"}function N7(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=N7(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var ld=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Vm("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 q("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 q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new sa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`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}}};ld.className="InputLayer";ue.registerClass(ld);function E7(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 q("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 r=e.dtype;return r==null&&(r="float32"),new ld({batchInputShape:t,name:e.name,dtype:r,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function xs(e){if(e==null)return;let t=[],r=[],n=[];for(let a in e){let s=e[a];if(typeof s!="number"){let i=s;t.push(i.data()),r.push(a),n.push(i)}}if(t.length>0){let a=await Promise.all(t);for(let s=0;s<a.length;++s)e[r[s]]=a[s][0];re(n)}}function R7(e){if(e!=null)for(let t in e){let r=e[t];typeof r!="number"&&r.dispose()}}var eV=125,xu=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){}},M7=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 r of this.callbacks)await r.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let r of this.callbacks)await r.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let r of this.callbacks)await r.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let r of this.callbacks)await r.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)}},tV=class extends xu{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let r=t.size==null?0:t.size;this.seen+=r;for(let n in t){let a=t[n];if(typeof a=="number")this.totals.hasOwnProperty(n)||(this.totals[n]=0),this.totals[n]=this.totals[n]+a*r;else{let s;n in this.totals?s=this.totals[n]:this.totals[n]=0;let i=K(()=>le(this.totals[n],L(a,r)));this.totals[n]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let r of this.params.metrics)this.totals[r]!=null&&(typeof this.totals[r]=="number"?t[r]=this.totals[r]/this.seen:K(()=>{let n=L(pe(1,this.seen),this.totals[r]);t[r]=n,this.totals[r].dispose(),hr(t[r])}))}},F7=class extends xu{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let r in t)this.history[r]==null&&(this.history[r]=[]),this.history[r].push(t[r])}async syncData(){let e=[],t=[],r=[];for(let a in this.history){let s=this.history[a];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(a),r.push(i)}}let n=await Promise.all(e);for(let a=0;a<n.length;++a)this.history[t[a]][r[a]].dispose(),this.history[t[a]][r[a]]=n[a][0]}},$7=class extends xu{constructor(e,t){super();if(this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||l7,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=eV),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");w.isNumber(this.yieldEvery)&&(this.maybeWait=lW(this.maybeWait.bind(this),this.yieldEvery,this.nowFunc)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,r){let n=[];this.yield!=null&&(await xs(r),n.push(this.yield(e,t,r))),n.push(this.nextFrameFunc()),await Promise.all(n)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await xs(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let r=[];this.epochEnd!=null&&(await xs(t),r.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&r.push(this.nextFrameFunc()),await Promise.all(r)}async onBatchBegin(e,t){this.batchBegin!=null&&(await xs(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let r=[];this.batchEnd!=null&&(await xs(t),r.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?r.push(this.nextFrameFunc()):w.isNumber(this.yieldEvery)&&r.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(r)}async onTrainBegin(e){this.trainBegin!=null&&(await xs(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await xs(e),await this.trainEnd(e))}};function P7(e,t){return e==null&&(e={}),e instanceof xu?[e]:Array.isArray(e)&&e[0]instanceof xu?e:It(e).map(r=>new $7(r,t))}var wa=class{constructor(){}static registerCallbackConstructor(e,t){w.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),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(r=>{if(r===e)throw new q("Duplicate callback constructor.")})}static clear(){wa.constructors={}}static createCallbacks(e){let t=[];for(let r in wa.constructors){let n=+r;e>=n&&t.push(...wa.constructors[n])}return t.map(r=>new r)}},hA=wa;hA.constructors={};function _7(e,t,r,n,a,s,i,o,l){let u=new F7,d=[new tV,...hA.createCallbacks(t)];e!=null&&d.push(...e),d.push(u);let h=new M7(d);return h.setParams({epochs:r,initialEpoch:n,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:u}}function la(e,t={},r=!1){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"layer",r)}function mf(e,t){return K(()=>{e.dtype!=="float32"&&(e=me(e,"float32"));let r=ke(gh(e),t,!0),n=rd(r.shape,tr()),a=Cr(Xa(r,n));return pe(e,a)})}function kl(e,t){return K(()=>Bt(gh(he(t,e)),-1))}function Gm(e,t){return K(()=>Bt(er(he(t,e)),-1))}function ud(e,t){return K(()=>{let r=he(e,t),n=un(er(e),tr(),Number.MAX_VALUE),a=er(pe(r,n));return L(100,Bt(a,-1))})}function rV(e,t){return K(()=>{let r=un(t,tr(),Number.MAX_VALUE),n=En(le(1,r)),a=un(e,tr(),Number.MAX_VALUE),s=En(le(1,a));return Bt(gh(he(n,s)),-1)})}function nV(e,t){return K(()=>{let r=Xa(0,he(1,L(e,t)));return Bt(gh(r),-1)})}function aV(e,t){return K(()=>{let r=Xa(0,he(1,L(e,t)));return Bt(r,-1)})}function sV(e,t){return K(()=>{let r=ke(L(e,t),-1),n=fr(L(he(1,e),t),-1);return Xa(0,le(1,he(n,r)))})}function iV(e,t){return K(()=>{let r=Math.log(2),n=he(t,e),a=he(le(n,nd(L(-2,n))),r);return Bt(a,-1)})}function Mp(e,t,r=!1){return K(()=>{if(r)t=id(t);else{let n=ke(t,t.shape.length-1,!0);t=pe(t,n)}return t=un(t,tr(),1-tr()),zt(ke(L(me(e,"float32"),En(t)),t.shape.length-1))})}function gf(e,t,r=!1){return K(()=>{let n=me(uh(kW(e)),"int32");t=un(t,tr(),1-tr());let a=t.shape,s=G(Np(n,a[a.length-1]),a);return Mp(s,t,r)})}function oV(e,t){if(!w.arraysEqual(e.shape,t.shape))throw new q(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return K(()=>{let r=$a(t),n=zt(er(t));return le(he(r,L(t,e)),hm(Nn(n)))})}function jm(e,t){return K(()=>{let r;return r=un(t,tr(),1-tr()),r=En(pe(r,he(1,r))),Bt(oV(e,r),-1)})}function lV(e,t){return K(()=>{let r=un(e,tr(),1),n=un(t,tr(),1);return ke(L(e,En(pe(r,n))),-1)})}function uV(e,t){return K(()=>{let r=En(le(tr(),t));return Bt(he(t,L(e,r)),-1)})}function cA(e,t){return K(()=>{let r=mf(e,-1),n=mf(t,-1),a=L(r,n);return zt(ke(a,-1))})}var yf={meanSquaredError:kl,meanAbsoluteError:Gm,meanAbsolutePercentageError:ud,meanSquaredLogarithmicError:rV,squaredHinge:nV,hinge:aV,categoricalHinge:sV,logcosh:iV,categoricalCrossentropy:Mp,sparseCategoricalCrossentropy:gf,binaryCrossentropy:jm,kullbackLeiblerDivergence:lV,poisson:uV,cosineProximity:cA};function z1(e){if(typeof e=="string"){if(e in yf)return yf[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new q(t)}else return e}function fA(e,t){return K(()=>{let r=L(.5,Rn(t)),n=$m(hn(t,r),e.dtype);return Bt(Tn(e,n),-1)})}function mA(e,t){return K(()=>$m(Tn(Cn(e,-1),Cn(t,-1)),"float32"))}function z7(e,t){return K(()=>me(ke(pa(Tn(e,1),Tn(t,1))),"float32"))}function dV(e,t){return K(()=>me(ke(pa(Tn(e,1),Tn(t,0))),"float32"))}function pV(e,t){return K(()=>me(ke(pa(Tn(e,0),Tn(t,1))),"float32"))}function O7(e,t){return K(()=>{let r=z7(e,t),n=pV(e,t),a=le(r,n);return me(Lr(hn(a,0),pe(r,a),0),"float32")})}function hV(e,t){return K(()=>{let r=z7(e,t),n=dV(e,t),a=le(r,n);return me(Lr(hn(a,0),pe(r,a),0),"float32")})}function D7(e,t){return jm(e,t)}function L7(e,t){return e.rank===t.rank&&(e=et(e,[e.rank-1])),t=Cn(t,-1),t.dtype!==e.dtype&&(t=me(t,e.dtype)),me(Tn(e,t),"float32")}var cV=kl,fV=kl,mV=Gm,gV=Gm,yV=ud,AV=ud,gA=Mp,xV=cA,B7=gf,Af={binaryAccuracy:fA,categoricalAccuracy:mA,precision:O7,categoricalCrossentropy:gA,sparseCategoricalCrossentropy:B7,mse:cV,MSE:fV,mae:mV,MAE:gV,mape:yV,MAPE:AV,cosine:xV};function bV(e){if(typeof e=="string"&&e in Af)return Af[e];if(typeof e!="string"&&e!=null)return e;throw new q(`Unknown metric ${e}`)}function zc(e){if(Ia(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let r of Object.keys(yf))if(yf[r]===e){t=r;break}if(t!==void 0)return t;for(let r of Object.keys(Af))if(Af[r]===e){t=r;break}return t!==void 0?t:e.name}}function vV(e){let t={Adagrad:()=>so.adagrad(.01),Adadelta:()=>so.adadelta(1,.95,tr()),Adam:()=>so.adam(.001,.9,.999,tr()),Adamax:()=>so.adamax(.002,.9,.999,tr(),0),RMSProp:()=>so.rmsprop(.001,.9,0,tr()),SGD:()=>so.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 q(`Unknown Optimizer ${e}`)}var O3=1*1024*1024;function D3(e,t,r=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!uy(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(r){let n=JSON.stringify(e);n.length>O3&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${n.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${O3}.`)}}function uy(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let r of t)if(typeof r!="string"||!uy(e[r]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!uy(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function wV(e,t,r,n=console.log){let a=IV(e),s=["Layer (type)","Input Shape","Output shape","Param #"];a?(t=t||90,r=r||[.32,.61,.89,1]):(t=t||115,r=r||[.24,.48,.7,.8,1]),r[r.length-1]<=1&&(r=r.map(d=>Math.floor(t*d)));let i;if(!a){s.push("Receives inputs"),i=[];for(let d in e.nodesByDepth)i.push(...e.nodesByDepth[d])}n("_".repeat(t)),xf(s,r,n),n("=".repeat(t));let o=e.layers;for(let d=0;d<o.length;++d)a?SV(o[d],r,n):CV(o[d],r,i,n),n((d===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=kV(e),u=ff(e.nonTrainableWeights);n(`Total params: ${l+u}`),n(`Trainable params: ${l}`),n(`Non-trainable params: ${u}`),n("_".repeat(t))}function kV(e){let t;return e.collectedTrainableWeights!=null?t=ff(e.collectedTrainableWeights):t=ff(e.trainableWeights),t}function IV(e){let t=!0,r=[],n=[];for(let a in e.nodesByDepth)r.push(e.nodesByDepth[a]);for(let a of r){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}n.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(n.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function xf(e,t,r=console.log){let n="";for(let a=0;a<e.length;++a)a>0&&(n=n.slice(0,n.length-1)+" "),n+=e[a],n=n.slice(0,t[a]),n+=" ".repeat(t[a]-n.length);r(n)}function SV(e,t,r){let n,a;try{a=e.inboundNodes.map(l=>JSON.stringify(l.inputShapes)).join(",")}catch(l){a="multiple"}try{n=JSON.stringify(e.outputShape)}catch(l){n="multiple"}let s=e.name,i=e.getClassName(),o=[`${s} (${i})`,a,n,e.countParams().toString()];xf(o,t,r)}function CV(e,t,r,n){let a,s;try{s=e.inboundNodes.map(h=>JSON.stringify(h.inputShapes)).join(",")}catch(h){s="multiple"}try{a=JSON.stringify(e.outputShape)}catch(h){a="multiple"}let i=[];for(let h of e.inboundNodes)if(!(r!=null&&r.length>0&&r.indexOf(h)===-1))for(let p=0;p<h.inboundLayers.length;++p){let c=h.inboundLayers[p].name,f=h.nodeIndices[p],m=h.tensorIndices[p];i.push(`${c}[${f}][${m}]`)}let o=e.name,l=e.getClassName(),u=i.length===0?"":i[0],d=[`${o} (${l})`,s,a,e.countParams().toString(),u];xf(d,t,n);for(let h=1;h<i.length;++h)xf(["","","","",i[h]],t,n)}function W7(e,t,r){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof r=="string"}function Fp(e,t){if(e===null)return null;if(typeof e=="string")return lo(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let r=[],n=e.length;for(let a=0;a<n;++a){let s=e[a];W7(t,a,s)?r.push(s):r.push(Fp(s,t))}return r}else{let r={};for(let n of Object.keys(e)){let a=e[n];if(n==="name"&&typeof a=="string")r[n]=a;else{let s=lo(n);r[s]=Fp(a,s)}}return r}}function dy(e,t){if(e==null)return null;if(typeof e=="string")return Ba(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let r=[],n=e.length;for(let a=0;a<n;++a){let s=e[a];W7(t,a,s)?r.push(s):r.push(dy(s,t))}return r}else{let r={};for(let n of Object.keys(e)){let a=e[n],s=Ba(n);(n==="name"||n==="className")&&typeof a=="string"?r[s]=a:r[s]=dy(a,n)}return r}}var yA="0.0.0";function TV(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(r){throw new q(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var ho=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof ho)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,r){if(this.id2Value[e.id]==null)this.id2Value[e.id]=TV(e,t),this.name2Id[e.name]=e.id,r!=null&&(this.id2Mask[e.id]=r);else throw new q(`Duplicate key: name=${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 sa){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof sa){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&re(this.id2Mask)}},O1={},L3={};function dp(e,t,r,n){let a=r==null?!1:r.training,s=Array.isArray(e),i=s?e:[e],o=i.map(f=>f.name),l=[],u=t.names();for(let f of o)u.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);n!=null&&(n.maxNumTensors=-1/0,n.minNumTensors=1/0);let d=o.join(",")+"|"+t.names().join(","),h,p;if(O1[d]==null){let f=NV(i,t);h=f.sorted,p=f.recipientCounts,O1[d]=h,L3[d]=p}h=O1[d],p={},a||Object.assign(p,L3[d]);let c=new ho(t);for(let f=0;f<h.length;++f){if(n!=null){let R=df().numTensors;R>n.maxNumTensors&&(n.maxNumTensors=R),R<n.minNumTensors&&(n.minNumTensors=R)}let m=h[f],g=m.sourceLayer;if(g instanceof ld)continue;let y=[],A=[],x=[],b=!1;for(let R of m.inputs){let _=c.getValue(R),M=c.getMask(R);y.push(_),A.push(M),M!=null&&(b=!0),a||(p[R.name]--,p[R.name]===0&&!t.hasKey(R)&&o.indexOf(R.name)===-1&&!_.isDisposed&&R.sourceLayer.stateful!==!0&&x.push(_))}b&&(r=r||{},r.mask=A[0]);let v=It(g.apply(y,r)),C=null;g.supportsMasking&&(C=g.computeMask(y,A));let S=RV(m),E=Array.isArray(S)?S:[S];for(let R=0;R<E.length;++R){c.hasKey(E[R])||c.add(E[R],v[R],Array.isArray(C)?C[0]:C);let _=o.indexOf(E[R].name);_!==-1&&(l[_]=v[R])}a||re(x)}return c.disposeMasks(),s?l:l[0]}function NV(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let r=[],n={};if(e.length===1){let a=B3(e[0],t);r=a.sorted,n=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=B3(s,t);for(let l of i)a.has(l.name)||(r.push(l),a.add(l.name));for(let l in o)n[l]==null&&(n[l]=new Set),o[l].forEach(u=>n[l].add(u))}}return{sorted:r,recipientCounts:EV(n)}}function EV(e){let t={};for(let r in e)t[r]=e[r].size;return t}function B3(e,t){let r=new Set,n=[],a={};for(let o of t.names())r.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(r.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),n.push(o),r.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)a[u.name]==null&&(a[u.name]=new Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function RV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var ka=class extends st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Vm(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],Is(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Is(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,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ia(x===0,"input layer has >1 nodes"),Ia(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}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 ld))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={},r={},n={},a={},s={},i=[],o=(y,A,x,b,v,C)=>{(b==null||v==null||C==null)&&(b=y.sourceLayer,v=y.nodeIndex,C=y.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new aa(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(ka.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let _=S.inputTensors[R],M=S.inboundLayers[R],I=S.nodeIndices[R],O=S.tensorIndices[R];o(_,A,x,M,I,O)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],C=y.nodeIndices[b],S=v.inboundNodes[C],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,E),r[S.id]=S}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort($c);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let v=s[x.id],C=s[b.id];return v<C?-1:v>C?1:0});for(let x of A)x instanceof ka&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort($c);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new aa(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new aa(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("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 r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}pA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${yA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=dy(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new ho;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return dp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=ko(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=cf(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort($c);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Jr(d)),p=cf(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],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}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ia(o in r),a.push(r[o])}return Jr(a)}runInternalGraph(e,t){t==null&&(t=ko(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort($c);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],C=A[x];r[b.id]=[v,C]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ia(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof ka?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=ka.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`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 q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=ka.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=ka.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=ka.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],C=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let S=a[b];if(S.inboundNodes.length<=v){i(m,g);return}let E=S.inboundNodes[v];y.push(E.outputTensors[C])}y.length>0&&m.apply(Jr(y),A)}function l(m){let g=m.name,y=la(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!oW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("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(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function MV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===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!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function V7(e,t){return MV(e,t,"classWeight")}async function U7(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Or(e);if(e.shape.length===2){if(e.shape[1]>1)return Cn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[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(r[o])}),St(i,"float32")}else return null}function FV(e,t){return L(e,t)}var $V=32;function G7(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=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=W3("input",e.inputNames,r),i=W3("output",e.outputNames,n),o=s[0].shape[0];w.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)})`),w.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++)w.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++)w.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 W3(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function PV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function _V(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.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 a=r.validationData!=null,s,i;if(a)if(V3(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=PV(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=P7(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=_7(d,h,r.epochs,null,null,zV(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.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, ${r.batchesPerEpoch*r.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:b,ys:v}=G7(e,x.value),C={};C.batch=A,C.size=b[0].shape[0],await p.onBatchBegin(A,C);let S=[];if(r.classWeight!=null){let _=V7(r.classWeight,e.outputNames);for(let M=0;M<_.length;++M)S.push(await U7(v[M],null,_[M]))}let E=b.concat(v).concat(S),R=o(E);re(E);for(let _=0;_<l.length;++_){let M=l[_],I=R[_];C[M]=I,hr(I)}await p.onBatchEnd(A,C),R7(C),A++,y++}if(n?y>=r.batchesPerEpoch:x.done){if(a){let b;V3(r.validationData)?b=It(await e.evaluateDataset(r.validationData,{batches:r.validationBatches})):b=It(e.evaluate(s,i,{batchSize:r.validationBatchSize==null?$V:r.validationBatchSize,verbose:0}));for(let v=0;v<e.metricsNames.length;++v)g[`val_${e.metricsNames[v]}`]=b[v]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,g),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function zV(e,t){let r=null;return t.batchesPerEpoch!=null?r=t.batchesPerEpoch:Number.isFinite(e.size)&&(r=e.size),r}function V3(e){return typeof e.iterator=="function"}function OV(e){return typeof e.next=="function"}async function DV(e,t,r){r=r||{};let n=r.batches!=null,a=e.testFunction,s=[];if(r.verbose>0)throw new Ve("Verbose mode is not implemented yet.");w.assert(!n||r.batches>0&&Number.isInteger(r.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(r.batches)}`);let i=OV(t)?t:await t.iterator(),o=0,l=0;for(;!n||l<r.batches;){let u=await i.next();if(s=K(()=>{if(u.value){let{xs:d,ys:h}=G7(e,u.value),p=d.concat(h),c=K(()=>a(p));if(re(p),l===0)for(let m=0;m<c.length;++m)s.push(Se(0));let f=p[0].shape[0];for(let m=0;m<c.length;++m){let g=c[m],y=s[m];s[m]=K(()=>le(s[m],L(f,g))),l>0&&re(y)}re(c),o+=f,++l}return s}),u.done){n&&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, ${r.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]=pe(s[u],o),re(d)}return Jr(s)}function py(e){w.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function pp(e,t,r){return e==null?[null]:Array.isArray(e)?e.map(n=>go(n,t,r-t)):go(e,t,r-t)}function AA(e,t){return K(()=>e==null?null:Array.isArray(e)?e.map(r=>AA(r,t)):k7(e,t.dtype==="int32"?t:me(t,"int32")))}function hy(e,t){let r=[],n=0,a=null;for(;n<e;)a=n+t,a>=e&&(a=e),r.push([n,a]),n=a;return r}async function LV(e,t,r,n,a,s,i,o,l,u,d,h,p,c,f){a==null&&(a=32),s==null&&(s=1),d==null&&(d=!0),p==null&&(p=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,c==null))throw new q("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(r,a,c,"steps_per_epoch"),y;g!=null&&(y=ca(0,g)),i==null&&(i=1);let{callbackList:A,history:x}=_7(o,i,s,p,g,c,a,m,h);A.setModel(e),e.history=x,await A.onTrainBegin(),e.stopTraining_=!1;for(let b=p;b<s;++b){await A.onEpochBegin(b);let v={};if(c!=null)throw new Ve("stepsPerEpoch mode is not implemented yet.");{if(d==="batch")throw new Ve("batch shuffling is not implemneted yet");d&&w.shuffle(y);let C=St(y),S=hy(g,a);for(let E=0;E<S.length;++E){let R={};if(await A.onBatchBegin(E,R),K(()=>{let _=S[E][0],M=S[E][1],I=go(C,_,M-_);R.batch=E,R.size=M-_;let O=AA(r,I),z=t(O);for(let j=0;j<n.length;++j){let X=n[j],D=z[j];R[X]=D,hr(D)}if(E===S.length-1&&m){let j=e.testLoop(l,u,a);for(let X=0;X<n.length;++X){let D=n[X],Q=j[X];hr(Q),v["val_"+D]=Q}}}),await A.onBatchEnd(E,R),R7(R),e.stopTraining_)break}C.dispose()}if(await A.onEpochEnd(b,v),e.stopTraining_)break}return await A.onTrainEnd(),await e.history.syncData(),e.history}async function BV(e,t,r,n={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let a,s,i,o,l,u,d,h,p;try{let c=n.batchSize==null?32:n.batchSize;py(c);let f=!1,m=await e.standardizeUserData(t,r,n.sampleWeight,n.classWeight,f,c);a=m[0],s=m[1],p=m[2];let g=!1,y;if(n.validationData!=null&&n.validationData.length>0){if(g=!0,n.validationData.length===2)l=n.validationData[0],u=n.validationData[1];else throw n.validationData.length===3?new Ve("validationData including sample weights is not supported yet."):new q(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);let E=!0,R=await e.standardizeUserData(l,u,null,null,E,c);d=R[0],h=R[1],y=d.concat(h)}else if(n.validationSplit!=null&&n.validationSplit>0&&n.validationSplit<1){g=!0;let E=Math.floor(a[0].shape[0]*(1-n.validationSplit)),R=a[0].shape[0];d=pp(a,E,R),i=a,a=pp(a,0,E),h=pp(s,E,R),o=s,s=pp(s,0,E),y=d.concat(h)}else n.validationSteps!=null&&(g=!0);let A=a.concat(s).concat(p);e.checkTrainableWeightsConsistency();let x=e.makeTrainFunction(),b=e.getDedupedMetricsNames(),v,C;g?(e.makeTestFunction(),v=e.testFunction,C=b.slice().concat(b.map(E=>"val_"+E))):(v=null,y=[],C=b.slice());let S=P7(n.callbacks,n.yieldEvery);return await LV(e,x,A,b,c,n.epochs,n.verbose,S,v,y,n.shuffle,C,n.initialEpoch,null,null)}finally{e.isTraining=!1,na(a,t),na(s,r),na(i,t),na(o,r),na(d,l),na(h,u),p!=null&&re(p)}}function j7(e){let t=[];e instanceof rt&&(e=[e]);for(let r=0;r<e.length;++r){let n=e[r];if(n.rank===1)t.push(mh(n,1));else{if(n.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(n)}}return t}function na(e,t){if(e==null)return;let r=[];if(t instanceof rt)r.push(t.id);else if(Array.isArray(t))t.forEach(a=>r.push(a.id));else if(t!=null)for(let a in t){let s=t[a];r.push(s.id)}let n=[];if(e instanceof rt)r.indexOf(e.id)===-1&&n.push(e);else if(Array.isArray(e))e.forEach(a=>{r.indexOf(a.id)===-1&&n.push(a)});else if(e!=null)for(let a in e){let s=e[a];r.indexOf(s.id)===-1&&n.push(s)}n.forEach(a=>{a.isDisposed||a.dispose()})}function WV(e){return e instanceof rt}function cy(e){return Array.isArray(e)}function U3(e){return!WV(e)&&!cy(e)}function G3(e,t,r,n=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(cy(e)&&e.length>0)i=!0;else if(U3(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new q(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(U3(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new q(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(cy(e)){if(e=e,e.length!==t.length)throw new q(`Error when checking model ${a}: 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 q(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=j7(s),r!=null)for(let i=0;i<t.length;++i){if(r[i]==null)continue;let o=s[i];if(o.shape.length!==r[i].length)throw new q(`Error when checking ${a}: expected ${t[i]} to have ${r[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<r[i].length;++l){if(l===0&&!n)continue;let u=o.shape[l],d=r[i][l];if(d!=null&&d>=0&&u!==d)throw new q(`${a} expected a batch of elements where each example has shape [${r[i].slice(1,r[i].length)}] (i.e.,tensor shape [*,${r[i].slice(1,r[i].length)}]) but the ${a} received an input with ${o.shape[0]} examples, each with shape [${o.shape.slice(1,o.shape.length)}] (tensor shape [${o.shape}])`)}}return s}function VV(e,t,r){let n=Is(e.map(s=>s.shape[0]));n.sort();let a=Is(t.map(s=>s.shape[0]));if(a.sort(),n.length>1)throw new q(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(a.length>1)throw new q(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(n.length>0&&a.length>0&&!w.arraysEqual(n,a))throw new q(`Input Tensors should have the same number of samples as target Tensors. Found ${n[0]} input sample(s) and ${a[0]} target sample(s).`)}function UV(e,t,r){let n=[kl,jm,Mp];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=r[a];if(i!=null){if(i===Mp&&s.shape[s.shape.length-1]===1)throw new q(`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(n.indexOf(i)!==-1){let l=s.shape.slice(1),u=o.slice(1);for(let d=0;d<l.length;++d){let h=l[d],p=u[d];if(p!=null&&h!==p)throw new q(`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 j3(e,t,r,n=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new q(`Error when checking model ${a}: 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 q(`The model expects ${t.length} ${a} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(r!=null)for(let i=0;i<t.length;++i){if(r[i]==null)continue;let o=s[i];if(o.shape.length!==r[i].length)throw new q(`Error when checking ${a}: expected ${t[i]} to have ${r[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<r[i].length;++l){if(l===0&&!n)continue;let u=o.shape[l],d=r[i][l];if(d!=null&&d!==u)throw new q(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(r[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function GV(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(n=>[]);let r;if(typeof e=="string"||typeof e=="function")r=[e];else if(Array.isArray(e)||typeof e=="object")r=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(r))return t.map(n=>r);{let n=[];for(let a of t){let s=r.hasOwnProperty(a)?r[a]:[];Array.isArray(s)||(s=[s]),n.push(s)}return n}}var jV="layers-model",Ga=class extends ka{constructor(e){super(e);this.isTraining=!1}summary(e,t,r=console.log){if(!this.built)throw new q("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");wV(this,e,t,r)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=vV(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Ya))throw new q("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new q(`Unknown entry in loss dictionary: "${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(z1(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new q(`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=>z1(s))}else{let s=z1(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 r=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],mo("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(r.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 n=GV(e.metrics,this.outputNames),a=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};mo("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(r.indexOf(s)!==-1)continue;let i=n[s];(o=>{let l="",u,d,h;for(let p of o){if(typeof p=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(p)!==-1){let f=this.internalOutputShapes[s];f[f.length-1]===1||this.lossFunctions[s]===jm?["accuracy","acc"].indexOf(p)!==-1?d=fA:["crossentropy","ce"].indexOf(p)!==-1&&(d=D7):this.lossFunctions[s]===gf?["accuracy","acc"].indexOf(p)!==-1?d=L7:["crossentropy","ce"].indexOf(p)!==-1&&(d=B7):["accuracy","acc"].indexOf(p)!==-1?d=mA:["crossentropy","ce"].indexOf(p)!==-1&&(d=gA);let m;["accuracy","acc"].indexOf(p)!==-1?m="acc":["crossentropy","ce"].indexOf(p)!==-1&&(m="ce"),h=d,u=l+m}else h=bV(p),u=l+zc(p);let c;mo(u,()=>{c=h}),a(s,u,c)}})(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,r={}){let n=r.batchSize==null?32:r.batchSize;py(n);let a=!0,s=this.standardizeUserDataXY(e,t,a,n);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,n,r.verbose,r.steps);return Jr(l)}finally{na(s[0],e),na(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),DV(this,e,t)}checkNumSamples(e,t,r,n="steps"){let a;if(r!=null){if(a=null,t!=null)throw new q(`If ${n} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?a=e[0].shape[0]:a=e.shape[0];else throw new q(`Either the input data should have a defined shape, or ${n} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new q("`outputs` is an empty Array, which is not allowed.");let r=Array.isArray(t),n=r?t:[t],a=this.retrieveSymbolicTensors(n),s=new ho;if(e instanceof rt&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new q(`The number of inputs provided (${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 q(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=dp(a,s);return r?i:i[0]}retrieveSymbolicTensors(e){let t=ko(null,e.length),r=e.length;for(let n of this.layers){let a=Array.isArray(n.output)?n.output:[n.output],s=a.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=a[o],r--),r===0)break}if(r===0)break}if(r>0){let n=[];throw t.forEach((a,s)=>{a==null&&n.push(e[s])}),new q(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(n)}`)}return t}predictLoop(e,t=32,r=!1){return K(()=>{let n=this.checkNumSamples(e);if(r)throw new Ve("Verbose predictLoop() is not implemented yet.");let a=hy(n,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)K(()=>{let o=a[i][0],l=a[i][1],u=pp(e,o,l),d=[];if(Array.isArray(u))for(let p=0;p<u.length;++p)d.push({key:this.inputs[p],value:u[p]});else d.push({key:this.inputs[0],value:u});let h=new ho(d);return dp(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Jr(s.map(i=>kt(i,0)))})}predict(e,t={}){let r=j7(e);j3(r,this.inputNames,this.feedInputShapes,!1);try{let n=t.batchSize==null?32:t.batchSize;return py(n),this.predictLoop(r,n)}finally{na(r,e)}}predictOnBatch(e){j3(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,r=!0,n){if(this.optimizer_==null)throw new aa("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===gf?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=G3(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=G3(t,this.feedOutputNames,a,!1,"target"),VV(e,t,null),UV(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&n!=null&&n>0&&e[0].shape[0]%n!==0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${n}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,r,n,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(r!=null)throw new Error("sample weight is not supported yet.");let l=null;if(n!=null){let u=V7(n,this.outputNames);l=[];for(let d=0;d<u.length;++d)l.push(await U7(o[d],null,u[d]))}return[i,o,l]}testLoop(e,t,r,n=0,a){return K(()=>{let s=this.checkNumSamples(t,r,a,"steps"),i=[];if(n>0)throw new Ve("Verbose mode is not implemented yet.");if(a!=null)throw new Ve("steps mode in testLoop() is not implemented yet");{let o=hy(s,r),l=St(ca(0,s));for(let u=0;u<o.length;++u){let d=o[u][0],h=o[u][1],p=go(l,d,h-d),c=AA(t,p),f=e(c);if(u===0)for(let m=0;m<f.length;++m)i.push(Se(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=le(i[m],L(h-d,g))}}for(let u=0;u<i.length;++u)i[u]=pe(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let r=0;r<e.length;++r){let n=e[r],a=n;E3(e,n)>1&&(a+=`_${E3(e.slice(0,r),n)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],r=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let c=0;c<this.inputs.length;++c)u.push({key:this.inputs[c],value:r[c]});let d=new ho(u),h=dp(this.outputs,d,{training:!0}),p;for(let c=0;c<this.lossFunctions.length;++c){let f=this.lossFunctions[c](n[c],h[c]);a[c]!=null&&(f=FV(f,a[c]));let m=Bt(f);t.push(m),c===0?p=f:p=le(p,f)}for(let c=0;c<this.metricsTensors.length;++c){let f;if(this.outputs.length>1&&c<this.outputs.length)f=t[c];else{let m=this.metricsTensors[c][0],g=this.metricsTensors[c][1];f=Bt(m(n[g],h[g]))}hr(f),s.push(f)}return p=Bt(p),this.calculateLosses().forEach(c=>{p=le(p,c)}),p},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>K(()=>{let t=[],r,n=e.slice(0,this.inputs.length),a=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:n[l]});let i=new ho(s),o=dp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Bt(u(a[l],o[l]));l===0?r=d:r=le(r,d),t.push(r)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Bt(u(a[d],o[d]));t.push(h)}return t})}async fit(e,t,r={}){return BV(this,e,t,r)}async fitDataset(e,t){return _V(this,e,t)}async trainOnBatch(e,t){let r=await this.standardizeUserData(e,t),n=r[0],a=r[1],s=this.makeTrainFunction()(n.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return re(s),na(r[0],e),na(r[1],t),Jr(i)}getNamedWeights(e){let t=[],r=e!=null&&e.trainableOnly,n=r?this.trainableWeights:this.weights,a=this.getWeights(r);for(let s=0;s<n.length;++s)r&&!n[s].trainable||t.push({name:n[s].originalName,tensor:a[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=df().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-df().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ba(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=>Ba(t))}else{let t=Object.keys(this.loss);e={};let r=this.loss;for(let n of t)if(typeof r[n]=="string")e[n]=Ba(r[n]);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[Ba(zc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ba(zc(e)));{let e={};for(let t in this.metrics)e[t]=Ba(zc(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=Fp(e.optimizer_config),r=la(t),n;if(typeof e.loss=="string")n=lo(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(s=>lo(s));else if(e.loss!=null){n={};for(let s in e.loss)n[s]=lo(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>lo(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=lo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Ir.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ir.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:jV,generatedBy:`TensorFlow.js tfjs-layers v${yA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Ir.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Ir.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(D3(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){D3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Ga.className="Model";ue.registerClass(Ga);var H7=class extends Ga{};H7.className="Functional";ue.registerClass(H7);async function HV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=Fp(r),a=la(n,t);if(e.weightsManifest!=null){let s=await Ir.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function qV(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Ir.getLoadHandlers(e,t);if(r.length===0)r.push(Ir.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return KV(e,void 0,t)}async function KV(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=la(Fp(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=XV(n.weightData,n.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),re(u),re(d.map(h=>h.tensor))}return o}function XV(e,t){let r=Ir.decodeWeights(e,t),n={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:r[s.name]}):n[s.name]=r[s.name]}),{modelWeights:n,optimizerWeights:a}}var fy=class extends Ga{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Vm("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 q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof fy||e instanceof Ga,r;if(t){if(r=e,r.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(r.inputs.length!==1)throw new q("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 q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=E7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=r.outputs,this.inputs=r.inputs;else{if(e.inboundNodes.length!==1)throw new q(`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 q("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=N7(this.outputs[0])}this.inboundNodes=[],new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ko(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))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=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Ga({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,r=console.log){this.built||this.build(),super.summary(e,t,r)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new aa("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,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new aa("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,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof fy))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=la(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("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 q("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 r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},Hm=fy;Hm.className="Sequential";ue.registerClass(Hm);function ZV(e){return new Ga(e)}function YV(e){return new Hm(e)}function JV(e,t){return t==null&&(t={}),qV(e,t)}function q7(e){return E7(e)}function QV(e,t){hA.registerCallbackConstructor(e,t)}var rn=class extends ue.Serializable{getConfig(){return{}}},K7=class extends rn{apply(e,t=1){return SW(e,t)}};K7.className="elu";ue.registerClass(K7);var X7=class extends rn{apply(e){return O2(e)}};X7.className="selu";ue.registerClass(X7);var Z7=class extends rn{apply(e){return $a(e)}};Z7.className="relu";ue.registerClass(Z7);var Y7=class extends rn{apply(e){return K(()=>dh(6,$a(e)))}};Y7.className="relu6";ue.registerClass(Y7);var J7=class extends rn{apply(e){return e}};J7.className="linear";ue.registerClass(J7);var Q7=class extends rn{apply(e){return Sr(e)}};Q7.className="sigmoid";ue.registerClass(Q7);var e4=class extends rn{apply(e){return TW(e)}};e4.className="hardSigmoid";ue.registerClass(e4);var t4=class extends rn{apply(e){return nd(e)}};t4.className="softplus";ue.registerClass(t4);var r4=class extends rn{apply(e){return CW(e)}};r4.className="softsign";ue.registerClass(r4);var n4=class extends rn{apply(e){return cu(e)}};n4.className="tanh";ue.registerClass(n4);var xA=class extends rn{apply(e,t=-1){return id(e,t)}};xA.className="softmax";ue.registerClass(xA);var a4=class extends rn{apply(e,t=-1){return C2(e,t)}};a4.className="logSoftmax";ue.registerClass(a4);var s4=class extends rn{apply(e,t=1){return K(()=>L(Sr(L(e,t)),e))}};s4.className="swish";ue.registerClass(s4);var i4=class extends rn{apply(e){return K(()=>L(e,cu(nd(e))))}};i4.className="mish";ue.registerClass(i4);function Os(e){return e.getClassName()}function D1(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Ds(e){if(e==null){let t={};return t.className="linear",t.config={},D1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},D1(t)}else return e instanceof rn?e:D1(e)}function bA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var o4=class extends ue.Serializable{},Ah=class extends o4{constructor(e){super();bA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,er(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,gh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ah.className="L1L2";ue.registerClass(Ah);function eU(e){return bA(e),new Ah({l1:e!=null?e.l1:null,l2:0})}function tU(e){return bA(e),new Ah({l2:e!=null?e.l2:null,l1:0})}var H3={l1l2:"L1L2"};function At(e){return Z2(e)}function q3(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in H3?H3[e]:e,config:{}};return q3(t)}else return e instanceof o4?e:q3(e)}var vA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let r=$a(e);return this.maxValue!=null&&(r=un(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};vA.className="ReLU";ue.registerClass(vA);var wA=class extends st{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 r=Ge(e);return pm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wA.className="LeakyReLU";ue.registerClass(wA);var kA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ar(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 q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ge(e),Am(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:nr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};kA.className="PReLU";ue.registerClass(kA);var IA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return lh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};IA.className="ELU";ue.registerClass(IA);var SA=class extends st{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 r=Ge(e);return L(r,me(hn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};SA.className="ThresholdedReLU";ue.registerClass(SA);var CA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new xA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ge(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};CA.className="Softmax";ue.registerClass(CA);function uu(e,t,r){if(typeof e=="number")return ko(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!vW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ua(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Sa(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+zs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function TA(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function l4(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function rU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=A2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=ya(o,r)),o})}function K3(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=TA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_s.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function nU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=l4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=v2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=ya(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var NA=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",NA.verifyArgs(t),this.rank=e,cr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=uu(t.kernelSize,e,"kernelSize"),this.strides=uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Vt(this.dataFormat),this.activation=Ds(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ia("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Y2(e.kernelSize,"number",1,3))throw new q(`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:Os(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},xh=class extends NA{constructor(e,t){super(e,t);this.kernel=null,xh.verifyArgs(t),this.filters=t.filters,cr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,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]:r}}],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r,n=this.bias==null?null:this.bias.read(),a=y7(this.activation.getClassName());if(a!=null&&this.rank===2)r=K3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=rU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=K3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=nU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ua(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:nr(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},u4=class extends xh{constructor(e){super(2,e);u4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Y2(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qm=u4;qm.className="Conv2D";ue.registerClass(qm);var d4=class extends xh{constructor(e){super(3,e);d4.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Km=d4;Km.className="Conv3D";ue.registerClass(Km);var EA=class extends qm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Sa(o,h,u,this.padding),f=Sa(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=b2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Sa(t[n],o,s,this.padding),t[a]=Sa(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};EA.className="Conv2DTranspose";ue.registerClass(EA);var RA=class extends Km{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Sa(l,f,h,this.padding),A=Sa(u,m,p,this.padding),x=Sa(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let v=Ak(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=nt(v,[0,4,1,2,3])),this.bias!==null&&(v=ya(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Sa(t[n],u,i,this.padding),t[a]=Sa(t[a],d,o,this.padding),t[s]=Sa(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv3DTranspose";ue.registerClass(RA);var p4=class extends xh{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 q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"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 qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=Lk(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseConstraint),e.pointwiseConstraint=nr(this.pointwiseConstraint),e}};p4.className="SeparableConv";var MA=class extends p4{constructor(e){super(2,e)}};MA.className="SeparableConv2D";ue.registerClass(MA);var h4=class extends xh{constructor(e){super(1,e);h4.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"&&!Y2(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},FA=h4;FA.className="Conv1D";ue.registerClass(FA);var $A=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let r=Pc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Pc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Pc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Pc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$A.className="Cropping2D";ue.registerClass($A);var PA=class extends st{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,Vt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,AW(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=Ge(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};PA.className="UpSampling2D";ue.registerClass(PA);function aU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ha()),Vt(a);let i=TA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=oh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var _A=class extends NA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r=aU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ua(t,this.kernelSize[0],this.padding,this.strides[0]),s=ua(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseRegularizer),e}};_A.className="DepthwiseConv2D";ue.registerClass(_A);function c4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function f4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ca(2,l));if(t=nt(t,u),s!=null)throw new Ve("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."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Ht(a,-1)),a=nt(a,u)),n&&(t=Mn(t,0),a!=null&&(a=Mn(a,0)));let d=[],h,p=r,c=t.shape[0],f=en(t),m;a!=null&&(m=en(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],C=he(Rn(v),v),S=le(L(x[0],v),L(p[0],C)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,C)));return{output:S,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=sr(d,1)),[h,g,p]})}var m4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Ym({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 qt({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 ca(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){oy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");oy(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("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(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>hr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof sa){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 K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=Ge(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=f4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=mh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?sy(t,[1,r]):t):this.cell.stateSize>1?[sy(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 r=this.cell.getConfig();return this.getClassName()===m4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=la(n,r);return new e(Object.assign(t,{cell:a}))}},Ja=m4;Ja.className="RNN";ue.registerClass(Ja);var bh=class extends st{},Xm=class extends bh{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,cr(this.units,"units"),this.activation=Ds(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ta(L(e,s),this.kernel.read()):a=Ta(e,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ta(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Xm.className="SimpleRNNCell";ue.registerClass(Xm);var zA=class extends Ja{constructor(e){e.cell=new Xm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};zA.className="SimpleRNN";ue.registerClass(zA);var Zm=class extends bh{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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ta(e,this.kernel.read());this.useBias&&(u=ya(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Kt(d,[2*this.units,this.units],d.rank-1),c=Ta(n,h),[f,m,g]=Kt(u,3,u.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ta(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Zm.className="GRUCell";ue.registerClass(Zm);var OA=class extends Ja{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 Zm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};OA.className="GRU";ue.registerClass(OA);var vh=class extends bh{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,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,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 n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Gn{apply(i,o){let l=a.apply([s]),u=new _m().apply([s]),d=a.apply([s*2]);return $3($3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ta(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ta(n,this.recurrentKernel.read())),this.useBias&&(h=ya(h,this.bias.read()));let[p,c,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};vh.className="LSTMCell";ue.registerClass(vh);var DA=class extends Ja{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 vh(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};DA.className="LSTM";ue.registerClass(DA);var Ym=class extends bh{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 K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){oy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{mo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(la(a,r));return new e({cells:n})}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 r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ly(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}pA(t)}};Ym.className="StackedRNNCells";ue.registerClass(Ym);function Ls(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):I7(t(),r),o=()=>yh(i,t,n);return!a||a<=1?hr(o().clone()):Array(a).fill(void 0).map(o).map(l=>hr(l.clone()))}var g4=class extends Ja{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}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 K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("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(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`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=>hr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ua(l,n[0],a,s[0],i[0]),h=ua(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};g4.className="ConvRNN2D";var Jm=class extends vh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,cr(this.filters,"filters"),this.kernelSize=uu(r,2,"kernelSize"),this.kernelSize.forEach(o=>cr(o,"kernelSize")),this.strides=uu(n||1,2,"strides"),this.strides.forEach(o=>cr(o,"strides")),this.padding=a||"valid",Pn(this.padding),this.dataFormat=s||"channelsLast",Vt(this.dataFormat),this.dilationRate=uu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>cr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);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 Gn{apply(d,h){let p=l.apply([u]),c=ln([u]),f=l.apply([u*2]);return nA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,Y)=>!ee||!ee[Y]?V:L(ee[Y],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,C]=Kt(this.kernel.read(),i,A),[S,E,R,_]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,C,_,this.padding);let[M,I,O,z]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,O),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Fs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?ya(a,r,this.dataFormat):a}recurrentConv(e,t){return Fs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Jm.className="ConvLSTM2DCell";ue.registerClass(Jm);var LA=class extends g4{constructor(e){let t=new Jm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};LA.className="ConvLSTM2D";ue.registerClass(LA);var Qm=class extends st{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,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return yh(()=>I7(r,this.rate,a,this.seed),()=>r,n)}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()}};Qm.className="Dropout";ue.registerClass(Qm);var BA=class extends Qm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};BA.className="SpatialDropout1D";ue.registerClass(BA);var WA=class extends st{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,cr(this.units,"units"),this.activation=Ds(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=y7(this.activation.getClassName()),a;return n!=null?a=Ta(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ta(r,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Dense";ue.registerClass(WA);var VA=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`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],Ss(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return IW(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};VA.className="Flatten";ue.registerClass(VA);var UA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ds(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.activation.apply(r)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};UA.className="Activation";ue.registerClass(UA);var GA=class extends st{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 K(()=>(e=Ge(e),wW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};GA.className="RepeatVector";ue.registerClass(GA);var jA=class extends st{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 r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ss(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){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 K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};jA.className="Reshape";ue.registerClass(jA);var HA=class extends st{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=ca(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};HA.className="Permute";ue.registerClass(HA);var qA=class extends st{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 r=Ge(e),n=-1;return pf(gu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=-1,a=!0,s=pf(gu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};qA.className="Masking";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,cr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ar(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 K(()=>this.maskZero?(e=Ge(e),gu(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);r.dtype!=="int32"&&(r=$m(r,"int32"));let n=k7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:nr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Embedding";ue.registerClass(KA);var Il=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}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 r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Is(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Is(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=zs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=mh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Ss(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ca(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ca(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Is(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Ht(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=pa(r,t[n]);return r})}},XA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};XA.className="Add";ue.registerClass(XA);var ZA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};ZA.className="Multiply";ue.registerClass(ZA);var YA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};YA.className="Average";ue.registerClass(YA);var JA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Xa(t,e[r]);return t})}};JA.className="Maximum";ue.registerClass(JA);var QA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=dh(t,e[r]);return t})}};QA.className="Minimum";ue.registerClass(QA);var ex=class extends Il{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>nA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Rn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Ht(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return f2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ex.className="Concatenate";ue.registerClass(ex);function ap(e,t){for(;e<0;)e+=t;return e}function sU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Ht(o,1)),o})}var tx=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>ap(a,e[s].shape.length)):n=[ap(this.axes,t.shape.length),ap(this.axes,r.shape.length)],this.normalize&&(t=mf(t,n[0]),r=mf(r,n[1])),sU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[ap(this.axes,e.length),ap(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Dot";ue.registerClass(tx);var rx=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return yh(()=>le(Pm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};rx.className="GaussianNoise";ue.registerClass(rx);var nx=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.rate>0&&this.rate<1?yh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Pm(r.shape,1,n))},()=>r,t.training||!1):r})}};nx.className="GaussianDropout";ue.registerClass(nx);var ax=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(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 K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return yh(()=>{let n=Ge(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=xl(sd(r),this.rate);o=$m(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>Ge(e),t.training||!1)}return e})}};ax.className="AlphaDropout";ue.registerClass(ax);function $p(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=uk(e,t,r,n,a,s);else if(e.rank===3)i=dk(e,t,r,n,a,s);else if(e.rank===4)i=pk(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function iU(e,t,r,n,a=.001){return K(()=>{let s=gm(e,n),i=s.mean,o=s.variance;return[$p(e,i,o,r,t,a),i,o]})}function oU(e,t,r,n,a=.001){return K(()=>{let s=gm(e,n),i=s.mean,o=s.variance,l=[];for(let c of ca(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[$p(e,u,d,p,h,a),i,o]})}function lU(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ca(0,e.rank-1))?iU(e,t,r,n,a):oU(e,t,r,n,a)}var sx=class extends st{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=Ge(e),a=n.shape,s=a.length,i=ca(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ko(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ca(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return $p(n,g,y,A,x,this.epsilon)}else return $p(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=lU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(he(b,y),x);g.write(he(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:nr(this.betaConstraint),gammaConstraint:nr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};sx.className="BatchNormalization";ue.registerClass(sx);var ix=class extends st{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Is(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=Ge(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=gm(r,this.axis,!0),o=ko(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Dn(s,h),i=Dn(i,h),u=Dn(u,p),d=Dn(d,p),$p(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};ix.className="LayerNormalization";ue.registerClass(ix);function uU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ha()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Vn(e,n)})}var ox=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ha():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;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?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(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?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>uU(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ox.className="ZeroPadding2D";ue.registerClass(ox);function e0(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=TA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=mm(e,t,r,o):i=om(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function y4(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=l4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=R2(e,t,r,o):i=g2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var A4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cr(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pn(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=ua(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=mh(Ge(e),2);let r=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},lx=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"max")}};lx.className="MaxPooling1D";ue.registerClass(lx);var ux=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"avg")}};ux.className="AveragePooling1D";ue.registerClass(ux);var x4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`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];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},dx=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"max")}};dx.className="MaxPooling2D";ue.registerClass(dx);var px=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"avg")}};px.className="AveragePooling2D";ue.registerClass(px);var b4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`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];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),n=ua(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},hx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"max")}};hx.className="MaxPooling3D";ue.registerClass(hx);var cx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"avg")}};cx.className="AveragePooling3D";ue.registerClass(cx);var v4=class extends st{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},fx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return Bt(r,1)})}};fx.className="GlobalAveragePooling1D";ue.registerClass(fx);var mx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return fr(r,1)})}};mx.className="GlobalMaxPooling1D";ue.registerClass(mx);var w4=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},gx=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};gx.className="GlobalAveragePooling2D";ue.registerClass(gx);var yx=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?fr(r,[1,2]):fr(r,[2,3])})}};yx.className="GlobalMaxPooling2D";ue.registerClass(yx);var k4=class extends st{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,r={}){let n=t.layer,a=la(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Ax=class extends k4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=Ge(e),f4((r,n)=>[Ge(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ax.className="TimeDistributed";ue.registerClass(Ax);function dU(e){wl(yW,"BidirectionalMergeMode",e)}var pU="concat",xx=class extends k4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=la(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=la(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?pU:e.mergeMode,dU(this.mergeMode),e.weights)throw new Ve("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,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Jr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new qt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof sa;for(let l of s)if(l instanceof sa!==o)throw new q("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 h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=Mn(a,1));let i;return this.mergeMode==="concat"?i=nA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){mo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),mo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}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 r=la(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ve("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let n=t;return n.layer=r,new e(n)}};xx.className="Bidirectional";ue.registerClass(xx);function hU(e){return new ld(e)}function cU(e){return new IA(e)}function fU(e){return new vA(e)}function mU(e){return new wA(e)}function gU(e){return new kA(e)}function yU(e){return new CA(e)}function AU(e){return new SA(e)}function xU(e){return new FA(e)}function bU(e){return new qm(e)}function vU(e){return new EA(e)}function wU(e){return new Km(e)}function kU(e){return new RA(e)}function IU(e){return new MA(e)}function SU(e){return new $A(e)}function CU(e){return new PA(e)}function TU(e){return new _A(e)}function NU(e){return new UA(e)}function EU(e){return new WA(e)}function RU(e){return new Qm(e)}function MU(e){return new BA(e)}function FU(e){return new VA(e)}function $U(e){return new GA(e)}function PU(e){return new jA(e)}function _U(e){return new HA(e)}function zU(e){return new KA(e)}function OU(e){return new XA(e)}function DU(e){return new YA(e)}function LU(e){return new ex(e)}function BU(e){return new JA(e)}function WU(e){return new QA(e)}function VU(e){return new ZA(e)}function UU(e){return new tx(e)}function GU(e){return new sx(e)}function jU(e){return new ix(e)}function HU(e){return new ox(e)}function bx(e){return new ux(e)}function qU(e){return bx(e)}function KU(e){return bx(e)}function vx(e){return new px(e)}function XU(e){return vx(e)}function ZU(e){return vx(e)}function wx(e){return new cx(e)}function YU(e){return wx(e)}function JU(e){return wx(e)}function QU(e){return new fx(e)}function eG(e){return new gx(e)}function I4(e){return new mx(e)}function S4(e){return new yx(e)}function C4(e){return new lx(e)}function T4(e){return new dx(e)}function tG(e){return new hx(e)}function rG(e){return new OA(e)}function nG(e){return new Zm(e)}function aG(e){return new DA(e)}function sG(e){return new vh(e)}function iG(e){return new zA(e)}function oG(e){return new Xm(e)}function lG(e){return new LA(e)}function uG(e){return new Jm(e)}function dG(e){return new Ja(e)}function pG(e){return new Ym(e)}function hG(e){return new xx(e)}function cG(e){return new Ax(e)}var fG=I4,mG=S4,gG=C4,yG=T4;function AG(e){return new rx(e)}function xG(e){return new nx(e)}function bG(e){return new ax(e)}function vG(e){return new qA(e)}var N4={};Le(N4,{MAPE:()=>FG,MSE:()=>_G,binaryAccuracy:()=>wG,binaryCrossentropy:()=>kG,categoricalAccuracy:()=>SG,categoricalCrossentropy:()=>CG,cosineProximity:()=>EG,mape:()=>$G,meanAbsoluteError:()=>RG,meanAbsolutePercentageError:()=>MG,meanSquaredError:()=>PG,mse:()=>zG,precision:()=>TG,recall:()=>NG,sparseCategoricalAccuracy:()=>IG});function wG(e,t){return fA(e,t)}function kG(e,t){return D7(e,t)}function IG(e,t){return L7(e,t)}function SG(e,t){return mA(e,t)}function CG(e,t){return gA(e,t)}function TG(e,t){return O7(e,t)}function NG(e,t){return hV(e,t)}function EG(e,t){return cA(e,t)}function RG(e,t){return Gm(e,t)}function MG(e,t){return ud(e,t)}function FG(e,t){return ud(e,t)}function $G(e,t){return ud(e,t)}function PG(e,t){return kl(e,t)}function _G(e,t){return kl(e,t)}function zG(e,t){return kl(e,t)}var E4={};Le(E4,{modelFromJSON:()=>HV});var R4={};Le(R4,{l1:()=>DG,l1l2:()=>OG,l2:()=>LG});function OG(e){return new Ah(e)}function DG(e){return eU(e)}function LG(e){return tU(e)}var M4=class extends xu{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof Ga))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function Oc(e,t){return e<t}function X3(e,t){return e>t}var F4=class extends M4{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Ve("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=Oc:this.mode==="max"?this.monitorFunc=X3:this.monitor.indexOf("acc")!==-1?this.monitorFunc=X3:this.monitorFunc=Oc,this.monitorFunc===Oc&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Oc?1/0:-1/0}async onEpochEnd(e,t){await xs(t);let r=this.getMonitorValue(t);r!=null&&(this.monitorFunc(r-this.minDelta,this.best)?(this.best=r,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 BG(e){return new F4(e)}var WG={earlyStopping:BG},VG=J();VG.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,e=>{e&&console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.")});var $4=(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_QINT16=15]="DT_QINT16",e[e.DT_QUINT16=16]="DT_QUINT16",e[e.DT_UINT16=17]="DT_UINT16",e[e.DT_COMPLEX128=18]="DT_COMPLEX128",e[e.DT_HALF=19]="DT_HALF",e[e.DT_RESOURCE=20]="DT_RESOURCE",e[e.DT_VARIANT=21]="DT_VARIANT",e[e.DT_UINT32=22]="DT_UINT32",e[e.DT_UINT64=23]="DT_UINT64",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",e[e.DT_QINT16_REF=115]="DT_QINT16_REF",e[e.DT_QUINT16_REF=116]="DT_QUINT16_REF",e[e.DT_UINT16_REF=117]="DT_UINT16_REF",e[e.DT_COMPLEX128_REF=118]="DT_COMPLEX128_REF",e[e.DT_HALF_REF=119]="DT_HALF_REF",e[e.DT_RESOURCE_REF=120]="DT_RESOURCE_REF",e[e.DT_VARIANT_REF=121]="DT_VARIANT_REF",e[e.DT_UINT32_REF=122]="DT_UINT32_REF",e[e.DT_UINT64_REF=123]="DT_UINT64_REF",e))($4||{}),Z3;(e=>{let t;(r=>{r[r.LEGACY=0]="LEGACY",r[r.V1=1]="V1",r[r.V2=2]="V2"})(t=e.CheckpointFormatVersion||(e.CheckpointFormatVersion={}))})(Z3||(Z3={}));var kx={};function UG(e,t){let r={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};kx[e]=r}function P4(e){return kx[e]}function GG(e){delete kx[e]}function k(e,t,r,n,a){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return _r(t.inputNames[s.inputIndexStart],r,n,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>_r(h,r,n,a));let u=_r(t.inputNames.slice(o)[0],r,n,a),d=u.dataSync();return s.type==="number"?d[0]:w.toNestedArray(u.shape,d)}let i=t.attrParams[e];return i&&i.value}function _r(e,t,r,n){let[a,s]=sn(e);if(n!=null){let o=n.getHashTableHandleByName(a);if(o!=null)return o}let i=r.currentContextIds.find(o=>!!t[bf(a,o)]);return i!==void 0?t[bf(a,i)][s]:void 0}function jG(e,t,r){return t[bf(e,r.currentContextId)]}function Ca(e,t){let[r,n,a]=sn(e);return[bf(r,t&&t.currentContextId),n,a]}function bf(e,t){return t?`${e}-${t}`:e}function sn(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let r=t[0],n=t.length===3?t[1]:void 0,a=Number(t[t.length-1]);return[r,a,n]}function Hc(e,t,r){let n=k("pad",e,t,r);if(n==="explicit"){n=k("explicitPaddings",e,t,r);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=n[s*2],a[s][1]=n[s*2+1];return a}return n}function Wa(e){return e.kept?e:Or(e)}var _4={};Le(_4,{json:()=>HG});var HG=[{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}]}],z4={};Le(z4,{json:()=>qG});var qG=[{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}]}],O4={};Le(O4,{json:()=>KG});var KG=[{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"}]}],D4={};Le(D4,{json:()=>XG});var XG=[{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"}]}],L4={};Le(L4,{json:()=>ZG});var ZG=[{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"}]}],B4={};Le(B4,{json:()=>YG});var YG=[{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}]}],W4={};Le(W4,{json:()=>JG});var JG=[{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"}]}],V4={};Le(V4,{json:()=>QG});var QG=[{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"}]}],U4={};Le(U4,{json:()=>ej});var ej=[{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"}]}],G4={};Le(G4,{json:()=>tj});var tj=[{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"}]}],j4={};Le(j4,{json:()=>rj});var rj=[{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}]}],H4={};Le(H4,{json:()=>nj});var nj=[{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"}]}],q4={};Le(q4,{json:()=>aj});var aj=[{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}]}],K4={};Le(K4,{json:()=>sj});var sj=[{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"}]}],X4={};Le(X4,{json:()=>ij});var ij=[{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}]}],Z4={};Le(Z4,{json:()=>oj});var oj=[{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"}]}],Y4={};Le(Y4,{json:()=>lj});var lj=[{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}]}],J4={};Le(J4,{json:()=>uj});var uj=[{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"}]}],Q4={};Le(Q4,{json:()=>dj});var dj=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]},{tfOpName:"BroadcastArgs",category:"transformation",inputs:[{start:0,name:"s0",type:"tensor"},{start:1,name:"s1",type:"tensor"}],attrs:[]}],Y3=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[_4,z4,O4,D4,L4,B4,W4,V4,U4,G4,j4,H4,q4,K4,X4,Z4,Y4,J4,Q4],t=[].concat(...e.map(r=>r.json));this.opMappers=t.reduce((r,n)=>(r[n.tfOpName]=n,r),{})}transformGraph(e,t={}){let r=e.node,n=[],a=[],s=[],i=r.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?n.push(f[m.name]):m.op==="Const"?a.push(f[m.name]):(m.input==null||m.input.length===0)&&s.push(f[m.name]),f),{}),o=[],l=[],u={},d={};t!=null&&(u=this.mapSignatureEntries(t.inputs),d=this.mapSignatureEntries(t.outputs));let h=Object.keys(i);h.forEach(f=>{let m=i[f];m.inputNames.forEach((g,y)=>{let[A,,x]=Ca(g),b=i[A];if(b.outputs!=null){let v=b.outputs.indexOf(x);if(v!==-1){let C=`${A}:${v}`;m.inputNames[y]=C}}m.inputs.push(b),b.children.push(m)})}),Object.keys(d).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(d).forEach(f=>{let[m]=Ca(f),g=i[m];g!=null&&(g.signatureKey=d[f],l.push(g))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=Ca(f),g=i[m];g&&(g.signatureKey=u[f],o.push(g))}):o=n;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let c={nodes:i,inputs:o,outputs:l,weights:a,placeholders:n,signature:t,functions:p};return s.length>0&&(c.initNodes=s),c}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,r)=>(t[e[r].name]=r,t),{})}mapNode(e){let t=P4(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let r={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(n=>n.startsWith("^")?n.substr(1):n),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(r.inputParams=t.inputs.reduce((n,a)=>(n[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},n),{})),t.attrs!=null&&(r.attrParams=t.attrs.reduce((n,a)=>{let s=a.type,i;switch(a.type){case"string":i=my(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=my(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=wy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=wy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=yy(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=yy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=vy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=vy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=gy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=gy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=Iy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Iy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=by(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=by(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=ky(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=ky(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=Ay(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Ay(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=xy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=xy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=J3(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=J3(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${a.type} for op: ${e.op}`)}return n[a.name]={value:i,type:s},n},{})),r}mapFunction(e){let t=e.nodeDef,r=[],n=[],a={};t!=null&&(a=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&n.push(u[d.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[d]=Ca(u.name),h={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Ix(u.type),type:"dtype"}},children:[]};h.signatureKey=u.name,s.push(h),a[d]=h}),Object.keys(a).forEach(u=>{let d=a[u];d.inputNames.forEach((h,p)=>{let[c,,f]=Ca(h),m=a[c];if(m.outputs!=null){let g=m.outputs.indexOf(f);if(g!==-1){let y=`${c}:${g}`;d.inputNames[p]=y}}d.inputs.push(m),m.children.push(d)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[d,h]=Ca(o[u.name]),p=a[d];p!=null&&(p.defaultOutput=h,i.push(p))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:n,placeholders:r,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,r)=>(t[r.name]=this.mapArgToTensorInfo(r),t),{}),outputs:e.signature.outputArg.reduce((t,r)=>(t[r.name]=this.mapArgToTensorInfo(r,e.ret),t),{})}}mapArgToTensorInfo(e,t){let r=e.name;return t!=null&&(r=t[r]),{name:r,dtype:e.type}}};function pj(e){let t=J().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 e6(e,t){let r=Array.isArray(e)?String.fromCharCode.apply(null,e):pj(e);return t?r:r.toLowerCase()}function my(e,t,r,n=!1){let a=e[t];return a!=null?e6(a.s,n):r}function gy(e,t,r){let n=e[t];return n?n.b:r}function yy(e,t,r){let n=e[t]||{},a=n.i!=null?n.i:n.f!=null?n.f:r;return typeof a=="number"?a:parseInt(a,10)}function Ix(e){switch(typeof e=="string"&&(e=$4[e]),e){case 1:case 19:return"float32";case 3:case 9:case 6:case 4:return"int32";case 10:return"bool";case 2:return"float32";case 7:return"string";default:return null}}function J3(e,t,r){let n=e[t];return n&&n.func?n.func.name:r}function Ay(e,t,r){let n=e[t];return n&&n.type?Ix(n.type):r}function xy(e,t,r){let n=e[t];return n&&n.list&&n.list.type?n.list.type.map(a=>Ix(a)):r}function t6(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function by(e,t,r){let n=e[t];return n&&n.shape?t6(n.shape):r}function vy(e,t,r){let n=e[t];return n?((n.list.f&&n.list.f.length?n.list.f:n.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):r}function wy(e,t,r,n=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>e6(s,n)):r}function ky(e,t,r){let n=e[t];return n&&n.list&&n.list.shape?n.list.shape.map(a=>t6(a)):r}function Iy(e,t,r){let n=e[t];return n&&n.list&&n.list.b?n.list.b:r}var hj=class{constructor(e,t,r){this.node=e,this.tensorMap=t,this.context=r,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(n=>this.getInput(n)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((n,a)=>(n[a]=this.getAttr(a),n),{}))}getInput(e){return _r(e,this.tensorMap,this.context)}getAttr(e,t){let r=this.node.rawAttrs[e];if(r.tensor!=null)return _r(e,this.tensorMap,this.context);if(r.i!=null||r.f!=null)return yy(this.node.rawAttrs,e,t);if(r.s!=null)return my(this.node.rawAttrs,e,t);if(r.b!=null)return gy(this.node.rawAttrs,e,t);if(r.shape!=null)return by(this.node.rawAttrs,e,t);if(r.type!=null)return Ay(this.node.rawAttrs,e,t);if(r.list!=null){if(r.list.i!=null||r.list.f!=null)return vy(this.node.rawAttrs,e,t);if(r.list.s!=null)return wy(this.node.rawAttrs,e,t);if(r.list.shape!=null)return ky(this.node.rawAttrs,e,t);if(r.list.b!=null)return Iy(this.node.rawAttrs,e,t);if(r.list.type!=null)return xy(this.node.rawAttrs,e,t)}return t}},cj=(e,t,r)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[le(k("a",e,t,r),k("b",e,t,r))];case"AddN":return[im(k("tensors",e,t,r))];case"FloorMod":case"Mod":return[ad(k("a",e,t,r),k("b",e,t,r))];case"Mul":return[L(k("a",e,t,r),k("b",e,t,r))];case"RealDiv":case"Div":return[pe(k("a",e,t,r),k("b",e,t,r))];case"DivNoNan":return[wk(k("a",e,t,r),k("b",e,t,r))];case"FloorDiv":return[sh(k("a",e,t,r),k("b",e,t,r))];case"Sub":return[he(k("a",e,t,r),k("b",e,t,r))];case"Minimum":return[dh(k("a",e,t,r),k("b",e,t,r))];case"Maximum":return[Xa(k("a",e,t,r),k("b",e,t,r))];case"Pow":return[Ps(k("a",e,t,r),k("b",e,t,r))];case"SquaredDifference":return[V2(k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fj=(e,t,r)=>{switch(e.op){case"Abs":case"ComplexAbs":return[er(k("x",e,t,r))];case"Acos":return[Jw(k("x",e,t,r))];case"Acosh":return[Qw(k("x",e,t,r))];case"Asin":return[tk(k("x",e,t,r))];case"Asinh":return[rk(k("x",e,t,r))];case"Atan":return[nk(k("x",e,t,r))];case"Atan2":return[ak(k("x",e,t,r),k("y",e,t,r))];case"Atanh":return[sk(k("x",e,t,r))];case"Ceil":return[ck(k("x",e,t,r))];case"Complex":return[Rs(k("real",e,t,r),k("imag",e,t,r))];case"Cos":return[um(k("x",e,t,r))];case"Cosh":return[w2(k("x",e,t,r))];case"Elu":return[lh(k("x",e,t,r))];case"Erf":return[Ik(k("x",e,t,r))];case"Exp":return[Nn(k("x",e,t,r))];case"Expm1":return[Sk(k("x",e,t,r))];case"Floor":return[uh(k("x",e,t,r))];case"Log":return[En(k("x",e,t,r))];case"Log1p":return[hm(k("x",e,t,r))];case"Imag":return[dm(k("x",e,t,r))];case"Neg":return[zt(k("x",e,t,r))];case"Reciprocal":return[Dk(k("x",e,t,r))];case"Real":return[Ep(k("x",e,t,r))];case"Relu":return[$a(k("x",e,t,r))];case"Round":return[_2(k("x",e,t,r))];case"Selu":return[O2(k("x",e,t,r))];case"Sigmoid":return[Sr(k("x",e,t,r))];case"Sin":return[D2(k("x",e,t,r))];case"Sign":return[Wk(k("x",e,t,r))];case"Sinh":return[L2(k("x",e,t,r))];case"Softplus":return[nd(k("x",e,t,r))];case"Sqrt":return[Cr(k("x",e,t,r))];case"Square":return[yt(k("x",e,t,r))];case"Tanh":return[cu(k("x",e,t,r))];case"Tan":return[Uk(k("x",e,t,r))];case"ClipByValue":return[un(k("x",e,t,r),k("clipValueMin",e,t,r),k("clipValueMax",e,t,r))];case"Relu6":return[P2(k("x",e,t,r))];case"Rsqrt":return[z2(_r(e.inputNames[0],t,r))];case"Prod":return[M2(k("x",e,t,r),k("axes",e,t,r))];case"LeakyRelu":return[pm(k("x",e,t,r),k("alpha",e,t,r))];case"Prelu":return[Am(k("x",e,t,r),k("alpha",e,t,r))];case"IsNan":return[Ck(_r(e.inputNames[0],t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function On(e,t,r=""){if(!(typeof e=="number"||typeof t=="number")){w.assert(e.length===t.length,()=>r+` Shapes ${e} and ${t} must match`);for(let n=0;n<e.length;n++){let a=e[n],s=t[n];w.assert(a<0||s<0||a===s,()=>r+` Shapes ${e} and ${t} must match`)}}}function Q3(e){return!(typeof e=="number"||e.some(t=>t<0))}function sp(e,t,r){let n=Sy(e,r),a=!Q3(n);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${n}`);if(a&&t.forEach(s=>{n=Sy(s.shape,n)}),!Q3(n))throw new Error(`Non-fully-defined elementShape: ${n}`);return n}function Sy(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 r=[];for(let n=0;n<e.length;++n){let a=e[n],s=t[n];if(a>=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);r[n]=a>=0?a:s}return r}var mj=class{constructor(e,t,r,n,a,s,i){this.name=e,this.dtype=t,this.maxSize=r,this.elementShape=n,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=Se(0),hr(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 r=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),On(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),r.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(r.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);r.tensor=t,hr(t),r.written=!0,this.tensors[e]=r}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((r,n)=>this.write(r,t[n]))}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 n=0;n<this.size();n++)e.push(n)}if(e.length===0)return ct([],[0].concat(this.elementShape));let r=this.readMany(e);return On(this.elementShape,r[0].shape,"TensorArray shape mismatch: "),sr(r,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return ct([],[0].concat(this.elementShape));let t=[];for(let n=0;n<this.size();n++)t.push(n);let r=this.readMany(t);return On(this.elementShape,r[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${r[0].shape})`),kt(r,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. ${t.shape[0]}`);let r=Math.max(...e);if(!this.dynamicSize&&r>=this.maxSize)throw new Error(`Max index must be < array size (${r}  vs. ${this.maxSize})`);this.writeMany(e,en(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 r=0,n=e.map(o=>(r+=o,r));if(r!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${r}, 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 a=r===0?0:t.size/r,s=[];K(()=>{t=G(t,[1,r,a]);for(let o=0;o<e.length;++o){let l=o===0?0:n[o-1],u=[0,l,0],d=[1,e[o],a];s[o]=G(Pe(t,u,d),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},wh=class{constructor(e,t,r,n=-1){this.tensors=e,this.elementShape=t,this.elementDtype=r,e!=null&&e.forEach(a=>{if(r!==a.dtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${a.dtype}`);On(t,a.shape,"TensorList shape mismatch: "),hr(a)}),this.idTensor=Se(0),this.maxNumElements=n,hr(this.idTensor)}get id(){return this.idTensor.id}copy(){return new wh([...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,r=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(r!==-1&&this.tensors.length!==r)throw new Error(`Operation expected a list with ${r} elements but got a list with ${this.tensors.length} elements.`);On(e,this.elementShape,"TensorList shape mismatch: ");let n=sp(this.elementShape,this.tensors,e);return K(()=>{let a=this.tensors.map(s=>G(s,n));return sr(a,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 r=sp(this.elementShape,this.tensors,e),n=this.tensors.pop();return On(n.shape,e,"TensorList shape mismatch: "),G(n,r)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(On(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");hr(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,r){if(r!==this.elementDtype)throw new Error(`Invalid data types; op elements ${r}, 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.`);On(this.tensors[e].shape,t,"TensorList shape mismatch: ");let n=sp(this.elementShape,this.tensors,t);return G(this.tensors[e],n)}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.`);On(this.elementShape,t.shape,"TensorList shape mismatch: "),hr(t),this.tensors[e]=t}gather(e,t,r){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);On(this.elementShape,r,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=sp(this.elementShape,this.tensors,r);return e.length===0?ct([],[0].concat(n)):K(()=>{let a=e.map(s=>G(this.tensors[s],n));return sr(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);On(this.elementShape,t,"TensorList shape mismatch: ");let r=sp(this.elementShape,this.tensors,t);return this.size()===0?ct([],[0].concat(r)):K(()=>{let n=this.tensors.map(a=>G(a,r));return kt(n,0)})}};function gj(e,t,r){let n=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!==r)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${r}`);let a=e.shape.slice(1);On(a,t,"TensorList shape mismatch: ");let s=en(e);return new wh(s,t,n)}function yj(e,t,r){return new wh([],e,t,r)}function Aj(e,t,r,n){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(n!=null&&n!==-1&&a>=n)throw new Error(`Max index must be < array size (${a}  vs. ${n})`);let s=new wh([],r,e.dtype,n),i=en(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function xj(e,t,r){let n=0,a=t.map(d=>(n+=d,n));if(n!==e.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: ${e.shape}`);let s=e.shape.slice(1),i=Sy(s,r),o=n===0?0:e.size/n,l=K(()=>{let d=[];e=G(e,[1,n,o]);for(let h=0;h<t.length;++h){let p=h===0?0:a[h-1],c=[0,p,0],f=[1,t[h],o];d[h]=G(Pe(e,c,f),i)}return e.dispose(),d}),u=new wh([],r,e.dtype,t.length);for(let d=0;d<l.length;d++)u.setItem(d,l[d]);return u}var bj=async(e,t,r)=>{switch(e.op){case"If":case"StatelessIf":{let n=k("thenBranch",e,t,r),a=k("elseBranch",e,t,r),s=k("cond",e,t,r),i=k("args",e,t,r);return(await s.data())[0]?r.functionMap[n].executeFunctionAsync(i,r.tensorArrayMap,r.tensorListMap):r.functionMap[a].executeFunctionAsync(i,r.tensorArrayMap,r.tensorListMap)}case"While":case"StatelessWhile":{let n=k("body",e,t,r),a=k("cond",e,t,r),s=k("args",e,t,r),i=await r.functionMap[a].executeFunctionAsync(s,r.tensorArrayMap,r.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 r.functionMap[n].executeFunctionAsync(u,r.tensorArrayMap,r.tensorListMap);let h=u.map(c=>c.id);d.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&h.indexOf(c.id)===-1&&c.dispose()});let p=await r.functionMap[a].executeFunctionAsync(u,r.tensorArrayMap,r.tensorListMap);l=await p[0].data(),p.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&h.indexOf(c.id)===-1&&c.dispose()})}return u}case"LoopCond":{let n=k("pred",e,t,r);return[Wa(n)]}case"Switch":{let n=k("pred",e,t,r),a=k("data",e,t,r);return a.kept||(a=Wa(a)),(await n.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let n=e.inputNames.find(a=>_r(a,t,r)!==void 0);if(n){let a=_r(n,t,r);return[Wa(a)]}return}case"Enter":{let n=k("frameName",e,t,r),a=k("tensor",e,t,r);return r.enterFrame(n),[Wa(a)]}case"Exit":{let n=k("tensor",e,t,r);return r.exitFrame(),[Wa(n)]}case"NextIteration":{let n=k("tensor",e,t,r);return r.nextIteration(),[Wa(n)]}case"TensorArrayV3":{let n=k("size",e,t,r),a=k("dtype",e,t,r),s=k("elementShape",e,t,r),i=k("dynamicSize",e,t,r),o=k("clearAfterRead",e,t,r),l=k("identicalElementShapes",e,t,r),u=k("name",e,t,r),d=new mj(u,a,n,s,l,i,o);return r.addTensorArray(d),[d.idTensor,Se(1)]}case"TensorArrayWriteV3":{let n=k("tensorArrayId",e,t,r),a=k("index",e,t,r),s=k("tensor",e,t,r),i=r.getTensorArray(n.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let n=k("tensorArrayId",e,t,r),a=k("index",e,t,r);return[r.getTensorArray(n.id).read(a)]}case"TensorArrayGatherV3":{let n=k("tensorArrayId",e,t,r),a=k("indices",e,t,r),s=k("dtype",e,t,r);return[r.getTensorArray(n.id).gather(a,s)]}case"TensorArrayScatterV3":{let n=k("tensorArrayId",e,t,r),a=k("indices",e,t,r),s=k("tensor",e,t,r),i=r.getTensorArray(n.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=k("tensorArrayId",e,t,r),a=r.getTensorArray(n.id),s=k("dtype",e,t,r);return[a.concat(s)]}case"TensorArraySplitV3":{let n=k("tensorArrayId",e,t,r),a=k("tensor",e,t,r),s=k("lengths",e,t,r),i=r.getTensorArray(n.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let n=k("tensorArrayId",e,t,r),a=r.getTensorArray(n.id);return[Se(a.size(),"int32")]}case"TensorArrayCloseV3":{let n=k("tensorArrayId",e,t,r),a=r.getTensorArray(n.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let n=k("tensorListId",e,t,r),a=k("index",e,t,r),s=k("tensor",e,t,r),i=r.getTensorList(n.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let n=k("tensorListId",e,t,r),a=k("index",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=k("indices",e,t,r),a=k("tensor",e,t,r),s=k("elementShape",e,t,r),i=k("numElements",e,t,r),o=Aj(a,n,s,i);return r.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=k("elementShape",e,t,r),a=k("elementDType",e,t,r),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,r),o=yj(n,a,i);return r.addTensorList(o),[o.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,r),a=k("indices",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).gather(a,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=k("numElements",e,t,r);return[r.getTensorList(n.id).stack(a,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=gj(n,a,s);return r.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id),s=k("dtype",e,t,r),i=k("elementShape",e,t,r);return[a.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,r),a=k("tensor",e,t,r),s=r.getTensorList(n.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r);return[r.getTensorList(n.id).popBack(a,s)]}case"TensorListSplit":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("lengths",e,t,r),i=xj(n,s,a);return r.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ev(e,t,r){let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=!s,o=a==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,r);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=k("strides",e,t,r),h=Hc(e,t,r),p=k("dataFormat",e,t,r).toUpperCase(),c=k("dilations",e,t,r),[f,m]=k("args",e,t,r);i&&(m=f,f=void 0);let g=k("leakyreluAlpha",e,t,r);return{stride:d,pad:h,dataFormat:p,dilations:c,biasArg:f,preluArg:m,activationFunc:a,leakyreluAlpha:g}}var vj=(e,t,r)=>{switch(e.op){case"Conv1D":{let n=k("stride",e,t,r),a=k("pad",e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilation",e,t,r);return[A2(k("x",e,t,r),k("filter",e,t,r),n,a,s,i)]}case"Conv2D":{let n=k("strides",e,t,r),a=Hc(e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilations",e,t,r);return[Fs(k("x",e,t,r),k("filter",e,t,r),[n[1],n[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=ev(e,t,r);return[_s.conv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=ev(e,t,r);return[_s.depthwiseConv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=k("outputShape",e,t,r),a=k("strides",e,t,r),s=Hc(e,t,r);return[b2(k("x",e,t,r),k("filter",e,t,r),n,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=k("strides",e,t,r),a=Hc(e,t,r),s=k("dilations",e,t,r),i=k("dataFormat",e,t,r).toUpperCase();return[oh(k("input",e,t,r),k("filter",e,t,r),[n[1],n[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilations",e,t,r);return[v2(k("x",e,t,r),k("filter",e,t,r),[n[1],n[2],n[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[om(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a)]}case"MaxPool":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[mm(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a)]}case"MaxPoolWithArgmax":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r),i=k("includeBatchInIndex",e,t,r),{result:o,indexes:l}=Pk(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a,i);return[o,l]}case"AvgPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[g2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"MaxPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[R2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"Dilation2D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dilations",e,t,r),i=n[1],o=n[2],l=s[1],u=s[2];return[vk(k("x",e,t,r),k("filter",e,t,r),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wj=(e,t,r)=>{switch(e.op){case"Fill":{let n=k("shape",e,t,r),a=k("dtype",e,t,r),s=k("value",e,t,r);return[rd(n,s,a)]}case"LinSpace":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("num",e,t,r);return[Tk(n,a,s)]}case"Multinomial":{let n=k("logits",e,t,r),a=k("numSamples",e,t,r),s=k("seed",e,t,r);return[zk(n,a,s)]}case"OneHot":{let n=k("indices",e,t,r),a=k("depth",e,t,r),s=k("onValue",e,t,r),i=k("offValue",e,t,r);return[Np(n,a,s,i)]}case"Ones":return[ln(k("shape",e,t,r),k("dtype",e,t,r))];case"OnesLike":return[Rn(k("x",e,t,r))];case"RandomUniform":return[sd(k("shape",e,t,r),k("minval",e,t,r),k("maxval",e,t,r),k("dtype",e,t,r))];case"Range":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("step",e,t,r);return[yu(n,a,s,k("dtype",e,t,r))]}case"TruncatedNormal":{let n=k("shape",e,t,r),a=k("mean",e,t,r),s=k("stdDev",e,t,r),i=k("seed",e,t,r);return[wm(n,a,s,k("dtype",e,t,r),i)]}case"Zeros":return[Wt(k("shape",e,t,r),k("dtype",e,t,r))];case"ZerosLike":return[at(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function L1(e,t,r){let n=k("boxes",e,t,r),a=k("scores",e,t,r),s=k("maxOutputSize",e,t,r),i=k("iouThreshold",e,t,r),o=k("scoreThreshold",e,t,r),l=k("softNmsSigma",e,t,r);return{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var kj=async(e,t,r)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=L1(e,t,r),u=await Ie.nonMaxSuppressionWithScoreAsync(n,a,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L1(e,t,r),l=k("padToMaxOutputSize",e,t,r),u=await Ie.nonMaxSuppressionPaddedAsync(n,a,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L1(e,t,r);return[await Ie.nonMaxSuppressionAsync(n,a,s,i,o)]}case"Where":{let n=me(k("condition",e,t,r),"bool"),a=[await U2(n)];return n.dispose(),a}case"ListDiff":return Bk(k("x",e,t,r),k("y",e,t,r));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ij=(e,t,r)=>{switch(e.op){case"TopKV2":{let n=k("x",e,t,r),a=k("k",e,t,r),s=k("sorted",e,t,r),i=Gk(n,a,s);return[i.values,i.indices]}case"Unique":{let n=k("x",e,t,r),a=ny(n);return[a.values,a.indices]}case"UniqueV2":{let n=k("x",e,t,r),a=k("axis",e,t,r),s=ny(n,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Sj=(e,t,r)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let n=k("default",e,t,r);return[_r(e.name,t,r)||n];case"Placeholder":return[_r(e.name,t,r)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=k("x",e,t,r);return[Wa(u)]}case"IdentityN":return k("x",e,t,r).map(u=>Wa(u));case"Snapshot":let a=k("x",e,t,r);return[Wa(a)];case"Shape":return[St(k("x",e,t,r).shape,"int32")];case"ShapeN":return k("x",e,t,r).map(u=>St(u.shape));case"Size":return[Se(k("x",e,t,r).size,"int32")];case"Rank":return[Se(k("x",e,t,r).rank,"int32")];case"NoOp":return[Se(1)];case"Print":let s=k("x",e,t,r),i=k("data",e,t,r),o=k("message",e,t,r),l=k("summarize",e,t,r);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`)}},Cj=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Se(0),this.tensorMap=new Map,hr(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 Se(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),K(()=>{let n=en(t),a=r.length,s=n.length;w.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=r[i],l=n[i];hr(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return K(()=>{let n=[];for(let a=0;a<r.length;a++){let s=r[a],i=this.findWithDefault(s,t);n.push(i)}return sr(n)})}findWithDefault(e,t){let r=this.tensorMap.get(e);return r!=null?r: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}`)}},Tj=async(e,t,r,n)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,r),s=k("valueDType",e,t,r),i=new Cj(a,s);return n.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,r,n),s=k("keys",e,t,r),i=k("values",e,t,r);return[await n.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,r,n),s=k("keys",e,t,r),i=k("defaultValue",e,t,r);return[await n.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=k("tableHandle",e,t,r,n);return[n.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nj=(e,t,r)=>{switch(e.op){case"ResizeBilinear":{let n=k("images",e,t,r),a=k("size",e,t,r),s=k("alignCorners",e,t,r),i=k("halfPixelCenters",e,t,r);return[Ie.resizeBilinear(n,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let n=k("images",e,t,r),a=k("size",e,t,r),s=k("alignCorners",e,t,r),i=k("halfPixelCenters",e,t,r);return[Ie.resizeNearestNeighbor(n,[a[0],a[1]],s,i)]}case"CropAndResize":{let n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ej=(e,t,r)=>{switch(e.op){case"Equal":return[Tn(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[gu(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[hn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[xl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[S2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[bl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[pa(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[fm(k("a",e,t,r))];case"LogicalOr":return[E2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Lr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[kk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);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]=k("args",e,t,r);return[_s.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[fu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[fu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Nk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[id(k("x",e,t,r))];case"LogSoftmax":return[C2(k("x",e,t,r))];case"SparseToDense":return[j2(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[fr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[$s(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[f2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[pf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Cn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[ek(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[M2(k("x",e,t,r),i,o)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[k2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[y2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[xk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[mu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[mu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[Mn(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[Mn(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[Vk(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[sr(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return en(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Dn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[Xk(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Zk(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[j2(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=up.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=up.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[up.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[up.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_j=(e,t,r)=>{switch(e.op){case"FFT":return[bm(k("x",e,t,r))];case"IFFT":return[Rp(k("x",e,t,r))];case"RFFT":return[vm(k("x",e,t,r))];case"IRFFT":return[W2(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zj=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=jc.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=jc.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[jc.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oj=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[Ht(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[et(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[_k(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Vn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[ym(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[lm(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[bk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[Ap(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[hk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function tv(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>cj(s,i,o));case"basic_math":return K(()=>fj(s,i,o));case"control":return bj(s,i,o);case"convolution":return K(()=>vj(s,i,o));case"creation":return K(()=>wj(s,i,o));case"dynamic":return kj(s,i,o);case"evaluation":return K(()=>Ij(s,i,o));case"image":return K(()=>Nj(s,i,o));case"graph":return K(()=>Sj(s,i,o));case"logical":return K(()=>Ej(s,i,o));case"matrices":return K(()=>Rj(s,i,o));case"normalization":return K(()=>Mj(s,i,o));case"reduction":return K(()=>Fj(s,i,o));case"slice_join":return K(()=>$j(s,i,o));case"sparse":return K(()=>Pj(s,i,o));case"spectral":return K(()=>_j(s,i,o));case"string":return K(()=>zj(s,i,o));case"transformation":return K(()=>Oj(s,i,o));case"hash_table":return Tj(s,i,o,n);case"custom":let l=P4(s.op);if(l&&l.customExecutor)return l.customExecutor(new hj(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,r);return w.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var rv=class{constructor(e={},t={},r={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=n,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 r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}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 nv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>sn(p)[0]),d=[];n!=null&&(d=n.map(p=>sn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((r6(p)||Vj(p)||Uj(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Dj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>sn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.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(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Lj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Bj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Wj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function r6(e){return Lj.indexOf(e.op)>=0}function Vj(e){return Bj.indexOf(e.op)>=0}function Uj(e){return Wj.indexOf(e.op)>=0}var Cy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new Cy(e.functions[r],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(r=>e[r].map(n=>n.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 r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=nv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return Dj(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=r.map(d=>this.graph.nodes[sn(d)[0]]),a=t.map(d=>sn(d)[0]),s=a.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return K(()=>{let d=new rv(this.weightMap,l,u,this.functionExecutorMap),h={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=sn(f),y=[];y[g]=e[f],h[m]=y});let p=this.getFrozenTensorIds(h),c={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let g=tv(m,h,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=g,this.checkTensorForDisposal(m.name,m,h,d,p,a,c)}}return this.parent==null&&d.dispose(p),t.map(f=>_r(f,h,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,r,n,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[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=jG(o.name,r,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!a.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[h,p]=Ca(t.name,n);this.intermediateTensors[h]?this.intermediateTensors[h][p]=u:(this.intermediateTensors[h]=[],this.intermediateTensors[h][p]=u)}delete i[u.id]}else d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,r=!1,n={},a={}){r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=J().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new rv(this.weightMap,n,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(u=>_r(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let n=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(n,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,n){let a=Object.keys(e),s=a.map(A=>this.graph.nodes[sn(A)[0]]),i=r.map(A=>sn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:h}=nv(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=sn(A),v=[];v[b]=e[A],c[x]=v});let f={},m=this.getFrozenTensorIds(c),g={};for(;p.length>0;){let A=this.processStack(s,p,t,c,g,m,i,f,l);await Promise.all(A)}d==null&&!n&&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=>!r6(A)&&!_r(A.name,c,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 [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${A}`)}return c}processStack(e,t,r,n,a,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();r.currentContext=d.contexts;let h="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,r)&&([h]=Ca(d.node.name,r)),n[d.node.name]==null){let p=tv(d.node,n,r,this._resourceManager);h||([h]=Ca(d.node.name,r));let c=r.currentContext;w.isPromise(p)?u.push(p.then(f=>(n[h]=f,r.currentContext=c,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l),f))):(n[h]=p,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l))}else this.processChildNodes(d.node,t,r,n,a,l)}return u}processChildNodes(e,t,r,n,a,s){e.children.forEach(i=>{let[o]=Ca(i.name,r);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})):i.inputNames.every(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let r=e[t],[n]=sn(t),a=this.graph.nodes[n];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===r.shape.length&&r.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${r.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&w.assert(r.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(e){let t={};for(let r in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let n=this._signature.inputs[r];t[n.name]=e[r]}else t[r]=e[r];return t}checkInputs(e){let t=Object.keys(e).filter(r=>{let[n]=sn(r);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[r]=sn(t);if(!this.graph.nodes[r])throw new Error(`The output '${t}' is not found in the graph`)})}},Gj=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},jj="?tfjs-format=file",Hj="model.json",t0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Gj}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ir.browserHTTPRequest(e,this.loadOptions);else{let t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cy(Y3.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Y3.Instance.transformGraph(e.modelInitializer);this.initializer=new Cy(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Ir.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[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 rt)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,r,n)=>(t[r]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function qj(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Hj}${jj}`);let r=new t0(e,t);return await r.load(),r}var Kj="0.0.0",n6={};Le(n6,{CSVDataset:()=>m6,Dataset:()=>dd,FileDataSource:()=>w6,TextLineDataset:()=>f6,URLDataSource:()=>k6,array:()=>yH,csv:()=>NH,func:()=>EH,generator:()=>RH,microphone:()=>FH,version_data:()=>$H,webcam:()=>MH,zip:()=>AH});var Xj=Eo(Mf()),Zj=Eo(Mf());function Yj(e,t){return vf(e,t)}function vf(e,t,r=new Map,n=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(n.has(e))throw new Error("Circular references are not supported.");if(r.has(e))return r.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(bu(e)){let s=Array.isArray(e)?[]:{};n.add(e);for(let i in e){let o=e[i],l=vf(o,t,r,n);s[i]=l}return n.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return r.set(e,a.value),a.value}function Jj(e,t=s6){return a6(e,t)}function a6(e,t,r=new Set){let n=e[0];if(r.has(n))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(bu(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let i in n){let o=e.map(u=>u[i]),l=a6(o,t,r);s[i]=l}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return a.value}function s6(e){return e===null?null:bu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function i6(e,t){let r=new Map;vf(e,t,r);for(let n of Array.from(r.keys())){let a=r.get(n);if(w.isPromise(a)){let s=await a;r.set(n,s)}}return vf(e,t,r)}function bu(e){let t=!1;if(J().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:r}=Gv();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof rt)&&!(e instanceof Promise)&&!t)}function Qj(e){return e==null||eH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof rt||w.isTypedArray(e)}function eH(e){return e===null||typeof e!="object"&&typeof e!="function"}function tH(e){return Yj(e,rH)}function rH(e){return e instanceof rt?{value:e.clone(),recurse:!1}:bu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var o6=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),r=this.get(t);return this.set(t,this.pop()),r}},l6=class extends o6{constructor(){super(l6.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),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},u6=l6;u6.INITIAL_CAPACITY=32;function d6(e){return new sH(e)}function Sx(e){return new iH(e)}function nH(e,t){return new p6(e,t)}function aH(e,t=h6.FAIL){return new mH(e,t)}var gr=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=[],r=await e.next();for(;!r.done;)t.push(r.value),r=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(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new cH(this,e)}filter(e){return new pH(this,e)}map(e){return new hH(this,e)}mapAsync(e){return new av(this,e)}serialMapAsync(e){return new av(this,e).serial()}flatmap(e){return new fH(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 dH(this,e,t)}columnMajorBatch(e,t=!0,r=s6){return this.rowMajorBatch(e,t).map(n=>Jj(n,r))}concatenate(e,t){return new p6(d6([this,e]),t)}take(e){return e<0||e==null?this:new uH(this,e)}skip(e){return e<0||e==null?this:new lH(this,e)}prefetch(e){return new c6(this,e)}shuffle(e,t){return new gH(this,e,t)}serial(){return new oH(this)}},sH=class extends gr{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:tH(e),done:!1}}},iH=class extends gr{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: ${e.message}`,e}}},oH=class extends gr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},lH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},uH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},dH=class extends gr{constructor(e,t,r=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},pH=class extends gr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;re(e.value)}}},hH=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},cH=class extends gr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},av=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Cx=class extends gr{constructor(){super();this.outputQueue=new u6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},fH=class extends Cx{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return!0}},p6=class extends gr{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 r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.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}},h6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(h6||{}),mH=class extends gr{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof gr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await i6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},c6=class extends gr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new o6(t)}summary(){return`${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()}},gH=class extends c6{constructor(e,t,r){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Zj.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},dd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),an(async()=>(await r.iterator()).columnMajorBatch(e,t,xH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,an(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,an(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return an(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return an(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return an(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,an(async()=>{let n=Sx(async()=>({value:await t.iterator(),done:!1}));return nH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,an(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=Xj.alea(t||w.now().toString());return an(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,an(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};dd.MAX_BUFFER_SIZE=1e4;function an(e,t=null){return new class extends dd{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function yH(e){return an(async()=>d6(e),e.length)}function AH(e){if(!bu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return an(async()=>{let r=await i6(e,n=>{if(n instanceof dd)return{value:n.iterator(),recurse:!1};if(bu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return aH(r,1)},t)}function xH(e){if(e===null)return null;let t=e[0];return Qj(t)?{value:bH(e),recurse:!1}:{value:null,recurse:!0}}function bH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?sr(e):ct(e)}var f6=class extends dd{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))}},Dc='"',ip=Symbol("out"),sv=Symbol("field"),Lc=Symbol("quote"),B1=Symbol("quoteafterquote"),iv=Symbol("quoteinquote"),m6=class extends dd{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 f6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" 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),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],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?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=ip;for(let i=0;i<a;i++)switch(s){case ip:switch(e.charAt(i)){case Dc:n=i+1,s=Lc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=ip;break;default:s=sv,n=i;break}break;case sv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=ip,n=i+1;break;default:}break;case Lc:switch(e.charAt(i)){case Dc:s=B1;break;default:}break;case B1:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=ip,n=i+1;break;case Dc:s=Lc;break;default:s=iv;break}break;case iv:switch(e.charAt(i)){case Dc:s=Lc;break;default:}break;default:}if(s===B1?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},g6=class extends gr{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(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new g6(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(r){throw new Error(`Error thrown while initializing video stream: ${r.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,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({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,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},y6=class extends gr{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=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=oa([s,a,o,i],[1,4])}else this.cropBox=oa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().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 r=new y6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$n.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},A6=class{},x6=class extends gr{split(e){return new vH(this,e)}},vH=class extends x6{constructor(e,t){super();this.upstream=e,this.impl=new wH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wH=class extends Cx{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},kH=class extends gr{decodeUTF8(){return new IH(this)}},IH=class extends x6{constructor(e){super();this.upstream=e,this.impl=new SH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SH=class extends Cx{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Gv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return J().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},b6=class extends kH{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(J().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 r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function CH(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=TH(e));let s=await(r||w.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new b6(i,t)}else throw new Error(s.statusText)}var TH=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 v6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var w6=class extends A6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(v6(this.input)&&J().get("IS_NODE")){let e=Gy();this.input=e.readFileSync(this.input.substr(7))}return new b6(this.input,this.options)}},k6=class extends A6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return v6(this.url)?new w6(this.url,this.fileOptions).iterator():CH(this.url,this.fileOptions)}};function NH(e,t={}){return new m6(new k6(e),t)}function EH(e){let t=Sx(e);return an(async()=>t)}function RH(e){return an(async()=>{let t=await e();return Sx(()=>t.next())})}async function MH(e,t){return y6.create(e,t)}async function FH(e){return g6.create(e)}var $H="0.0.0";function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var PH=Un.whereImpl,I6=class extends Su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new zp(this,kr())}nextDataId(){return I6.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&N.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 n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:r,refCount:1}),n}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,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,r,n,a){this.data.set(e,{values:t,dtype:n,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:r}=this.data.get(e);if(t==="complex64"){let n=this.readSync(r.real.dataId),a=this.readSync(r.imag.dataId);return N.mergeRealAndImagArrays(n,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}makeOutput(e,t,r){let n=this.write(e,t,r);return kr().makeTensorFromDataId(n,t,r,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:r}=this.data.get(e);r!=null&&(this.disposeData(r.real.dataId,!0),this.disposeData(r.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Te([e],"where");let t=this.readSync(e.dataId);return PH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},Tx=I6;Tx.nextDataId=0;var r0={};Le(r0,{addImpl:()=>C6,bincountImpl:()=>Ex,bincountReduceImpl:()=>T6,ceilImpl:()=>N6,concatImpl:()=>Rx,equalImpl:()=>E6,expImpl:()=>M6,expm1Impl:()=>$6,floorImpl:()=>P6,gatherNdImpl:()=>_6,gatherV2Impl:()=>z6,greaterEqualImpl:()=>D6,greaterImpl:()=>O6,lessEqualImpl:()=>B6,lessImpl:()=>L6,linSpaceImpl:()=>W6,logImpl:()=>V6,maxImpl:()=>U6,maximumImpl:()=>G6,minimumImpl:()=>j6,multiplyImpl:()=>Mx,negImpl:()=>H6,notEqualImpl:()=>q6,prodImpl:()=>K6,rangeImpl:()=>$x,rsqrtImpl:()=>X6,sigmoidImpl:()=>wq,simpleAbsImpl:()=>S6,sliceImpl:()=>kf,sparseFillEmptyRowsImpl:()=>Y6,sparseReshapeImpl:()=>J6,sparseSegmentReductionImpl:()=>Px,sqrtImpl:()=>Sq,squaredDifferenceImpl:()=>Q6,stridedSliceImpl:()=>eI,stringNGramsImpl:()=>tI,stringSplitImpl:()=>rI,stringToHashBucketFastImpl:()=>nI,subImpl:()=>aI,tileImpl:()=>sI,topKImpl:()=>oI,transposeImpl:()=>Fx,uniqueImpl:()=>lI});function S6(e){let t=new Float32Array(e.length);for(let r=0;r<e.length;++r)t[r]=Math.abs(e[r]);return t}var _H=e=>{let{x:t}=e.inputs,r=e.backend;Te(t,"abs");let n=new Float32Array(w.sizeFromShape(t.shape)),a=r.data.get(t.dataId).values;return n=S6(a),r.makeOutput(n,t.shape,t.dtype)},zH={kernelName:Mo,backendName:"cpu",kernelFunc:_H};function Zt(e){return(t,r,n,a,s)=>{let i=N.assertAndGetBroadcastShape(t,r),o=i.length,l=w.computeStrides(i),u=w.sizeFromShape(i),d=w.getTypedArrayFromDType(s,u),h=t.length,p=r.length,c=w.computeStrides(t),f=w.computeStrides(r),m=N.getBroadcastDims(t,i),g=N.getBroadcastDims(r,i);if(m.length+g.length===0)for(let y=0;y<d.length;++y)d[y]=e(n[y%n.length],a[y%a.length]);else for(let y=0;y<d.length;++y){let A=w.indexToLoc(y,o,l),x=A.slice(-h);m.forEach(S=>x[S]=0);let b=w.locToIndex(x,h,c),v=A.slice(-p);g.forEach(S=>v[S]=0);let C=w.locToIndex(v,p,f);d[y]=e(n[b],a[C])}return[d,i]}}function on(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=r.makeTensorInfo(n.shape,"complex64"),l=r.data.get(o.dataId);return l.complexTensorInfos={real:r.makeTensorInfo(n.shape,"float32",s),imag:r.makeTensorInfo(a.shape,"float32",i)},o}var OH={kernelName:Dp,backendName:"cpu",kernelFunc:on};function wf(e,t,r="float32"){if(r==="complex64"){let a=wf(e,t,"float32"),s=wf(e,t,"float32");return on({inputs:{real:a,imag:s},backend:e})}let n=w.makeZerosTypedArray(w.sizeFromShape(t),r);return e.makeTensorInfo(t,r,n)}function Ma(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var DH={kernelName:ii,backendName:"cpu",kernelFunc:Ma};function Io(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.real,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var LH={kernelName:qp,backendName:"cpu",kernelFunc:Io};function Bs(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Ma({inputs:{x:a},backend:r});let i=wf(r,a.shape,a.dtype),o=Bs({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=on({inputs:{real:o,imag:i},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Io({inputs:{input:a},backend:r}),o=Bs({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Ma({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=r.data.get(a.dataId).values,o=Int32Array.from(i);return r.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=r.data.get(a.dataId).values,o=w.toTypedArray([0],a.dtype),[l,u]=Zt((d,h)=>d!==h?1:0)(a.shape,[],i,o,"bool");return r.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var BH={kernelName:Hs,backendName:"cpu",kernelFunc:Bs};function yr(e,t,r,n){return r==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;Te([i,o],e);let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,h=i.dtype==="string"?N.fromUint8ToStringArray(u):u,p=i.dtype==="string"?N.fromUint8ToStringArray(d):d,c=n||i.dtype,[f,m]=t(i.shape,o.shape,h,p,c);return l.makeTensorInfo(m,c,f)}:({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=Bs({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),d=l.data.get(u.dataId),h=d.complexTensorInfos.real,p=d.complexTensorInfos.imag,c=l.data.get(h.dataId).values,f=l.data.get(p.dataId).values,m=Bs({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,b=l.data.get(A.dataId).values,[v,C,S]=r(i.shape,o.shape,c,f,x,b),E=l.makeTensorInfo(S,"float32",v),R=l.makeTensorInfo(S,"float32",C),_=on({inputs:{real:E,imag:R},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(E),l.disposeIntermediateTensorInfo(R),_}else{let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,h=n||i.dtype,[p,c]=t(i.shape,o.shape,u,d,h);return l.makeTensorInfo(c,h,p)}}}function Nx(e){return(t,r,n,a,s,i)=>{let o=N.assertAndGetBroadcastShape(t,r),l=w.sizeFromShape(o),u=o.length,d=w.computeStrides(o),h=w.getTypedArrayFromDType("float32",l),p=w.getTypedArrayFromDType("float32",l),c=N.getBroadcastDims(t,o),f=N.getBroadcastDims(r,o),m=N.mergeRealAndImagArrays(n,a),g=N.mergeRealAndImagArrays(s,i),y=t.length,A=w.computeStrides(t),x=r.length,b=w.computeStrides(r);if(c.length+f.length===0)for(let v=0;v<h.length;v++){let C=v%m.length,S=v%g.length,E=e(m[C*2],m[C*2+1],g[S*2],g[S*2+1]);h[v]=E.real,p[v]=E.imag}else for(let v=0;v<h.length;v++){let C=w.indexToLoc(v,u,d),S=C.slice(-y);c.forEach(I=>S[I]=0);let E=w.locToIndex(S,y,A),R=C.slice(-x);f.forEach(I=>R[I]=0);let _=w.locToIndex(R,x,b),M=e(m[E*2],m[E*2+1],g[_*2],g[_*2+1]);h[v]=M.real,p[v]=M.imag}return[h,p,o]}}var C6=Zt((e,t)=>e+t),WH=Nx((e,t,r,n)=>({real:e+r,imag:t+n})),kh=yr(Ha,C6,WH),VH={kernelName:Ha,backendName:"cpu",kernelFunc:kh};function Ex(e,t,r,n,a){let s=w.sizeFromShape(n),i=w.makeZerosTypedArray(a,r);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>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function T6(e,t,r,n=!1){let a=e.shape[0],s=e.shape[1],i=We([a,r],t.dtype);for(let o=0;o<a;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>=r||(n?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 zi(e){return(t,r,n)=>{let a=w.getTypedArrayFromDType(r,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],n);return a}}function mt(e,t,r){return({inputs:n,attrs:a,backend:s})=>{let{x:i}=n;if(Te(i,e),i.dtype==="string"||r==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=w.sizeFromShape(i.shape),d=r||i.dtype,h=w.getArrayFromDType(d,u);for(let p=0;p<u;++p)h[p]=t(l[p],a);return o.makeTensorInfo(i.shape,d,h)}}function pd(e,t,r){return({inputs:n,attrs:a,backend:s})=>{let{x:i}=n;if(Te(i,e),i.dtype==="string"||r==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=r||i.dtype,d=t(l,u,a);return o.makeTensorInfo(i.shape,u,d)}}var N6=zi(e=>Math.ceil(e)),UH=pd(qs,N6),GH={kernelName:qs,backendName:"cpu",kernelFunc:UH};function Rx(e,t,r,n){let a=w.getArrayFromDType(r,w.sizeFromShape(t));if(n&&r!=="string"){let s=0;e.forEach(i=>{let o=w.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=r==="string"?N.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;u<i.shape[0];++u){let d=u*t[1]+s;for(let h=0;h<i.shape[1];++h)a[d+h]=o[l++]}s+=i.shape[1]})}return a}var E6=Zt((e,t)=>e===t?1:0),R6=yr(Oo,E6,null,"bool"),jH={kernelName:Oo,backendName:"cpu",kernelFunc:R6},M6=zi(e=>Math.exp(e)),F6=pd(ti,M6,"float32"),HH={kernelName:ti,backendName:"cpu",kernelFunc:F6},$6=zi(e=>Math.expm1(e)),qH=pd(Lo,$6),KH={kernelName:Lo,backendName:"cpu",kernelFunc:qH},P6=zi(e=>Math.floor(e)),XH=pd(ri,P6),ZH={kernelName:ri,backendName:"cpu",kernelFunc:XH};function _6(e,t,r,n,a,s,i,o,l){let u=We([n,s],r);for(let d=0;d<n;d++){let h=[],p=0;for(let c=0;c<a;c++){let f=e[d*a+c];p+=f*i[c],h.push(f)}if(p<0||p>=l/s)throw new Error(`Invalid indices: ${h} does not index into ${o}`);for(let c=0;c<s;c++)u.values[d*s+c]=t.get(...t.indexToLoc(p*s+c))}return u}function z6(e,t,r){let n=We(r,e.dtype);for(let a=0;a<n.size;++a){let s=n.indexToLoc(a).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);0<=u&&u<e.values.length&&(n.values[a]=e.values[u])}return n}var O6=Zt((e,t)=>e>t?1:0),YH=yr(Uo,O6,null,"bool"),JH={kernelName:Uo,backendName:"cpu",kernelFunc:YH},D6=Zt((e,t)=>e>=t?1:0),QH=yr(si,D6,null,"bool"),eq={kernelName:si,backendName:"cpu",kernelFunc:QH},L6=Zt((e,t)=>e<t?1:0),tq=yr(Go,L6,null,"bool"),rq={kernelName:Go,backendName:"cpu",kernelFunc:tq},B6=Zt((e,t)=>e<=t?1:0),nq=yr(jo,B6,null,"bool"),aq={kernelName:jo,backendName:"cpu",kernelFunc:nq};function W6(e,t,r){let n=(t-e)/(r-1),a=w.makeZerosTypedArray(r,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+n;return a}var V6=zi(e=>Math.log(e)),sq=pd(li,V6),iq={kernelName:li,backendName:"cpu",kernelFunc:sq};function U6(e,t,r,n){let a=w.getTypedArrayFromDType(n,w.sizeFromShape(r));for(let s=0;s<a.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)}a[s]=o}return a}var G6=Zt((e,t)=>Math.max(e,t)),oq=yr(di,G6),lq={kernelName:di,backendName:"cpu",kernelFunc:oq},j6=Zt((e,t)=>Math.min(e,t)),uq=yr(fi,j6),dq={kernelName:fi,backendName:"cpu",kernelFunc:uq},Mx=Zt((e,t)=>e*t),pq=Nx((e,t,r,n)=>({real:e*r-t*n,imag:e*n+t*r})),n0=yr(gi,Mx,pq),hq={kernelName:gi,backendName:"cpu",kernelFunc:n0};function H6(e,t,r){let n=w.createScalarValue(-1,r);return Mx([],t,n,e,r)}function cq(e){let{inputs:t,backend:r}=e,{x:n}=t;Te(n,"neg");let a=r.data.get(n.dataId).values,[s,i]=H6(a,n.shape,n.dtype);return r.makeTensorInfo(i,n.dtype,s)}var fq={kernelName:qo,backendName:"cpu",kernelFunc:cq},q6=Zt((e,t)=>e!==t?1:0),mq=yr(Ko,q6,null,"bool"),gq={kernelName:Ko,backendName:"cpu",kernelFunc:mq};function Fx(e,t,r,n,a){let s=t.length,i=w.sizeFromShape(t),o=w.computeStrides(t),l=w.computeStrides(a),u=w.getTypedArrayFromDType(r,w.sizeFromShape(a));for(let d=0;d<i;++d){let h=w.indexToLoc(d,s,o),p=new Array(h.length);for(let f=0;f<p.length;f++)p[f]=h[n[f]];let c=w.locToIndex(p,s,l);u[c]=e[d]}return u}function Fn(e){let{inputs:t,attrs:r,backend:n}=e,{x:a}=t,{perm:s}=r;Te(a,"transpose");let i=a.shape.length,o=new Array(i);for(let d=0;d<o.length;d++)o[d]=a.shape[s[d]];let l=n.data.get(a.dataId).values,u=Fx(l,a.shape,a.dtype,s,o);return{dataId:n.write(u,o,a.dtype),shape:o,dtype:a.dtype}}var yq={kernelName:Fi,backendName:"cpu",kernelFunc:Fn};function K6(e,t,r,n){let[a,s]=N.computeOutAndReduceShapes(e,n),i=Dr(t,"int32"),o=w.makeZerosTypedArray(w.sizeFromShape(a),i),l=w.sizeFromShape(s);for(let u=0;u<o.length;++u){let d=u*l,h=1;for(let p=0;p<l;++p)h*=r[d+p];o[u]=h}return{outVals:o,outShape:a,outDtype:i}}function Aq(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Te(a,"prod");let o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=N.getAxesPermutation(l,o),d=l,h=a,p=[];u!=null&&(h=Fn({inputs:{x:a},backend:r,attrs:{perm:u}}),p.push(h),d=N.getInnerMostAxes(d.length,o));let c=r.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:g}=K6(h.shape,h.dtype,c,d),y=m;return i&&(y=N.expandShapeToKeepDim(m,l)),p.forEach(A=>r.disposeIntermediateTensorInfo(A)),r.makeTensorInfo(y,g,f)}var xq={kernelName:el,backendName:"cpu",kernelFunc:Aq};function $x(e,t,r,n){let a=e===t,s=e<t&&r<0,i=t<e&&r>1;if(a||s||i)return w.makeZerosTypedArray(0,n);let o=Math.abs(Math.ceil((t-e)/r)),l=w.makeZerosTypedArray(o,n);t<e&&r===1&&(r=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+r;return l}var X6=zi(e=>1/Math.sqrt(e)),bq=pd(ki,X6),vq={kernelName:ki,backendName:"cpu",kernelFunc:bq},wq=zi(e=>1/(1+Math.exp(-e))),Z6=mt(Si,e=>1/(1+Math.exp(-e))),kq={kernelName:Si,backendName:"cpu",kernelFunc:Z6};function kf(e,t,r,n,a){let s=_t.isSliceContinous(n,t,r),i=w.sizeFromShape(r),o=w.computeStrides(n);if(s){let h=_t.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?N.fromUint8ToStringArray(e):e,u=We(n,a,l),d=We(r,a);for(let h=0;h<d.size;++h){let p=d.indexToLoc(h),c=p.map((f,m)=>f+t[m]);d.set(u.get(...c),...p)}return a==="string"?N.fromStringArrayToUint8(d.values):d.values}function So(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n;Te(a,"slice");let[o,l]=_t.parseSliceParams(a,s,i);_t.assertParamsValid(a,o,l);let u=r.data.get(a.dataId).values,d=kf(u,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,d)}var Iq={kernelName:il,backendName:"cpu",kernelFunc:So};function Y6(e,t,r,n,a,s,i){let o=t[0],l=s[0],u=new Array(l),d=new Array(o),h=t[1];if(l===0){if(o!==0)throw new Error(N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));let g=w.getArrayFromDType(r,0),y=w.getArrayFromDType(a,0);return[g,[0,h],y,u,d]}let p=!0,c=0,f=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*h];if(y<0)throw new Error(N.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++f[y],p=p&&y>=c,c=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,y=n;for(let A=0;A<o;++A)d[A]=A;return[g,[o,h],y,u,d]}else{let g=f[l-1],y=w.getArrayFromDType(r,g*h),A=w.getArrayFromDType(a,g),x=new Array(l).fill(0);for(let b=0;b<o;++b){let v=e[b*h],C=x[v],S=(v===0?0:f[v-1])+C;x[v]++;for(let E=0;E<h;++E)y[S*h+E]=e[b*h+E];A[S]=n[b],d[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let v=b===0?0:f[b-1];y[v*h+0]=b;for(let C=1;C<h;++C)y[v*h+C]=0;A[v]=i}return[y,[g,h],A,u,d]}}function J6(e,t,r,n,a){let s=w.sizeFromShape(n),i=t[0],o=a.length,l=[],u=1,d=-1;for(let m=0;m<o;++m){let g=a[m];if(g===-1){if(d!==-1)throw new Error(N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(d,m));d=m,l.push(1)}else{if(g<0)throw new Error(N.getSparseReshapeNegativeOutputDimErrorMessage(m,g));u*=g,l.push(g)}}if(d!==-1){if(u<=0)throw new Error(N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let m=Math.trunc(s/u);if(u*m!==s)throw new Error(N.getSparseReshapeInputOutputMultipleErrorMessage(n,l));l[d]=m}if(w.sizeFromShape(l)!==s)throw new Error(N.getSparseReshapeInputOutputMismatchErrorMessage(n,l));let h=n.length,p=[];if(h>0){p[h-1]=1;for(let m=h-2;m>=0;--m)p[m]=p[m+1]*n[m+1]}let c=[];if(o>0){c[o-1]=1;for(let m=o-2;m>=0;--m)c[m]=c[m+1]*l[m+1]}let f=w.getArrayFromDType(r,i*o);for(let m=0;m<i;++m){let g=0;for(let y=0;y<h;++y)g+=e[m*h+y]*p[y];for(let y=0;y<o;++y)f[m*o+y]=Math.trunc(g/c[y]),g%=c[y]}return[f,[i,o],l]}function Px(e,t,r,n,a,s=!1,i=0){let o=n.length,l=[t[0],e.length/t[0]],u=l[1],d=o>0?a[o-1]+1:0;if(d<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=t.slice();h[0]=d;let p=h.reduce((A,x)=>A*x,1),c=w.getArrayFromDType(r,p);if(o===0)return d>0&&c.fill(i),[c,h];if(d<=0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let f=0,m=1,g=0,y=a[f];for(;;){let A=0;if(m<o){if(A=a[m],y===A){++m;continue}if(y>=A)throw new Error(N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=d)throw new Error(N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,d));y>g&&c.fill(i,g*u,y*u);for(let x=f;x<m;++x){let b=n[x];if(b<0||b>=l[0])throw new Error(N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x,n[x],l[0]));for(let v=0;v<u;v++)c[y*u+v]+=e[b*u+v]}if(s)for(let x=0;x<u;x++)c[y*u+x]/=m-f;if(f=m,++m,g=y+1,y=A,m>o)break}return g<d&&c.fill(i,g*u,d*u),[c,h]}var Sq=zi(e=>Math.sqrt(e)),Cq=mt(Ci,e=>Math.sqrt(e)),Tq={kernelName:Ci,backendName:"cpu",kernelFunc:Cq},Q6=Zt((e,t)=>{let r=e-t;return r*r}),Nq=yr(Ei,Q6),Eq={kernelName:Ei,backendName:"cpu",kernelFunc:Nq};function eI(e,t,r,n){let a=We(e,t.dtype);for(let s=0;s<a.size;s++){let i=a.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*r[l]+n[l];a.set(t.get(...o),...i)}return a}var Rq=class{constructor(e,t,r,n,a,s){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(r),this.rightPad=w.encodeString(n),this.padWidth=a,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let r=this.getPadWidth(t);return Math.max(0,e+2*r-t+1)}createNGrams(e,t,r,n,a,s){for(let i=0;i<a;++i){let o=this.getPadWidth(s),l=Math.max(0,o-i),u=Math.max(0,o-(a-(i+1))),d=s-(l+u),h=t+(l>0?0:i-o),p=0;p+=l*this.leftPad.length;for(let g=0;g<d;++g)p+=e[h+g].length;p+=u*this.rightPad.length,p+=(l+u+d-1)*this.separator.length,r[n+i]=new Uint8Array(p);let c=r[n+i],f=0,m=g=>g.forEach(y=>c[f++]=y);for(let g=0;g<l;++g)m(this.leftPad),m(this.separator);for(let g=0;g<d-1;++g)m(e[h+g]),m(this.separator);if(d>0){m(e[h+d-1]);for(let g=0;g<u;++g)m(this.separator),m(this.rightPad)}else{for(let g=0;g<u-1;++g)m(this.rightPad),m(this.separator);m(this.rightPad)}}}compute(e,t){let r=e.length,n=t.length;if(n>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l<n;++l){let u=t[l]>=o;if(u=u&&t[l]<=r,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${r}]`);o=t[l]}if(o!==r)throw new Error(`Last split value must be data size. Expected ${r}, got ${o}`)}let a=n-1,s=w.getArrayFromDType("int32",n);if(r===0||n===0){let o=new Array(r);for(let l=0;l<=a;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=a;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(d=>{u+=this.getNumNGrams(l,d)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[a]);for(let o=0;o<a;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(d=>{let h=t[o+1]-t[o],p=this.getNumNGrams(h,d);this.createNGrams(e,l,i,u,p,d),u+=p}),this.preserveShort&&u===s[o]){let d=t[o+1]-t[o];if(d===0)continue;let h=d+2*this.padWidth,p=1;this.createNGrams(e,l,i,u,p,h)}}return[i,s]}};function tI(e,t,r,n,a,s,i,o){return new Rq(r,n,a,s,i,o).compute(e,t)}function Mq(e,t,r,n){if(!e.length)return;if(t.length===0){for(let s=0;s<e.length;++s)n.push(e.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=e.indexOf(s);for(;i!==-1;){let o=e.subarray(0,i);(!r||o.length!==0)&&n.push(o),e=e.subarray(i+1),i=e.indexOf(s)}(!r||e.length!==0)&&n.push(e);return}let a=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(a,s);(!r||i.length!==0)&&n.push(i),a=s+1}}function rI(e,t,r){let n=e.length,a=[],s=0,i=0,o=new Array(n);for(let p=0;p<n;++p){let c=a.length;Mq(e[p],t,r,a);let f=a.length-c;o[p]=f,s+=f,i=Math.max(i,f)}let l=w.getArrayFromDType("int32",s*2),u=new Array(s),d=[n,i],h=0;for(let p=0;p<n;++p)for(let c=0;c<o[p];++c)l[h*2]=p,l[h*2+1]=c,u[h]=a[h],++h;return[l,u,d]}function nI(e,t){let r=w.getArrayFromDType("int32",e.length);for(let n=0;n<e.length;++n)r[n]=w.fingerPrint64(e[n]).modulo(t).getLowBitsUnsigned();return r}var aI=Zt((e,t)=>e-t),Fq=Nx((e,t,r,n)=>({real:e-r,imag:t-n})),_x=yr(Ri,aI,Fq),$q={kernelName:Ri,backendName:"cpu",kernelFunc:_x};function sI(e,t){let r=new Array(e.rank);for(let a=0;a<r.length;a++)r[a]=e.shape[a]*t[a];let n=We(r,e.dtype);for(let a=0;a<n.values.length;++a){let s=n.indexToLoc(a),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);n.values[a]=e.values[o]}return n}var hp=(e,t)=>{let r=t.value-e.value;return r===0?e.index-t.index:r};function iI(e,t,r=0,n=e.length-1){for(;n>r;){if(n-r>600){let o=n-r+1,l=t-r+1,u=Math.log(o),d=.5*Math.exp(2*u/3),h=.5*Math.sqrt(u*d*(o-d)/o)*Math.sign(l-o/2),p=Math.max(r,Math.floor(t-l*d/o+h)),c=Math.min(n,Math.floor(t+(o-l)*d/o+h));iI(e,t,p,c)}let a=e[t],s=r,i=n;for(w.swap(e,r,t),hp(e[n],a)>0&&w.swap(e,r,n);s<i;){for(w.swap(e,s,i),s++,i--;hp(e[s],a)<0;)s=s+1;for(;hp(e[i],a)>0;)i=i-1}hp(e[r],a)===0?w.swap(e,r,i):(i=i+1,w.swap(e,i,n)),i<=t&&(r=i+1),t<=i&&(n=i-1)}}function oI(e,t,r,n,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=w.getTypedArrayFromDType(r,i*n),u=w.getTypedArrayFromDType("int32",i*n);for(let h=0;h<i;h++){let p=h*o,c=e.subarray(p,p+o),f=new Array(c.length);c.forEach((A,x)=>f[x]={value:A,index:x}),n<f.length&&(iI(f,n),f=f.slice(0,n)),a&&f.sort(hp);let m=h*n,g=l.subarray(m,m+n),y=u.subarray(m,m+n);for(let A=0;A<n;A++)g[A]=f[A].value,y[A]=f[A].index}let d=t.slice();return d[d.length-1]=n,[We(d,r,l),We(d,"int32",u)]}function lI(e,t,r,n){let a=w.parseAxisParam(t,r)[0],s=[1,r[0],1];for(let f=0;f<a;f++)s[0]*=r[f];s[1]=r[a];for(let f=a+1;f<r.length;f++)s[2]*=r[f];let i={},o=new Int32Array(r[a]),l=new rr(s,n,e),u=[],d=s[0]===1&&s[2]===1;for(let f=0;f<r[a];f++){let m;if(d)m=e[f].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,f,A));m=g.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,u.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let p=new rr(h,n);u.forEach((f,m)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)p.set(l.get(g,f,y),g,m,y)});let c=r.slice();return c[a]=h[1],{outputValues:p.values,outputShape:c,indices:o}}var Pq="0.0.0";Al("cpu",()=>new Tx,1);var uI=mt(ei,e=>e>=0?e:Math.exp(e)-1),_q={kernelName:ei,backendName:"cpu",kernelFunc:uI};function dI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n;Te([a],"leakyRelu");let i=w.sizeFromShape(a.shape),o=r.data.get(a.dataId).values,l=w.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return r.makeTensorInfo(a.shape,"float32",l)}var zq={kernelName:oi,backendName:"cpu",kernelFunc:dI},Oq=Zt((e,t)=>e<0?t*e:e);function pI(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t;Te([n,a],"prelu");let s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,[o,l]=Oq(n.shape,a.shape,s,i,"float32");return r.makeTensorInfo(l,"float32",o)}var Dq={kernelName:xi,backendName:"cpu",kernelFunc:pI},hI=mt(bi,e=>Math.max(0,e)),Lq={kernelName:bi,backendName:"cpu",kernelFunc:hI},cI=mt(wi,e=>Math.min(Math.max(0,e),6)),Bq={kernelName:wi,backendName:"cpu",kernelFunc:cI};function zx(e,t,r,n,a){if(r==="linear")return Ma({inputs:{x:t},backend:e});if(r==="relu")return hI({inputs:{x:t},backend:e});if(r==="elu")return uI({inputs:{x:t},backend:e});if(r==="relu6")return cI({inputs:{x:t},backend:e});if(r==="prelu")return pI({inputs:{x:t,alpha:n},backend:e});if(r==="leakyrelu")return dI({inputs:{x:t},backend:e,attrs:{alpha:a}});if(r==="sigmoid")return Z6({inputs:{x:t},backend:e});throw new Error(`Activation ${r} has not been implemented for the CPU backend.`)}function Mt(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=w.sizeFromShape(a.shape),o=w.inferFromImplicitShape(s,i),l=w.sizeFromShape(o);w.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),r.incRef(a.dataId);let u=r.data.get(a.dataId);if(u.complexTensorInfos!=null){let d=u.complexTensorInfos.real,h=u.complexTensorInfos.imag;d.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var Wq={kernelName:tl,backendName:"cpu",kernelFunc:Mt};function fI(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;Te([a,s],"matMul");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=Mt({inputs:{x:a},backend:r,attrs:{shape:x}}),C=Mt({inputs:{x:s},backend:r,attrs:{shape:b}}),S=i?v.shape[1]:v.shape[2],E=i?v.shape[2]:v.shape[1],R=o?C.shape[1]:C.shape[2],_=Math.max(g,y),M=r.data.get(v.dataId).values,I=r.data.get(C.dataId).values,O=w.computeStrides(v.shape),z=w.computeStrides(C.shape),[j,X,D]=i?[O[0],1,O[1]]:[O[0],O[1],1],[Q,V,ee]=o?[1,z[1],z[0]]:[z[1],1,z[0]],Y=E*R,se=We([_,E,R],v.dtype),Z=se.values,ae=r.blockSize;for(let de=0;de<_;de++)for(let Ae=0;Ae<E;Ae+=ae)for(let be=0;be<R;be+=ae)for(let Ee=0;Ee<S;Ee+=ae){let Me=Math.min(Ae+ae,E),De=Math.min(be+ae,R),Be=Math.min(Ee+ae,S);for(let Ze=Ae;Ze<Me;Ze++)for(let ot=be;ot<De;ot++){let dt=0;for(let pt=Ee;pt<Be;pt++){let $e=Math.min(de,g-1)*j,vt=Math.min(de,y-1)*ee,gt=M[$e+Ze*X+pt*D],Rr=I[pt*Q+ot*V+vt];dt+=gt*Rr}Z[de*Y+(Ze*R+ot)]+=dt}}return r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(C),r.makeTensorInfo(A,se.dtype,se.values)}var Vq={kernelName:js,backendName:"cpu",kernelFunc:fI};function Uq(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p,c,f,m=[];p=fI({inputs:{a,b:s},attrs:{transposeA:l,transposeB:u},backend:r}),i&&(c=kh({inputs:{a:p,b:i},backend:r}),m.push(p),p=c),d&&(f=zx(r,p,d,o,h),m.push(p),p=f);for(let g of m)r.disposeIntermediateTensorInfo(g);return p}var Gq={kernelName:Ts,backendName:"cpu",kernelFunc:Uq},jq=mt(Tu,e=>Math.acos(e)),Hq={kernelName:Tu,backendName:"cpu",kernelFunc:jq},qq=mt(Nu,e=>Math.acosh(e)),Kq={kernelName:Nu,backendName:"cpu",kernelFunc:qq};function Xq(e){let{inputs:t,backend:r}=e,n=t;Te(t,"addN");let a=n.map(o=>r.data.get(o.dataId).values),s=We(n[0].shape,n[0].dtype),i=s.values;for(let o=0;o<n.length;o++){let l=a[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return r.makeTensorInfo(s.shape,s.dtype,s.values)}var Zq={kernelName:Vs,backendName:"cpu",kernelFunc:Xq};function Yq(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Te(a,"all");let o=w.parseAxisParam(s,a.shape),l=o,u=N.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=Fn({inputs:{x:a},backend:r,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,a.shape.length)),N.assertAxesAreInnerMostDims("all",l,d.shape.length);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(h),d.dtype),m=r.data.get(d.dataId).values;for(let y=0;y<f.length;++y){let A=y*c,x=m[A];for(let b=0;b<c;++b){let v=m[A+b];x=x&&v}f[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,f);if(i){let y=N.expandShapeToKeepDim(h,o),A=Mt({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var Jq={kernelName:Eu,backendName:"cpu",kernelFunc:Yq};function Qq(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Te(a,"any");let o=w.parseAxisParam(s,a.shape),l=o,u=N.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=Fn({inputs:{x:a},backend:r,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,a.shape.length)),N.assertAxesAreInnerMostDims("any",l,d.shape.length);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(h),d.dtype),m=r.data.get(d.dataId).values;for(let y=0;y<f.length;++y){let A=y*c,x=m[A];for(let b=0;b<c;++b){let v=m[A+b];x=x||v}f[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,f);if(i){let y=N.expandShapeToKeepDim(h,o),A=Mt({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var eK={kernelName:Ru,backendName:"cpu",kernelFunc:Qq};function tK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Te(a,"argMax");let i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Fn({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,h]=N.computeOutAndReduceShapes(l.shape,i),p=w.sizeFromShape(d),c=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(h),m=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v>A&&(A=v,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var rK={kernelName:Us,backendName:"cpu",kernelFunc:tK};function nK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Te(a,"argMin");let i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Fn({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,h]=N.computeOutAndReduceShapes(l.shape,i),p=w.sizeFromShape(d),c=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(h),m=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v<A&&(A=v,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var aK={kernelName:Mu,backendName:"cpu",kernelFunc:nK},sK=mt(Fu,e=>Math.asin(e)),iK={kernelName:Fu,backendName:"cpu",kernelFunc:sK},oK=mt($u,e=>Math.asinh(e)),lK={kernelName:$u,backendName:"cpu",kernelFunc:oK},uK=mt(Pu,e=>Math.atan(e)),dK={kernelName:Pu,backendName:"cpu",kernelFunc:uK},pK=Zt((e,t)=>Math.atan2(e,t)),hK=yr(zu,pK),cK={kernelName:zu,backendName:"cpu",kernelFunc:hK},fK=mt(_u,e=>Math.atanh(e)),mK={kernelName:_u,backendName:"cpu",kernelFunc:fK};function Ox(e,t,r,n,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,u=a.dilationWidth,d=a.effectiveFilterHeight,h=a.effectiveFilterWidth,p=a.padInfo.top,c=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(a.outShape,r),g=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],A=a.outShape[2]*a.outShape[3],x=a.outShape[3];for(let b=0;b<a.batchSize;++b){let v=b*y,C=b*n[0];for(let S=0;S<a.inChannels;++S)for(let E=0;E<a.outHeight;++E){let R=E*i-p,_=Math.max(0,R),M=Math.min(a.inHeight,d+R),I=v+E*A;for(let O=0;O<a.outWidth;++O){let z=O*o-c,j=Math.max(0,z),X=Math.min(a.inWidth,h+z),D=f,Q=0,V=0;for(let Y=_;Y<M;Y+=l){let se=C+Y*n[1];for(let Z=j;Z<X;Z+=u){let ae=se+Z*n[2],de=e[ae+S];s==="max"&&de>D?D=de:s==="avg"&&(Q+=de,V++)}if(isNaN(D))break}let ee=I+O*x+S;g[ee]=s==="avg"?Q/V:D}}}return m}function mI(e,t,r,n,a=!1,s=!1){let i=We(n.outShape,"int32"),o=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,d=n.dilationWidth,h=n.effectiveFilterHeight,p=n.effectiveFilterWidth,c=n.padInfo.top,f=n.padInfo.left,m=We(t,r,e);for(let g=0;g<n.batchSize;++g)for(let y=0;y<n.inChannels;++y)for(let A=0;A<n.outHeight;++A){let x=A*o-c,b=x;for(;b<0;)b+=u;let v=Math.min(n.inHeight,h+x);for(let C=0;C<n.outWidth;++C){let S=C*l-f,E=S;for(;E<0;)E+=d;let R=Math.min(n.inWidth,p+S),_=Number.NEGATIVE_INFINITY,M=-1;for(let I=b;I<v;I+=u){let O=I-x;for(let z=E;z<R;z+=d){let j=z-S,X=m.get(g,I,z,y);X>_&&(_=X,a?M=s?((g*n.inHeight+I)*n.inWidth+z)*n.inChannels+y:(I*n.inWidth+z)*n.inChannels+y:M=O*p+j)}}i.set(M,g,A,C,y)}}return i}function gI(e,t,r,n,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,u=a.dilationDepth,d=a.dilationHeight,h=a.dilationWidth,p=a.effectiveFilterDepth,c=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,g=a.padInfo.top,y=a.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(a.outShape,r),b=x.values,v=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],C=a.outShape[2]*a.outShape[3]*a.outShape[4],S=a.outShape[3]*a.outShape[4],E=a.outShape[4];for(let R=0;R<a.batchSize;++R){let _=R*v,M=R*n[0];for(let I=0;I<a.inChannels;++I)for(let O=0;O<a.outDepth;++O){let z=O*i-m,j=z;for(;j<0;)j+=u;let X=Math.min(a.inDepth,p+z),D=_+O*C;for(let Q=0;Q<a.outHeight;++Q){let V=Q*o-g,ee=V;for(;ee<0;)ee+=d;let Y=Math.min(a.inHeight,c+V),se=D+Q*S;for(let Z=0;Z<a.outWidth;++Z){let ae=Z*l-y,de=ae;for(;de<0;)de+=h;let Ae=Math.min(a.inWidth,f+ae),be=se+Z*E,Ee=A,Me=0,De=0;for(let Ze=j;Ze<X;Ze+=u){let ot=M+Ze*n[1];for(let dt=ee;dt<Y;dt+=d){let pt=ot+dt*n[2];for(let $e=de;$e<Ae;$e+=h){let vt=pt+$e*n[3],gt=e[vt+I];if(s==="max"&&gt>Ee?Ee=gt:s==="avg"&&(Me+=gt,De++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let Be=be+I;b[Be]=s==="avg"?Me/De:Ee}}}}return x}function gK(e,t){let r=We(t.outShape,"int32"),n=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,p=t.padInfo.front,c=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*n-p,x=A;for(;x<0;)x+=i;let b=Math.min(t.inDepth,u+A);for(let v=0;v<t.outHeight;++v){let C=v*a-c,S=C;for(;S<0;)S+=o;let E=Math.min(t.inHeight,d+C);for(let R=0;R<t.outWidth;++R){let _=R*s-f,M=_;for(;M<0;)M+=l;let I=Math.min(t.inWidth,h+_),O=Number.NEGATIVE_INFINITY,z=-1;for(let j=x;j<b;j+=i){let X=j-A;for(let D=S;D<E;D+=o){let Q=D-C;for(let V=M;V<I;V+=l){let ee=V-_,Y=e.get(m,j,D,V,g);Y>=O&&(O=Y,z=X*d*h+Q*d+ee)}}}r.set(z,m,y,v,R,g)}}}return r}function yK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Te(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l),h;if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))h=Ma({inputs:{x:a},backend:r});else{let p=r.data.get(a.dataId).values,c=w.computeStrides(a.shape),f=Ox(p,a.shape,a.dtype,c,d,"avg");h=r.makeTensorInfo(d.outShape,a.dtype,f.values)}return h}var AK={kernelName:Gs,backendName:"cpu",kernelFunc:yK};function xK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Te(a,"avgPool3d");let d=N.computePool3DInfo(a.shape,s,i,1,o,l,u),h=r.data.get(a.dataId).values,p=gI(h,a.shape,a.dtype,w.computeStrides(a.shape),d,"avg");return r.makeTensorInfo(p.shape,"float32",p.values)}var bK={kernelName:Op,backendName:"cpu",kernelFunc:xK};function vK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Te([a,s],"avgPool3DGrad");let d=N.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,f=d.filterDepth,m=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,v=d.effectiveFilterHeight,C=d.effectiveFilterWidth,S=b-1-d.padInfo.front,E=C-1-d.padInfo.left,R=v-1-d.padInfo.top,_=We(s.shape,"float32"),M=1/(f*m*g),I=r.bufferSync(a);for(let O=0;O<d.batchSize;++O)for(let z=0;z<d.inChannels;++z)for(let j=0;j<d.inDepth;++j)for(let X=0;X<d.inHeight;++X)for(let D=0;D<d.inWidth;++D){let Q=j-S,V=X-R,ee=D-E,Y=0;for(let se=0;se<b;se+=y){let Z=(Q+se)/h;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let ae=0;ae<v;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let Ae=0;Ae<C;Ae+=x){let be=(ee+Ae)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(Y+=I.get(O,Z,de,be,z))}}}_.set(Y*M,O,j,X,D,z)}return r.makeTensorInfo(_.shape,_.dtype,_.values)}var wK={kernelName:_f,backendName:"cpu",kernelFunc:vK};function kK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Te([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,v=We(i.shape,"float32"),C=1/(c*f),S=r.data.get(a.dataId).values,E=We(a.shape,"float32",S);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let I=0;I<d.inWidth;++I){let O=M-b,z=I-x,j=0;for(let X=0;X<y;X+=m){let D=(O+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(z+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,_))}}v.set(j*C,R,M,I,_)}return r.makeTensorInfo(v.shape,v.dtype,v.values)}var IK={kernelName:Pf,backendName:"cpu",kernelFunc:kK};function SK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Te([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let d=r.data.get(a.dataId).values,h=r.data.get(o.dataId).values,p=r.data.get(l.dataId).values,c=s?r.data.get(s.dataId).values:new Float32Array([1]),f=i?r.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(d.length),g=f.length,y=c.length,A=p.length,x=h.length,b=0,v=0,C=0,S=0;for(let E=0;E<d.length;++E)m[E]=f[b++]+(d[E]-h[v++])*c[C++]/Math.sqrt(p[S++]+u),b>=g&&(b=0),v>=x&&(v=0),C>=y&&(C=0),S>=A&&(S=0);return r.makeTensorInfo(a.shape,a.dtype,m)}var CK={kernelName:ai,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Fn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=So({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var NK={kernelName:Fo,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Ex(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var RK={kernelName:zf,backendName:"cpu",kernelFunc:EK};function MK(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var FK={kernelName:Of,backendName:"cpu",kernelFunc:MK},$K=mt(qa,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),PK={kernelName:qa,backendName:"cpu",kernelFunc:$K},_K=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(w.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},zK={kernelName:Lp,backendName:"cpu",kernelFunc:_K};function vu(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.imag,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var OK={kernelName:Up,backendName:"cpu",kernelFunc:vu};function wu(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return Ma({inputs:{x:o[0]},backend:r});let l=o.map(m=>m.shape);if(N.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>Io({inputs:{input:b},backend:r})),g=o.map(b=>vu({inputs:{input:b},backend:r})),y=wu({inputs:m,backend:r,attrs:{axis:s}}),A=wu({inputs:g,backend:r,attrs:{axis:s}}),x=on({inputs:{real:y,imag:A},backend:r});return m.forEach(b=>r.disposeIntermediateTensorInfo(b)),g.forEach(b=>r.disposeIntermediateTensorInfo(b)),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),x}let u=o.map(m=>{let g=w.sizeFromShape(m.shape.slice(s));return Mt({inputs:{x:m},backend:r,attrs:{shape:[-1,g]}})}),d=u.map(m=>({vals:r.data.get(m.dataId).values,shape:m.shape}));i=N.computeOutShape(u.map(m=>m.shape),1);let h=u[0].shape[0]===1,p=Rx(d,i,t[0].dtype,h),c=N.computeOutShape(o.map(m=>m.shape),s),f=r.makeTensorInfo(c,t[0].dtype,p);return u.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var DK={kernelName:$o,backendName:"cpu",kernelFunc:wu};function yI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n;Te([a,s],"conv2d");let h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new rr(p.outShape,a.dtype),v=w.computeStrides(a.shape),C=w.computeStrides(s.shape),S=v[0],E=x?v[1]:v[2],R=x?v[2]:1,_=x?1:v[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],O=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,X=r.data.get(s.dataId).values,D=b.values;for(let Q=0;Q<p.batchSize;++Q){let V=Q*S,ee=Q*M;for(let Y=0;Y<p.outHeight;++Y){let se=ee+Y*I,Z=Y*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=Z+ae*m;if(de<0||de>=p.inHeight)continue;let Ae=ae*C[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let Me=se+Ee*O,De=Ee*p.strideWidth-y;for(let Be=0;Be<f;++Be){let Ze=De+Be*g;if(Ze<0||Ze>=p.inWidth)continue;let ot=Ae+Be*C[1],dt=be+Ze*R,pt=ot;for(let $e=0;$e<p.inChannels;++$e){let vt=j[dt+$e*_];for(let gt=0;gt<p.outChannels;++gt)D[Me+gt*z]+=vt*X[pt+gt];pt+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var LK={kernelName:Ks,backendName:"cpu",kernelFunc:yI};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"conv2dBackpropFilter");let h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new rr(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=new rr(a.shape,a.dtype,v),E=new rr(s.shape,s.dtype,C);for(let R=0;R<m;++R){let _=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let I=0;I<g;++I){let O=Math.max(0,Math.ceil((x-I)/f)),z=Math.min(p.outWidth,(p.inWidth+x-I)/f);for(let j=0;j<p.inChannels;++j)for(let X=0;X<p.outChannels;++X){let D=0;for(let Q=0;Q<p.batchSize;++Q)for(let V=_;V<M;++V){let ee=R+V*c-b;for(let Y=O;Y<z;++Y){let se=I+Y*f-x;y?D+=S.get(Q,ee,se,j)*E.get(Q,V,Y,X):D+=S.get(Q,j,ee,se)*E.get(Q,X,V,Y)}}A.set(D,R,I,j,X)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var WK={kernelName:Df,backendName:"cpu",kernelFunc:BK};function VK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=w.computeStrides(s.shape),p=w.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new rr(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=h,{batchSize:C,filterHeight:S,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:O,outWidth:z,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=S-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],Y=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<C;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let Be=De-D,Ze=Math.max(0,Math.ceil(Be/j)),ot=Math.min(O,(S+Be)/j);for(let dt=0;dt<M;++dt){let pt=dt-Q,$e=Math.max(0,Math.ceil(pt/X)),vt=Math.min(z,(E+pt)/X),gt=0;for(let ur=Ze;ur<ot;++ur){let Xr=ur*j-Be;for(let Jt=$e;Jt<vt;++Jt){let dr=Jt*X-pt,Xn=ae*Ee+de*ur+Ae*Jt,Zr=x*(S-1-Xr)+b*(E-1-dr)+v*Me;for(let Qt=0;Qt<I;++Qt){let xn=y[Xn+be*Qt],bn=A[Zr+Qt];gt+=xn*bn}}}let Rr=ee*Ee+Y*De+se*dt+Z*Me;g[Rr]=gt}}return r.makeTensorInfo(m.shape,m.dtype,m.values)}var UK={kernelName:Xs,backendName:"cpu",kernelFunc:VK};function GK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Te([a,s],"conv3d");let u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new rr(u.outShape,a.dtype),v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=b.values,E=w.computeStrides(a.shape),R=w.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let M=_*E[0],I=_*b.strides[0];for(let O=0;O<u.outDepth;++O){let z=I+O*b.strides[1],j=O*u.strideDepth-y;for(let X=0;X<d;++X){let D=j+X*c;if(D<0||D>=u.inDepth)continue;let Q=X*R[0],V=M+D*E[1];for(let ee=0;ee<u.outHeight;++ee){let Y=z+ee*b.strides[2],se=ee*u.strideHeight-x;for(let Z=0;Z<h;++Z){let ae=se+Z*f;if(ae<0||ae>=u.inHeight)continue;let de=Q+Z*R[1],Ae=V+ae*E[2];for(let be=0;be<u.outWidth;++be){let Ee=Y+be*u.outChannels,Me=be*u.strideWidth-A;for(let De=0;De<p;++De){let Be=Me+De*m;if(Be<0||Be>=u.inWidth)continue;let Ze=de+De*R[2],ot=Ae+Be*u.inChannels,dt=Ze;for(let pt=0;pt<u.inChannels;++pt){let $e=v[ot+pt];for(let vt=0;vt<u.outChannels;++vt)S[Ee+vt]+=$e*C[dt+vt];dt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var jK={kernelName:Bp,backendName:"cpu",kernelFunc:GK};function HK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Te([a,s],"conv3dBackpropFilterV2");let u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=N.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,f=h.strideWidth,m=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new rr(h.filterShape,"float32"),x=A.values,[b,v,C,S]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,I]=d,O=r.data.get(a.dataId).values,[z,j,X,D]=u,Q=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let Y=0;Y<m;++Y){let se=Math.max(0,Math.ceil((Q-Y)/p)),Z=Math.min(h.outDepth,(h.inDepth+Q-Y)/p),ae=Y*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*v+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/f)),Be=Math.min(h.outWidth,(h.inWidth+V-Me)/f),Ze=Me*C+Ee;for(let ot=0;ot<h.inChannels;++ot){let dt=ot*S+Ze;for(let pt=0;pt<h.outChannels;++pt){let $e=0;for(let vt=0;vt<h.batchSize;++vt){let gt=vt*z,Rr=vt*R;for(let ur=se;ur<Z;++ur){let Xr=(Y+ur*p-Q)*j+gt,Jt=ur*_+Rr;for(let dr=Ae;dr<be;++dr){let Xn=(de+dr*c-ee)*X+Xr,Zr=dr*M+Jt;for(let Qt=De;Qt<Be;++Qt){let xn=(Me+Qt*f-V)*D+Xn,bn=Qt*I+Zr;$e+=O[xn+ot]*E[bn+pt]}}}}x[dt+pt]=$e}}}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var qK={kernelName:Lf,backendName:"cpu",kernelFunc:HK};function KK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n;Te([a],"conv3dBackpropInputV2");let u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=N.computeConv3DInfo(l,s.shape,o,1,i),p=new rr(h.inShape,"float32"),c=p.values,[f,m,g,y]=p.strides,A=r.data.get(a.dataId).values,[x,b,v,C]=u,S=r.data.get(s.dataId).values,[E,R,_,M]=d,{batchSize:I,filterDepth:O,filterHeight:z,filterWidth:j,inChannels:X,inDepth:D,inHeight:Q,inWidth:V,outChannels:ee,outDepth:Y,outHeight:se,outWidth:Z,strideDepth:ae,strideHeight:de,strideWidth:Ae}=h,be=O-1-h.padInfo.front,Ee=z-1-h.padInfo.top,Me=j-1-h.padInfo.left;for(let De=0;De<I;++De)for(let Be=0;Be<X;++Be)for(let Ze=0;Ze<D;++Ze){let ot=Ze-be,dt=Math.max(0,Math.ceil(ot/ae)),pt=Math.min(Y,(O+ot)/ae);for(let $e=0;$e<Q;++$e){let vt=$e-Ee,gt=Math.max(0,Math.ceil(vt/de)),Rr=Math.min(se,(z+vt)/de);for(let ur=0;ur<V;++ur){let Xr=ur-Me,Jt=Math.max(0,Math.ceil(Xr/Ae)),dr=Math.min(Z,(j+Xr)/Ae),Xn=0;for(let Zr=dt;Zr<pt;++Zr){let Qt=Zr*ae-ot;for(let xn=gt;xn<Rr;++xn){let bn=xn*de-vt;for(let ps=Jt;ps<dr;++ps){let Xi=ps*Ae-Xr,Xh=x*De+b*Zr+v*xn+C*ps,hs=E*(O-1-Qt)+R*(z-1-bn)+_*(j-1-Xi)+M*Be;for(let Da=0;Da<ee;++Da){let Wd=A[Xh+Da],Dl=S[hs+Da];Xn+=Wd*Dl}}}}c[f*De+m*Ze+g*$e+y*ur+Be]=Xn}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var XK={kernelName:Bf,backendName:"cpu",kernelFunc:KK},ZK=mt(Zs,e=>Math.cos(e)),YK={kernelName:Zs,backendName:"cpu",kernelFunc:ZK},JK=mt(Ys,e=>Math.cosh(e)),QK={kernelName:Ys,backendName:"cpu",kernelFunc:JK};function eX(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,f=s.shape[0],[m,g]=o,y=We([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,v=w.computeStrides(a.shape),C=w.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=A[E],_=A[E+1],M=A[E+2],I=A[E+3],O=x[S];if(O>=d)continue;let z=m>1?(M-R)*(h-1)/(m-1):0,j=g>1?(I-_)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let D=m>1?R*(h-1)+X*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let ee=V+Q*C[2]+X*C[1]+S*C[0];y.values[ee]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),ee=D-Q;for(let Y=0;Y<g;Y++){let se=g>1?_*(p-1)+Y*j:.5*(_+I)*(p-1);if(se<0||se>p-1){for(let Ae=0;Ae<c;Ae++){let be=Ae+Y*C[2]+X*C[1]+S*C[0];y.values[be]=u}continue}let Z=Math.floor(se),ae=Math.ceil(se),de=se-Z;for(let Ae=0;Ae<c;Ae++){let be=Ae+Z*v[2]+Q*v[1]+O*v[0],Ee=b[be];be=Ae+ae*v[2]+Q*v[1]+O*v[0];let Me=b[be];be=Ae+Z*v[2]+V*v[1]+O*v[0];let De=b[be];be=Ae+ae*v[2]+V*v[1]+O*v[0];let Be=b[be],Ze=Ee+(Me-Ee)*de,ot=De+(Be-De)*de;be=Ae+Y*C[2]+X*C[1]+S*C[0],y.values[be]=Ze+(ot-Ze)*ee}}}else for(let Q=0;Q<g;++Q){let V=g>1?_*(p-1)+Q*j:.5*(_+I)*(p-1);if(V<0||V>p-1){for(let se=0;se<c;se++){let Z=se+Q*C[2]+X*C[1]+S*C[0];y.values[Z]=u}continue}let ee=Math.round(V),Y=Math.round(D);for(let se=0;se<c;se++){let Z=se+ee*v[2]+Y*v[1]+O*v[0],ae=se+Q*C[2]+X*C[1]+S*C[0];y.values[ae]=b[Z]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var tX={kernelName:_o,backendName:"cpu",kernelFunc:eX};function rX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Te(a,"cumsum");let l=N.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=Fn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,a.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=${d}`);let h=Dr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=Fn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var nX={kernelName:Po,backendName:"cpu",kernelFunc:rX};function aX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Ex(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=T6(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var sX={kernelName:Wf,backendName:"cpu",kernelFunc:aX};function iX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],d=a.shape[3],h=l*s,p=u*s,c=d/(s*s),f=r.data.get(a.dataId).values,m=new Float32Array(o*h*p*c),g=0;for(let y=0;y<o;++y)for(let A=0;A<h;++A){let x=Math.floor(A/s),b=A%s;for(let v=0;v<p;++v){let C=Math.floor(v/s),S=v%s,E=(b*s+S)*c;for(let R=0;R<c;++R){let _=R+E+d*(C+u*(x+l*y));m[g++]=f[_]}}}return r.makeTensorInfo([o,h,p,c],a.dtype,m)}var oX={kernelName:zo,backendName:"cpu",kernelFunc:iX};function AI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n;Te([a,s],"depthwiseConv2DNative");let d=w.computeStrides(a.shape),h=w.computeStrides(s.shape),p=l;p==null&&(p=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=N.computeConv2DInfo(a.shape,s.shape,i,p,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:A}=c,x=A.left,b=A.top,v=c.outChannels/c.inChannels,C=new rr(c.outShape,a.dtype),S=r.data.get(a.dataId).values,E=r.data.get(s.dataId).values,R=C.values;for(let _=0;_<c.batchSize;++_){let M=_*d[0],I=_*C.strides[0];for(let O=0;O<c.outHeight;++O){let z=I+O*C.strides[1],j=O*c.strideHeight-b;for(let X=0;X<f;++X){let D=j+X*g;if(D<0||D>=c.inHeight)continue;let Q=X*h[0],V=M+D*d[1];for(let ee=0;ee<c.outWidth;++ee){let Y=z+ee*C.strides[2],se=ee*c.strideWidth-x;for(let Z=0;Z<m;++Z){let ae=se+Z*y;if(ae<0||ae>=c.inWidth)continue;let de=Q+Z*h[1],Ae=V+ae*c.inChannels,be=Y,Ee=de;for(let Me=0;Me<c.inChannels;++Me){let De=S[Ae+Me];for(let Be=0;Be<v;++Be)R[be+Be]+=De*E[Ee+Be];be+=v,Ee+=v}}}}}}return r.makeTensorInfo(C.shape,C.dtype,C.values)}var lX={kernelName:Js,backendName:"cpu",kernelFunc:AI};function uX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"depthwiseConv2dNativeBackpropFilter");let h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:f,filterWidth:m}=h,g=new rr(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,v=new rr(a.shape,a.dtype,b),C=r.data.get(s.dataId).values,S=new rr(s.shape,s.dtype,C);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((A-E)/p)),_=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let M=0;M<m;++M){let I=Math.max(0,Math.ceil((y-M)/c)),O=Math.min(h.outWidth,(h.inWidth+y-M)/c);for(let z=0;z<h.outChannels;++z){let j=Math.trunc(z/x),X=z%x,D=0;for(let Q=0;Q<h.batchSize;++Q)for(let V=R;V<_;++V){let ee=E+V*p-A;for(let Y=I;Y<O;++Y){let se=M+Y*c-y;D+=v.get(Q,ee,se,j)*S.get(Q,V,Y,z)}}g.set(D,E,M,j,X)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var dX={kernelName:Vf,backendName:"cpu",kernelFunc:uX};function pX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n;Te([a,s],"depthwiseConv2DNativeBackpropInput");let h=w.computeStrides(a.shape),p=w.computeStrides(s.shape),c=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),f=new rr(c.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=r.data.get(a.dataId).values,[b,v,C]=h,S=r.data.get(s.dataId).values,[E,R,_]=p,{batchSize:M,filterHeight:I,filterWidth:O,inChannels:z,inHeight:j,inWidth:X,outChannels:D,outHeight:Q,outWidth:V,strideHeight:ee,strideWidth:Y}=c,se=I-1-c.padInfo.top,Z=O-1-c.padInfo.left,ae=D/z;for(let de=0;de<M;++de)for(let Ae=0;Ae<z;++Ae)for(let be=0;be<j;++be){let Ee=be-se,Me=Math.max(0,Math.ceil(Ee/ee)),De=Math.min(Q,(I+Ee)/ee);for(let Be=0;Be<X;++Be){let Ze=Be-Z,ot=Math.max(0,Math.ceil(Ze/Y)),dt=Math.min(V,(O+Ze)/Y),pt=0;for(let $e=Me;$e<De;++$e){let vt=$e*ee-Ee;for(let gt=ot;gt<dt;++gt){let Rr=gt*Y-Ze,ur=b*de+v*$e+C*gt,Xr=E*(I-1-vt)+R*(O-1-Rr)+_*Ae;for(let Jt=0;Jt<ae;++Jt){let dr=Ae*ae+Jt,Xn=x[ur+dr],Zr=S[Xr+Jt];pt+=Xn*Zr}}}m[g*de+y*be+A*Be+Ae]=pt}}return r.makeTensorInfo(f.shape,f.dtype,f.values)}var hX={kernelName:Uf,backendName:"cpu",kernelFunc:pX};function cX(e){let{inputs:t,backend:r}=e,{x:n}=t,a=w.sizeFromShape(n.shape),s=r.data.get(n.dataId).values,i=We([a,a],n.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*a+u]=s[u];let l=[...n.shape,...n.shape];return r.makeTensorInfo(l,i.dtype,i.values)}var fX={kernelName:Gf,backendName:"cpu",kernelFunc:cX},mX={kernelName:Wp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:r})=>{let{x:n,filter:a}=e,{strides:s,pad:i,dilations:o}=r,l=t,u=l.data.get(n.dataId).values,d=n.shape.length,h=l.data.get(a.dataId).values,p=a.shape.length,{batchSize:c,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:C,filterWidth:S,dilationHeight:E,dilationWidth:R,outShape:_}=N.computeDilation2DInfo(n.shape,a.shape,s,i,"NHWC",o),M=w.sizeFromShape(_),I=_.length,O=w.getArrayFromDType(n.dtype,M);for(let z=0;z<c;++z)for(let j=0;j<y;++j){let X=j*b-x.top;for(let D=0;D<A;++D){let Q=D*v-x.left;for(let V=0;V<g;++V){let ee=Number.MIN_SAFE_INTEGER;for(let se=0;se<C;++se){let Z=X+se*E;if(Z>=0&&Z<f)for(let ae=0;ae<S;++ae){let de=Q+ae*R;if(de>=0&&de<m){let Ae=w.locToIndex([z,Z,de,V],d,w.computeStrides(n.shape)),be=w.locToIndex([se,ae,V],p,w.computeStrides(a.shape)),Ee=u[Ae]+h[be];Ee>ee&&(ee=Ee)}}}let Y=w.locToIndex([z,j,D,V],I,w.computeStrides(_));O[Y]=ee}}}return{dataId:l.write(w.toTypedArray(O,n.dtype),_,n.dtype),shape:_,dtype:n.dtype}}},gX={kernelName:af,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:r})=>{let{x:n,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=r,u=t,d=w.toNestedArray(n.shape,u.data.get(n.dataId).values),h=w.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:p,inHeight:c,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:C,dilationHeight:S,dilationWidth:E,outShape:R}=N.computeDilation2DInfo(n.shape,a.shape,i,o,"NHWC",l);w.assert(s.rank===R.length,()=>`Error in ${af}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let _=w.toNestedArray(R,u.data.get(s.dataId).values),M=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let I=0;I<p;++I)for(let O=0;O<g;++O){let z=O*x-A.top;for(let j=0;j<y;++j){let X=j*b-A.left;for(let D=0;D<m;++D){let Q=Number.MIN_SAFE_INTEGER,V=0,ee=0;for(let Y=0;Y<v;++Y){let se=z+Y*S;if(se>=0&&se<c)for(let Z=0;Z<C;++Z){let ae=X+Z*E;if(ae>=0&&ae<f){let de=d[I][se][ae][D]+h[Y][Z][D];de>Q&&(Q=de,V=Y,ee=Z)}}}M[V][ee][D]+=_[I][O][j][D]}}}return{dataId:u.write(w.toTypedArray(M,n.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},yX={kernelName:nf,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:r})=>{let{x:n,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=r,u=t,d=w.toNestedArray(n.shape,u.data.get(n.dataId).values),h=w.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:p,inHeight:c,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:C,dilationHeight:S,dilationWidth:E,outShape:R}=N.computeDilation2DInfo(n.shape,a.shape,i,o,"NHWC",l);w.assert(s.rank===R.length,()=>`Error in ${nf}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let _=w.toNestedArray(R,u.data.get(s.dataId).values),M=w.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I<p;++I)for(let O=0;O<g;++O){let z=O*x-A.top;for(let j=0;j<y;++j){let X=j*b-A.left;for(let D=0;D<m;++D){let Q=Number.MIN_SAFE_INTEGER,V=z<0?0:z,ee=X<0?0:X;for(let Y=0;Y<v;++Y){let se=z+Y*S;if(se>=0&&se<c)for(let Z=0;Z<C;++Z){let ae=X+Z*E;if(ae>=0&&ae<f){let de=d[I][se][ae][D]+h[Y][Z][D];de>Q&&(Q=de,V=se,ee=ae)}}}M[I][V][ee][D]+=_[I][O][j][D]}}}return{dataId:u.write(w.toTypedArray(M,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Ih(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Te(a,"sum");let o;a.dtype==="bool"?o=Bs({inputs:{x:a},backend:r,attrs:{dtype:"int32"}}):o=Ma({inputs:{x:a},backend:r});let l=o.shape.length,u=w.parseAxisParam(s,o.shape),d=N.getAxesPermutation(u,l),h=u,p=o;d!=null&&(p=Fn({inputs:{x:o},backend:r,attrs:{perm:d}}),h=N.getInnerMostAxes(h.length,l)),N.assertAxesAreInnerMostDims("sum",h,p.shape.length);let[c,f]=N.computeOutAndReduceShapes(p.shape,h),m=N.upcastType(p.dtype,"int32"),g=wf(r,c,m),y=w.sizeFromShape(f),A=r.data.get(g.dataId).values,x=r.data.get(p.dataId).values;for(let b=0;b<A.length;++b){let v=b*y,C=0;for(let S=0;S<y;++S)C+=x[v+S];A[b]=C}if(i){let b=N.expandShapeToKeepDim(g.shape,u),v=g;g=Mt({inputs:{x:g},backend:r,attrs:{shape:b}}),r.disposeIntermediateTensorInfo(v)}return r.disposeIntermediateTensorInfo(o),d!=null&&r.disposeIntermediateTensorInfo(p),g}var AX={kernelName:Ti,backendName:"cpu",kernelFunc:Ih};function xX(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Fn({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=Mt({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=n0({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=Ih({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var bX={kernelName:Vp,backendName:"cpu",kernelFunc:xX};function vX(e){let{inputs:t,backend:r}=e,{dy:n,y:a}=t;Te([n,a],"eluGrad");let s=new Float32Array(w.sizeFromShape(a.shape)),i=r.data.get(a.dataId).values,o=r.data.get(n.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 r.makeTensorInfo(a.shape,"float32",s)}var wX={kernelName:jf,backendName:"cpu",kernelFunc:vX},kX=N.ERF_P,IX=N.ERF_A1,SX=N.ERF_A2,CX=N.ERF_A3,TX=N.ERF_A4,NX=N.ERF_A5,EX=mt(Ou,e=>{let t=Math.sign(e),r=Math.abs(e),n=1/(1+kX*r);return t*(1-((((NX*n+TX)*n+CX)*n+SX)*n+IX)*n*Math.exp(-r*r))}),RX={kernelName:Ou,backendName:"cpu",kernelFunc:EX};function If(e){let{inputs:t,backend:r,attrs:n}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Mt({inputs:{x:a},backend:r,attrs:{shape:o}})}var MX={kernelName:Do,backendName:"cpu",kernelFunc:If},FX=Zt((e,t)=>e/t),Dx=yr(Qs,FX),Ty={kernelName:Qs,backendName:"cpu",kernelFunc:Dx};function xI(e,t,r){let n=e.shape,a=n[0],s=n[1],i=r.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[a,s],d=w.sizeFromShape(u),h=w.getTypedArrayFromDType("float32",d),p=w.getTypedArrayFromDType("float32",d);for(let g=0;g<a;g++){let y=So({inputs:{x:o},backend:r,attrs:{begin:[g,0],size:[1,s]}}),A=So({inputs:{x:l},backend:r,attrs:{begin:[g,0],size:[1,s]}}),x=on({inputs:{real:y,imag:A},backend:r}),{real:b,imag:v}=$X(x,t,r),C=N.mergeRealAndImagArrays(b,v);for(let S=0;S<s;S++){let E=N.getComplexWithIndex(C,S);h[g*s+S]=E.real,p[g*s+S]=E.imag}r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(x)}let c=r.makeTensorInfo(u,"float32",h),f=r.makeTensorInfo(u,"float32",p),m=on({inputs:{real:c,imag:f},backend:r});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),m}function $X(e,t,r){let n=w.sizeFromShape(e.shape),a=r.data.get(e.dataId),s=r.data.get(a.complexTensorInfos.real.dataId).values,i=r.data.get(a.complexTensorInfos.imag.dataId).values;if(PX(n)){let o=Ny(s,i,n,t,r),l=[e.shape[0],e.shape[1]];if(t){let u=r.makeTensorInfo(l,"float32",o.real),d=r.makeTensorInfo(l,"float32",o.imag),h=r.makeTensorInfo([],"float32",w.createScalarValue(n,"float32")),p=Ma({inputs:{x:h},backend:r}),c=Ty.kernelFunc({inputs:{a:u,b:h},backend:r}),f=Ty.kernelFunc({inputs:{a:d,b:p},backend:r}),m=r.data.get(c.dataId).values,g=r.data.get(f.dataId).values;return r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return o}else{let o=N.mergeRealAndImagArrays(s,i),l=_X(o,n,t);return N.splitRealAndImagArrays(l)}}function PX(e){return(e&e-1)===0}function Ny(e,t,r,n,a){if(r===1)return{real:e,imag:t};let s=N.mergeRealAndImagArrays(e,t),i=r/2,o=N.complexWithEvenIndex(s),l=o.real,u=o.imag,d=[l.length],h=a.makeTensorInfo(d,"float32",l),p=a.makeTensorInfo(d,"float32",u),c=on({inputs:{real:h,imag:p},backend:a}),f=N.complexWithOddIndex(s),m=f.real,g=f.imag,y=[m.length],A=a.makeTensorInfo(y,"float32",m),x=a.makeTensorInfo(y,"float32",g),b=on({inputs:{real:A,imag:x},backend:a}),v=Ny(l,u,i,n,a),C=v.real,S=v.imag,E=[C.length],R=a.makeTensorInfo(E,"float32",C),_=a.makeTensorInfo(E,"float32",S),M=on({inputs:{real:R,imag:_},backend:a}),I=Ny(m,g,i,n,a),O=I.real,z=I.imag,j=[O.length],X=a.makeTensorInfo(j,"float32",O),D=a.makeTensorInfo(j,"float32",z),Q=on({inputs:{real:X,imag:D},backend:a}),V=N.exponents(r,n),ee=[V.real.length],Y=a.makeTensorInfo(ee,"float32",V.real),se=a.makeTensorInfo(ee,"float32",V.imag),Z=on({inputs:{real:Y,imag:se},backend:a}),ae=n0({inputs:{a:Z,b:Q},backend:a}),de=kh({inputs:{a:M,b:ae},backend:a}),Ae=_x({inputs:{a:M,b:ae},backend:a}),be=Io({inputs:{input:de},backend:a}),Ee=Io({inputs:{input:Ae},backend:a}),Me=vu({inputs:{input:de},backend:a}),De=vu({inputs:{input:Ae},backend:a}),Be=wu({inputs:[be,Ee],backend:a,attrs:{axis:0}}),Ze=wu({inputs:[Me,De],backend:a,attrs:{axis:0}}),ot=a.data.get(Be.dataId).values,dt=a.data.get(Ze.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(A),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(b),a.disposeIntermediateTensorInfo(R),a.disposeIntermediateTensorInfo(_),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(D),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(Y),a.disposeIntermediateTensorInfo(se),a.disposeIntermediateTensorInfo(Z),a.disposeIntermediateTensorInfo(ae),a.disposeIntermediateTensorInfo(de),a.disposeIntermediateTensorInfo(Ae),a.disposeIntermediateTensorInfo(be),a.disposeIntermediateTensorInfo(Me),a.disposeIntermediateTensorInfo(Ee),a.disposeIntermediateTensorInfo(De),a.disposeIntermediateTensorInfo(Be),a.disposeIntermediateTensorInfo(Ze),{real:ot,imag:dt}}function _X(e,t,r){let n=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let l=N.exponent(a*o,t,r),u=N.getComplexWithIndex(e,o);s+=u.real*l.real-u.imag*l.imag,i+=u.real*l.imag+u.imag*l.real}r&&(s/=t,i/=t),N.assignToTypedArray(n,s,i,a)}return n}function zX(e){let{inputs:t,backend:r}=e,{input:n}=t,a=w.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=a/s,o=Mt({inputs:{x:n},backend:r,attrs:{shape:[i,s]}}),l=xI(o,!1,r),u=Mt({inputs:{x:l},backend:r,attrs:{shape:n.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(l),u}var OX={kernelName:Hf,backendName:"cpu",kernelFunc:zX};function Lx(e){let{backend:t,attrs:r}=e,{shape:n,value:a,dtype:s}=r,i=s||w.inferDtype(a),o=w.getArrayFromDType(i,w.sizeFromShape(n));return LX(o,a,i),t.makeTensorInfo(n,i,o)}var DX={kernelName:Du,backendName:"cpu",kernelFunc:Lx};function LX(e,t,r){e.fill(t)}var BX={kernelName:Bo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,a=r,s=w.getTypedArrayFromDType(n.dtype,w.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,d=a.data.get(n.dataId).values;for(let h=0;h<i;h++){let p=h*l*o*u;for(let c=0;c<o;c++){let f=c*(l*u);for(let m=0;m<l;m++){let g=m*u;for(let y=0;y<u;y++){let A=Math.round(l-m-1),x=p+f+g+y,b=d[x];if(A>=0&&A<l){let v=A*u,C=p+f+v+y;b=d[C]}s[x]=b}}}}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},WX=Zt((e,t)=>Math.floor(e/t)),VX=yr(ni,WX,null,"int32"),UX={kernelName:ni,backendName:"cpu",kernelFunc:VX};function GX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=yI({inputs:{x:a,filter:s},backend:r,attrs:{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p}});if(i){let g=m;m=kh({inputs:{a:m,b:i},backend:r}),r.disposeIntermediateTensorInfo(g)}if(c){let g=m;m=zx(r,m,c,o,f),r.disposeIntermediateTensorInfo(g)}return m}var jX={kernelName:Ns,backendName:"cpu",kernelFunc:GX};function HX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=AI({inputs:{x:a,filter:s},backend:r,attrs:{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p}});if(i){let g=m;m=kh({inputs:{a:m,b:i},backend:r}),r.disposeIntermediateTensorInfo(g)}if(c){let g=m;m=zx(r,m,c,o,f),r.disposeIntermediateTensorInfo(g)}return m}var qX={kernelName:Es,backendName:"cpu",kernelFunc:HX};function KX(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=w.sizeFromShape(n.shape),i=a.shape,o=i[i.length-1],[l,u,d,h]=N.prepareAndValidate(n,a);if(u===0)return r.makeTensorInfo(l,n.dtype,[]);let p=r.data.get(a.dataId).values,c=r.bufferSync(n),f=_6(p,c,n.dtype,u,o,d,h,n.shape,s);return r.makeTensorInfo(l,n.dtype,f.values)}var XX={kernelName:Vo,backendName:"cpu",kernelFunc:KX};function ZX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n;Te([a,s],"gatherV2");let l=w.parseAxisParam(i,a.shape)[0],u=r.data.get(s.dataId).values,d=a.shape[l];for(let b=0;b<u.length;++b){let v=u[b];w.assert(v<=d-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${d-1}]`)}let h=o;o==null&&(h=0);let p=w.sizeFromShape(s.shape),c=N.segment_util.collectGatherOpShapeInfo(a,s,l,h),f=Mt({inputs:{x:a},backend:r,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),m=Mt({inputs:{x:s},backend:r,attrs:{shape:[c.batchSize,p/c.batchSize]}}),g=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],y=r.bufferSync(m),A=r.bufferSync(f),x=z6(A,y,g);return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),r.makeTensorInfo(c.outputShape,x.dtype,x.values)}var YX={kernelName:Wo,backendName:"cpu",kernelFunc:ZX};function JX(e){let{inputs:t,backend:r}=e,{input:n}=t,a=w.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=a/s,o=Mt({inputs:{x:n},backend:r,attrs:{shape:[i,s]}}),l=xI(o,!0,r),u=Mt({inputs:{x:l},backend:r,attrs:{shape:n.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(l),u}var QX={kernelName:qf,backendName:"cpu",kernelFunc:JX},eZ=mt(Lu,e=>Number.isFinite(e)?1:0,"bool"),tZ={kernelName:Lu,backendName:"cpu",kernelFunc:eZ},rZ=mt(Bu,e=>Math.abs(e)===1/0?1:0,"bool"),nZ={kernelName:Bu,backendName:"cpu",kernelFunc:rZ},aZ=mt(Wu,e=>Number.isNaN(e)?1:0,"bool"),sZ={kernelName:Wu,backendName:"cpu",kernelFunc:aZ};function iZ(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=W6(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var oZ={kernelName:Kf,backendName:"cpu",kernelFunc:iZ},lZ=mt(Vu,e=>Math.log1p(e)),uZ={kernelName:Vu,backendName:"cpu",kernelFunc:lZ},dZ=Zt((e,t)=>e&&t),pZ=yr(Ho,dZ,null,"bool"),hZ={kernelName:Ho,backendName:"cpu",kernelFunc:pZ},cZ=mt(Uu,e=>e?0:1,"bool"),fZ={kernelName:Uu,backendName:"cpu",kernelFunc:cZ},mZ=Zt((e,t)=>e||t),gZ=yr(Gp,mZ,null,"bool"),yZ={kernelName:Gp,backendName:"cpu",kernelFunc:gZ};function AZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Te(a,"LRN");let u=a.shape[3],d=u-1,h=r.data.get(a.dataId).values,p=w.sizeFromShape(a.shape),c=new Float32Array(p);function f(m){let g=m%u,y=m-g+Math.max(0,g-s),A=m-g+Math.min(g+s,d),x=0;for(;y<=A;y++){let b=h[y];x+=b*b}return x}for(let m=0;m<p;m++){let g=f(m),y=h[m]*Math.pow(i+o*g,-l);c[m]=y}return r.makeTensorInfo(a.shape,a.dtype,c)}var xZ={kernelName:jp,backendName:"cpu",kernelFunc:AZ};function bZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n;Te(i,"LRNGrad");let h=w.sizeFromShape(i.shape),p=i.shape[3],c=r.data.get(i.dataId).values,f=r.data.get(a.dataId).values,m=r.data.get(s.dataId).values,g=new Float32Array(h),y=h;for(let A=0;A<y;A++){let x=A%p,b=A-x+Math.max(0,x-o),v=A-x+Math.min(p,x+o+1),C=0;for(let S=b;S<v;S++)C+=Math.pow(f[S],2);C=u*C+l;for(let S=b;S<v;S++){let E=-2*u*d*f[S]*m[A]/C;A===S&&(E+=Math.pow(C,-d)),E*=c[A],g[S]+=E}}return r.makeTensorInfo(i.shape,a.dtype,g)}var vZ={kernelName:Xf,backendName:"cpu",kernelFunc:bZ};function bI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=r,l=a.shape,u=l.length,d=w.parseAxisParam(s,l),h=d,p=N.getAxesPermutation(h,u),c=o.data.get(a.dataId).values;if(p!=null){let b=new Array(u);for(let v=0;v<b.length;v++)b[v]=l[p[v]];c=Fx(c,l,a.dtype,p,b),h=N.getInnerMostAxes(h.length,u),l=b}Te(a,"max"),N.assertAxesAreInnerMostDims("max",h,u);let[f,m]=N.computeOutAndReduceShapes(l,h),g=w.sizeFromShape(m),y=U6(c,g,f,a.dtype),A=o.write(y,f,a.dtype),x=f;return i&&(x=N.expandShapeToKeepDim(f,d)),{dataId:A,shape:x,dtype:a.dtype}}var wZ={kernelName:ui,backendName:"cpu",kernelFunc:bI};function kZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Te(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l),h;if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))h=Ma({inputs:{x:a},backend:r});else{let p=r.data.get(a.dataId).values,c=w.computeStrides(a.shape),f=Ox(p,a.shape,a.dtype,c,d,"max");h=r.makeTensorInfo(d.outShape,a.dtype,f.values)}return h}var IZ={kernelName:pi,backendName:"cpu",kernelFunc:kZ};function SZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Te(a,"maxPool3d");let d=N.computePool3DInfo(a.shape,s,i,1,o,l,u),h=r.data.get(a.dataId).values,p=gI(h,a.shape,a.dtype,w.computeStrides(a.shape),d,"max");return r.makeTensorInfo(p.shape,"float32",p.values)}var CZ={kernelName:Hp,backendName:"cpu",kernelFunc:SZ};function TZ(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Te([a,s],"maxPool3DGrad");let d=N.computePool3DInfo(s.shape,i,o,1,l,u),h=r.bufferSync(s),p=gK(h,d),c=d.strideDepth,f=d.strideHeight,m=d.strideWidth,g=d.dilationDepth,y=d.dilationHeight,A=d.dilationWidth,x=d.effectiveFilterDepth,b=d.effectiveFilterHeight,v=d.effectiveFilterWidth,C=x-1-d.padInfo.front,S=v-1-d.padInfo.left,E=b-1-d.padInfo.top,R=We(s.shape,"float32"),_=r.bufferSync(a);for(let M=0;M<d.batchSize;++M)for(let I=0;I<d.inChannels;++I)for(let O=0;O<d.inDepth;++O)for(let z=0;z<d.inHeight;++z)for(let j=0;j<d.inWidth;++j){let X=O-C,D=z-E,Q=j-S,V=0;for(let ee=0;ee<x;ee+=g){let Y=(X+ee)/c;if(!(Y<0||Y>=d.outDepth||Math.floor(Y)!==Y))for(let se=0;se<b;se+=y){let Z=(D+se)/f;if(!(Z<0||Z>=d.outHeight||Math.floor(Z)!==Z))for(let ae=0;ae<v;ae+=A){let de=(Q+ae)/m;if(de<0||de>=d.outWidth||Math.floor(de)!==de)continue;let Ae=x*b*v-1-p.get(M,Y,Z,de,I),be=ee*b*v+se*v+ae,Ee=Ae===be?1:0;Ee!==0&&(V+=_.get(M,Y,Z,de,I)*Ee)}}}R.set(V,M,O,z,j,I)}return r.makeTensorInfo(R.shape,R.dtype,R.values)}var NZ={kernelName:Yf,backendName:"cpu",kernelFunc:TZ};function EZ(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;Te([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=r.data.get(o.dataId).values,f=We(p.outShape,o.dtype,mI(c,o.shape,o.dtype,p).values),m=p.strideHeight,g=p.strideWidth,y=p.dilationHeight,A=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,v=b-1-p.padInfo.left,C=x-1-p.padInfo.top,S=We(o.shape,"float32"),E=r.data.get(a.dataId).values,R=We(a.shape,"float32",E);for(let _=0;_<p.batchSize;++_)for(let M=0;M<p.inChannels;++M)for(let I=0;I<p.inHeight;++I)for(let O=0;O<p.inWidth;++O){let z=I-C,j=O-v,X=0;for(let D=0;D<x;D+=y){let Q=(z+D)/m;if(!(Q<0||Q>=p.outHeight||Math.floor(Q)!==Q))for(let V=0;V<b;V+=A){let ee=(j+V)/g;if(ee<0||ee>=p.outWidth||Math.floor(ee)!==ee)continue;let Y=x*b-1-f.get(_,Q,ee,M),se=D*b+V,Z=Y===se?1:0;Z!==0&&(X+=R.get(_,Q,ee,M)*Z)}}S.set(X,_,I,O,M)}return r.makeTensorInfo(S.shape,S.dtype,S.values)}var RZ={kernelName:Zf,backendName:"cpu",kernelFunc:EZ};function MZ(e,t,r,n,a){let s=w.computeStrides(t),i=Ox(e,t,r,s,a,"max"),o=mI(e,t,r,a,!0,n);return[i.values,o.values]}var FZ={kernelName:Jf,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;Te(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,d=N.computePool2DInfo(n.shape,a,s,[1,1],i),[h,p]=MZ(u,n.shape,n.dtype,o,d),c=l.write(h,d.outShape,n.dtype),f=l.write(p,d.outShape,n.dtype);return[{dataId:c,shape:d.outShape,dtype:n.dtype},{dataId:f,shape:d.outShape,dtype:"int32"}]}};function $Z(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=w.parseAxisParam(s,a.shape),l=N.computeOutAndReduceShapes(a.shape,o)[1],u=w.sizeFromShape(l),d=[],h=r.makeTensorInfo([],"float32",new Float32Array([u]));d.push(h);let p=Bs({inputs:{x:a},backend:r,attrs:{dtype:"float32"}});d.push(p);let c=Dx({inputs:{a:p,b:h},backend:r});d.push(c);let f=Ih({inputs:{x:c},backend:r,attrs:{axis:s,keepDims:i}});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var PZ={kernelName:hi,backendName:"cpu",kernelFunc:$Z};function _Z(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Te(a,"min");let o=w.parseAxisParam(s,a.shape),l=o,u=N.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=Fn({inputs:{x:a},backend:r,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",l,d.shape.length);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(h),d.dtype),m=r.data.get(d.dataId).values;for(let y=0;y<f.length;++y){let A=y*c,x=m[A];for(let b=0;b<c;++b){let v=m[A+b];(Number.isNaN(v)||v<x)&&(x=v)}f[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,f);if(i){let y=N.expandShapeToKeepDim(h,o),A=Mt({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var zZ={kernelName:ci,backendName:"cpu",kernelFunc:_Z};function OZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,mode:i}=n;Te(a,"mirrorPad");let o=s.map((A,x)=>A[0]+a.shape[x]+A[1]),l=s.map(A=>A[0]),u=s.map((A,x)=>A[0]+a.shape[x]),d=i==="reflect"?0:1,h=r.data.get(a.dataId).values,p=a.shape.length,c=w.computeStrides(a.shape),f=w.sizeFromShape(o),m=o.length,g=w.computeStrides(o),y=w.getTypedArrayFromDType(a.dtype,f);for(let A=0;A<f;A++){let x=w.indexToLoc(A,m,g);for(let v=0;v<m;v++)x[v]<l[v]?x[v]=l[v]*2-x[v]-d:x[v]>=u[v]&&(x[v]=(u[v]-1)*2-x[v]+d);x=x.map((v,C)=>v-l[C]);let b=w.locToIndex(x,p,c);y[A]=h[b]}return{dataId:r.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var DZ={kernelName:mi,backendName:"cpu",kernelFunc:OZ},LZ=Zt((e,t)=>{let r=e%t;return e<0&&t<0||e>=0&&t>=0?r:(r+t)%t}),BZ=yr(Gu,LZ),WZ={kernelName:Gu,backendName:"cpu",kernelFunc:BZ},VZ=Eo(Mf());function vI(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=a.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=w.parseAxisParam([o],a.shape),u=bI({inputs:{x:a},backend:r,attrs:{reductionIndices:l,keepDims:!1}}),d=N.expandShapeToKeepDim(u.shape,l),h=Mt({inputs:{x:u},backend:r,attrs:{shape:d}}),p=_x({inputs:{a,b:h},backend:r}),c=F6({inputs:{x:p},backend:r}),f=Ih({inputs:{x:c},backend:r,attrs:{axis:l,keepDims:!1}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Dx({inputs:{a:c,b:m},backend:r});return r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var UZ={kernelName:Ni,backendName:"cpu",kernelFunc:vI};function GZ(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n;Te(a,"multinomial");let l=o?a:vI({inputs:{logits:a},backend:r,attrs:{dim:-1}}),u=l.shape[0],d=l.shape[1],h=r.data.get(l.dataId).values,p=[u,s],c=w.makeZerosTypedArray(w.sizeFromShape(p),"int32");for(let f=0;f<u;++f){let m=f*d,g=new Float32Array(d-1);g[0]=h[m];for(let x=1;x<g.length;++x)g[x]=g[x-1]+h[m+x];let y=VZ.alea(i.toString()),A=f*s;for(let x=0;x<s;++x){let b=y();c[A+x]=g.length;for(let v=0;v<g.length;v++)if(b<g[v]){c[A+x]=v;break}}}return o||r.disposeIntermediateTensorInfo(l),r.makeTensorInfo(p,"int32",c)}var jZ={kernelName:Qf,backendName:"cpu",kernelFunc:GZ},HZ=Un.nonMaxSuppressionV3Impl;function qZ(e){let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n;Te(a,"NonMaxSuppression");let u=r.data.get(a.dataId).values,d=r.data.get(s.dataId).values,{selectedIndices:h}=HZ(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var KZ={kernelName:Xo,backendName:"cpu",kernelFunc:qZ},XZ=Un.nonMaxSuppressionV4Impl;function ZZ(e){let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n;Te(a,"NonMaxSuppressionPadded");let d=r.data.get(a.dataId).values,h=r.data.get(s.dataId).values,{selectedIndices:p,validOutputs:c}=XZ(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var YZ={kernelName:ju,backendName:"cpu",kernelFunc:ZZ},JZ=Un.nonMaxSuppressionV5Impl;function QZ(e){let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n;Te(a,"NonMaxSuppressionWithScore");let d=r.data.get(a.dataId).values,h=r.data.get(s.dataId).values,p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=JZ(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var eY={kernelName:Zo,backendName:"cpu",kernelFunc:QZ};function tY(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n;Te(a,"oneHot");let l=w.sizeFromShape(a.shape),u=new Float32Array(l*s);u.fill(o);let d=r.data.get(a.dataId).values;for(let h=0;h<l;++h)d[h]>=0&&d[h]<s&&(u[h*s+d[h]]=i);return r.makeTensorInfo([...a.shape,s],"int32",u)}var rY={kernelName:Jo,backendName:"cpu",kernelFunc:tY};function Sf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(n.dtype==="complex64"){let a=Io({inputs:{input:n},backend:r}),s=Sf({inputs:{x:a},backend:r}),i=vu({inputs:{input:n},backend:r}),o=Sf({inputs:{x:i},backend:r}),l=on({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Lx({backend:r,attrs:{shape:n.shape,value:0,dtype:n.dtype}})}var nY={kernelName:ml,backendName:"cpu",kernelFunc:Sf};function wI(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(n.dtype==="complex64"){let a=Io({inputs:{input:n},backend:r}),s=wI({inputs:{x:a},backend:r}),i=vu({inputs:{input:n},backend:r}),o=Sf({inputs:{x:i},backend:r}),l=on({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Lx({backend:r,attrs:{shape:n.shape,value:1,dtype:n.dtype}})}var aY={kernelName:Yo,backendName:"cpu",kernelFunc:wI};function kI(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return If({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=If({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=wu({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var sY={kernelName:Qo,backendName:"cpu",kernelFunc:kI};function iY(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;Te(a,"pad");let o=s.map((y,A)=>y[0]+a.shape[A]+y[1]),l=s.map(y=>y[0]),u=r.data.get(a.dataId).values,d=w.sizeFromShape(a.shape),h=a.shape.length,p=w.computeStrides(a.shape),c=w.sizeFromShape(o),f=o.length,m=w.computeStrides(o),g=w.getTypedArrayFromDType(a.dtype,c);i!==0&&g.fill(i);for(let y=0;y<d;y++){let A=w.indexToLoc(y,h,p).map((b,v)=>b+l[v]),x=w.locToIndex(A,f,m);g[x]=u[y]}return{dataId:r.write(g,o,a.dtype),shape:o,dtype:a.dtype}}var II={kernelName:yi,backendName:"cpu",kernelFunc:iY},oY=Zt((e,t)=>Math.pow(e,t)),lY=yr(Ai,oY),uY={kernelName:Ai,backendName:"cpu",kernelFunc:lY};function dY(e){let{backend:t,attrs:r}=e,{start:n,stop:a,dtype:s,step:i}=r,o=$x(n,a,i,s);return t.makeTensorInfo([o.length],s,o)}var pY={kernelName:Hu,backendName:"cpu",kernelFunc:dY},hY=mt(qu,e=>1/e),cY={kernelName:qu,backendName:"cpu",kernelFunc:hY};function fY(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n;Te(a,"resizeBilinear");let l=w.computeStrides(a.shape),[u,d]=o,[h,p,c,f]=a.shape,m=r.data.get(a.dataId).values,g=new Float32Array(w.sizeFromShape([h,u,d,f])),y=[s&&u>1?p-1:p,s&&d>1?c-1:c],A=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=0,b=y[0]/A[0],v=y[1]/A[1];for(let C=0;C<h;C++)for(let S=0;S<u;S++){let E;i?E=b*(S+.5)-.5:E=b*S;let R=Math.max(0,Math.floor(E)),_=E-R,M=Math.min(p-1,Math.ceil(E)),I=C*l[0]+R*l[1],O=C*l[0]+M*l[1];for(let z=0;z<d;z++){let j;i?j=v*(z+.5)-.5:j=v*z;let X=Math.max(0,Math.floor(j)),D=j-X,Q=Math.min(c-1,Math.ceil(j)),V=I+X*l[2],ee=O+X*l[2],Y=I+Q*l[2],se=O+Q*l[2];for(let Z=0;Z<f;Z++){let ae=m[V+Z],de=m[ee+Z],Ae=m[Y+Z],be=m[se+Z],Ee=ae+(Ae-ae)*D,Me=de+(be-de)*D,De=Ee+(Me-Ee)*_;g[x++]=De}}}return r.makeTensorInfo([h,u,d,f],"float32",g)}var mY={kernelName:vi,backendName:"cpu",kernelFunc:fY};function gY(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n;Te([s,a],"resizeBilinearGrad");let o=w.computeStrides(a.shape),[l,u,d,h]=a.shape,[,p,c]=s.shape,f=new Float32Array(l*u*d*h),m=[i&&p>1?u-1:u,i&&c>1?d-1:d],g=[i&&p>1?p-1:p,i&&c>1?c-1:c],y=m[0]/g[0],A=m[1]/g[1],x=r.data.get(s.dataId).values,b=0;for(let v=0;v<l;v++){let C=v*o[0];for(let S=0;S<p;S++){let E=S*y,R=Math.floor(E),_=Math.min(Math.ceil(E),u-1),M=C+R*o[1],I=C+_*o[1],O=E-R,z=1-O;for(let j=0;j<c;j++){let X=j*A,D=Math.floor(X),Q=Math.min(Math.ceil(X),d-1),V=X-D,ee=1-V,Y=M+D*o[2],se=M+Q*o[2],Z=I+D*o[2],ae=I+Q*o[2],de=z*ee,Ae=z*V,be=O*ee,Ee=O*V;for(let Me=0;Me<h;Me++){let De=x[b++];f[Y+Me]+=De*de,f[se+Me]+=De*Ae,f[Z+Me]+=De*be,f[ae+Me]+=De*Ee}}}}return r.makeTensorInfo([l,d,u,h],"float32",f)}var yY={kernelName:tm,backendName:"cpu",kernelFunc:gY};function AY(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n;Te(a,"resizeNearestNeighbor");let l=w.computeStrides(a.shape),[u,d]=o,[h,p,c,f]=a.shape,m=r.data.get(a.dataId).values,g=new Float32Array(h*u*d*f),y=[s&&u>1?p-1:p,s&&d>1?c-1:c],A=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=y[0]/A[0],b=y[1]/A[1],v=0;for(let C=0;C<h;C++){let S=C*l[0];for(let E=0;E<u;E++){let R=i?x*(E+.5):x*E,_=Math.min(p-1,s?Math.round(R):Math.floor(R));i&&(_=Math.max(0,_));let M=S+_*l[1];for(let I=0;I<d;I++){let O=i?b*(I+.5):b*I,z=Math.min(c-1,s?Math.round(O):Math.floor(O));i&&(z=Math.max(0,z));let j=M+z*l[2];for(let X=0;X<f;X++){let D=m[j+X];g[v++]=D}}}}return r.makeTensorInfo([h,u,d,f],a.dtype,g)}var xY={kernelName:Ku,backendName:"cpu",kernelFunc:AY};function bY(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n;Te([s,a],"resizeNearestNeighborGrad");let o=w.computeStrides(a.shape),l=w.computeStrides(s.shape),[u,d,h,p]=a.shape,[,c,f]=s.shape,m=new Float32Array(u*d*h*p),g=r.data.get(s.dataId).values,y=[i&&c>1?d-1:d,i&&f>1?h-1:h],A=[i&&c>1?c-1:c,i&&f>1?f-1:f],x=y[0]/A[0],b=y[1]/A[1],v=1/x,C=1/b,S=Math.ceil(v)*2+2,E=Math.ceil(C)*2+2;for(let R=0;R<u;R++){let _=R*o[0];for(let M=0;M<d;M++){let I=_+M*o[1],O=Math.floor(M*v),z=Math.floor(O-S/2);for(let j=0;j<h;j++){let X=I+j*o[2],D=Math.floor(j*C),Q=Math.floor(D-E/2);for(let V=0;V<p;V++){let ee=0;for(let Y=0;Y<S;Y++){let se=Y+z;if(se<0||se>=c)continue;let Z=_+se*l[1],ae=se*x,de=Math.min(d-1,i?Math.round(ae):Math.floor(ae));if(M===de)for(let Ae=0;Ae<E;Ae++){let be=Ae+Q;if(be<0||be>=f)continue;let Ee=Z+be*l[2],Me=be*b,De=Math.min(h-1,i?Math.round(Me):Math.floor(Me));j===De&&(ee+=g[Ee+V])}}m[X+V]=ee}}}}return r.makeTensorInfo(a.shape,a.dtype,m)}var vY={kernelName:em,backendName:"cpu",kernelFunc:bY};function wY(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n;Te(a,"reverse");let i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return Ma({inputs:{x:a},backend:r});let l=new rr(a.shape,a.dtype),u=r.bufferSync(a);for(let d=0;d<l.size;d++){let h=l.indexToLoc(d),p=h.slice();o.forEach(c=>p[c]=a.shape[c]-1-p[c]),l.set(u.get(...p),...h)}return r.makeTensorInfo(l.shape,l.dtype,l.values)}var kY={kernelName:rl,backendName:"cpu",kernelFunc:wY},IY={kernelName:gl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=w.getTypedArrayFromDType(n.dtype,w.sizeFromShape(n.shape)),[u,d,h,p]=n.shape,[c,f]=N.getImageCenter(i,d,h),m=255,g=Math.sin(a),y=Math.cos(a),A=o.data.get(n.dataId).values;for(let x=0;x<u;x++){let b=x*h*d*p;for(let v=0;v<d;v++){let C=v*(h*p);for(let S=0;S<h;S++){let E=S*p;for(let R=0;R<p;R++){let _=[u,v,S,R],M=_[2],I=_[1],O=(M-c)*y-(I-f)*g,z=(M-c)*g+(I-f)*y;O=Math.round(O+c),z=Math.round(z+f);let j=s;if(typeof s!="number"&&(R===3?j=m:j=s[R]),O>=0&&O<h&&z>=0&&z<d){let D=z*(h*p),Q=O*p,V=b+D+Q+R;j=A[V]}let X=b+C+E+R;l[X]=j}}}}return{dataId:o.write(l,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},SY=mt(nl,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}),CY={kernelName:nl,backendName:"cpu",kernelFunc:SY};function SI(e,t,r,n,a,s,i,o,l,u){let d=[n/a,a],h=e.values,p=t.values;if(n===0)return We(r,t.dtype);let c=We(d,t.dtype);c.values.fill(l);for(let f=0;f<s;f++){let m=[],g=0;for(let y=0;y<i;y++){let A=h[f*i+y];m.push(A),g+=A*o[y]}if(g<0||g>=n/a)throw new Error(`Invalid indices: ${m} does not index into ${r}`);for(let y=0;y<a;y++)u?c.values[g*a+y]+=p[f*a+y]:c.values[g*a+y]=t.rank===0?p[0]:p[f*a+y]}return c}function TY(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=!0,c=r.bufferSync(a),f=r.bufferSync(s),m=SI(c,f,i,h,u,l,o,d,0,p);return r.makeTensorInfo(i,m.dtype,m.values)}var NY={kernelName:al,backendName:"cpu",kernelFunc:TY};function EY(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t;Te([n,a,s],"select");let i=n.shape.length,o=r.data.get(n.dataId).values,l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Dr(a.dtype,s.dtype),h=w.makeZerosTypedArray(w.sizeFromShape(a.shape),d),p=0,c=i===0||i>1||a.shape.length===1?1:w.sizeFromShape(a.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<c;m++)o[f]===1?h[p++]=l[f]:h[p++]=u[f];return r.makeTensorInfo(a.shape,d,h)}var RY={kernelName:sl,backendName:"cpu",kernelFunc:EY},MY=N.SELU_SCALEALPHA,FY=N.SELU_SCALE,$Y=mt(Xu,e=>e>=0?FY*e:MY*(Math.exp(e)-1)),PY={kernelName:Xu,backendName:"cpu",kernelFunc:$Y},_Y=mt(Zu,e=>e<0?-1:e>0?1:0),zY={kernelName:Zu,backendName:"cpu",kernelFunc:_Y},OY=mt(Ii,e=>Math.sin(e)),DY={kernelName:Ii,backendName:"cpu",kernelFunc:OY},LY=mt(ol,e=>Math.sinh(e)),BY={kernelName:ol,backendName:"cpu",kernelFunc:LY},WY=11920928955078125e-23,ov=Math.log(WY)+2,VY=mt(Yu,e=>{let t=e>-ov,r=e<ov,n=Math.exp(e),a;return r?a=n:t?a=e:a=Math.log(1+n),a}),UY={kernelName:Yu,backendName:"cpu",kernelFunc:VY};function GY(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;Te([a],"spaceToBatchND");let o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=II.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=Mt({inputs:{x:u},backend:r,attrs:{shape:d}}),f=Fn({inputs:{x:c},backend:r,attrs:{perm:h}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),m}var jY={kernelName:ll,backendName:"cpu",kernelFunc:GY};function HY(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
        ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
        ${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
        ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let o=r.data.get(n.dataId).values,l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=r.data.get(i.dataId).values[0],[h,p,c,f,m]=Y6(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var qY={kernelName:Kp,backendName:"cpu",kernelFunc:HY};function KY(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.data.get(a.dataId).values),o=r.data.get(n.dataId).values,l=Array.from(r.data.get(s.dataId).values),[u,d,h]=J6(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var XY={kernelName:Ju,backendName:"cpu",kernelFunc:KY};function ZY(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
          ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
          ${s.shape}`);if(a.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=r.data.get(n.dataId).values,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,[u,d]=Px(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var YY={kernelName:Xp,backendName:"cpu",kernelFunc:ZY};function JY(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
         ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
         ${s.shape}`);if(a.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=r.data.get(n.dataId).values,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,[u,d]=Px(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var QY={kernelName:Zp,backendName:"cpu",kernelFunc:JY};function eJ(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=N.calculateShapes(s,a,o),c=!1,f=r.bufferSync(a),m=r.bufferSync(s),g=r.data.get(i.dataId).values[0],y=SI(f,m,o,p,d,u,l,h,g,c);return r.makeTensorInfo(o,y.dtype,y.values)}var tJ={kernelName:Yp,backendName:"cpu",kernelFunc:eJ};function rJ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),d=a.shape.slice();return l.map(h=>{let p=[...d];p[o]=h;let c=So({inputs:{x:a},backend:r,attrs:{begin:u,size:p}});return u[o]+=h,c})}var nJ={kernelName:ul,backendName:"cpu",kernelFunc:rJ},aJ={kernelName:Qu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t;Te(r,"square");let a=n.data.get(r.dataId).values,s=new Float32Array(a.length);for(let i=0;i<a.length;++i){let o=a[i];s[i]=o*o}return{dataId:n.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},sJ=mt($i,(e,t)=>{let r=t;return isNaN(e)?NaN:e>0?1:r.alpha}),iJ={kernelName:$i,backendName:"cpu",kernelFunc:sJ};function oJ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n;Te(a,"stridedSlice");let{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=Mt({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let C=_t.computeOutShape(A,x,b),S=So({inputs:{x:a},backend:r,attrs:{begin:A,size:C}});v=Mt({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(S)}else{let C=r.bufferSync(a),S=eI(c,C,b,A);v=r.makeTensorInfo(f,S.dtype,S.values)}return v}var lJ={kernelName:dl,backendName:"cpu",kernelFunc:oJ};function uJ(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.data.get(d.dataId).values,c=r.data.get(h.dataId).values,[f,m]=tI(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var dJ={kernelName:Jp,backendName:"cpu",kernelFunc:uJ};function pJ(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{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=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values[0],[u,d,h]=rI(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var hJ={kernelName:rm,backendName:"cpu",kernelFunc:pJ};function cJ(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.data.get(s.dataId).values,o=nI(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var fJ={kernelName:nm,backendName:"cpu",kernelFunc:cJ},mJ=mt(pl,e=>Math.tan(e)),gJ={kernelName:pl,backendName:"cpu",kernelFunc:mJ},yJ=mt(Mi,e=>Math.tanh(e)),AJ={kernelName:Mi,backendName:"cpu",kernelFunc:yJ};function xJ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;Te(a,"tile");let i=sI(r.bufferSync(a),s);return r.makeTensorInfo(i.shape,i.dtype,i.values)}var bJ={kernelName:Ka,backendName:"cpu",kernelFunc:xJ};function vJ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n;Te(a,"topk");let o=r.data.get(a.dataId).values,[l,u]=oI(o,a.shape,a.dtype,s,i);return[r.makeTensorInfo(l.shape,l.dtype,l.values),r.makeTensorInfo(u.shape,u.dtype,u.values)]}var wJ={kernelName:hl,backendName:"cpu",kernelFunc:vJ};function kJ(e){let{inputs:t,attrs:r,backend:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=w.computeStrides(a.shape),A=y[0],x=y[1],b=y[2],v=w.getTypedArrayFromDType(a.dtype,w.sizeFromShape(g));v.fill(l);let C=n.data.get(a.dataId).values,S=n.data.get(s.dataId).values;for(let E=0;E<d;++E){let R=s.shape[0]===1?S:S.subarray(E*8,E*8+8);for(let _=0;_<f;++_)for(let M=0;M<m;++M)for(let I=0;I<c;++I){let O,z=R[6]*M+R[7]*_+1;if(z===0)continue;let j=(R[0]*M+R[1]*_+R[2])/z,X=(R[3]*M+R[4]*_+R[5])/z,D=lv(j,p,o),Q=lv(X,h,o);switch(i){case"nearest":O=EJ(C,h,p,A,x,b,E,Q,D,I,l);break;case"bilinear":O=RJ(C,h,p,A,x,b,E,Q,D,I,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let V=E*A+_*x+M*b+I;v[V]=O}return n.makeTensorInfo(g,a.dtype,v)}return{dataId:n.write(v,g,a.dtype),shape:a.shape,dtype:a.dtype}}var IJ={kernelName:cl,backendName:"cpu",kernelFunc:kJ};function lv(e,t,r){switch(r){case"reflect":return SJ(e,t);case"wrap":return CJ(e,t);case"nearest":return NJ(e,t);case"constant":default:return TJ(e,t)}}function SJ(e,t){let r=e;if(r<0)if(t<=1)r=0;else{let n=2*t;r<n&&(r=n*Math.trunc(-r/n)+r),r=r<-t?r+n:-r-1}else if(r>t-1)if(t<=1)r=0;else{let n=2*t;r-=n*Math.trunc(r/n),r>=t&&(r=n-r-1)}return w.clamp(0,r,t-1)}function CJ(e,t){let r=e;if(r<0)if(t<=1)r=0;else{let n=t-1;r+=t*(Math.trunc(-r/n)+1)}else if(r>t-1)if(t<=1)r=0;else{let n=t-1;r-=t*Math.trunc(r/n)}return w.clamp(0,r,t-1)}function TJ(e,t){return e}function NJ(e,t){return w.clamp(0,e,t-1)}function cp(e,t,r,n,a,s,i,o,l,u,d){let h=i*n+o*a+l*s+u;return 0<=o&&o<t&&0<=l&&l<r?e[h]:d}function EJ(e,t,r,n,a,s,i,o,l,u,d){let h=Math.round(o),p=Math.round(l);return cp(e,t,r,n,a,s,i,h,p,u,d)}function RJ(e,t,r,n,a,s,i,o,l,u,d){let h=Math.floor(o),p=Math.floor(l),c=h+1,f=p+1,m=(f-l)*cp(e,t,r,n,a,s,i,h,p,u,d)+(l-p)*cp(e,t,r,n,a,s,i,h,f,u,d),g=(f-l)*cp(e,t,r,n,a,s,i,c,p,u,d)+(l-p)*cp(e,t,r,n,a,s,i,c,f,u,d);return(c-o)*m+(o-h)*g}function MJ(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;Te(s,"unique");let i=n.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:u}=lI(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var FJ={kernelName:am,backendName:"cpu",kernelFunc:MJ};function $J(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a.shape.length,o=a.shape[s],l=new Array(i-1),u=0;for(let c=0;c<i;c++)c!==s&&(l[u++]=a.shape[c]);let d=new Array(i).fill(0),h=a.shape.slice();h[s]=1;let p=new Array(o);for(let c=0;c<p.length;c++){d[s]=c;let f=So({inputs:{x:a},backend:r,attrs:{begin:d,size:h}});p[c]=Mt({inputs:{x:f},backend:r,attrs:{shape:l}}),r.disposeIntermediateTensorInfo(f)}return p}var PJ={kernelName:fl,backendName:"cpu",kernelFunc:$J};function _J(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n;Te(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,u=[],d=[],h=o-l,p=s;for(let f=0;f<h;++f){let m=If({inputs:{input:p},backend:r,attrs:{dim:f+1}});p=m,d.push(m)}for(let f=0;f<i;++f){let m=w.createScalarValue(f,"int32"),g=r.makeTensorInfo([],"int32",m),y=R6({inputs:{a:g,b:p},backend:r}),A=Bs({inputs:{x:y},backend:r,attrs:{dtype:"float32"}}),x=n0({inputs:{a:A,b:a},backend:r}),b=Ih({inputs:{x},backend:r,attrs:{axis:0,keepDims:!1}});u.push(b),d.push(g),d.push(y),d.push(A),d.push(x),d.push(b)}let c=kI({inputs:u,backend:r,attrs:{axis:0}});return d.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var zJ={kernelName:Qp,backendName:"cpu",kernelFunc:_J},OJ=[Gq,zH,Hq,Kq,VH,Zq,Jq,eK,rK,aK,iK,lK,dK,cK,mK,AK,bK,wK,IK,Vq,CK,NK,RK,FK,BH,GH,PK,OH,zK,DK,LK,WK,UK,jK,qK,XK,YK,QK,tX,nX,sX,oX,lX,dX,hX,fX,mX,gX,yX,bX,_q,wX,jH,RX,HH,MX,KH,OX,DX,BX,ZH,UX,jX,qX,XX,YX,JH,eq,DH,QX,OK,tZ,nZ,sZ,zq,rq,aq,oZ,iq,uZ,hZ,fZ,yZ,xZ,vZ,wZ,lq,IZ,CZ,NZ,RZ,FZ,PZ,zZ,dq,DZ,WZ,jZ,hq,fq,KZ,YZ,eY,gq,rY,aY,sY,II,uY,Dq,xq,pY,LH,Ty,cY,Lq,Bq,Wq,mY,yY,xY,vY,kY,IY,CY,vq,NY,RY,PY,kq,zY,DY,BY,Iq,UZ,UY,jY,qY,XY,YY,QY,tJ,nJ,Tq,aJ,Eq,iJ,lJ,dJ,hJ,fJ,$q,AX,gJ,AJ,bJ,wJ,IJ,yq,FJ,PJ,zJ,nY];for(let e of OJ)Wn(e);var CI={};Le(CI,{assertNotComplex:()=>cd,bindCanvasToFramebuffer:()=>XJ,bindColorTextureToFramebuffer:()=>Kc,bindTextureToProgramUniformSampler:()=>WI,bindTextureUnit:()=>DI,bindVertexBufferToProgramAttribute:()=>Ey,callAndCheck:()=>we,canBeRepresented:()=>TI,createFragmentShader:()=>RI,createFramebuffer:()=>OI,createProgram:()=>MI,createStaticIndexBuffer:()=>PI,createStaticVertexBuffer:()=>$I,createTexture:()=>_I,createVertexShader:()=>EI,getBatchDim:()=>Co,getExtensionOrThrow:()=>fp,getFramebufferErrorMessage:()=>VI,getMaxTexturesInShader:()=>HI,getNumChannels:()=>qJ,getProgramUniformLocation:()=>BI,getProgramUniformLocationOrThrow:()=>LI,getRowsCols:()=>To,getShapeAs3D:()=>Xc,getTextureShapeFromLogicalShape:()=>GI,getWebGLDisjointQueryTimerVersion:()=>qI,getWebGLErrorMessage:()=>NI,getWebGLMaxTextureSize:()=>jI,hasExtension:()=>Sn,isCapableOfRenderingToFloatTexture:()=>KI,isDownloadFloatTextureEnabled:()=>XI,isReshapeFree:()=>Pp,isWebGLFenceEnabled:()=>ZI,isWebGLVersionEnabled:()=>My,linkProgram:()=>FI,resetMaxTextureSize:()=>ZJ,resetMaxTexturesInShader:()=>YJ,unbindColorTextureFromFramebuffer:()=>Ry,unbindTextureUnit:()=>KJ,validateFramebuffer:()=>mp,validateProgram:()=>qc,validateTextureSize:()=>zI});var uo={},W1={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function a0(e,t){uo[e]=t}function fa(e,t){if(!(e in uo)||t!=null){let n=LJ(e,t);if(n!==null)uo[e]=n;else return console.log("Could not get context for WebGL version",e),null}let r=uo[e];return r==null||r.isContextLost()?(delete uo[e],fa(e)):(r.disable(r.DEPTH_TEST),r.disable(r.STENCIL_TEST),r.disable(r.BLEND),r.disable(r.DITHER),r.disable(r.POLYGON_OFFSET_FILL),r.disable(r.SAMPLE_COVERAGE),r.enable(r.SCISSOR_TEST),r.enable(r.CULL_FACE),r.cullFace(r.BACK),uo[e])}function DJ(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 LJ(e,t){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let r=t==null?DJ(e):t;return r.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete uo[e]},!1),e===1?r.getContext("webgl",W1)||r.getContext("experimental-webgl",W1):r.getContext("webgl2",W1)}function Sh(e,t){return[t,e]}function BJ(e,t){return e*t}function Bc(e){let t=w.sizeFromShape(e),r=Math.ceil(t/4);return w.sizeToSquarishShape(r)}function hd(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function WJ(e,t){let[r,n]=hd(e,t);return r*n*4}function Bx(e,t){let r=e,n,a,s,i,o,l,u,d,h,p;return J().getNumber("WEBGL_VERSION")===2?(n=r.R32F,a=r.R16F,s=r.RGBA16F,i=r.RGBA32F,o=r.RED,u=4,d=1,h=r.HALF_FLOAT,p=r.FLOAT,l=r.RGBA8):(n=e.RGBA,a=e.RGBA,s=e.RGBA,i=r.RGBA,o=e.RGBA,u=4,d=4,h=t!=null?t.HALF_FLOAT_OES:null,p=e.FLOAT,l=e.RGBA),{internalFormatFloat:n,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:d,textureTypeHalfFloat:h,textureTypeFloat:p}}function we(e,t){let r=t();return J().getBool("DEBUG")&&VJ(e),r}function VJ(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+NI(e,t))}var UJ=596e-10,GJ=65504;function TI(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||UJ<Math.abs(e)&&Math.abs(e)<GJ)}function NI(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function fp(e,t){return Qa(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function EI(e,t){let r=Qa(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(we(e,()=>e.shaderSource(r,t)),we(e,()=>e.compileShader(r)),e.getShaderParameter(r,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(r)),new Error("Failed to compile vertex shader.");return r}function RI(e,t){let r=Qa(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(we(e,()=>e.shaderSource(r,t)),we(e,()=>e.compileShader(r)),e.getShaderParameter(r,e.COMPILE_STATUS)===!1)throw HJ(t,e.getShaderInfoLog(r)),new Error("Failed to compile fragment shader.");return r}var jJ=/ERROR: [0-9]+:([0-9]+):/g;function HJ(e,t){let r=jJ.exec(t);if(r==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let n=+r[1],a=e.split(`
`),s=a.length.toString().length+2,i=a.map((h,p)=>w.rightPad((p+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,n-1),u=i.slice(n-1,n),d=i.slice(n);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${w.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(d.join(`
`))}function MI(e){return Qa(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function FI(e,t){if(we(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 qc(e,t){if(we(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function $I(e,t){let r=Qa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),we(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),r}function PI(e,t){let r=Qa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,r)),we(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),r}function qJ(){return J().getNumber("WEBGL_VERSION")===2?1:4}function _I(e){return Qa(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function zI(e,t){let r=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let n=`[${e}x${t}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(e>r||t>r){let n=`[${e}x${t}]`,a=`[${r}x${r}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+a+".")}}function OI(e){return Qa(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Ey(e,t,r,n,a,s,i){let o=e.getAttribLocation(t,r);return o===-1?!1:(we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),we(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),we(e,()=>e.enableVertexAttribArray(o)),!0)}function DI(e,t,r){UI(e,r),we(e,()=>e.activeTexture(e.TEXTURE0+r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function KJ(e,t){UI(e,t),we(e,()=>e.activeTexture(e.TEXTURE0+t)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function LI(e,t,r){return Qa(e,()=>e.getUniformLocation(t,r),'uniform "'+r+'" not present in program.')}function BI(e,t,r){return e.getUniformLocation(t,r)}function WI(e,t,r,n){we(e,()=>DI(e,t,n)),we(e,()=>e.uniform1i(r,n))}function XJ(e){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),we(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function Kc(e,t,r){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,r)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function Ry(e,t){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function mp(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+VI(e,t))}function VI(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 ${t}`}}function Qa(e,t,r){let n=we(e,()=>t());if(n==null)throw new Error(r);return n}function UI(e,t){let r=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=t+e.TEXTURE0;if(n<e.TEXTURE0||n>r){let a=`[gl.TEXTURE0, gl.TEXTURE${r}]`;throw new Error(`textureUnit must be in ${a}.`)}}function Co(e,t=2){return w.sizeFromShape(e.slice(0,e.length-t))}function To(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 Xc(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Co(e),...To(e)]),t}function GI(e,t=!1){let r=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(r=r*2,e=e.map((a,s)=>s>=e.length-2?w.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=w.squeezeShape(e).newShape);let n=w.sizeFromShape(e);if(e.length<=1&&n<=r)return[1,n];if(e.length===2&&e[0]<=r&&e[1]<=r)return e;if(e.length===3&&e[0]*e[1]<=r&&e[2]<=r)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=r&&e[1]*e[2]<=r)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=r&&e[3]<=r)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=r&&e[1]*e[2]*e[3]<=r)return[e[0],e[1]*e[2]*e[3]];if(t){let a=Co(e),s=2,i=2;return e.length&&([s,i]=To(e)),n=a*(s/2)*(i/2),w.sizeToSquarishShape(n).map(o=>o*2)}return w.sizeToSquarishShape(n)}function Wc(e){return e%2===0}function Pp(e,t){if(e=e.slice(-2),t=t.slice(-2),w.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let r=e.slice(-1)[0],n=t.slice(-1)[0];if(r===n||Wc(r)&&Wc(n)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Wc(e[0])&&Wc(t[0])}var Zc,Yc;function jI(e){if(Zc==null){let t=fa(e);Zc=t.getParameter(t.MAX_TEXTURE_SIZE)}return Zc}function ZJ(){Zc=null}function YJ(){Yc=null}function HI(e){if(Yc==null){let t=fa(e);Yc=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Yc)}function qI(e){if(e===0)return 0;let t,r=fa(e);return Sn(r,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Sn(r,"EXT_disjoint_timer_query")?t=1:t=0,t}function Sn(e,t){return e.getExtension(t)!=null}function My(e){try{if(fa(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function KI(e){if(e===0)return!1;let t=fa(e);if(e===1){if(!Sn(t,"OES_texture_float"))return!1}else if(!Sn(t,"EXT_color_buffer_float"))return!1;return Fy(t)}function XI(e){if(e===0)return!1;let t=fa(e);if(e===1){if(!Sn(t,"OES_texture_float")||!Sn(t,"WEBGL_color_buffer_float"))return!1}else{if(Sn(t,"EXT_color_buffer_float"))return Fy(t);let r="EXT_color_buffer_half_float";if(Sn(t,r)){let n=t.getExtension(r);return JJ(t,n)}return!1}return Fy(t)}function Fy(e){let t=Bx(e),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let n=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,n,a,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,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(s),i}function JJ(e,t){let r=Bx(e,t),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,r.internalFormatHalfFloat,a,s,0,r.textureFormatFloat,r.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(i),o}function ZI(e){return e!==2?!1:fa(e).fenceSync!=null}function cd(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=J();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>My(2)?2:My(1)?1:0);Fe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Fe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Fe.get("WEBGL_VERSION")===2);Fe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Fe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Fe.registerFlag("WEBGL_PACK",()=>Fe.getBool("HAS_WEBGL"));Fe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_CLIP",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_REDUCE",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_CONV_IM2COL",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>jI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>HI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:qI(e)});Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Fe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!nh.isMobile());Fe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>KI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Fe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Fe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Fe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>XI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>ZI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Fe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Fe.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Fe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>nh.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Fe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Fe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Fe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Fe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Ur(){let e,t,r,n,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",r="out",n="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

      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",r="varying",n="varying",a="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:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Sl(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function s0(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function QJ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function eQ(e,t,r="index"){let n=e.map((s,i)=>i),a=QJ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function Wx(e){let t=w.computeStrides(e).map(r=>r.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function Vx(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var YI=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`,{getBroadcastDims:JI}=N;function tQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=Ux(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
`),s=e.map(p=>rQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
`),i=t.texShape,o=Ur(),l=sQ(o),u,d,h=lQ(o);return t.isPacked?(u=nQ(t.logicalShape,i,r.enableShapeUniforms),d=oQ(o)):(u=aQ(t.logicalShape,i,r.enableShapeUniforms),d=iQ(o)),r.packedInputs&&(h+=hQ),[h,l,d,a,u,s,r.userCode].join(`
`)}function fd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return IQ(e,t);case 1:return CQ(e,t);case 2:return NQ(e,t);case 3:return RQ(e,t);case 4:return FQ(e,t);case 5:return $Q(e);case 6:return PQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function QI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return kQ(e);case 1:return SQ(e,t);case 2:return TQ(e,t);case 3:return EQ(e,t);default:return MQ(e,t)}}function rQ(e,t,r=!1,n){let a="";r?a+=QI(e,n):a+=fd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=_Q(e,t):a+=zQ(e,t)),a}function nQ(e,t,r){switch(e.length){case 0:return e8();case 1:return cQ(e,t,r);case 2:return vQ(e,t,r);case 3:return mQ(e,t,r);default:return yQ(e,t,r)}}function aQ(e,t,r){switch(e.length){case 0:return e8();case 1:return fQ(e,t,r);case 2:return wQ(e,t,r);case 3:return gQ(e,t,r);case 4:return AQ(e,t,r);case 5:return xQ(e,t);case 6:return bQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function sQ(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function iQ(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function oQ(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function lQ(e){return`${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);
    }

    ${uQ}
    ${dQ}
    ${pQ}
  `}var uQ=`
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);
}
`,dQ=`
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);
}
`,pQ=`
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);
}
`,hQ=`
  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 e8(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function cQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${n[1]}.0);
      }
    `:n[1]===1?r?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${n[0]}.0);
      }
    `:r?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
    }
  `}function fQ(e,t,r){return t[0]===1?r?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?r?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:r?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function mQ(e,t,r){if(r)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=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 / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec3(b, r, c);
    }
  `}function gQ(e,t,r){if(r)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${s0(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let n=Sl(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec3(r, c, d);
    }
  `}function yQ(e,t,r){if(r)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
      int b${u} = index / ${i};
      index -= b${u} * ${i};
    `+o,l=`b${u}, `+l;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      ${o}

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec${e.length}(${l});
    }
  `}function AQ(e,t,r){if(r)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${s0(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let n=Sl(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function xQ(e,t){let r=Sl(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${r}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function bQ(e,t){let r=Sl(["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;

      ${r}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function vQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
      }
    `;let a=Math.ceil(e[1]/2);return r?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${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 wQ(e,t,r){return w.arraysEqual(e,t)?r?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?r?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?r?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:r?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function Cl(e){return`offset${e}`}function kQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ur();return`
    vec4 ${r}() {
      return ${n.texture2D}(${t}, halfCR);
    }
  `}function IQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
      float ${n}() {
        return sampleTexture(${r}, halfCR);
      }
    `;let i=Cl(r);if(t)return`
    float ${n}() {
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
      return sampleTexture(${r}, uv);
    }
  `;let[o,l]=e.shapeInfo.texShape;return`
    float ${n}() {
      vec2 uv = uvFromFlat(${o}, ${l}, ${i});
      return sampleTexture(${r}, uv);
    }
  `}function SQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Ur();if(t)return`
    vec4 ${n}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${r}, uv);
    }
  `;let i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${r}, uv);
    }
  `}function CQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${md(e)}
      }
    `;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
      float ${n}(int index) {
        return sampleTexture(${r}, halfCR);
      }
    `;let o=Cl(r);return i===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
        return sampleTexture(${r}, uv);
      }
    `:s===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${r}TexShape[1]), 0.5);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${r}, uv);
      }
    `:t?`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${o});
      return sampleTexture(${r}, uv);
    }
  `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
      return sampleTexture(${r}, uv);
    }
  `}function TQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ur();if(s!=null&&w.arraysEqual(r,s))return t?`
      vec4 ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);

        return ${l.texture2D}(${n}, uv);
      }
    `:`
      vec4 ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);

        return ${l.texture2D}(${n}, uv);
      }
    `;if(t)return`
    vec4 ${a}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${l.texture2D}(${n}, uv);
    }
  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],d=Math.ceil(r[1]/2);return`
    vec4 ${a}(int row, int col) {
      vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
      return ${l.texture2D}(${n}, uv);
    }
  `}function NQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
      float ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `;let p=s[0],c=s[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:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let p=gd(e,l),c=["row","col"];return`
      ${fd(p,t)}
      float ${a}(int row, int col) {
        return ${a}(${yd(c,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
        ${md(e)}
      }
    `;let u=s[0],d=s[1],h=Cl(n);return d===1?t?`
      float ${a}(int row, int col) {
        float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${n}, uv);
    }
  `:u===1?t?`
      float ${a}(int row, int col) {
        float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:t?`
      float ${a}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${n}Shape[1] + col + ${h};
        vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
        return sampleTexture(${n}, uv);
      }
    `:`
  float ${a}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${r[1]} + col + ${h};
    vec2 uv = uvFromFlat(${u}, ${d}, index);
    return sampleTexture(${n}, uv);
  }
`}function EQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=gd(e,p),m=["b","row","col"];return`
        ${QI(f,t)}
        vec4 ${a}(int b, int row, int col) {
          return ${a}(${yd(m,c)});
        }
      `}let o=Ur();if(t)return`
    vec4 ${a}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${o.texture2D}(${n}, uv);
    }
  `;let l=i[0],u=i[1],d=Math.ceil(r[2]/2),h=d*Math.ceil(r[1]/2);return`
    vec4 ${a}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${l}, ${u}, ${h}, ${d}, b, row, col);
      return ${o.texture2D}(${n}, uv);
    }
  `}function RQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=gd(e,u),g=["row","col","depth"];return`
        ${fd(m,t)}
        float ${a}(int row, int col, int depth) {
          return ${a}(${yd(g,l)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${md(e)}
      }
    `;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
      float ${a}(int row, int col, int depth) {
        int stride1 = ${n}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
        float ${a}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${p}.0, ${h}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(p===i&&c==null)return t?`
      float ${a}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${a}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${r[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);
      return sampleTexture(${n}, uv);
    }
  `;let f=Cl(n);return t?`
    float ${a}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${n}Shape[1] * ${n}Shape[2];
      int stride1 = ${n}Shape[2];
      int index = row * ${s} + col * ${i} + depth + ${f};
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
      return sampleTexture(${n}, uv);
    }
    `:`
      float ${a}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${f};
        vec2 uv = uvFromFlat(${h}, ${p}, index);
        return sampleTexture(${n}, uv);
      }
  `}function MQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Ur();if(t)return`
    vec4 ${n}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${r}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${a.texture2D}(${r}, uv);
    }
  `;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],d=l[1],h=Math.ceil(s[i-1]/2),p=h*Math.ceil(s[i-2]/2),c="int b, int row, int col",f=`b * ${p} + (row / 2) * ${h} + (col / 2)`;for(let m=2;m<i-1;m++)c=`int b${m}, `+c,p*=s[i-m-1],f=`b${m} * ${p} + `+f;return`
    vec4 ${n}(${c}) {
      int index = ${f};
      int texR = index / ${d};
      int texC = index - texR * ${d};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
      return ${a.texture2D}(${r}, uv);
    }
  `}function FQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=gd(e,l),x=["row","col","depth","depth2"];return`
      ${fd(A,t)}
      float ${a}(int row, int col, int depth, int depth2) {
        return ${a}(${yd(x,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${i}, ${s}, 1)));
        ${md(e)}
      }
    `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],f=`int stride2 = ${n}Shape[3];`,m=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
      float ${a}(int row, int col, int depth, int depth2) {
        ${f}
        ${m}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${c}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(c===s&&d==null)return t?`
      float ${a}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${r[1]*r[2]}, ${r[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${c}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let y=Cl(n);return t?`
    float ${a}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${m}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${a}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
      return sampleTexture(${n}, uv);
    }
  `}function $Q(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=gd(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${fd(m)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${yd(g,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${o}, ${i}, ${s}, ${a})) +
          depth3;
        ${md(e)}
      }
    `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${c}.0, ${p}.0);
        return sampleTexture(${r}, uv);
      }
    `;if(c===a&&d==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${c}.0, ${p}.0);
        return sampleTexture(${r}, uv);
      }
    `;let f=Cl(r);return`
    float ${n}(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 * ${a} + depth3 + ${f};
      vec2 uv = uvFromFlat(${p}, ${c}, index);
      return sampleTexture(${r}, uv);
    }
  `}function PQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=gd(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${fd(g)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${yd(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 ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${d}, ${u}, ${l}, ${o})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${md(e)}
      }
    `;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],f=p[1];if(f===d&&h==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${o}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${c}.0);
        return sampleTexture(${r}, uv);
      }
    `;if(f===i&&h==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${c}.0);
        return sampleTexture(${r}, uv);
      }
    `;let m=Cl(r);return`
    float ${n}(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 + ${m};
      vec2 uv = uvFromFlat(${c}, ${f}, index);
      return sampleTexture(${r}, uv);
    }
  `}function md(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
    for (int i = 0; i < ${r}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function _Q(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=JI(e.shapeInfo.logicalShape,t.logicalShape),l=bt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
`);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",f=w.sizeFromShape(e.shapeInfo.logicalShape)===1,m=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)c=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(f&&!m)i===1?c=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:c=`
        return vec4(outputValue.x);
      `;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?c="return vec4(outputValue.x);":o.indexOf(g)>-1?c="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(c="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${a}() {
      ${l} coords = getOutputCoords();
      ${d}
      vec4 outputValue = get${n}(${p});
      ${c}
    }
  `}function zQ(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"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&&w.arraysEqual(i,s))return`
      float ${a}() {
        return sampleTexture(${r}, resultUV);
      }
    `;let u=bt(l),d=JI(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${c[g+h]}`).join(", "),`
    float ${a}() {
      ${u} coords = getOutputCoords();
      ${p}
      return get${n}(${f});
    }
  `}function bt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ux(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function gd(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function yd(e,t){return t.map(r=>e[r]).join(", ")}function OQ(e,t,r,n){let a=r.map((b,v)=>{let C={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(C.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[v],shapeInfo:C}}),s=a.map(b=>b.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=tQ(a,i,t),l=RI(e.gl,o),u=e.createProgram(l),d=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let p=!1,c={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let v=t.variableNames[b];c[v]=e.getUniformLocation(u,v,p),c[`offset${v}`]=e.getUniformLocation(u,`offset${v}`,p),t.enableShapeUniforms&&(f[`${v}Shape`]=e.getUniformLocation(u,`${v}Shape`,p),m[`${v}TexShape`]=e.getUniformLocation(u,`${v}TexShape`,p))}let g,y,A;t.enableShapeUniforms&&(g=e.getUniformLocation(u,"outShape",p),A=e.getUniformLocation(u,"outShapeStrides",p),y=e.getUniformLocation(u,"outTexShape",p));let x=[];return t.customUniforms&&t.customUniforms.forEach((b,v)=>{x[v]=e.getUniformLocation(u,b.name,p)}),{program:t,fragmentShader:l,source:o,webGLProgram:u,uniformLocations:c,customUniformLocations:x,inShapeInfos:s,outShapeInfo:i,infLoc:d,nanLoc:h,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:A,outTexShapeLocation:y}}function uv(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((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function DQ(e,t,r,n,a){t.program.enableShapeUniforms||(uv(t.inShapeInfos,r),uv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=Ux(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function LQ(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=Ux(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${J().getNumber("WEBGL_VERSION")}`,s}function nn(e){return J().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var BQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?s0(["r","c","d"],e):Sl(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${t.output} = result;
      }
    `}},WQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?s0(["r","c","d"],e):Sl(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${t.output} = result;
      }
    `}},VQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Ur();this.outputShape=e,this.userCode=`
      ${YI}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},UQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Ur();this.outputShape=e,this.userCode=`
      ${YI}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},GQ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
      ${this.enableShapeUniforms?Vx():Wx(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / texShape[1];
        int c = imod(flatIndex, texShape[1]);
        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
        vec4 values = ${r.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];
        }

        ${r.output} = vec4(${n}, 0., 0., 0.);
      }
    `}},jQ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
          localCoords = coords;
          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${i};
          if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${s};

            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);

            flatIndex = idiv(flatIndex, 4, 1.);

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${r.texture2D}(A, uv);

            if (offset == 0) {
              result[${o}] = values[0];
            } else if (offset == 1) {
              result[${o}] = values[1];
            } else if (offset == 2) {
              result[${o}] = values[2];
            } else {
              result[${o}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?Vx():Wx(e)}

        void main() {
          ivec3 coords = getOutputCoords();

          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;

          ${n}

          ${r.output} = ${a};
        }
    `}},t8={};Le(t8,{bindVertexProgramAttributeStreams:()=>d8,createBufferFromOutputTexture:()=>c8,createFloat16MatrixTexture:()=>i8,createFloat16PackedMatrixTexture:()=>u8,createFloat32MatrixTexture:()=>s8,createIndexBuffer:()=>a8,createPackedMatrixTexture:()=>l8,createUnsignedBytesMatrixTexture:()=>o8,createVertexBuffer:()=>n8,createVertexShader:()=>r8,downloadByteEncodedFloatMatrixFromOutputTexture:()=>m8,downloadFloat32MatrixFromBuffer:()=>f8,downloadMatrixFromPackedOutputTexture:()=>y8,downloadPackedMatrixFromBuffer:()=>g8,getInternalFormatForFloat16MatrixTexture:()=>jx,getInternalFormatForFloat16PackedMatrixTexture:()=>Kx,getInternalFormatForFloat32MatrixTexture:()=>Gx,getInternalFormatForPackedMatrixTexture:()=>qx,getInternalFormatForUnsignedBytesMatrixTexture:()=>Hx,uploadDenseMatrixToTexture:()=>p8,uploadPixelDataToTexture:()=>h8});function r8(e){let t=Ur(),r=`${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 EI(e,r)}function n8(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 $I(e,t)}function a8(e){let t=new Uint16Array([0,1,2,2,1,3]);return PI(e,t)}function Ch(e,t,r,n,a,s){zI(t,r);let i=_I(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),J().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(o,0,n,t,r,0,a,s,null)):we(e,()=>e.texStorage2D(o,1,n,t,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[r,t]}}function Gx(e){return e.internalFormatFloat}function s8(e,t,r,n){let[a,s]=Sh(t,r);return Ch(e,a,s,Gx(n),n.textureFormatFloat,e.FLOAT)}function jx(e){return e.internalFormatHalfFloat}function i8(e,t,r,n){let[a,s]=Sh(t,r);return Ch(e,a,s,jx(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Hx(e){return e.downloadTextureFormat}function o8(e,t,r,n){let[a,s]=Sh(t,r);return Ch(e,a,s,Hx(n),e.RGBA,e.UNSIGNED_BYTE)}function qx(e){return e.internalFormatPackedFloat}function l8(e,t,r,n){let[a,s]=hd(t,r);return Ch(e,a,s,qx(n),e.RGBA,e.FLOAT)}function Kx(e){return e.internalFormatPackedHalfFloat}function u8(e,t,r,n){let[a,s]=hd(t,r);return Ch(e,a,s,Kx(n),e.RGBA,n.textureTypeHalfFloat)}function d8(e,t,r){return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),Ey(e,t,"clipSpacePos",r,3,20,0)&&Ey(e,t,"uv",r,2,20,12)}function p8(e,t,r,n,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(r*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(r*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),J().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,r,n,e.RGBA,o,i)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,r,n,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function h8(e,t,r){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),r.data instanceof Uint8Array?J().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,r.width,r.height,e.RGBA,e.UNSIGNED_BYTE,r.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,r.width,r.height,0,e.RGBA,e.UNSIGNED_BYTE,r.data)):J().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,r)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function c8(e,t,r,n){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*r;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,r,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function f8(e,t,r){let n=e,a=new Float32Array(r);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,a),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),a}function m8(e,t,r,n){let[a,s]=Sh(t,r),i=4,o=new Uint8Array(BJ(t*r,i));return we(e,()=>e.readPixels(0,0,a,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function g8(e,t,r,n,a,s,i,o){let l=e,u=new Float32Array(WJ(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 y8(e,t,r){let n=new Float32Array(t*r*4);return we(e,()=>e.readPixels(0,0,r,t,e.RGBA,e.FLOAT,n)),n}var du=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,a0(t,e)):this.gl=fa(t);let r="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=fp(this.gl,a),Sn(this.gl,s))this.textureHalfFloatExtension=fp(this.gl,s);else if(J().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(r),Sn(this.gl,n))this.colorBufferHalfFloatExtension=fp(this.gl,n);else if(J().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(r="EXT_color_buffer_float",Sn(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(Sn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=n8(this.gl),this.indexBuffer=a8(this.gl),this.framebuffer=OI(this.gl),this.textureConfig=Bx(this.gl,this.textureHalfFloatExtension)}get debug(){return J().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;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),s8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),i8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),o8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),h8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,r,n){this.throwIfDisposed(),p8(this.gl,e,t,r,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),u8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),l8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ry(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r){return this.downloadMatrixDriver(e,()=>m8(this.gl,t,r,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,r,n,a,s){return g8(this.gl,e,t,r,n,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return f8(this.gl,e,t)}createBufferFromTexture(e,t,r){this.bindTextureToFrameBuffer(e);let n=c8(this.gl,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,r;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,a=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=()=>{let s=n.clientWaitSync(a,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:t,isFencePassed:r}}downloadMatrixFromPackedTexture(e,t,r){return this.downloadMatrixDriver(e,()=>y8(this.gl,t,r))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=r8(t));let r=MI(t);return we(t,()=>t.attachShader(r,this.vertexShader)),we(t,()=>t.attachShader(r,e)),FI(t,r),this.debug&&qc(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=d8(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&qc(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,r=!0){return this.throwIfDisposed(),r?LI(this.gl,e,t):BI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,r){this.throwIfDisposed(),this.throwIfNoProgram(),WI(this.gl,e,t,r)}setOutputMatrixTexture(e,t,r){this.setOutputMatrixTextureDriver(e,r,t)}setOutputPackedMatrixTexture(e,t,r){this.throwIfDisposed();let[n,a]=hd(t,r);this.setOutputMatrixTextureDriver(e,n,a)}setOutputMatrixWriteRegion(e,t,r,n){this.setOutputMatrixWriteRegionDriver(r,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,r,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&qc(this.gl,this.program),mp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,J().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(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.createQuery();return r.beginQuery(n.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{let r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),n=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=HQ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Kc(this.gl,e,this.framebuffer),this.debug&&mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Kc(this.gl,this.outputTexture,this.framebuffer),this.debug&&mp(this.gl)):Ry(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();let n=this.gl;Kc(n,e,this.framebuffer),this.debug&&mp(n),this.outputTexture=e,we(n,()=>n.viewport(0,0,t,r)),we(n,()=>n.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,n){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,r,n))}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 HQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:qQ,bincountImpl:A8,bincountReduceImpl:KQ,ceilImpl:XQ,concatImpl:ZQ,equalImpl:YQ,expImpl:JQ,expm1Impl:QQ,floorImpl:eee,gatherNdImpl:tee,gatherV2Impl:ree,greaterImpl:nee,greaterEqualImpl:aee,lessImpl:see,lessEqualImpl:iee,linSpaceImpl:oee,logImpl:lee,maxImpl:uee,maximumImpl:dee,minimumImpl:pee,multiplyImpl:hee,negImpl:cee,notEqualImpl:fee,prodImpl:mee,rangeImpl:gee,rsqrtImpl:yee,sigmoidImpl:Aee,simpleAbsImpl:x8,sliceImpl:xee,sparseFillEmptyRowsImpl:bee,sparseReshapeImpl:vee,sparseSegmentReductionImpl:b8,sqrtImpl:wee,stridedSliceImpl:kee,stringNGramsImpl:Iee,stringSplitImpl:See,stringToHashBucketFastImpl:Cee,subImpl:Tee,tileImpl:Nee,topKImpl:Eee,transposeImpl:Xx,uniqueImpl:Ree}=r0;function v8(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function zr(e,t){return t===1?[e]:v8(e,t)}function Mee(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var Fee=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=nn(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let t=zr("rc",this.rank),r=bt(this.rank),n=this.getOutOfBoundsCondition(t),a=this.getSetup(t),s=this.getOutput(t);this.userCode=`
        void main() {
          ${r} rc = getOutputCoords();

          if(${n}) {
            setOutput(vec4(0));
          } else {
            ${a}

            setOutput(vec4(${s}));
          }
        }
      `}}getSourceCoordsArr(e){let t=[];for(let r=0;r<=1;r++)for(let n=0;n<=1;n++){let a=`${r===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)a=`${e[e.length-1-s]},`+a;t.push(a)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let r=this.rank-2;r<this.rank;r++)t+=`${e[r]} >= ${this.enableShapeUniforms?`outShape[${r}]`:this.outputShape[r]}`,r<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),r=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${t[0]};
      int c = ${t[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${r};
      bool rEdge = rp1 >= ${n};
    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
            cEdge ? 0. : getA(${t[1]}),
            rEdge ? 0. : getA(${t[2]}),
            rEdge || cEdge ? 0. : getA(${t[3]})`}},w8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
        ${a}
        ${n>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[${n}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${n>0?"}":""}
      `}this.userCode=`
      ${$ee(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?Vx():Wx(e)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};

        ${r}

        setOutput(result);
      }
    `}};function $ee(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?eQ(["r","c","d"],"inputShape"):Sl(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var Pee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=pv(t,r),a=hv(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=dv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=pv(r,n),s=hv(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=dv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function _ee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function dv(e,t,r,n,a){let s=zee(t,n),i;if(a){let[l,u]=hd(e[0],e[1]);i=l*u}else{let[l,u]=Sh(e[0],e[1]);i=l*u}let o=_ee(r,s);return i*o}function zee(e,t){switch(e){case 3:return qx(t);case 4:return Kx(t);case 1:return Gx(t);case 0:return jx(t);case 2:return Hx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Oee(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function pv(e,t){if(e===1)return 3;if(e===0||e==null)return Oee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function hv(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ua=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}},jn="if (isnan(x)) return x;",Dee="return x;",cv="return abs(x);",Lee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bee=jn+`
  return (x < 0.0) ? 0.0 : x;
`,Wee=jn+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Jl="return x;",Vee="return 1.0 / (1.0 + exp(-1.0 * x));",Uee="return x;",Gee=`
  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;
`,jee=`
  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;
`,Hee=`
  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;
`,qee="return 1.0 / (1.0 + exp(-1.0 * x));",co=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}},Kee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let t=e.length,r=zr("rc",t),n=bt(t),a=Mee(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
      void main() {
        ${n} rc = getOutputCoords();
        vec4 packedInput = getA(${a});

        setOutput(getChannel(packedInput, ${i}));
      }
    `}},Xee=Un.whereImpl,Zee=1e-7,Yee=1e-4,V1={};function Jee(e){return e in V1||(V1[e]={}),V1[e]}var Qee=J().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ete=600;function tte(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*ete/1024/1024}var k8=class extends Su{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof du)t=e;else{let r=fa(J().getNumber("WEBGL_VERSION"),e);t=new du(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=fa(J().getNumber("WEBGL_VERSION"));t=new du(r),this.binaryCache=Jee(J().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Pee(this.gpgpu),this.numMBBeforeWarning=tte(),this.texData=new zp(this,kr())}nextDataId(){return k8.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}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,r,n,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new co(i,Jl):h=new Ua(i,Jl);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new co(n,Jl):c=new Ua(n,Jl);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(J().getBool("DEBUG")&&!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().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"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Bc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kr().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new co(a,Jl):p=new Ua(a,Jl);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=kr().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!TI(r))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Bc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=J().getBool("WEBGL_PACK")&&n===!0,i=s?Xc(t):t,o=s?new UQ(i):new VQ(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(J().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:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,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(n,r),this.textureManager.releaseTexture(t,n,a,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.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Qee){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Xee(e.shape,t)}packedUnaryOp(e,t,r){let n=new co(e.shape,t),a=this.compileAndRun(n,[e],r);return kr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=x8(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cv,e.dtype);let t=new Ua(e.shape,cv),r=this.compileAndRun(t,[e]);return kr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return kr().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Kee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Fee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Co(e.shape),...To(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Co(t),...To(t)],s=new w8(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Xc(a),o;n?o=new WQ(i):o=new BQ(i);let l=!0,u=[t!=null?t:Bc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Bc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Pp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=LQ(e,u,d),p=this.getAndSaveBinary(h,()=>OQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),DQ(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().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=K(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Zee:Yee}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=GI(r,o),t.texShape=d),a!=null){let h=Xc(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=hd(d[0],d[1])),o?p=new jQ(h,m):p=new GQ(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),C=this.texData.get(v.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(v.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=rte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},Th=k8;Th.nextDataId=0;function rte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var nte="0.0.0";function I8(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}nh.isBrowser()&&Al("webgl",()=>new Th,2);var ate={forceHalfFloat:I8},S8=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,ku=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},i0=`
  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;
`,Nh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=nn(a);let s="";if(n)if(a===0||w.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${bt(a)} coords = getOutputCoords();
        `,a===1)this.enableShapeUniforms?s+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=zr("coords",a);this.enableShapeUniforms?s+=`
            bool nextRowOutOfBounds =
              (${i[a-2]} + 1) >= outShape[${a} - 2];
            bool nextColOutOfBounds =
              (${i[a-1]} + 1) >= outShape[${a} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:s+=`
            bool nextRowOutOfBounds =
              (${i[a-2]} + 1) >= ${this.outputShape[a-2]};
            bool nextColOutOfBounds =
              (${i[a-1]} + 1) >= ${this.outputShape[a-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 dn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var ste={kernelName:ii,backendName:"webgl",kernelFunc:dn};function Oi(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=dn({inputs:{x:n},backend:r}),l=dn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ite={kernelName:Dp,backendName:"webgl",kernelFunc:Oi},C8="return (a < 0.) ? b * a : a;",T8=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function ote(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(T8,a.shape,i.shape):new ku(C8,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var lte={kernelName:oi,backendName:"webgl",kernelFunc:ote},N8="return (a < 0.) ? b * a : a;",E8=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function ute(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(E8,n.shape,a.shape):new ku(N8,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var dte={kernelName:xi,backendName:"webgl",kernelFunc:ute},Ad="if (isnan(x)) return x;",pte=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,hte=`
  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 it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new co(i.shape,t):d=new Ua(i.shape,e),o.runWebGLProgram(d,[i],l)}}function Ar({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new ku(e,l.shape,u.shape);return d.runWebGLProgram(E,[C,S],Dr(b.dtype,v.dtype))}),A=Oi({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Dr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Nh(t,l.shape,u.shape,r):c=new ku(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function o0(e,t=!1){if(e==="linear")return t?Uee:Dee;if(e==="relu")return t?jee:Bee;if(e==="elu")return t?Gee:Lee;if(e==="relu6")return t?Hee:Wee;if(e==="prelu")return t?E8:N8;if(e==="leakyrelu")return t?T8:C8;if(e==="sigmoid")return t?qee:Vee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var R8=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=nn(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:l?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:m=`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=`
      ${m}
      // Don't use uniform for sharedDimensionPacked for performance.
      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, ${h});
          vec4 b = getMatrixB(batchB, ${p});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${c[0]} * ${f[0]});
          result += (${c[1]} * ${f[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${y}

        ${g}

        setOutput(result);
      }
    `}},fv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},mv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),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));
      }
    `}},gv="return a * b;";function Zx(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new mv(fv.REAL,n.shape,a.shape),d=new mv(fv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=hee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Nh(gv,n.shape,a.shape):i=new ku(gv,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var cte={kernelName:gi,backendName:"webgl",kernelFunc:Zx};function fte(e,t,r){let n=[Co(e.shape),...To(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Co(t),...To(t)],i=new w8(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!Pp(a.shape,l)&&!(d.texture!==null&&Pp(d.shape,l))?fte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var mte={kernelName:tl,backendName:"webgl",kernelFunc:ve},yv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
        if (inIdx < 0 || inIdx >= ${a}) {
          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 * ${r};

        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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},gte=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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(r/4)*4,d=r%4,h=`
      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);
          }
        }
      }
    `,p="vec4";t==="all"?(i="1.0",h=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,p="bvec4"):t==="any"&&(i="0.0",h=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,p="bvec4");let c="";a%r>0&&(c=`
        if (inIdx < 0 || inIdx >= ${a}) {
          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) {
        ${c}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

        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;
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${h}
        }

        int inIdx = inOffset + ${u};
        if (${d===1}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${h}
        } else if (${d===2}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${h}
        } else if (${d===3}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${h}
        }
        setOutput(${l});
      }
    `}};function yte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Tl(e,t,r,n){let a=yte(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new yv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new yv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new gte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Ate=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=bt(this.rank),a=xte(t);this.userCode=`
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${a}));
    }
    `}};function xte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var bte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=bt(this.rank),a=v8("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
    void main() {
      ${n} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${o}) {
        result[1] = ${l};
      }
      --${a[this.rank-1]};
      if(++${a[this.rank-2]} < ${r[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function l0(e,t,r){let n=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bte(e.shape,t):new Ate(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function vte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=l0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=rh(e.dtype),A=Tl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function u0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return vte(a,s,i,r)}var wte={kernelName:Ti,backendName:"webgl",kernelFunc:u0};function Br(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=Xx(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=l0(a,s,i);return u}var kte={kernelName:Fi,backendName:"webgl",kernelFunc:Br},M8=1e3;function Cf({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),S=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=r?C.shape[1]:C.shape[2],M=s!=null,I=i!=null,O=l==="leakyrelu",z=l!=null?o0(l,!0):null,j=M||I||O||z!=null,X;if((c===1||f===1)&&_>M8&&j===!1){let Q=C,V=S;r&&(Q=Br({inputs:{x:C},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=Br({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,Y=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;Y&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=Zx({inputs:{a:se,b:ae},backend:a});X=u0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Dr(e.dtype,t.dtype),V=new R8(b,v,[R,c,f],r,n,M,z,I,O),ee=[C,S];if(s!=null&&ee.push(s),I&&ee.push(i),O){let Y=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(Y),E.push(Y)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Ite(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Cf({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ste={kernelName:Ts,backendName:"webgl",kernelFunc:Ite},Av="return abs(x);";function Cte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=x8(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,Av):a=new Ua(n.shape,Av),r.runWebGLProgram(a,[n],n.dtype)}var Tte={kernelName:Mo,backendName:"webgl",kernelFunc:Cte},Nte=jn+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,Ete=it({opSnippet:Nte}),Rte={kernelName:Tu,backendName:"webgl",kernelFunc:Ete},Mte=jn+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Fte=it({opSnippet:Mte}),$te={kernelName:Nu,backendName:"webgl",kernelFunc:Fte},xv="return a + b;",Pte=Ar({opSnippet:xv,packedOpSnippet:xv,supportsComplex:!0,cpuKernelImpl:qQ}),_te={kernelName:Ha,backendName:"webgl",kernelFunc:Pte},zte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
      void main() {
        ${r.join(`
        `)}

        float result = ${n};
        setOutput(result);
      }
    `}},Ote=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
      void main() {
        ${r.join(`
        `)}

        vec4 result = ${n};
        setOutput(result);
      }
    `}};function Jc(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return dn({inputs:{x:n[0]},backend:r});if(n.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Jc({inputs:n.slice(0,o),backend:r}),u=Jc({inputs:n.slice(o),backend:r});return Jc({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new Ote(n[0].shape,s):new zte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Dte={kernelName:Vs,backendName:"webgl",kernelFunc:Jc};function Lte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Bte={kernelName:Eu,backendName:"webgl",kernelFunc:Lte};function Wte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Vte={kernelName:Ru,backendName:"webgl",kernelFunc:Wte},Ute=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"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 * ${n};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${n}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},Gte=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=bt(o),u=zr("coords",o),d,h;if(s===1){h=o+1;let S=bt(h);d=`
        ${S} sourceLocR = ${S}(${u.join()}, 0);
        ++${u[o-1]};
        ${S} sourceLocG = ${S}(${u.join()}, 0);
        ++${u[o-2]};
        ${S} sourceLocA = ${S}(${u.join()}, 0);
        --${u[o-1]};
        ${S} sourceLocB = ${S}(${u.join()}, 0);
        --${u[o-2]};`}else h=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 p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],f=p.map(S=>"int "+S),m=zr("sourceLocR",h-1).concat("inIdx.r"),g=zr("sourceLocG",h-1).concat("inIdx.g"),y=zr("sourceLocB",h-1).concat("inIdx.b"),A=zr("sourceLocA",h-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=n?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${A.join()})));`,v=`vec4(
            getAChannel(${m.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,C=n?"":`
      float getBestIndicesAChannel(${f.join()}) {
        return getChannel(getBestIndicesA(${p.join()}),
                                          vec2(${p.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${f.join()}) {
        return getChannel(getA(${p.join()}),
                               vec2(${p.slice(-2).join()}));
      }
      ${C}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
        ${d}
        ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},
          sourceLocB${c}, sourceLocA${c}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${v};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${b}
          vec4 candidate = ${v};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function F8(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Ute(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=F8(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function $8(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new Gte(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=$8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function P8(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=F8(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return $8(e,t,n)}function jte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=P8(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Hte={kernelName:Us,backendName:"webgl",kernelFunc:jte};function qte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=P8(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Kte={kernelName:Mu,backendName:"webgl",kernelFunc:qte},Xte=jn+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,Zte=it({opSnippet:Xte}),Yte={kernelName:Fu,backendName:"webgl",kernelFunc:Zte},Jte=jn+"return log(x + sqrt(x * x + 1.0));",Qte=it({opSnippet:Jte}),ere={kernelName:$u,backendName:"webgl",kernelFunc:Qte},tre=jn+`
  return atan(x);
`,rre=it({opSnippet:tre}),nre={kernelName:Pu,backendName:"webgl",kernelFunc:rre},are=pte+`
  return atan(a, b);
`,sre=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+hte+`
  return result;
`,ire=Ar({opSnippet:are,packedOpSnippet:sre}),ore={kernelName:zu,backendName:"webgl",kernelFunc:ire},lre=jn+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernelName:_u,backendName:"webgl",kernelFunc:ure},_p=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)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,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let S=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${p}, ${c});

        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 < ${h};
                wC += ${u}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${S} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${n?a?m:g:`wR * ${h} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,v=s%4,C=`
      if (${f}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${A}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${p}, ${c});
      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 < ${b}; wC += 4) {
            int xC = xCCorner + wC * ${u};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              getValue(batch, xR, xC + 3 * ${u}, d)
            );

            ${C}
          }

          int xC = xCCorner + ${b};
          if (${v===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${C}
          } else if (${v===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              initializationValue,
              initializationValue
            );

            ${C}
          } else if (${v===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              initializationValue
            );

            ${C}
          }
        }
        setOutput(${x});
      }
    `}},Yx=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)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,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=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"),r){let R=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${m}, ${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 < ${p};
              wD += ${u}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${c};
                wR += ${d}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${f};
                  wC += ${h}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${R} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${n?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let C=Math.floor(s/4)*4,S=s%4,E=`
      if (${A}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${m}, ${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 < ${p};
            wD += ${u}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${c};
            wR += ${d}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${C}; wC += 4) {
              int xC = xCCorner + wC * ${h};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                getValue(batch, xD, xR, xC + 2 * ${h}, ch),
                getValue(batch, xD, xR, xC + 3 * ${h}, ch)
              );

              ${E}
            }

            int xC = xCCorner + ${C};
            if (${S===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${S===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${S===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                getValue(batch, xD, xR, xC + 2 * ${h}, ch),
                initializationValue
              );

              ${E}
            }
          }
          setOutput(${v});
        }
      }
    `}};function pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;cd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new _p(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var hre={kernelName:Gs,backendName:"webgl",kernelFunc:pre};function cre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new Yx(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var fre={kernelName:Op,backendName:"webgl",kernelFunc:cre},mre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=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,h=1/(t*r);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${d});
      const float avgMultiplier = float(${h});

      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) / ${n}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${l};
            wC+= ${i}) {
            float dyC = float(dyCCorner + wC) / ${a}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},gre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
      const ivec3 pads = ivec3(${c}, ${f}, ${m});
      const float avgMultiplier = float(${g});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${d};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${a}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${h};
              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 < ${p};
                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 yre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new gre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Are={kernelName:_f,backendName:"webgl",kernelFunc:yre};function xre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;cd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new mre(d);return r.runWebGLProgram(h,[a],i.dtype)}var bre={kernelName:Pf,backendName:"webgl",kernelFunc:xre};function vre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Cf({a,b:s,transposeA:i,transposeB:o,backend:r})}var wre={kernelName:js,backendName:"webgl",kernelFunc:vre},kre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),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)));
      }
    `}},Ire=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),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);
      }
    `}},Sre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=J().getBool("WEBGL_PACK_NORMALIZATION")?new Ire(n.shape,a.shape,s.shape,d,h,l):new kre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Cre={kernelName:ai,backendName:"webgl",kernelFunc:Sre},Tre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Nre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${$y[i]} = start[${i}] + coords.${$y[i]};`);n=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${a.join(`
`)}
      `,this.userCode=`
      void main() {
        ${n}
        setOutput(getSource(${r}));
      }
    `}},$y=["x","y","z","w","u","v"];function Nre(e){if(e===1)return"sourceLoc";if(e<=6)return $y.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ere=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=bt(this.rank),r=zr("coords",this.rank),n=zr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
      result.x = ${s};
      if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
        ++${n[this.rank-1]};
        result.y = ${s};
        --${n[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${r[this.rank-1]};
      if (++${r[this.rank-2]} < ${e[this.rank-2]}) {
        ++${n[this.rank-2]};
        result.z = ${s};
        if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
          ++${n[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${n[d]} = ${r[d]} + start[${d}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `}};function Rre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function xd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=xee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ere(l):new Tre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Rre(a,o,l,r)}var Mre={kernelName:il,backendName:"webgl",kernelFunc:xd},Fre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=xd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},$re={kernelName:Fo,backendName:"webgl",kernelFunc:Fre};function Pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=A8(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var _re={kernelName:zf,backendName:"webgl",kernelFunc:Pre};function zre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Ore={kernelName:Of,backendName:"webgl",kernelFunc:zre},Dre="return float(a != b);",_8=Ar({opSnippet:Dre,cpuKernelImpl:fee,dtype:"bool"}),Lre={kernelName:Ko,backendName:"webgl",kernelFunc:_8};function Eh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Bre={kernelName:qp,backendName:"webgl",kernelFunc:Eh},Wre="return float(int(x));";function Vre(e,t){let r=new Ua(e.shape,Wre),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Py(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return dn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Py({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Oi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Eh({inputs:{input:a},backend:r}),o=Py({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=dn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Vre(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=_8({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Ure={kernelName:Hs,backendName:"webgl",kernelFunc:Py},bv="return ceil(x);",Gre=it({opSnippet:bv,packedOpSnippet:bv,cpuKernelImpl:XQ}),jre={kernelName:qs,backendName:"webgl",kernelFunc:Gre},Hre=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}},qre=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function Kre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;J().getBool("WEBGL_PACK_CLIP")?o=new qre(a.shape):o=new Hre(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var Xre={kernelName:qa,backendName:"webgl",kernelFunc:Kre},Zre=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 vv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Yre(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Zre(n.shape),i=[vv(n,a.complexTensorInfos.real),vv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Jre={kernelName:Lp,backendName:"webgl",kernelFunc:Yre},Qre=class{constructor(e){this.outputShape=[],this.outputShape=N.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 r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${r.join(`
        `)}
      }
    `}},ene=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=bt(n),s=zr("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
        return getChannel(
            getT0(${d}), vec2(${u.join()}));
        }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
        if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
          return getChannel(
            getT${f}(${Vc(i,l,m)}),
            vec2(${Vc(u,l,m)}));
        }`}let p=o.length,c=o[o.length-1];h+=`
        return getChannel(
          getT${p}(${Vc(i,l,c)}),
          vec2(${Vc(u,l,c)}));`,this.userCode=`
      float getValue(${i.map(f=>"int "+f)}) {
        ${h}
      }

      void main() {
        ${a} coords = getOutputCoords();
        vec4 result = vec4(getValue(${s}), 0., 0., 0.);

        ${s[n-1]} = ${s[n-1]} + 1;
        if (${s[n-1]} < ${r[n-1]}) {
          result.g = getValue(${s});
        }

        ${s[n-2]} = ${s[n-2]} + 1;
        if (${s[n-2]} < ${r[n-2]}) {
          result.a = getValue(${s});
        }

        ${s[n-1]} = ${s[n-1]} - 1;
        if (${s[n-2]} < ${r[n-2]} &&
            ${s[n-1]} < ${r[n-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function Vc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function d0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var tne={kernelName:Up,backendName:"webgl",kernelFunc:d0};function au(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Eh({inputs:{input:m},backend:r})),h=e.map(m=>d0({inputs:{input:m},backend:r})),p=au(d,t,r),c=au(h,t,r),f=Oi({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=ZQ(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=au(e.slice(0,d),t,r),p=au(e.slice(d),t,r),c=au([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new ene(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=rne(e,t,r),o=new Qre(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function rne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function z8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return dn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),au(o,s,r)}var nne={kernelName:$o,backendName:"webgl",kernelFunc:z8},O8=class{constructor(e,t=!1,r=null,n=!1,a=!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,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:a?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:x=`
          float activation(float x) {
            ${r}
          }
        `,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&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 < ${h}; wR++) {
          int xR = xRCorner + wR * ${u};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${p}; wC++) {
            int xC = xCCorner + wC * ${d};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${c}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${m}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${f===1}) {

              if (${m}) {
                dotProd +=
                    getX(batch, xR, xC, ${c}) *
                    getW(wR, wC, ${c}, d2);
              } else {
                dotProd +=
                    getX(batch, ${c}, xR, xC) *
                    getW(wR, wC, ${c}, d2);
              }

            } else if (${f===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2)
              );

              if (${m}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${f===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2),
                getW(wR, wC, ${c} + 2, d2)
              );

              if (${m}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1),
                  getX(batch, xR, xC, ${c} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC),
                  getX(batch, ${c} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${v}
        ${b}
        setOutput(result);
      }
    `}},ane=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${a}, ${s}, ${i});
      const ivec3 pads = ivec3(${t}, ${r}, ${n});

      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 < ${h}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${p}; wC++) {
              int xC = xCCorner + wC * ${u};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${c}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${c}) *
                  getW(wF, wR, wC, ${c}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1),
                  getX(batch, xF, xR, xC, ${c} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2),
                  getW(wF, wR, wC, ${c} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},sne=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let{dataFormat:r}=t,n=Ur(),a=r==="channelsLast",s=a?0:1,i=a?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
          blockIndex = rc.y + ${d};
          pos = rc.x + ${u};

          ${o}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${s}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${i}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${a}) {
                  innerDims = vec2(d1, ch);
                  result[${u*2+d}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${u*2+d}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${l}

        ${n.output} = result;
      }
    `}};function D8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>M8)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Pp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let C=Cf({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),S=n.texData.get(C.dataId);w.assert(S.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,S.shape=r.outShape,g=dn({inputs:{x:C},backend:n}),g.shape=r.outShape,y.push(C)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Cf({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function L8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),C=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(C);let S=new sne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(S,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,O=o==="leakyrelu",z=o?o0(o,!0):null,j=new R8(_.shape,C.shape,[1,g,r.outChannels],A,x,M,z,I,O),X=[_,C];if(a&&X.push(a),I&&X.push(s),O){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=D8({x:a,filter:s,convInfo:p,backend:r});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=L8({x:a,filter:s,convInfo:p,backend:r});else{let m=new O8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var one={kernelName:Ks,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=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} - ${n};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${a};

              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);
      }
    `}},une=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-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) / ${n}.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 < ${r}; wC++) {
            float dyC = float(dyCCorner + wC) / ${a}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${r} - 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);
      }
    `}},dne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=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} - ${a};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${r} - ${s};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${n} - ${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);
      }
    `}},pne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-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) / ${a}.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 < ${r}; 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 = ${r} - 1 - wR;

            for (int wC = 0; wC < ${n}; 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 = ${n} - 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 hne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new lne(p);return r.runWebGLProgram(c,[a,s],"float32")}var cne={kernelName:Df,backendName:"webgl",kernelFunc:hne};function fne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new une(p);return r.runWebGLProgram(c,[a,s],"float32")}var mne={kernelName:Xs,backendName:"webgl",kernelFunc:fne};function gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new ane(u);return r.runWebGLProgram(d,[a,s],"float32")}var yne={kernelName:Bp,backendName:"webgl",kernelFunc:gne};function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new dne(u);return r.runWebGLProgram(d,[a,s],"float32")}var xne={kernelName:Lf,backendName:"webgl",kernelFunc:Ane};function bne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new pne(u);return r.runWebGLProgram(d,[a,s],"float32")}var vne={kernelName:Bf,backendName:"webgl",kernelFunc:bne},wne=Ad+`
  return cos(x);
`,kne=it({opSnippet:wne}),Ine={kernelName:Zs,backendName:"webgl",kernelFunc:kne},Sne=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,Cne=it({opSnippet:Sne}),Tne={kernelName:Ys,backendName:"webgl",kernelFunc:Cne},Nne=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${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 > ${c} ) {
          setOutput(float(${a}));
          return;
        }
        float in_x = ${b};
        if( in_x < 0.0 || in_x > ${f} ) {
          setOutput(float(${a}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${p} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}},Ene=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Nne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Rne={kernelName:_o,backendName:"webgl",kernelFunc:Ene},wv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${kv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${bt(n)} coords = getOutputCoords();
        int end = ${Iv(n,"coords")};
        float val = ${a};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${Iv(n,"coords")} = idx;
          val += getX(${kv(n,"coords")});
        }
        setOutput(val);
      }
    `}};function kv(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 ${e} is not yet supported`)}function Iv(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Mne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=Br({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=dn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new wv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new wv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=Br({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Fne={kernelName:Po,backendName:"webgl",kernelFunc:Mne};function $ne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=A8(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=KQ(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Pne={kernelName:Wf,backendName:"webgl",kernelFunc:$ne},_ne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,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 zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new _ne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var One={kernelName:zo,backendName:"webgl",kernelFunc:zne},B8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:a?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:l=`
          float activation(float x) {
            ${r}
          }
        `,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${l}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${o};
        int q = d2 - d1 * ${o};

        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 < ${s}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${i}; wC++) {
            int xC = xCCorner + wC * dilations[1];

            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${d}
        ${u}
        setOutput(result);
      }
    `}},W8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
          vec4 xTexelC${g*2};
          int xTexelC${g*2}Ready;
          vec4 xTexelC${g*2+1};
          int xTexelC${g*2+1}Ready;
          vec4 xC${g};`;p+=`
    for (int r = 0; r < ${u}; r++) {
      `;for(let g=0;g<d;g++)p+=`
          xTexelC${g*2} = vec4(0.0);
          xTexelC${g*2}Ready = 0;
          xTexelC${g*2+1} = vec4(0.0);
          xTexelC${g*2+1}Ready = 0;
          xC${g} = vec4(0.0);`;p+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let g=0;g<(h+1)/2;g++){let y=g*2;if(p+=`
          xC = xCCorner + ${y*l};
          `,o===1){if(y<d&&(i%2===1?(p+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }
              `,l===1&&y>0?p+=`
                xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
                `:p+=`
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }

                    xC${y} = vec4(previous.zw, xTexelC${y}.xy);
                  } else {
                    xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
                  }
                  `):p+=`
                if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xC${y} = xTexelC${y};
                `,y+1<d)){let A=i%2===0?w.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(p+=`
                  xCOffset = xC + imod(pads[1], 2) + ${A};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                    xTexelC${y+1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${y+1}.zw = vec2(0.0);
                    }
                    xTexelC${y+1}Ready = 1;
                  }
                  `,l>1&&(p+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                      xTexelC${y} = getX(batch, xR, xCOffset, d1);
                      xTexelC${y}Ready = 1;
                    }
                    `),p+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
                  `):A===1?p+=`
                    xC${y+1} = xTexelC${y};
                    `:p+=`
                    xCOffset = xC + ${A};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                      xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${y+1}.zw = vec2(0.0);
                      }
                      xTexelC${y+1}Ready = 1;
                    }

                    xC${y+1} = xTexelC${y+1};
                    `}}else y<d&&(i%2===1?(p+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${y+1}.zw = vec2(0.0);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
              `,y+1<d&&(p+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
                `)):(p+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y+1}.zw = vec2(0.);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(
                  xTexelC${y}.xy, xTexelC${y+1}.xy);
              `,y+1<d&&(p+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
                `)));y<d&&(p+=`
            wTexel = getW(r, ${y}, d1, q);
            dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
          `,y+1<d&&(p+=`
              wTexel = getW(r, ${y+1}, d1, q);
              dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
            `))}p+=`
    }
  `,p+=`
      }
    `;let c="",f="";r&&(n?c=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:a?c=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:c=`vec4 activation(vec4 x) {
          ${r}
        }`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${c}

      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);

        ${p}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${m}
        ${f}
        setOutput(result);
      }
    `}};function Dne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new W8(h):p=new B8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Lne={kernelName:Js,backendName:"webgl",kernelFunc:Dne},Bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=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} - ${n};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${a};

              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);
      }
    `}},Wne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-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) / ${n}.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 < ${r}; wC++) {
            float dyC = float(dyCCorner + wC) / ${a}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${r} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${o}; dm++) {
              int d2 = d1 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Vne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new Bne(h);return r.runWebGLProgram(p,[a,s],"float32")}var Une={kernelName:Vf,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Wne(h);return r.runWebGLProgram(p,[a,s],"float32")}var jne={kernelName:Uf,backendName:"webgl",kernelFunc:Gne},Hne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function qne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Hne(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Kne={kernelName:Gf,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
      const ivec2 strides = ivec2(${a}, ${s});
      const ivec2 pads = ivec2(${d}, ${h});
      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 < ${r}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function Zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Xne(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Yne={kernelName:Wp,backendName:"webgl",kernelFunc:Zne};function Jne(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Br({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Zx({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=u0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var Qne={kernelName:Vp,backendName:"webgl",kernelFunc:Jne},eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",tae=`
  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;
`,rae=it({opSnippet:eae,packedOpSnippet:tae}),nae={kernelName:ei,backendName:"webgl",kernelFunc:rae},aae="return (b >= 1.0) ? a : a * (b + 1.0);",sae=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,iae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(sae,n.shape,a.shape):new ku(aae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},oae={kernelName:jf,backendName:"webgl",kernelFunc:iae},lae=`
  return vec4(equal(a, b));
`,uae="return float(a == b);",dae=Ar({opSnippet:uae,packedOpSnippet:lae,dtype:"bool",cpuKernelImpl:YQ}),pae={kernelName:Oo,backendName:"webgl",kernelFunc:dae},hae=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.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));
`,cae=it({opSnippet:hae}),fae={kernelName:Ou,backendName:"webgl",kernelFunc:cae},mae=Ad+`
  return exp(x);
`,gae=`
  vec4 result = exp(x);
  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;
`,V8=it({opSnippet:mae,packedOpSnippet:gae,cpuKernelImpl:JQ,dtype:"float32"}),yae={kernelName:ti,backendName:"webgl",kernelFunc:V8};function _y(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Aae={kernelName:Do,backendName:"webgl",kernelFunc:_y},Sv="return exp(x) - 1.0;",xae=it({opSnippet:Sv,packedOpSnippet:Sv,cpuKernelImpl:QQ}),bae={kernelName:Lo,backendName:"webgl",kernelFunc:xae},Cv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.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 = ${a};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${i}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${n});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${n}; 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 U8(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Cv("real",l,t),d=new Cv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function vae(e){let{inputs:t,backend:r}=e,{input:n}=t;return U8(n,!1,r)}var wae={kernelName:Hf,backendName:"webgl",kernelFunc:vae},kae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function Rh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new kae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Iae={kernelName:Du,backendName:"webgl",kernelFunc:Rh},Sae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Cae={kernelName:Bo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Sae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Tv="return floor(x);",Tae=it({opSnippet:Tv,packedOpSnippet:Tv,cpuKernelImpl:eee}),Nae={kernelName:ri,backendName:"webgl",kernelFunc:Tae},Eae=`
  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;
  }
`,Rae=`
  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);
`,Mae=Ar({opSnippet:Eae,packedOpSnippet:Rae,dtype:"int32"}),Fae={kernelName:ni,backendName:"webgl",kernelFunc:Mae},$ae=class{constructor(e){this.variableNames=["A"];let t=Ur(),[r,n]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${r}.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));
      }
    `}},Pae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ur(),[r,n]=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(${n}.0, ${r}.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;
      }
    `}},_ae={kernelName:kp,backendName:"webgl",kernelFunc:zae},Ql;function zae(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(Ql==null&&(Ql=document.createElement("canvas").getContext("2d")),Ql.canvas.width=l,Ql.canvas.height=u,Ql.drawImage(a,0,0,l,u),a=Ql.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=J().getBool("WEBGL_PACK")?new Pae(h):new $ae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function Oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),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=D8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=L8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,C=c==="leakyrelu",S=c?o0(c,!1):null,E=new O8(g,b,S,v,C),R=[a,s];if(i&&R.push(i),o&&R.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Dae={kernelName:Ns,backendName:"webgl",kernelFunc:Oae};function Lae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?o0(p,y):null,x=[a,s],b=i!=null,v=o!=null,C=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let S;y?S=new W8(g,b,A,v,C):S=new B8(g,b,A,v,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(S,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var Bae={kernelName:Es,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=bt(t.length),a=bt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${n} strides = ${n}(${this.strides});
         void main() {
          ${a} 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 Vae(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=tee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new Wae(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var Uae={kernelName:Vo,backendName:"webgl",kernelFunc:Vae},Gae=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=bt(this.rank),n=jae(e,2);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${n}));
      }
    `}};function jae(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function G8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(J().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=ree(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Gae(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Hae={kernelName:Wo,backendName:"webgl",kernelFunc:G8},qae="return float(a > b);",Kae=`
  return vec4(greaterThan(a, b));
`,Xae=Ar({opSnippet:qae,packedOpSnippet:Kae,cpuKernelImpl:nee,dtype:"bool"}),Zae={kernelName:Uo,backendName:"webgl",kernelFunc:Xae},Yae="return float(a >= b);",Jae=`
  return vec4(greaterThanEqual(a, b));
`,Qae=Ar({opSnippet:Yae,packedOpSnippet:Jae,dtype:"bool",cpuKernelImpl:aee}),ese={kernelName:si,backendName:"webgl",kernelFunc:Qae};function tse(e){let{inputs:t,backend:r}=e,{input:n}=t;return U8(n,!0,r)}var rse={kernelName:qf,backendName:"webgl",kernelFunc:tse},nse="return float(!isnan(x) && !isinf(x));",ase=it({opSnippet:nse,dtype:"bool"}),sse={kernelName:Lu,backendName:"webgl",kernelFunc:ase},ise="return float(isinf(x));",ose=it({opSnippet:ise,dtype:"bool"}),lse={kernelName:Bu,backendName:"webgl",kernelFunc:ose},use="return float(isnan(x));",dse=it({opSnippet:use,dtype:"bool"}),pse={kernelName:Wu,backendName:"webgl",kernelFunc:dse},hse="return float(a < b);",cse=`
  return vec4(lessThan(a, b));
`,fse=Ar({opSnippet:hse,packedOpSnippet:cse,cpuKernelImpl:see,dtype:"bool"}),mse={kernelName:Go,backendName:"webgl",kernelFunc:fse},gse="return float(a <= b);",yse=`
  return vec4(lessThanEqual(a, b));
`,Ase=Ar({opSnippet:gse,packedOpSnippet:yse,cpuKernelImpl:iee,dtype:"bool"}),xse={kernelName:jo,backendName:"webgl",kernelFunc:Ase};function bse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=oee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var vse={kernelName:Kf,backendName:"webgl",kernelFunc:bse},wse=Ad+`
  return x < 0.0 ? 0./0. : log(x);
`,kse=`
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`,Ise=it({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:lee}),Sse={kernelName:li,backendName:"webgl",kernelFunc:Ise},Cse=Ad+`
  return log(1.0 + x);
`,Tse=it({opSnippet:Cse}),Nse={kernelName:Vu,backendName:"webgl",kernelFunc:Tse},Ese="return float(a >= 1.0 && b >= 1.0);",Rse=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Mse=Ar({opSnippet:Ese,packedOpSnippet:Rse,dtype:"bool"}),Fse={kernelName:Ho,backendName:"webgl",kernelFunc:Mse},$se="return float(!(x >= 1.0));",Pse=it({opSnippet:$se}),_se={kernelName:Uu,backendName:"webgl",kernelFunc:Pse},zse="return float(a >= 1.0 || b >= 1.0);",Ose=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Dse=Ar({opSnippet:zse,packedOpSnippet:Ose,dtype:"bool"}),Lse={kernelName:Gp,backendName:"webgl",kernelFunc:Dse},Bse=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${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 * ${o};
        setOutput(val);
      }
    `}},Wse=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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);
      }
    `}},Vse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new Wse(a.shape,s,i,o,l):new Bse(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Use={kernelName:jp,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,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(${n}) * norm + float(${r});

          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(${n})
                * float(${a})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${a});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},jse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Gse(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Hse={kernelName:Xf,backendName:"webgl",kernelFunc:jse};function qse(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function j8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[d[C]];let b=Xx(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=l0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=uee(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=qse(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Kse={kernelName:ui,backendName:"webgl",kernelFunc:j8},Xse=S8+`
  return max(a, b);
`,Zse=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+i0+`
  return result;
`,Yse=Ar({opSnippet:Xse,packedOpSnippet:Zse,cpuKernelImpl:dee}),Jse={kernelName:di,backendName:"webgl",kernelFunc:Yse};function Qse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;cd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new _p(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var eie={kernelName:pi,backendName:"webgl",kernelFunc:Qse};function tie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new Yx(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var rie={kernelName:Hp,backendName:"webgl",kernelFunc:tie},nie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*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 < ${a};
          wR += ${n}) {
          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) / ${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);
            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);
      }
    `}},aie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${d}, ${h}, ${p});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${o};
           wD += ${a}) {
          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) / ${r}.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) / ${n}.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 = ${c} -
                  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 sie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Yx(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new aie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var iie={kernelName:Yf,backendName:"webgl",kernelFunc:sie};function oie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;cd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new _p(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new nie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var lie={kernelName:Zf,backendName:"webgl",kernelFunc:oie};function uie(e,t,r,n){let a=new _p(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new _p(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var die={kernelName:Jf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=uie(n,o,d,l);return[h,p]}};function pie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var hie={kernelName:hi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=n.shape[d[S]];let v=Xx(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let C=i.texData.get(f.dataId);C.values=v}else f=l0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=pie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function cie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var fie={kernelName:ci,backendName:"webgl",kernelFunc:cie},mie=S8+`
  return min(a, b);
`,gie=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+i0+`
  return result;
`,yie=Ar({opSnippet:mie,packedOpSnippet:gie,cpuKernelImpl:pee}),Aie={kernelName:fi,backendName:"webgl",kernelFunc:yie},xie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=bt(n),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,n),l=r==="reflect"?0:1;if(n===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=`
      ${a} start = ${a}(${s});
      ${a} end = ${a}(${i});

      void main() {
        ${a} outC = getOutputCoords();
        for (int i = 0; i < ${n}; 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};
          }
        }
        ${a} coords = outC - start;
        setOutput(getX(${o}));
      }
    `}},bie=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,f)=>c[0]+e[f]+c[1]);let n=e.length,a=bt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,f)=>c[0]+e[f]).join(","),o=zr("rc",n),l=zr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
        ${a} source = rc;
        if (source < start) {
          source = start * 2 - source - ${h};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${h};
        }
        source -= start;
      `;p=`
        ${a} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${d});
        ${o[n-1]} += 1;
        if(${u}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${d});
        }
      `}else{let c=`
        ${a} source = rc;
        ${a} lt = ${a}(lessThan(source, start));
        ${a} gte = ${a}(greaterThanEqual(source, end));
        ${a} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${h}) +
                gte * ((end - 1) * 2 - source + ${h});
        source -= start;
      `;p=`
        ${a} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${d});
        ${o[n-1]} += 1;
        if(${u}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${d});
        }
        rc = outputLoc;
        ${o[n-2]} += 1;
        if(${o[n-2]} < ${this.outputShape[n-2]}) {
          ${c}
          result[2] = getChannel(getX(${l.join()}), ${d});
          ${o[n-1]} += 1;
          if(${u}) {
            ${c}
            result[3] = getChannel(getX(${l.join()}), ${d});
          }
        }
      `}this.userCode=`
      const ${a} start = ${a}(${s});
      const ${a} end = ${a}(${i});

      void main() {
        ${a} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}},vie=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bie(n.shape,a,s):new xie(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},wie={kernelName:mi,backendName:"webgl",kernelFunc:vie},kie=`if (b == 0.0) return NAN;
  return mod(a, b);`,Iie=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+i0+`
  return result;
`,Sie=Ar({opSnippet:kie,packedOpSnippet:Iie}),Cie={kernelName:Gu,backendName:"webgl",kernelFunc:Sie},Tie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${t-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}},Nie=`
if (a == b) {
  return 1.0;
};
return a / b;`,Eie=`
  // 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;
`,H8=Ar({opSnippet:Nie,packedOpSnippet:Eie,checkOutOfBounds:!0}),Rie={kernelName:Qs,backendName:"webgl",kernelFunc:H8},Nv="return a - b;",q8=Ar({opSnippet:Nv,packedOpSnippet:Nv,supportsComplex:!0,cpuKernelImpl:Tee}),Mie={kernelName:Ri,backendName:"webgl",kernelFunc:q8};function K8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=j8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=q8({inputs:{a,b:u},backend:r}),h=V8({inputs:{x:d},backend:r}),p=u0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=H8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Fie={kernelName:Ni,backendName:"webgl",kernelFunc:K8};function $ie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:K8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Tie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Pie={kernelName:Qf,backendName:"webgl",kernelFunc:$ie},_ie=jn+`
  return -x;
`,zie=`
  vec4 result = -x;
  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;
`;function Oie(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=cee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,zie):a=new Ua(n.shape,_ie),r.runWebGLProgram(a,[n],n.dtype)}var Die={kernelName:qo,backendName:"webgl",kernelFunc:Oie},Lie=Un.nonMaxSuppressionV3Impl;function Bie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Lie(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wie={kernelName:Xo,backendName:"webgl",kernelFunc:Bie},Vie=Un.nonMaxSuppressionV4Impl;function Uie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Vie(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Gie={kernelName:ju,backendName:"webgl",kernelFunc:Uie},jie=Un.nonMaxSuppressionV5Impl;function Hie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=jie(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var qie={kernelName:Zo,backendName:"webgl",kernelFunc:Hie},Kie=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${r}),
                      float(index == coords.y)));
      }
    `}},Xie=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new Kie(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Zie={kernelName:Jo,backendName:"webgl",kernelFunc:Xie};function Tf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Eh({inputs:{input:n},backend:r}),s=Tf({inputs:{x:a},backend:r}),i=d0({inputs:{input:n},backend:r}),o=Tf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Rh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Yie={kernelName:ml,backendName:"webgl",kernelFunc:Tf};function X8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Eh({inputs:{input:n},backend:r}),s=X8({inputs:{x:a},backend:r}),i=d0({inputs:{input:n},backend:r}),o=Tf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Rh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Jie={kernelName:Yo,backendName:"webgl",kernelFunc:X8};function Qie(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return _y({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=_y({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=z8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var eoe={kernelName:Qo,backendName:"webgl",kernelFunc:Qie},toe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=bt(n),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,n);if(n===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${a} start = ${a}(${s});
      ${a} end = ${a}(${i});

      void main() {
        ${a} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${a} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}},roe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let n=e.length,a=bt(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=zr("rc",n),l=zr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
       if(${u}) {
      `,n===1?"":`}
       rc = outputLoc;
       ${o[n-2]} += 1;
       if(${o[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":`  ${o[n-1]} += 1;
         if(${u}) {`],p=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let f=0,m=n===1?2:4;f<m;f++)c+=`
        ${h[f]}
        if (${p}) {
          result[${f}] = float(value);
        } else {
          ${a} source = rc - start;
          result[${f}] = getChannel(getX(${l.join()}), ${d});
        }
      `;c+=n===1?"} ":"}}",this.userCode=`
      const ${a} start = ${a}(${s});
      const ${a} end = ${a}(${i});

      void main() {
        ${a} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${c}
        setOutput(result);
      }
    `}},Z8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Rh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new roe(a.shape,s,i):new toe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},noe={kernelName:yi,backendName:"webgl",kernelFunc:Z8},aoe=`
  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);
`,soe=`
  // 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));
  `+i0+`
  return result;
`,ioe=Ar({opSnippet:aoe,packedOpSnippet:soe}),ooe={kernelName:Ai,backendName:"webgl",kernelFunc:ioe};function loe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=Br({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=mee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=rh(a.dtype),x=Tl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var uoe={kernelName:el,backendName:"webgl",kernelFunc:loe},Y8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=gee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},doe={kernelName:Hu,backendName:"webgl",kernelFunc:Y8},poe="return 1.0 / x;",hoe=it({opSnippet:poe}),coe={kernelName:qu,backendName:"webgl",kernelFunc:hoe},foe=jn+`
  return (x < 0.0) ? 0.0 : x;
`,moe=`
  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;
`,goe=it({opSnippet:foe,packedOpSnippet:moe}),yoe={kernelName:bi,backendName:"webgl",kernelFunc:goe},Aoe=jn+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,xoe=`
  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;
`,boe=it({opSnippet:Aoe,packedOpSnippet:xoe}),voe={kernelName:wi,backendName:"webgl",kernelFunc:boe},woe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="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 = ${h};

        // 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);
      }
    `}},koe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="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 = ${h};

        // 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 < ${r-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}};function Ioe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new koe(a.shape,l,u,s,i):new woe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var Soe={kernelName:vi,backendName:"webgl",kernelFunc:Ioe},Coe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${d});

        const float invHeightScale = float(${h});
        const float invWidthScale = float(${p});

        const int winHeight = int(${c});
        const int winWidth = int(${f});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${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), ${n-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), ${a-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 Toe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Coe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Noe={kernelName:tm,backendName:"webgl",kernelFunc:Toe},Eoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":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 coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}},Roe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":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 coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${r-1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `}};function Moe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Roe(a.shape,l,u,s,i):new Eoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Foe={kernelName:Ku,backendName:"webgl",kernelFunc:Moe},$oe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${d});

        const float invHeightScale = float(${h});
        const float invWidthScale = float(${p});

        const int winHeight = int(${c});
        const int winWidth = int(${f});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${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(${n}) - 1),
                ${r} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${a}) - 1),
                ${r} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function Poe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new $oe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var _oe={kernelName:em,backendName:"webgl",kernelFunc:Poe},zoe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>n(o)).join(","),s=bt(r);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${a}));
      }
    `}},Ooe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=zr("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=bt(r);r===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${a}){
              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 = ${o(n.slice())};
          if(${a}){
            result.g = ${l(n.slice())};
          }
          if(${s}) {
            result.b = ${u(n.slice())};
            if(${a}) {
              result.a = ${d(n.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function Doe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return dn({inputs:{x:a},backend:r});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ooe(a.shape,o):new zoe(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Loe={kernelName:rl,backendName:"webgl",kernelFunc:Doe},Boe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${a}
          if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${r}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Woe={kernelName:gl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new Boe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},Voe=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,Uoe=it({opSnippet:Voe}),Goe={kernelName:nl,backendName:"webgl",kernelFunc:Uoe},joe="return inversesqrt(x);",Hoe=it({opSnippet:joe,cpuKernelImpl:yee}),qoe={kernelName:ki,backendName:"webgl",kernelFunc:Hoe},J8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=bt(a.length),l=bt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${a});

        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 * ${c};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${p};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function Koe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new J8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var Xoe={kernelName:al,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=bt(r);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${n});
        if (cVal >= 1.0) {
          setOutput(getA(${a}));
        } else {
          setOutput(getB(${a}));
        }
      }
    `}};function Yoe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Zoe(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Dr(a.dtype,s.dtype))}var Joe={kernelName:sl,backendName:"webgl",kernelFunc:Yoe},Qoe=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${N.SELU_SCALEALPHA};
  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,ele=it({opSnippet:Qoe}),tle={kernelName:Xu,backendName:"webgl",kernelFunc:ele},rle=Ad+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,nle=`
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  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;
`,ale=it({opSnippet:rle,packedOpSnippet:nle,cpuKernelImpl:Aee}),sle={kernelName:Si,backendName:"webgl",kernelFunc:ale},ile=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,ole=it({opSnippet:ile}),lle={kernelName:Zu,backendName:"webgl",kernelFunc:ole},ule=Ad+`
  return sin(x);
`,dle=it({opSnippet:ule}),ple={kernelName:Ii,backendName:"webgl",kernelFunc:dle},hle=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,cle=it({opSnippet:hle}),fle={kernelName:ol,backendName:"webgl",kernelFunc:cle},mle=`
  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;
`,gle=it({opSnippet:mle}),yle={kernelName:Yu,backendName:"webgl",kernelFunc:gle},Ale=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.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<a.shape.length;++y)l.push([0,0]);let u=[],d=Z8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},xle={kernelName:ll,backendName:"webgl",kernelFunc:Ale};function ble(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=bee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var vle={kernelName:Kp,backendName:"webgl",kernelFunc:ble};function wle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=vee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var kle={kernelName:Ju,backendName:"webgl",kernelFunc:wle};function Ile(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=b8(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Sle={kernelName:Xp,backendName:"webgl",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=b8(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var Tle={kernelName:Zp,backendName:"webgl",kernelFunc:Cle};function Nle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new J8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var Ele={kernelName:Yp,backendName:"webgl",kernelFunc:Nle};function Rle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=xd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Mle={kernelName:ul,backendName:"webgl",kernelFunc:Rle},Ev="return sqrt(x);",Fle=it({opSnippet:Ev,packedOpSnippet:Ev,cpuKernelImpl:wee}),$le={kernelName:Ci,backendName:"webgl",kernelFunc:Fle},Ple="return x * x;",_le=it({opSnippet:Ple}),zle={kernelName:Qu,backendName:"webgl",kernelFunc:_le},Rv="return (a - b) * (a - b);",Ole=Ar({opSnippet:Rv,packedOpSnippet:Rv}),Dle={kernelName:Ei,backendName:"webgl",kernelFunc:Ole};function Lle({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=jn+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Ua(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var Ble={kernelName:$i,backendName:"webgl",kernelFunc:Lle},Wle=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=bt(r.length),s=bt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${a} begin = ${a}(${e});
      ${a} strides = ${a}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),E=xd({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),E=We(a.shape,a.dtype,S),R=kee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let S=new Wle(A,b,c);v=r.runWebGLProgram(S,[a],a.dtype)}let C=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),C}var Ule={kernelName:dl,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Iee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var jle={kernelName:Jp,backendName:"webgl",kernelFunc:Gle};function Hle(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{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=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=See(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var qle={kernelName:rm,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Cee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Xle={kernelName:nm,backendName:"webgl",kernelFunc:Kle},Zle="return tan(x);",Yle=it({opSnippet:Zle}),Jle={kernelName:pl,backendName:"webgl",kernelFunc:Yle},Qle=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,eue=it({opSnippet:Qle}),tue={kernelName:Mi,backendName:"webgl",kernelFunc:eue},rue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=bt(this.rank),a=nue(e);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${a}));
      }
    `}};function nue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function Q8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Nee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new rue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var aue={kernelName:Ka,backendName:"webgl",kernelFunc:Q8},sue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},iue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function no(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Mv(e){let t=1;for(;t<e;)t*=2;return t}function oue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=J().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=J().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=Eee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Rh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&no(r,c);let g=Mv(s),y=Mv(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),O=new sue(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(O,I,"int32",z),no(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new iue([f,R/2]),I=[[d],[A===null?1:0],[g]],O=A;A=r.runWebGLProgram(M,_,"int32",I),no(r,O);let z=g/2,j=z*2;for(let X=z;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=xd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),no(r,v);let C=G8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});no(r,m);let S=u.slice(0,-1);S.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:S},backend:r}),no(r,v);let E=C;return C=ve({inputs:{x:C},attrs:{shape:S},backend:r}),no(r,E),[C,A]}var lue={kernelName:hl,backendName:"webgl",kernelFunc:oue},uue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){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 < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${a});
              }
              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(${a});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${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 due(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new uue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var pue={kernelName:cl,backendName:"webgl",kernelFunc:due};function hue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;cd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Ree(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var cue={kernelName:am,backendName:"webgl",kernelFunc:hue};function fue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=xd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var mue={kernelName:fl,backendName:"webgl",kernelFunc:fue},gue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
        sumValue += dot(values, segFilter);
    `,p="";a%r>0&&(p=`
        if (inIdx < 0 || inIdx >= ${a}) {
          return initializationValue;
        }
      `);let c="";a%r>0&&(c=`
        if (inIdx < 0 || inIdx >= ${a}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${o};

      float getValue(int batch, int inIdx) {
        ${p}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${c}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${s})) * float(${r}));
        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
          );

          ${h}
        }

        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
          );

          ${h}
        } else if (${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
          );

          ${h}
        } else if (${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
          );

          ${h}
        }
        setOutput(${l});
      }
    `}};function yue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=rh(a.dtype),g=(b,v,C,S,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},O=new gue(I,v),z=r.compileAndRun(O,[b,C],S);if(l.push(z),z.shape[1]===E)return z;let j=Y8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=Q8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(z,v,X,S,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=Br({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Aue={kernelName:Qp,backendName:"webgl",kernelFunc:yue},xue=[Ste,Tte,Rte,$te,_te,Dte,Bte,Vte,Hte,Kte,Yte,ere,nre,ore,dre,hre,fre,Are,bre,wre,Cre,$re,_re,Ore,Ure,jre,Xre,ite,Jre,nne,one,cne,mne,yne,xne,vne,Ine,Tne,Rne,Fne,Pne,One,Lne,Une,jne,Kne,Yne,Qne,nae,oae,pae,fae,yae,Aae,bae,wae,Iae,Cae,Nae,Fae,_ae,Dae,Bae,Uae,Hae,Zae,ese,ste,rse,tne,sse,lse,pse,lte,mse,xse,vse,Sse,Nse,Fse,_se,Lse,Use,Hse,Kse,Jse,eie,rie,iie,lie,die,hie,fie,Aie,wie,Cie,Pie,cte,Die,Wie,Gie,qie,Lre,Zie,Jie,eoe,noe,ooe,dte,uoe,doe,Bre,Rie,coe,yoe,voe,mte,Soe,Noe,Foe,_oe,Loe,Woe,Goe,qoe,Xoe,Joe,tle,sle,lle,ple,fle,Mre,Fie,yle,xle,vle,kle,Sle,Tle,Ele,Mle,$le,zle,Dle,Ble,Ule,jle,qle,Xle,Mie,wte,Jle,tue,aue,lue,pue,kte,cue,mue,Aue,Yie];for(let e of xue)Wn(e);var Pa=J();Pa.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Pa.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Pa.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Pa.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Pa.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Pa.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Pa.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Pa.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function bue(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function mr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function Qc(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Jx(){return`
  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Di(){return`
  ${Jx()}
  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
          @builtin(global_invocation_id) GlobalId : vec3<u32>,
          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
    localId = LocalId;
    globalId = GlobalId;
    numWorkgroups = NumWorkgroups;
`}function tt(){return`
    ${Di()}
      let index = getGlobalIndex();
`}function vue(e,t,r,n=!1){let a=[];if(a.push(`
    let workGroupSizeX = ${r.workGroupSize[0]}u;
    let workGroupSizeY = ${r.workGroupSize[1]}u;
    let workGroupSizeZ = ${r.workGroupSize[2]}u;

    var<private> localId: vec3<u32>;
    var<private> globalId: vec3<u32>;
    var<private> numWorkgroups: vec3<u32>;

    // Only used when the y/z dimension of workgroup size is 1.
    fn getGlobalIndex() -> i32 {
      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
        return i32(globalId.x);
      }

      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
          localId.y * workGroupSizeX + localId.x;
      let workGroupID = (globalId - localId)/vec3<u32>(
          workGroupSizeX, workGroupSizeY, workGroupSizeZ);

      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
        workGroupID.y * numWorkgroups.x + workGroupID.x) *
        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
        localInvocationIndex);
    }
  `),n===!0)return a.push(`
      struct Matrix0 {
        numbers: array<${Qc(t.dtype,r.isVec4)}>;
      };
      struct Uniform {
        size            : i32;
        numChannels     : i32;
        outShapeStrides : vec2<i32>;
        dispatchSize    : vec3<u32>;
      };

      @group(0) @binding(0) var<storage, write> result : Matrix0;
      @group(0) @binding(2) var<uniform> uniforms: Uniform;
    `),[Fv,a.join(`
`),$v(t.shape),r.getUserCode()].join(`
`);let s="struct Uniforms { NAN : f32; ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${mr(e[h].shape.length)}; `}),s+=`outShape : ${mr(t.shape.length)} ; `;let i=t.shape.length-1;s+=`
       outShapeStrides: ${mr(i)}; `,r.size&&(s+="size : i32; "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
    struct Matrix0 {
        numbers: array<atomic<i32>>;
    };

    @group(0) @binding(0) var<storage, read_write> result : Matrix0;
  `):a.push(`
    struct Matrix0 {
        numbers: array<${Qc(t.dtype,r.isVec4)}>;
    };

    @group(0) @binding(0) var<storage, write> result : Matrix0;
  `),r.variableNames.forEach((d,h)=>{a.push(`
    struct Matrix${1+h} {
      numbers: array<${Qc(e[h].dtype,r.isVec4)}>;
    };
    @group(0) @binding(${1+h}) var<storage, read> ${d} : Matrix${1+h};
    `)}),s!==""&&a.push(`
    @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms : Uniforms;
    `);let[o,l]=Tue(t.shape,r.dispatchLayout),u=[Fv,a.join(`
`),$v(t.shape),o,wue(t.shape.length)];if(r.atomic||u.push(kue(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Iue(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
`);u.push(d)}return u.push(r.getUserCode()),u.join(`
`)}var Fv=`
  // Checks whether coordinates lie within the bounds of the shape.
  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
    return all(coord >= vec2<i32>(0)) && all(coord < shape);
  }
  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
    return all(coord >= vec3<i32>(0)) && all(coord < shape);
  }
  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
    return all(coord >= vec4<i32>(0)) && all(coord < shape);
  }

  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
    return coord;
  }
  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
    return dot(coords, vec2<i32>(shape.y, 1));
  }
  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
  }
  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
    return dot(coords, vec4<i32>(
        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
  }

  fn idiv(a: i32, b: i32, sign: f32) -> i32 {
    var res: i32 = a / b;
    let mod: i32 = a % b;
    if (sign < 0. && mod != 0) {
      res = res - 1;
    }
    return res;
  }

  // NaN defination in IEEE 754-1985 is :
  //   - sign = either 0 or 1.
  //   - biased exponent = all 1 bits.
  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
  fn isnan(val: f32) -> bool {
    let floatToUint: u32 = bitcast<u32>(val);
    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
  }
  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
  }
`;function wue(e){let t="";switch(e){case 0:case 1:t+=`
        fn getOutputIndexFromCoords(coords : i32) -> i32 {
          return coords;
        }
        `;break;case 2:t+=`
        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
        }
        `;break;case 3:t+=`
        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
        }
        `;break;case 4:t+=`
        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
          return dot(coords, vec4<i32>(
            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
        }
        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function kue(e,t,r){let n=e.length,a=Qc(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
      result.numbers[flatIndex] = ${a}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
      result.numbers[flatIndex] = ${a}(value);
    }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
      result.numbers[flatIndex] = ${a}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
      result.numbers[flatIndex] = ${a}(value);
    }`,n>=2){let i=["d0","d1","d2","d3"].slice(0,n),o=mr(n);r?s+=`
      fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex / 4, value);
      }
      fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex / 4, value);
      }
    `:s+=`
      fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex, value);
      }
      fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : i32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `}return s}function Iue(e,t,r,n){let a=Sue(e,r);return e.shape.length<=t.length&&(a+=Cue(e,t,r,n)),a}function Sue(e,t){let r=e.name,n=e.shape.length,a=mr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
        fn ${s}() -> vec4<f32> {
          return vec4<f32>(${r}.numbers[0]);
        }
      `:`
      fn ${s}() ->f32 {
        return f32(${r}.numbers[0]);
      }
    `;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
      fn ${s}(${o}) -> vec4<f32> {
        return vec4<f32>(${r}.numbers[getIndexFromCoords${u}(${a}(${i.join(",")}),
          ${l}) / 4]);
      }
      `:`
    fn ${s}(${o}) -> f32 {
      return f32(${r}.numbers[getIndexFromCoords${u}(${a}(${i.join(",")}),
        ${l})]);
    }
   `}function Cue(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=mr(l);if(w.arraysEqual(e.shape,t)&&n)return r?`
        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
          return vec4<f32>(${a}.numbers[globalIndex]);
        }

        fn ${i}Coords(coords : ${u}) -> vec4<f32> {
          return vec4<f32>(${a}.numbers[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
        }
        `:`
      fn ${i}Index(globalIndex : i32) -> f32 {
        return f32(${a}.numbers[globalIndex]);
      }

      fn ${i}Coords(coords : ${u}) -> f32 {
        return f32(${a}.numbers[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
      }
      `;let d=N.getBroadcastDims(e.shape,t),h=l-o,p="";if(o===0)return r?`
      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
        return get${s}();
      }

      fn ${i}Coords(coords : ${u}) -> vec4<f32> {
        return get${s}();
      }
    `:`
      fn ${i}Index(globalIndex : i32) -> f32{
        return get${s}();
      }

      fn ${i}Coords(coords : ${u}) -> f32{
        return get${s}();
      }
    `;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords[${g+h}] = 0;`).join(`
`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=mr(o),y=e.shape.map((A,x)=>`coords[${x+h}]`).join(", ");c=`${g}(${y})`}else c="coords";let f=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,m=`${o}D`;return r?`
      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
        var coords = getCoordsFromIndex(globalIndex);
        ${p}
        return ${a}.numbers[getIndexFromCoords${m}(${c}, ${f}) / 4];
      }

      fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
        var coords = coordsIn;
        ${p}
        return ${a}.numbers[getIndexFromCoords${m}(${c}, ${f}) / 4];
      }
    `:`
    fn ${i}Index(globalIndex : i32) -> f32 {
      var coords = getCoordsFromIndex(globalIndex);
      ${p}
      return f32(${a}.numbers[getIndexFromCoords${m}(${c}, ${f})]);
    }

    fn ${i}Coords(coordsIn : ${u}) -> f32 {
      var coords = coordsIn;
      ${p}
      return f32(${a}.numbers[getIndexFromCoords${m}(${c}, ${f})]);
    }
  `}function Tue(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${mr(s)}{
      let globalIndex = getGlobalIndex();
      return getCoordsFromIndex(globalIndex);
    }
    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=bue(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=mr(l),h=`fn getOutputCoords() -> ${d} {
    ${i}
  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function $v(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=mr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
      return vec2<i32>(d0, d1);
    }`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
    fn getCoordsFromIndex(index : i32) -> ${n} {
      ${s}
      return ${n}(${a.join(",")});
    }
  `}var eS={};Le(eS,{ArrayBufferToTypedArray:()=>rS,GPUBytesPerElement:()=>zy,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Qx,computeWorkGroupSizeForMatMul:()=>tS,computeWorkPerThreadForConv2d:()=>eb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>tb,tilesFitEvenlyIntoShape:()=>ja});var yo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function ja(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(yo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(yo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(yo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function Qx(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function tS(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function eb(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function zy(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function rS(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function tb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Nue="return a + b;",Eue="return areal * breal - aimag * bimag;",Rue="return areal * bimag + aimag * breal;",Mue="return a / b;",Fue="return a * b;",$ue="return (a - b) * (a - b);",Pue="return a - b;",_ue="return f32(a == b);",zue="return vec4<f32>(a == b);",Oue="return f32(a > b);",Due="return vec4<f32>(a > b);",Lue="return f32(a >= b);",Bue="return vec4<f32>(a >= b);",Wue="return f32(a < b);",Vue="return vec4<f32>(a < b);",Uue="return f32(a <= b);",Gue="return vec4<f32>(a <= b);",jue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Hue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`,que=`
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `,nS=`
  if (isNaN.r) {
    resultTemp.r = uniforms.NAN;
  }
  if (isNaN.g) {
    resultTemp.g = uniforms.NAN;
  }
  if (isNaN.b) {
    resultTemp.b = uniforms.NAN;
  }
  if (isNaN.a) {
    resultTemp.a = uniforms.NAN;
  }
  `,Kue=`
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `,Xue=`
  let ia = vec4<i32>(round(a));
  let ib = vec4<i32>(round(b));
  let cond = ib != vec4<i32>(0);
  var resultTemp = vec4<i32>(0);
  let s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    resultTemp[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    resultTemp[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    resultTemp[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    resultTemp[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4<f32>(resultTemp);
  `,Zue="return f32(a != b);",Yue="return vec4<f32>(a != b);",Jue=`
  if(a < 0.0 && floor(b) < b) {
    return uniforms.NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  if (round(abs(b) % 2.0) != 1.0) {
    return pow(abs(a), b);
  }
  return sign(a) * pow(abs(a), b);
  `,Que=`
  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
  let isModRound1 = vec4<f32>(isModRound1Bool);
  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
  var resultTemp = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  let isExpZero = b == vec4<f32>(0.0);
  if (isExpZero.r) {
    resultTemp.r = 1.0;
  }
  if (isExpZero.g) {
    resultTemp.g = 1.0;
  }
  if (isExpZero.b) {
    resultTemp.b = 1.0;
  }
  if (isExpZero.a) {
    resultTemp.a = 1.0;
  }
  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
  ${nS}
  return resultTemp;
  `,ede="if (a < 0.0) { return b * a; }  return a;",tde=`
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;function Pv(e,t){let r=t?nS:que;return t?`
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = isnanVec4(a) | isnanVec4(b);
    `+r+`
    return resultTemp;
  `:r+`
    return ${e}(a, b);
  `}function Mh(e,t){switch(e){case 0:return Fue;case 1:return Nue;case 2:return Pue;case 3:return Mue;case 4:return t?zue:_ue;case 5:return t?Due:Oue;case 6:return t?Bue:Lue;case 7:return t?Vue:Wue;case 8:return t?Gue:Uue;case 9:return t?Hue:jue;case 10:return t?Yue:Zue;case 11:return $ue;case 12:return t?Xue:Kue;case 14:return t?tde:ede;case 15:return Pv("max",t);case 16:return Pv("min",t);case 13:return t?Que:Jue;case 17:return Eue;case 18:return Rue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var rde="return abs(a);",nde="return ceil(a);",ade="return cos(a);",sde=`
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`,ide="return exp(a) - 1.0;",ode="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",lde=`
  var resFloat = exp(a) - vec4<f32>(1.0);
  if (a.r >= 0.0) {
    resFloat.r = a.r;
  }
  if (a.g >= 0.0) {
    resFloat.g = a.g;
  }
  if (a.b >= 0.0) {
    resFloat.b = a.b;
  }
  if (a.a >= 0.0) {
    resFloat.a = a.a;
  }
  return resFloat;
`,ude="return exp(a);",dde="return floor(a);",pde="return a;",hde=`if (a < 0.0) { return 1.0/0.0; }
  return log(a);`,cde="return f32(!(a >= 1.0));",fde="return -a;",mde="return (a < 0.0) ? b * a : a;",gde="if (a < 0.0) { return uniforms.alpha * a; } return a;",yde="if(a < 0.0) { return 0.0; } return a;",Ade="return clamp(a, 0.0, 6.0);",xde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",bde=`
  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
  let isNaN = isnanVec4(a);

  if (isNaN.r) {
    resFloat.r = a.r;
  }
  if (isNaN.g) {
    resFloat.g = a.g;
  }
  if (isNaN.b) {
    resFloat.b = a.b;
  }
  if (isNaN.a) {
    resFloat.a = a.a;
  }
  return resFloat;
`,vde="return 1.0/sqrt(a);",wde="return 1.0 / (1.0 + exp(-1.0 * a));",kde="return sin(a);",Ide=`
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`,Sde="return sqrt(a);",Cde="return a * a;",Tde=`
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Nde="return f32(i32((a)));";function su(e,t){switch(e){case 0:return rde;case 2:return ade;case 3:return sde;case 1:return nde;case 4:return t?lde:ode;case 5:return ude;case 6:return ide;case 7:return dde;case 8:return pde;case 9:return hde;case 10:return cde;case 11:return fde;case 12:return mde;case 15:return gde;case 13:return t?bde:yde;case 14:return t?xde:Ade;case 16:return vde;case 19:return wde;case 17:return kde;case 18:return Ide;case 20:return Sde;case 21:return Cde;case 22:return Tde;case 23:return Nde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function es(e,t=!1){if(e===null)return null;if(e==="linear")return su(8);if(e==="relu")return su(13,t);if(e==="elu")return su(4,t);if(e==="relu6")return su(14,t);if(e==="prelu")return Mh(14,t);if(e==="sigmoid")return su(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function aS(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
  var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;

  let RowPerThread = ${e[1]};
  let ColPerThread = ${e[0]};
  let TileInner = ${n};

  ${Di()}

    let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
    let tileCol = i32(localId.x);

    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
    let globalCol = i32(globalId.x);
    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;

    var acc: array<vec4<f32>, RowPerThread>;
    var ACached : vec4<f32>;
    var BCached : array<vec4<f32>, 4>;

    // Loop over shared dimension.
    var globalColA = tileCol;
    let RowPerThreadB = TileInner / i32(workGroupSizeY);
    let tileRowB = i32(localId.y) * RowPerThreadB;
    for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileCol;
            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
        }
        globalColA = globalColA + TileInner / ColPerThread;

        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol;
            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];

            for (var i = 0; i < RowPerThread; i = i + 1) {
                ACached = mm_Asub[tileRow + i][k];
                acc[i] = BCached[0] * ACached.x + acc[i];
                acc[i] = BCached[1] * ACached.y + acc[i];
                acc[i] = BCached[2] * ACached.z + acc[i];
                acc[i] = BCached[3] * ACached.w + acc[i];
            }
        }

        workgroupBarrier();
    }

    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        mm_write(globalRow + innerRow,
                 globalCol,
                 acc[innerRow], globalId);
    }
  }`}var Ede=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[ja(n,this.aShape.slice(1)),ja(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
        }
        return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
        }
        return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=es(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${s}
                }`:r=`
            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
              ${s}
            }`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}
      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
        let batch = i32(globalId.z);
        ${e};
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
        let batch = i32(globalId.z);
        ${t};
      }

      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
        {
          var value = valueIn;
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col * 4);
          ${a}
          ${n}
          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
        }
      }
      ${aS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
    `}};function rb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
    var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
    var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
    ${Di()}
      let tileRow = i32(localId.y) * ${e[1]};
      let tileCol = i32(localId.x) * ${e[0]};

      let globalRow = i32(globalId.y) * ${e[1]};
      let globalCol = i32(globalId.x) * ${e[0]};

      let numTiles = (uniforms.dimInner - 1) / ${a} + 1;

      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
      var ACached : f32;
      var BCached : array<f32, ${e[0]}>;

      // Without this initialization strange values show up in acc.
      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
          acc[innerRow][innerCol] = 0.0;
        }
      }

      let ColPerThreadA = ${a} / ${t[0]};
      let tileColA = i32(localId.x) * ColPerThreadA;
      let RowPerThreadB = ${a} / ${t[1]};
      let tileRowB = i32(localId.y) * RowPerThreadB;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileColA + innerCol;

            mm_Asub[inputRow][inputCol] = mm_readA(
                globalRow + innerRow,
                t * ${a} + inputCol, globalId);
          }
        }
        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol + innerCol;

            mm_Bsub[inputRow][inputCol] = mm_readB(
              t * ${a} + inputRow,
              globalCol + innerCol, globalId);
          }
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${a}; k = k + 1) {
          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
            BCached[inner] = mm_Bsub[k][tileCol + inner];
          }

          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
            ACached = mm_Asub[tileRow + innerRow][k];
            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
            }
          }
        }

        workgroupBarrier();
      }

      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {

          if ((globalCol + innerCol) < uniforms.dimBOuter &&
              (globalRow + innerRow) < uniforms.dimAOuter) {
            mm_write(globalRow + innerRow,
                     globalCol + innerCol,
                     acc[innerRow][innerCol], globalId);
          }
        }
      }
    }
  `}function Rde(e){return`
    let TileSize = ${e[0]*4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

    ${Di()}
      let tileCol = i32(localId.x);
      let globalCol = i32(globalId.x);
      let globalRow = i32(globalId.y);

      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;

      // Without this initialization strange values show up in acc.
      var acc = 0.0;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        let colA = t * TileSize + tileCol * 4;
        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
                                mm_readA(globalRow, colA + 1, globalId),
                                mm_readA(globalRow, colA + 2, globalId),
                                mm_readA(globalRow, colA + 3, globalId));
        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < TileSize / 4; k = k + 1) {
          let rowB = t * TileSize + k * 4;
          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
                              mm_readB(rowB + 1, globalCol, globalId),
                              mm_readB(rowB + 2, globalCol, globalId),
                              mm_readB(rowB + 3, globalCol, globalId));

          let ACached = mm_Asub[k];
          acc = acc + dot(ACached, BCached);
        }

        workgroupBarrier();
      }

      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
        mm_write(globalRow, globalCol, acc, globalId);
      }
    }
  `}var sS=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=tS(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[ja(a,this.aShape.slice(1)),ja(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
           }
           return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
           }
           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
           }
           return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
           }
           return 0.0;`;let r="",n="";if(this.activation){let s=es(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${s}
            }`:r=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${s}
              }
            `,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }

      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        var value = valueIn;
        let batch = i32(globalId.z);
        let outCoord = vec3<i32>(batch, row, col);
        ${a}
        ${n}
        setOutputAtCoords(batch, row, col, value);
      }
      ${this.outputShape[1]>1?rb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Rde(this.workGroupSize)}
    `}};function Mde(){return`
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Di()}
      let coords = getOutputCoords();
      let batch = coords[0];
      let row = coords[1];
      let col = coords[2];
      var sum = 0.0;
      let Length = uniforms.dimInner;
      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
        let dataA = mm_readA(batch, row, k);
        let dataB = mm_readB(batch, k, col);
        sum = sum + dataA * dataB;
      }
      sumValues[localId.x] = sum;
      workgroupBarrier();

      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
          currentSize = currentSize / 2u) {
        if (localId.x < currentSize)
        {
          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
        }
        workgroupBarrier();
      }

      if (localId.x == 0u) {
        sum = sumValues[0] + sumValues[1];
        mm_write(batch, row, col, sum);
      }
    }
  `}var Fde=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=es(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${s}
            }`:r=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${s}
              }
            `,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        ${e}
      }

      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }

      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
        var value = valueIn;
        let outCoord = vec3<i32>(batch, row, col);
        ${a}
        ${n}
        setOutputAtCoords(batch, row, col, value);
      }
      ${Mde()}
    `}};function $de(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
  var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
  var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
  var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
  var<workgroup> mm_Bsub2 : array<array<f32, ${r}>, ${n}>;

  // If the output size is small for matrix multiplication, avoid to use vec4
  // and handle some elements per thread to optimally utilize the ALU.
  // Introduces two shared memory buffers, some logical threads could handle
  // arithmetic operations and others handle IO operations between barrier api,
  // makes ALUs and load/store units work simultaneously, could improves
  // the performance.
  ${Di()}
    let tileRow = i32(localId.y);
    let tileCol = i32(localId.x);
    let globalRow = i32(globalId.y);
    let globalCol = i32(globalId.x);

    // uniforms.dimInner should be greater than 0.
    let numTiles = (uniforms.dimInner - 1) / ${n} + 1;
    var acc = 0.0;

    var globalColA = tileCol;
    var globalRowB = tileRow;
    for (var t = 0; t < numTiles; t = t + 1) {
      if (t == 0) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${n};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${n};
        }
      } else {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${n};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${n};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${n}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
          }
        }
      }
      workgroupBarrier();
      if (t != 0) {
        t = t + 1;
      }

      if (t < numTiles) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub2[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${n};
          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${n};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${n}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
          }
        }
      }
      workgroupBarrier();
    }
    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
    if (tileRow >= ${t} && writeCol >= 0) {
      mm_write(writeCol, globalCol, acc, globalId);
    }
  }
  `}var Pde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
        }
        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
         }
         return 0.0;`,r="",n="";if(this.activation){let s=es(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            let b = getPreluActivationWeightsByOutputCoords(outCoord);
            ${s}
            }`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            ${s}
        }`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }
      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }
      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col);
          var value = valueIn;
          ${a}
          ${n}
          setOutputAtCoords(batch, row, col, value);
        }
      }
      ${$de(this.workGroupSize)}
    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var _de={kernelName:tl,backendName:"webgpu",kernelFunc:qe};function nb({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),S=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new Fde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new Pde(b,v,[R,c,f],s,l,i):_?M=new Ede(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new sS(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[C,S];s&&I.push(s),i&&I.push(i);let O=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}],z=a.runWebGPUProgram(M,I,e.dtype,O),j=qe({inputs:{x:z},backend:a,attrs:{shape:x}});E.push(z);for(let X of E)a.disposeData(X.dataId);return j}function zde(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return nb({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ode={kernelName:Ts,backendName:"webgpu",kernelFunc:zde},_v=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${Mh(this.op,!1)}
      }

      ${tt()}
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `}},Dde=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
         let b = getBByOutputCoords(coords);`;return`
        fn binaryOperation(a : f32, b : f32) -> f32 {
          ${Mh(this.op,!1)}
        }
        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
        ${tt()}

          // Fill in the shared memory buffer. Here we need a loop to make sure
          // that all data in A|B are uploaded when |sharedMemorySize| is larger
          // than work group size.
          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
          }
          workgroupBarrier();

          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
            let flatIndex = index * ${this.workPerThread} + i;
            if(flatIndex < uniforms.size) {
              let coords = getCoordsFromIndex(flatIndex);

              ${t}
              setOutputAtIndex(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `}},Lde=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${Mh(this.op,this.isVec4)}
      }
      ${tt()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
    `}},iS=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${Mh(this.op,!1)}
      }
      ${tt()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
      `}};function zv(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new Lde(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Dde(e,t,r,a):new iS(e,t,r)}function Ln(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Bde={kernelName:ii,backendName:"webgpu",kernelFunc:Ln};function bd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Ln({inputs:{x:n},backend:r}),l=Ln({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Wde={kernelName:Dp,backendName:"webgpu",kernelFunc:bd},Fh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
      fn unaryOperation(a : f32) -> f32 {
        ${su(this.op,!1)}
      }
      ${tt()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `}};function xr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Fh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Gr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=zv(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Dr(y.dtype,A.dtype))});else{let g=new _v(17,i.shape,o.shape),y=new _v(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=bd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Dr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=zv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Vde,ceilImpl:Ude,concatImpl:Gde,equalImpl:jde,expImpl:Hde,expm1Impl:qde,floorImpl:Kde,gatherNdImpl:Xde,gatherV2Impl:Zde,greaterEqualImpl:Yde,greaterImpl:Jde,lessEqualImpl:Qde,lessImpl:epe,logImpl:tpe,maxImpl:rpe,maximumImpl:npe,minimumImpl:ape,multiplyImpl:spe,negImpl:ipe,notEqualImpl:ope,prodImpl:lpe,rangeImpl:upe,rsqrtImpl:dpe,simpleAbsImpl:ppe,sliceImpl:hpe,stridedSliceImpl:cpe,stringNGramsImpl:fpe,subImpl:mpe,tileImpl:gpe,topKImpl:ype,transposeImpl:Ape,uniqueImpl:bAe}=r0,xpe=xr({opType:0,cpuKernelImpl:ppe}),bpe={kernelName:Mo,backendName:"webgpu",kernelFunc:xpe},vpe=Gr({opSnippet:1,cpuKernelImpl:Vde,supportsComplex:!0}),wpe={kernelName:Ha,backendName:"webgpu",kernelFunc:vpe},kpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
      ${tt()}
        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndex);
            ${e.join(`
        `)}
            setOutputAtIndex(flatIndex, ${t});
          }
        }
      }
    `}};function Ipe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Ln({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=new kpe(s);return r.runWebGPUProgram(i,n,a)}var Spe={kernelName:Vs,backendName:"webgpu",kernelFunc:Ipe},oS=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;return`
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${e}

      // In order to get a flattened index into the input tensor, we need to
      // add back the index along the reduced dimension to |outputCoords|.
      // This function outputs the offset to the first value along
      // |axis| and the stride to get the next value of the input along |axis|.
      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
        let outputCoords = getCoordsFromIndex(outputIndex);
        var i = ${this.outputShape.length-1};

        var stride = 1;
        var inputStride = 1;
        var offset = 0;

        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
          let length = ${r(`${this.inputShape.length} - r`)};
          if (${this.inputShape.length} - r == uniforms.axis) {
            inputStride = stride;
          } else {
            offset = offset + ${t("outputCoords","i")} * stride;
            i = i - 1;
          }
          stride = stride * length;
        }

        return vec2<i32>(offset, inputStride);
      }

      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
        return coordInfo[0] + coordInfo[1] * index;
      }

      ${tt()}
        let outputIndex = index / i32(workGroupSizeX);
        let coordInfo = getInputCoordInfo(outputIndex);
        let Length = ${r("uniforms.axis")};

        var bestIndex = i32(localId.x);
        var bestValue = uniforms.infinityValue;

        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
            k = k + i32(workGroupSizeX)) {
          let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
          if (!isnan(candidate) && candidate ${this.op} bestValue) {
            bestValue = candidate;
            bestIndex = k;
          }
        }
        xBestValues[localId.x] = bestValue;
        xBestIndices[localId.x] = bestIndex;
        workgroupBarrier();

        var reduceSize = min(u32(Length), workGroupSizeX);
        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
            currentSize = reduceSize / 2u) {
          let interval = DIV_CEIL(reduceSize, 2u);
          if (localId.x < currentSize) {
            let candidate = xBestValues[localId.x + interval];
            if (candidate ${this.op} bestValue) {
              bestValue = candidate;
              xBestValues[localId.x] = bestValue;
              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
            }
          }
          reduceSize = interval;
          workgroupBarrier();
        }

        if (localId.x == 0u && outputIndex < uniforms.size) {
          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
        }
      }
    `}},Cpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
      let TILE_DIM = ${this.workGroupSize[0]};
      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
      ${Jx()}
      fn main(@builtin(local_invocation_id) localId : vec3<u32>,
              @builtin(workgroup_id) workgroupId : vec3<u32>) {
        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
        let width = uniforms.outShape[0];
        let height = uniforms.outShape[1];
        if (x < width && y < height) {
          tile[localId.y][localId.x] =
              A.numbers[y * width + x];
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
        if (x < height && y < width) {
          setOutputAtIndex((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `}},Tpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=mr(this.outputShape.length),t=Npe(this.newDim);return`
      ${tt()}

        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let resRC = getCoordsFromIndex(flatIndex);
            setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(
              ${e}(${t}), uniforms.aShape)]);
          }
        }
      }
    `}};function Npe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Nl(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Ape(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new Cpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new Tpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Epe={kernelName:Fi,backendName:"webgpu",kernelFunc:Nl};function Rpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new oS(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Mpe={kernelName:Us,backendName:"webgpu",kernelFunc:Rpe};function Fpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new oS(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var $pe={kernelName:Mu,backendName:"webgpu",kernelFunc:Fpe},lS=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
      ${tt()}
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
          let batch = coords[0];
          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
          let xRCorner = xRCCorner.x;
          let xCCorner = xRCCorner.y;

          var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
          var count = 0.0;

          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
            let xR = xRCorner + wR;

            if (xR < 0 || xR >= uniforms.convDims.x) {
              continue;
            }

            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
              let xC = xCCorner + wC;
              if (xC < 0 || xC >= uniforms.convDims.y) {
                continue;
              }

              let value = getX(batch, xR, xC, coords[3]);
              ${e}
            }
          }

          setOutputAtIndex(index, ${t});
        }
      }
    `}},uS=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
      ${tt()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let batch = coords[0];
          let d = coords[3];

          let xRCCorner = coords.yz * uniforms.stride;
          let xRCorner = xRCCorner.x;
          let xCCorner = xRCCorner.y;

          let value = getX(batch, xRCorner, xCCorner, d);
          setOutputAtIndex(index, value);
        }
      }
    `}};function Ppe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new uS(d):(h=new lS(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var _pe={kernelName:Gs,backendName:"webgpu",kernelFunc:Ppe};function zpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return nb({a,b:s,transposeA:i,transposeB:o,backend:r})}var Ope={kernelName:js,backendName:"webgpu",kernelFunc:zpe},Dpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${mr(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=mr(this.rank),t=Lpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${Oy[a]} = uniforms.start[${a}] + coords.${Oy[a]};`),`
      ${tt()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${r.join(`
`)}
          setOutputAtIndex(index, getSource(${t}));
        }
      }
    `}},Oy=["x","y","z","w","u","v"];function Lpe(e){if(e===1)return"sourceLoc";if(e<=6)return Oy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function vd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=hpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Dpe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var Bpe={kernelName:il,backendName:"webgpu",kernelFunc:vd},Wpe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=vd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Vpe={kernelName:Fo,backendName:"webgpu",kernelFunc:Wpe},dS=Gr({opSnippet:10,dtype:"bool",cpuKernelImpl:ope}),Upe={kernelName:Ko,backendName:"webgpu",kernelFunc:dS};function $h(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.real},backend:r})}var Gpe={kernelName:qp,backendName:"webgpu",kernelFunc:$h};function jpe(e,t){let r=new Fh(e.shape,23),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Dy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Ln({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Dy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=bd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=$h({inputs:{input:a},backend:r}),o=Dy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Ln({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return jpe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=dS({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Hpe={kernelName:Hs,backendName:"webgpu",kernelFunc:Dy},qpe=xr({opType:1,cpuKernelImpl:Ude}),Kpe={kernelName:qs,backendName:"webgpu",kernelFunc:qpe},Xpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
      ${tt()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          var clampedValue : vec4<f32>;
          for (var i = 0; i < 4; i = i + 1) {
            if (isnan(value[i])) {
              clampedValue[i] = value[i];
            } else {
              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
            }
          }

          setOutputAtIndex(index, clampedValue);
        }
      }
    `}},Zpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
      ${tt()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isnan(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `}};function Ype(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(a.shape)%4===0?o=new Xpe(a.shape):o=new Zpe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var Jpe={kernelName:qa,backendName:"webgpu",kernelFunc:Ype},Qpe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
      ${tt()}
        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndex);
            let yR = coords.x;
            let yC = coords.y;

            ${e.join(`
        `)}
          }
        }
      }
    `}};function p0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.imag},backend:r})}var ehe={kernelName:Up,backendName:"webgpu",kernelFunc:p0};function Ly(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>$h({inputs:{input:A},backend:r})),f=e.map(A=>p0({inputs:{input:A},backend:r})),m=Ly(c,t,r),g=Ly(f,t,r),y=bd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Gde(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=the(e,t,r),o=s.map(c=>c.shape),l=new Qpe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function the(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function pS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Ln({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),Ly(o,s,r)}var rhe={kernelName:$o,backendName:"webgpu",kernelFunc:pS},nhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
      inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
    ${tt()}

      for(var i = 0; i<${this.workPerThread}; i = i + 1) {
        let flatIndex = index * ${this.workPerThread} + i;

        let rc = getCoordsFromIndex(flatIndex);

        if(flatIndex < uniforms.size) {
          let blockIndex = rc[0];
          let pos = rc[1];

          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
          var value = 0.0;
          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
              uniforms.pad[0];
            let d1 = offsetX + uniforms.dilation[0] * ((pos %
              uniforms.itemsPerBlockRow) / uniforms.inChannels);
            let ch = pos % uniforms.inChannels;
            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
              value = getA(d0, d1, ch);
            }
          }
          setOutputAtIndex(flatIndex, value);
        }
      }
    }
  `}},ahe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ja(e,[r,a]),ja(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
    let divBy4Remainder${e} = flatIndex${e} % 4;
    let divBy4Index${e} = flatIndex${e} / 4;
    let curData${e} = x.numbers[divBy4Index${e}];
    if (divBy4Remainder${e} == 0) {
      temp = curData${e};
    } else {
      // TODO: This could end up being a redundant load with another one in
      // the same shader invocation. Perhaps there's an opportunity for
      // optimization
      let nextData${e} = x.numbers[divBy4Index${e} + 1];
      if (divBy4Remainder${e} == 1) {
        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
      } else if (divBy4Remainder${e} == 2) {
        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
      } else if (divBy4Remainder${e} == 3) {
        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
      }
    }
    `}getUserCode(){let e=aS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
        let outCol = r % uniforms.outShape[2];
        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
        let inChCoord = c % uniforms.xShape[3];
        var coord = vec4<i32>(
            batch,
            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
            inChCoord);
        var resData = vec4<f32>(0.0);
        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
          // the 'same' padding type.
          if (coordsInBounds4D(coord, uniforms.xShape)) {
            resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];
          } else {
            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
          ${this.getSampleAWithRemainder(1)}
          resData = temp;
          if (WCol == (uniforms.filterDims[1] - 1)) {
            coord = vec4<i32>(
              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
              ${this.getSampleAWithRemainder(2)}
            if (inChCoord == 0) {
              resData = vec4<f32>(resData.xyz, temp.x);
            } else if (inChCoord == 1) {
              resData = vec4<f32>(resData.xy, temp.xy);
            } else {
              resData = vec4<f32>(resData.x, temp.xyz);
            }
          }
          `}
        return resData;`,r=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${t}
         }
         return vec4<f32>(0.0);
        `,n=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter / 4 + col];
         }
         return vec4<f32>(0.0);
        `,a="",s="";if(this.activation){let o=es(this.activation,this.isVec4);if(this.hasPreluActivationWeights)a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${o}
        }`;else{if(this.hasLeakyreluAlpha)throw a=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
          let b = getLeakyreluAlphaByOutputCoords(outCoord);
          ${o}
        }`,new Error("Leakyrelu is not supported.");a=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          ${o}
        }`}s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
        ${a}
        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          let r = row;
          let c = col * 4;
          var batch = i32(globalId.z);
          ${r}
        }

        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          ${n}
        }

        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
          var batch = i32(globalId.z);
          var value = valueInput;
          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
          {
            let outCoord = vec4<i32>(
              batch,
              row / uniforms.outShape[2],
              row % uniforms.outShape[2],
              col * 4);
            ${i}
            ${s}
            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${e}
      `}},she=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qx(this.dispatchLayout,this.outputShape),this.elementsPerThread=eb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ja(n,[s,o]),ja(a,[o,i])]}getUserCode(){let e=rb(this.elementsPerThread,this.workGroupSize),t=`
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
    let coord = vec4<i32>(
        batch,
        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
        col % uniforms.xShape[3]);
    // The bounds checking is always needed since we use it to pad zero for the
    // 'same' padding type.
    if(coordsInBounds4D(coord, uniforms.xShape)) {
      return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;`,r=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${t}
    }
    return 0.0;
    `,n=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter + col];
	 }
	 return 0.0;
	 `,a="",s="";if(this.activation){let o=es(this.activation,!1);this.hasPreluActivationWeights?a=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${o}
                }`:a=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
                    ${o}
                  }
                `,s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
    ${a}
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      ${r}
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      ${n}
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      ${i}
      ${s}
      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }
    ${e}
  `}},ihe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${n}
             }`:e=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                    ${n}
                  }
                `,t="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${e}
      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
        let coord = vec4<i32>(batch, row, col, chan);
        if(coordsInBounds4D(coord, uniforms.xShape)) {
          return getX(batch, row, col, chan);
        }
        return 0.0;
      }

      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
        let coord = vec4<i32>(row, col, xChannel, outChannel);
        if(coordsInBounds4D(coord, uniforms.wShape)) {
          return getW(row, col, xChannel, outChannel);
        }
        return 0.0;
      }

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          ${r}
          ${t}
          setOutputAtCoords(batch, row, col, chan, value);
        }
      }

      ${Di()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let outChannel = coords[3];

        var acc = 0.0;

        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
              let v = readInp(batch, coordRow, coordCol, xChannel);
              let f = readFilt(row, col, xChannel, outChannel);
              acc = acc + v * f;
            }
          }
        }

        writeResult(batch, coords[1], coords[2], outChannel, acc);
      }
    `}};function ohe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=nb({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function lhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,C=[v,b],S=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new nhe(C,x),O=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],z=n.runWebGPUProgram(I,[_],_.dtype,O),j=qe({inputs:{x:z},backend:n,attrs:{shape:[1,C[0],C[1]]}});R.push(z),R.push(j);let X=[1,C[0],C[1]],D=new sS(X,[1,v,r.outChannels],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,Y=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s);let Z=n.runWebGPUProgram(D,se,j.dtype,Y),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function hS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return ohe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(J().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return lhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=J().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&r.outChannels>=32,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new ihe(r,l,o,u);else{p?d=new ahe(r,l,o,u):d=new she(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),n.runWebGPUProgram(d,m,e.dtype,f)}function uhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return hS({x:a,filter:s,convInfo:p,backend:n})}var dhe={kernelName:Ks,backendName:"webgpu",kernelFunc:uhe},phe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qx(this.dispatchLayout,this.outputShape),this.elementsPerThread=eb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
      return 0.0;
    }
    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
      return 0.0;
    }
    let coord = vec4<i32>(
        batch,
        i32(xR),
        i32(xC),
        col % uniforms.outBackprop[3]);
    return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      let coordX = uniforms.filterDims.x - 1 -
          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
      let coordY = uniforms.filterDims.y - 1 -
          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
          coordX >= 0 && coordY >= 0) {
        let coord = vec4<i32>(coordX, coordY, col,
            row % uniforms.outBackprop[3]);
        return W.numbers[getIndexFromCoords4D(coord, uniforms.wShape)];
      }
      return 0.0;
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }

    ${rb(this.elementsPerThread,this.workGroupSize)}
  `}},hhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
    ${tt()} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d1 = coords[${r}];

        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
        let dyRCorner = dyCorner.x;
        let dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;
        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
          let wRPerm = uniforms.filterDims.x - 1 - wR;
          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
              wRPerm < 0) {
            continue;
          }
          let idyR = dyR;

          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
            let wCPerm = uniforms.filterDims.y - 1 - wC;
            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
                fract(dyC) > 0.0 || wCPerm < 0) {
              continue;
            }
            let idyC = dyC;

            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
              if (${this.isChannelsLast}) {
                let xValue = getDy(batch, idyR, idyC, d2);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              } else {
                let xValue = getDy(batch, d2, idyR, idyC);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              }

            }
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
  `}};function che(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(J().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new hhe(p);else{f=new phe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var fhe={kernelName:Xs,backendName:"webgpu",kernelFunc:che},mhe=xr({opType:2}),ghe={kernelName:Zs,backendName:"webgpu",kernelFunc:mhe},yhe=xr({opType:3}),Ahe={kernelName:Ys,backendName:"webgpu",kernelFunc:yhe},xhe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
      ${tt()}
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let height_ratio = f32(${r});
        let width_ratio = f32(${s});
        let b = coords[0];
        let y = coords[1];
        let x = coords[2];
        let d = coords[3];
        // get box vals
        let y1 = getBoxes(b, 0);
        let x1 = getBoxes(b, 1);
        let y2 = getBoxes(b, 2);
        let x2 = getBoxes(b, 3);
        // get image in batch index
        let bInd = i32(round(getBoxInd(b)));
        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
          return;
        }
        let height_scale = ${n};
        let width_scale = ${i};
        let in_y = ${a};
        if( in_y < 0.0 || in_y > ${e} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let in_x = ${o};
        if( in_x < 0.0 || in_x > ${t} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
        if(${this.methodId} == 1) {
          // Compute the four integer indices.
          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
          let top = topLeft + (topRight - topLeft) * fracCR.x;
          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          let newValue = top + (bottom - top) * fracCR.y;
          setOutputAtIndex(index, newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          let sourceNearestCR = vec2<i32>(floor(
            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
          let newValue = getImage(
            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutputAtIndex(index, newValue);
        }
      }
    }
    `}},bhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new xhe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},vhe={kernelName:_o,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
      ${tt()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let h = ${this.getHeightCoordString()};
          let w = ${this.getWidthCoordString()};
          let d = ${this.getDepthCoordString()};

          let in_h = h / uniforms.blockSize;
          let offset_h = h % uniforms.blockSize;
          let in_w = w / uniforms.blockSize;
          let offset_w = w % uniforms.blockSize;
          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
            ${this.getOutputDepthSize()};
          let in_d = d + offset_d;

          let rlt = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function khe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new whe(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Ihe={kernelName:zo,backendName:"webgpu",kernelFunc:khe},cS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${n}
        }`:e=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
            ${n}
          }
        `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
      ${e}

      ${Jx()}
      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
        let batch = 0;
        let r = i32(globalId.x);
        let c = i32(globalId.y) * 4;
        let d2 = i32(globalId.z) * 4;
        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
        let d1 = d2;
        let q = 0;

        let xRCorner = xRCCorner.x;
        let xCCorner = xRCCorner.y;

        var wVals : array<vec4<f32>, 9>;
        wVals[0] = getW(0, 0, d1, q);
        wVals[1] = getW(0, 1, d1, q);
        wVals[2] = getW(0, 2, d1, q);
        wVals[3] = getW(1, 0, d1, q);
        wVals[4] = getW(1, 1, d1, q);
        wVals[5] = getW(1, 2, d1, q);
        wVals[6] = getW(2, 0, d1, q);
        wVals[7] = getW(2, 1, d1, q);
        wVals[8] = getW(2, 2, d1, q);

        var xVals : array<array<vec4<f32>, 6>, 3>;
        for (var wR = 0; wR < 3; wR = wR + 1) {
          let xR = xRCorner + wR * uniforms.dilation[0];
          for (var wC = 0; wC < 6; wC = wC + 1) {
            let xC = xCCorner + wC * uniforms.dilation[1];
            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
              xVals[wR][wC] = vec4<f32>(0.0);
            } else {
              xVals[wR][wC] = getX(batch, xR, xC, d1);
            }
          }
        }

        var dotProd : array<vec4<f32>, 4>;
        dotProd[0] = vec4<f32>(0.0);
        dotProd[1] = vec4<f32>(0.0);
        dotProd[2] = vec4<f32>(0.0);
        dotProd[3] = vec4<f32>(0.0);

        for (var wR = 0; wR < 3; wR = wR + 1) {
          for (var wC = 0; wC < 3; wC = wC + 1) {
            let indexW = wR * 3 + wC;
            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
          }
        }

        for (var i = 0; i < 4; i = i + 1) {
          let coords = vec4<i32>(batch, r, c + i, d2);
          if (coordsInBounds4D(coords, uniforms.outShape)) {
            ${r}
            ${t}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `}},fS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
      channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${n}
        }`:e=`
          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
            ${n}
          }
        `,t="dotProd = activation(dotProd, coords);"}let r=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
      ${e}

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
          value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          setOutputAtCoords(batch, row, col, chan, value);
        }
      }

      ${Di()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
        let d2 = coords[3];
        let d1 = d2 / uniforms.channelMul;
        let q = d2 - d1 * uniforms.channelMul;

        let inputRowStart = xRCCorner.x;
        let inputColStart = xRCCorner.y;
        let inputRowEnd = inputRowStart + uniforms.filterHeight *
            uniforms.dilation[0];
        let inputColEnd = inputColStart + uniforms.filterWidth *
            uniforms.dilation[1];

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;

        // Extract if checking out of for loop for performance.
        if (inputRowStart >= 0 && inputColStart >= 0 &&
          inputRowEnd < uniforms.inDims[0] &&
              inputColEnd < uniforms.inDims[1]) {
            // Here using a constant value |this.convInfo.filterHeight| instead
            // of uniform value is in order to loop unrolling.
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

                let xVal = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          } else {
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

              if (xR < 0 || xR >= uniforms.inDims[0]) {
                continue;
              }

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

                if (xC < 0 || xC >= uniforms.inDims[1]) {
                  continue;
                }

                let xVal = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          }

        ${r}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `}};function She(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.filterHeight===3&&h.inChannels%4===0?c=new cS(h):(c=new fS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Che={kernelName:Js,backendName:"webgpu",kernelFunc:She},mS=Gr({opSnippet:0,cpuKernelImpl:spe,supportsComplex:!0}),The={kernelName:gi,backendName:"webgpu",kernelFunc:mS},Nhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
         if (isnan(candidate)) {
          bestValue = uniforms.NAN;
         } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
           {  bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let r=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
       }

       ${`
         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
       `}
       fn getOffset(outputIndex : i32) -> i32 {
         let outputCoords = getCoordsFromIndex(outputIndex);
         let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
          return offset;
       }
       ${tt()}
         let outputIndex = index / i32(workGroupSizeX);
         let offset = getOffset(outputIndex);
         var bestValue = ${t};
         let Length = uniforms.reduceSize;
         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[offset + k]);
           ${e}
         }
         xBestValues[localId.x] = bestValue;
         workgroupBarrier();

         var reduceSize = min(u32(Length), workGroupSizeX);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (localId.x < currentSize) {
            let candidate = xBestValues[localId.x + interval];
            ${e}
            xBestValues[localId.x] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (localId.x == 0u && outputIndex < uniforms.size) {
          ${r}
        }
       }
     `}};function Ph(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Nl({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=rpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=lpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":rh(e.dtype),x=[{type:"int32",data:[m]}],b=new Nhe(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function ab(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"sum",r)}var Ehe={kernelName:Ti,backendName:"webgpu",kernelFunc:ab};function Rhe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Nl({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=mS({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=ab({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var Mhe={kernelName:Vp,backendName:"webgpu",kernelFunc:Rhe},Fhe=xr({opType:4}),$he={kernelName:ei,backendName:"webgpu",kernelFunc:Fhe},Phe=Gr({opSnippet:4,dtype:"bool",cpuKernelImpl:jde}),_he={kernelName:Oo,backendName:"webgpu",kernelFunc:Phe},gS=xr({opType:5,cpuKernelImpl:Hde,dtype:"float32"}),zhe={kernelName:ti,backendName:"webgpu",kernelFunc:gS};function By(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Ohe={kernelName:Do,backendName:"webgpu",kernelFunc:By},Dhe=xr({opType:6,cpuKernelImpl:qde}),Lhe={kernelName:Lo,backendName:"webgpu",kernelFunc:Dhe},Bhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
    ${tt()}
      if (index < uniforms.size) {
        setOutputAtIndex(index, uniforms.value);
      }
    }
  `}};function wd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Bhe(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var Whe={kernelName:Du,backendName:"webgpu",kernelFunc:wd},Vhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
      ${tt()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputAtIndex(index, outputValue);
        }
      }
    `}},Uhe={kernelName:Bo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Vhe(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},Ghe=xr({opType:7,cpuKernelImpl:Kde}),jhe={kernelName:ri,backendName:"webgpu",kernelFunc:Ghe},Hhe=Gr({opSnippet:12,dtype:"int32"}),qhe={kernelName:ni,backendName:"webgpu",kernelFunc:Hhe},Khe=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},yS=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=vue(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function AS(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function Ov(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=AS(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>yS(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var Xhe={kernelName:kp,backendName:"webgpu",kernelFunc:Zhe},eu;function Zhe(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(J().getBool("WEBGPU_USE_IMPORT")&&i)return Ov({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(eu==null&&(eu=document.createElement("canvas").getContext("2d")),eu.canvas.width=d,eu.canvas.height=h,eu.drawImage(a,0,0,d,h),a=eu.canvas),u||l||i||o)return Ov({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var Yhe=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
      ${tt()}
        if (index < uniforms.size)
        {
          let xValue = getXByOutputIndex(index);
          let meanValue = getMeanByOutputIndex(index);
          let varianValue = getVarianceByOutputIndex(index);
          let offsetValue = ${e};
          let scaleValue = ${t};
          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
        }
      }
  `}},Jhe={kernelName:ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Yhe(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function Qhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return hS({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var ece={kernelName:Ns,backendName:"webgpu",kernelFunc:Qhe};function tce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p}=n,c=d;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${c}'`);let f=N.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!0),m=[a,s],g=i!=null,y=o!=null;g&&m.push(i),y&&m.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],x;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4===0?x=new cS(f,g,p,y):(x=new fS(f,g,p,y),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),r.runWebGPUProgram(x,m,"float32",A)}var rce={kernelName:Es,backendName:"webgpu",kernelFunc:tce},nce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${mr(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
        ${tt()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          var flattenIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexTemp = i32(round(getIndices(coords[0], j)));
            let strideNum = ${e};
            flattenIndex = flattenIndex + indexTemp * strideNum;
          }

          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
        }
      }
      `}};function ace(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=Xde(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new nce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var sce={kernelName:Vo,backendName:"webgpu",kernelFunc:ace},ice=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=oce(this.aShape,"i32");return`
      ${tt()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `}};function oce(e,t="int"){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push(`${t}(getIndices(resRC.x, resRC.z))`):n.push(`${r[a]}`);return n.join()}function xS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),C=Zde(v,x,f);return h.forEach(S=>r.disposeData(S.dataId)),r.makeTensorInfo(u.outputShape,C.dtype,C.values)}let m=new ice(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var lce={kernelName:Wo,backendName:"webgpu",kernelFunc:xS},uce=Gr({opSnippet:5,cpuKernelImpl:Jde,dtype:"bool"}),dce={kernelName:Uo,backendName:"webgpu",kernelFunc:uce},pce=Gr({opSnippet:6,dtype:"bool",cpuKernelImpl:Yde}),hce={kernelName:si,backendName:"webgpu",kernelFunc:pce};function cce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Fh(a.shape,15);return o.uniforms="alpha : f32;",r.runWebGPUProgram(o,[a],"float32",i)}var fce={kernelName:oi,backendName:"webgpu",kernelFunc:cce},mce=Gr({opSnippet:7,dtype:"bool",cpuKernelImpl:epe}),gce={kernelName:Go,backendName:"webgpu",kernelFunc:mce},yce=Gr({opSnippet:8,dtype:"bool",cpuKernelImpl:Qde}),Ace={kernelName:jo,backendName:"webgpu",kernelFunc:yce},xce=xr({opType:9,cpuKernelImpl:tpe}),bce={kernelName:li,backendName:"webgpu",kernelFunc:xce},vce=Gr({opSnippet:9,dtype:"bool"}),wce={kernelName:Ho,backendName:"webgpu",kernelFunc:vce},kce=xr({opType:10}),Ice={kernelName:Uu,backendName:"webgpu",kernelFunc:kce};function bS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Ph(a,s,i,"max",r)}var Sce={kernelName:ui,backendName:"webgpu",kernelFunc:bS},Cce=Gr({opSnippet:15,cpuKernelImpl:npe}),Tce={kernelName:di,backendName:"webgpu",kernelFunc:Cce};function Nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});h=new uS(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new lS(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var Ece={kernelName:pi,backendName:"webgpu",kernelFunc:Nce};function Rce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Ph(a,i,s,"mean",r)}var Mce={kernelName:hi,backendName:"webgpu",kernelFunc:Rce};function Fce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"min",r)}var $ce={kernelName:ci,backendName:"webgpu",kernelFunc:Fce},Pce=Gr({opSnippet:16,cpuKernelImpl:ape}),_ce={kernelName:fi,backendName:"webgpu",kernelFunc:Pce},zce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>;`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=mr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${tt()}
        if (index < uniforms.size) {
          let start = ${i}(${t});
          let end = ${i}(${r});
          var outC = getCoordsFromIndex(index);
          for (var i = 0; i < ${e}; i = i + 1) {
            if (${s} < ${n}) {
              ${s} = ${n} * 2 - ${s} - ${this.offset};
            } else if(${s} >= ${a}) {
              ${s} = (${a} - 1) * 2 - ${s} + ${this.offset};
            }
          }
          let coords = outC - start;
          setOutputAtIndex(index, getX(${o}));
        }
      }
    `}},Oce={kernelName:mi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new zce(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function Dce(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=ipe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Fh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Lce={kernelName:qo,backendName:"webgpu",kernelFunc:Dce};function Bce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Un.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wce={kernelName:Xo,backendName:"webgpu",kernelFunc:Bce};function Vce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Un.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Uce={kernelName:Zo,backendName:"webgpu",kernelFunc:Vce};function Nf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=$h({inputs:{input:n},backend:r}),s=Nf({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=bd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return wd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Gce={kernelName:ml,backendName:"webgpu",kernelFunc:Nf};function vS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=$h({inputs:{input:n},backend:r}),s=vS({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=bd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return wd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var jce={kernelName:Yo,backendName:"webgpu",kernelFunc:vS};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return By({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=By({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=pS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var qce={kernelName:Qo,backendName:"webgpu",kernelFunc:Hce},Kce=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=mr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${tt()}
        if (index < uniforms.size) {
          let start = ${a};
          let end = ${s};
          let outC = getCoordsFromIndex(index);

          if (${i} || ${o}) {
            setOutputAtIndex(index, uniforms.constantValue);
          } else {
            let coords = outC - start;
            setOutputAtIndex(index, getX(${l}));
          }
        }
      }
    `}},wS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return Ln({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return wd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Kce(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},Xce={kernelName:yi,backendName:"webgpu",kernelFunc:wS},Zce=Gr({opSnippet:13}),Yce={kernelName:Ai,backendName:"webgpu",kernelFunc:Zce};function Jce(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new iS(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Qce={kernelName:xi,backendName:"webgpu",kernelFunc:Jce};function efe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"prod",r)}var tfe={kernelName:el,backendName:"webgpu",kernelFunc:efe},rfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=upe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},nfe={kernelName:Hu,backendName:"webgpu",kernelFunc:rfe},kS=Gr({opSnippet:3}),afe={kernelName:Qs,backendName:"webgpu",kernelFunc:kS},sfe=xr({opType:13}),ife={kernelName:bi,backendName:"webgpu",kernelFunc:sfe},ofe=xr({opType:14}),lfe={kernelName:wi,backendName:"webgpu",kernelFunc:ofe},ufe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
      ${tt()}
        if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

          let effectiveInSize = vec2<f32>(
            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveOutSize = vec2<f32>(
            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

          // Fractional source index
          let sourceFracIndexRC =
            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);

          // Compute the four integer indices.
          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
          let sourceCeilRC = vec2<i32>(
            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));

          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);

          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);

          let top = topLeft + (topRight - topLeft) * fracRC.y;
          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
          let newValue = top + (bottom - top) * fracRC.x;

          setOutputAtIndex(index, newValue);
        }
      }
    `}};function dfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new ufe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var pfe={kernelName:vi,backendName:"webgpu",kernelFunc:dfe},hfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
      ${tt()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

          let effectiveInSize = vec2<f32>(
            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveOutSize = vec2<f32>(
            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

          // Fractional source index
          let sourceFracIndexRC = ${e};

          // Compute the coordinators of nearest neighbor point.
          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
          let sourceNearestRC = vec2<i32>(
            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);

          setOutputAtIndex(index, newValue);
        }
      }
    `}};function cfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new hfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var ffe={kernelName:Ku,backendName:"webgpu",kernelFunc:cfe},mfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
          cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
        ${tt()}

          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
                uniforms.sinRadians;
            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
                uniforms.cosRadians;
            let coordX = i32(round(coordXFloat + uniforms.centerX));
            let coordY = i32(round(coordYFloat + uniforms.centerY));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `}},gfe={kernelName:gl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new mfe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},yfe=xr({opType:16,cpuKernelImpl:dpe}),Afe={kernelName:ki,backendName:"webgpu",kernelFunc:yfe},xfe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Xe(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=mr(a.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
        return index;
      }
      `):this.updatesRank===2&&(n="coords[0], coords[1]",a="vec2<i32>(flattenedIndex, coords[1])",s=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
        let d0 = index / uniforms.updatesShape[1];
        let d1 = index - d0 * uniforms.updatesShape[1];
        return vec2<i32>(d0, d1);
      }
      `);let i=`getUpdates(${n})`,o=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
     var assumed = atomicLoad(&(result.numbers[flatIndex]));
     var success = 0;
     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
       assumed = resValue[0];
       success = resValue[1];
     }
     `;return`
    ${s}

      ${tt()}

        if (index < uniforms.size) {
          let coords = getUpdatesCoordsFromFlatIndex(index);
          var flattenedIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexInside = i32(round(${t}));
            flattenedIndex = flattenedIndex + indexInside * ${r};
          }
          let updateValue = ${i};
          let flatIndex = getOutputIndexFromCoords(${a});

         ${o}
        }
      }`}};function bfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=wd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new xfe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var vfe={kernelName:al,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
      ${tt()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputAtIndex(index, getA(${t}));
          } else {
            setOutputAtIndex(index, getB(${t}));
          }
        }
      }
    `}};function kfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new wfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Dr(a.dtype,s.dtype))}var Ife={kernelName:sl,backendName:"webgpu",kernelFunc:kfe},Sfe=xr({opType:19}),Cfe={kernelName:Si,backendName:"webgpu",kernelFunc:Sfe},Tfe=xr({opType:17}),Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:Tfe},Efe=xr({opType:18}),Rfe={kernelName:ol,backendName:"webgpu",kernelFunc:Efe},IS=Gr({opSnippet:2,cpuKernelImpl:mpe,supportsComplex:!0}),Mfe={kernelName:Ri,backendName:"webgpu",kernelFunc:IS};function Ffe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=bS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=IS({inputs:{a,b:u},backend:r}),h=gS({inputs:{x:d},backend:r}),p=ab({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=kS({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var $fe={kernelName:Ni,backendName:"webgpu",kernelFunc:Ffe},Pfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=wS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},_fe={kernelName:ll,backendName:"webgpu",kernelFunc:Pfe},zfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=mr(a.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
      ${tt()}

        let globalIndex = index * ${this.workPerThread};
        if (globalIndex < uniforms.size) {
          var sum = vec4<f32>(0.0);
          var found = vec4<bool>(false);
          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
            var flattenedIndex = 0;
            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
              let indexInside = i32(round(${this.indicesSnippet}));
              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
            }
            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
              let curIndex = globalIndex + innerIndex;
              let coords = getCoordsFromIndex(curIndex);
              if (flattenedIndex == coords[0]) {
                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
                found[innerIndex] = true;
              }
            }
          }
          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
            let curIndex = globalIndex + innerIndex;
            if (curIndex < uniforms.size)
            {
              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
            }
          }
        }
      }`}};function Ofe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new zfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Dfe={kernelName:Yp,backendName:"webgpu",kernelFunc:Ofe};function Lfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=vd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Bfe={kernelName:ul,backendName:"webgpu",kernelFunc:Lfe},Wfe=xr({opType:20}),Vfe={kernelName:Ci,backendName:"webgpu",kernelFunc:Wfe},Ufe={kernelName:Qu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Fh(r.shape,21);return n.runWebGPUProgram(a,[r],r.dtype)}},Gfe=Gr({opSnippet:11}),jfe={kernelName:Ei,backendName:"webgpu",kernelFunc:Gfe},Hfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=mr(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           setOutputAtIndex(index, getX(${t}));
         }
       }
     `}};function qfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let C=_t.computeOutShape(A,x,b),S=vd({inputs:{x:a},backend:r,attrs:{begin:A,size:C}});v=qe({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeData(S.dataId)}else if(r.shouldExecuteOnCPU([a])){let C=r.readSync(a.dataId),S=We(a.shape,a.dtype,C),E=cpe(c,S,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let C=new Hfe(c),S=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(C,[a],a.dtype,S);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Kfe={kernelName:dl,backendName:"webgpu",kernelFunc:qfe};function Xfe(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=fpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var Zfe={kernelName:Jp,backendName:"webgpu",kernelFunc:Xfe},Yfe=xr({opType:22}),Jfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Yfe},Qfe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=eme(this.rank,"uniforms.");return`
      ${tt()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `}};function eme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function tme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=gpe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Qfe(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var rme={kernelName:Ka,backendName:"webgpu",kernelFunc:tme},nme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
        dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
        ${tt()}
          if (index < uniforms.size) {
            let outC = getCoordsFromIndex(index);
            let batch = outC[0];
            let elemIdx = outC[1];
            // We compare elements pair-wise within a group of size 2 * inc.
            // The comparing rule for each group alternates between ascending
            // and descending. Within each group, we compare each pair at
            // positions i and i+inc. To decide whether an element at position i
            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
            // inc, it is in the first half of the group, we denote it as x0,
            // otherwise we denote it as x1.
            // For example, as shown in the Bitonic top K paper referenced
            // above, Figure5(a) shows that element[1] is in the second half of
            // the group when group size is 2, but it is in the first half of
            // the group when group size is 4.
            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
            var i = 0;
            if (isFirstInPair) {
              i = elemIdx;
            } else {
              i = elemIdx - uniforms.inc;
            }

            var i0 = 0;
            if (uniforms.firstPass == 1) {
              i0 = i;
            } else {
              i0 = i32(getIndices(batch, i));
            }

            var i1 = 0;
            if (uniforms.firstPass == 1) {
              i1 = i + uniforms.inc;
            } else {
              i1 = i32(getIndices(batch, i + uniforms.inc));
            }

            var x0 = f32(0.0);
            var x1 = f32(0.0);
            if (i0 < uniforms.inputSize) {
              x0 = getX(batch, i0);
            } else {
              x0 = uniforms.negativeInf;
            }
            if (i1 < uniforms.inputSize) {
              x1 = getX(batch, i1);
            } else {
              x1 = uniforms.negativeInf;
            }

            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
            if (reverse == isGreater) {
              // Elements in opposite order of direction
              let iTemp = i0;
              i0 = i1;
              i1 = iTemp;
            }
            if (isFirstInPair) {
              setOutputAtIndex(index, f32(i0));
            } else {
              setOutputAtIndex(index, f32(i1));
            }
          }
        }
      `}},ame=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
        ${tt()}
          if (index < uniforms.size) {
            let outC = getCoordsFromIndex(index);
            let batch = outC[0];
            let elemIdx = outC[1];
            // The output size is half of the previous size.
            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
            // (k=4), we only need to output the indices at positions |, the
            // indices at positions _ can be thrown away, see Figure5(b) After
            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
            // above.
            // For example, the paper shows we only need to output the orange
            // bars. The output sequence should look like this | | | | | | | |.
            // Because the sequence is halved, to map the output index back to
            // the previous sequence to find the corresponding value, we need
            // to double the index. When we double the index, we basically
            // interpolate a position, so 2i looks like
            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
            // position of each 2k positions by - elemIdx % k. E.g. for output
            // at index 4,5,6,7, we want to get the corresponding element at
            // original index 8,9,10,11, for output at index 8,9,10,11,
            // we want to get the corresponding element at original index
            // 16,17,18,19, so on and so forth.

            var i = 0;
            if (elemIdx < uniforms.k) {
              i = elemIdx;
            } else {
              i = elemIdx * 2 - elemIdx % uniforms.k;
            }
            var i0 = 0;
            if (uniforms.firstPass == 1) {
              i0 = i;
            } else {
              i0 = i32(getIndices(batch, i));
            }
            var i1 = 0;
            if (uniforms.firstPass == 1) {
              i1 = i + uniforms.k;
            } else {
              i1 = i32(getIndices(batch, i + uniforms.k));
            }

            let x0 = getX(batch, i0);
            var x1 = f32(0.0);
            if (i1 < uniforms.inputSize) {
              x1 = getX(batch, i1);
            } else {
              x1 = x0;
            }

            if (x0 >= x1) {
              setOutputAtIndex(index, f32(i0));
            } else {
              setOutputAtIndex(index, f32(i1));
            }
          }
        }
      `}};function tu(e,t){t!==null&&e.disposeData(t.dataId)}function Dv(e){let t=1;for(;t<e;)t*=2;return t}function sme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,C]=ype(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(C.shape,C.dtype,C.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,wd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=Dv(s),p=Dv(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,C)=>{let S=f(),E=new nme(C),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,S,"int32",R),tu(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let C=b;C>=1;C/=2)m(v,C,[u,p])}for(let b=p;b>h;b/=2){let v=f(),C=new ame([u,b/2]),S=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(C,v,"int32",S),tu(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=vd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),tu(r,g);let y=xS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});tu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),tu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),tu(r,x),[y,c]}var ime={kernelName:hl,backendName:"webgpu",kernelFunc:sme},ome=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
          fn mapCoord(outCoord : f32, len : f32) -> f32{
            var inCoord = outCoord;
            if(uniforms.fillModeId == 2) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  if (inCoord < sz2) {
                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
                    inCoord;
                  }
                  if (inCoord < -len) {
                    inCoord = inCoord + sz2;
                  } else {
                    inCoord = -inCoord - 1.0;
                  }
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
                  if (inCoord >= len) {
                    inCoord = sz2 - inCoord - 1.0;
                  }
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 3) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 4) {
              return clamp(outCoord, 0.0, len - 1.0);
            }
            return outCoord;
          }
          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
            channel : i32) -> f32 {
            var outputValue : f32;
            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
                outputValue = getImage(batch, coordY, coordX, channel);
            } else {
              outputValue = uniforms.fillValue;
            }
            return outputValue;
          }

          ${tt()}
            if (index < uniforms.size) {
              let coords = getCoordsFromIndex(index);
              var outputValue : f32;
              let batch = coords[0];
              let x = coords[2];
              let y = coords[1];
              let channel = coords[3];
              let xf = f32(x);
              let yf = f32(y);
              let a1 = getTransforms(batch, 0);
              let a2 = getTransforms(batch, 1);
              let a3 = getTransforms(batch, 2);
              let b1 = getTransforms(batch, 3);
              let b2 = getTransforms(batch, 4);
              let b3 = getTransforms(batch, 5);
              let c1 = getTransforms(batch, 6);
              let c2 = getTransforms(batch, 7);
              let projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = uniforms.fillValue;
              } else {
                let inX = (a1 * xf + a2 * yf + a3) / projection;
                let inY = (b1 * xf + b2 * yf + b3) / projection;
                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));

                if (uniforms.interpolationModeId == 1) {
                  let coordY = i32(round(mapY));
                  let coordX = i32(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  let yFloor = floor(mapY);
                  let xFloor = floor(mapX);
                  let yCeil = yFloor + 1.0;
                  let xCeil = xFloor + 1.0;
                  let valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
                  let valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutputAtIndex(index, outputValue);
            }
          }
        `}};function lme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new ome(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var ume={kernelName:cl,backendName:"webgpu",kernelFunc:lme};function dme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=vd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var pme={kernelName:fl,backendName:"webgpu",kernelFunc:dme},hme=[Ode,bpe,wpe,Spe,Mpe,$pe,_pe,Ope,Vpe,Hpe,Kpe,Jpe,Wde,rhe,dhe,fhe,ghe,Ahe,vhe,Ihe,Che,Mhe,$he,_he,zhe,Ohe,Lhe,Whe,Uhe,Xhe,jhe,qhe,Jhe,ece,rce,sce,lce,dce,hce,Bde,ehe,fce,gce,Ace,bce,wce,Ice,Sce,Tce,Ece,Mce,$ce,_ce,Oce,The,Lce,Wce,Uce,Upe,jce,qce,Xce,Yce,Qce,tfe,nfe,Gpe,afe,ife,lfe,_de,pfe,ffe,gfe,Afe,vfe,Ife,Cfe,Nfe,Rfe,Bpe,Kfe,Zfe,$fe,_fe,Dfe,Bfe,Vfe,Ufe,jfe,Mfe,Ehe,Jfe,rme,ime,ume,Epe,pme,Gce];for(let e of hme)Wn(e);var cme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=Lv(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=Lv(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Lv(e,t){return`${e}_${t}`}var SS=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
      @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};

      ${tt()}
        let flatIndexBase = index * uniforms.numChannels;
        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
          let flatIndex = flatIndexBase + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndexBase);
            let values = ${e};
            result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},fme=class extends SS{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},mme=J().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Bv=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},CS=class extends Su{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!tb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new cme(this.device),this.tensorMap=new zp(this,kr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return CS.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*zy(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*zy(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new SS),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new fme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=rS(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=Bv(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,E)=>{if(S.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(S.dataId),{dtype:this.tensorMap.get(S.dataId).dtype,shape:S.shape,name:e.variableNames[E]}}),h=d.map(S=>S.dtype).concat(a.dtype),p=d.map(S=>N.getBroadcastDims(S.shape,a.shape)),c=d.map(S=>w.arraysEqual(S.shape,a.shape)).join("_"),f=p.map(S=>S.join("_")).join(";"),m=AS(e,i,h,f,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(m,()=>yS(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Khe(this.device,g,t.map(S=>this.tensorToBinding(S)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let v=this.getComputePass();x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(S=>{this.commandQueueOwnedIds.add(S.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let C={byteSize:u.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};return this.uniformDisposalQueue.push(C),J().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}runFromPixelsProgram(e,t,r,n,a){e.dispatch=Bv(this.device,e);let s=this.device.createBindGroup({layout:r.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:n},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,s),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=mme){return J().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},sb=CS;sb.nextDataId=0;var TS={};Le(TS,{WebGPUBackend:()=>sb,webgpu_util:()=>eS});tb()&&Al("webgpu",async()=>{J().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:J().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?n.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new sb(s,a)},3);var Ut=(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",e))(Ut||{}),h0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(h0||{}),NS;function gme(e){NS=e.wasm.cwrap(Ts,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function yme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.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:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=h0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,C=new Uint8Array(new Int32Array(a.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return NS(p,C,a.shape.length,c,S,s.shape.length,l,u,g,f,m,h||0,v),b}var Ame={kernelName:Ts,backendName:"wasm",setupFunc:gme,kernelFunc:yme};function br(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var xme=br(Mo);function jr(e,t,r){let n;function a(i){n=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,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var bme=!0,vme=jr(Ha,bme),ES;function wme(e){ES=e.wasm.cwrap(Vs,null,["array","number","number","number"])}function kme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return ES(s,a.length,Ut[n.dtype],i),n}var Ime={kernelName:Vs,backendName:"wasm",setupFunc:wme,kernelFunc:kme};function c0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Sme={kernelName:ii,backendName:"wasm",kernelFunc:c0},RS;function Cme(e){RS=e.wasm.cwrap(Fi,null,["number","array","number","number","number","array","number"])}function Iu(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Nme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Tme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=c0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return RS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Tme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Nme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Eme={kernelName:Fi,backendName:"wasm",kernelFunc:Iu,setupFunc:Cme};function Li(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Iu({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var MS;function Rme(e){MS=e.wasm.cwrap(Eu,null,["number, number, number"])}function Mme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;MS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Fme={kernelName:Eu,backendName:"wasm",setupFunc:Rme,kernelFunc:Mme},FS;function $me(e){FS=e.wasm.cwrap(Ru,null,["number, number, number"])}function Pme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;FS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var _me={kernelName:Ru,backendName:"wasm",setupFunc:$me,kernelFunc:Pme},$S;function zme(e){$S=e.wasm.cwrap(Us,null,["number","number","number","number","number"])}function Ome(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Li(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return $S(o,Ut[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Dme={kernelName:Us,backendName:"wasm",kernelFunc:Ome,setupFunc:zme},PS;function Lme(e){PS=e.wasm.cwrap(Gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${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 b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return PS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var Wme={kernelName:Gs,backendName:"wasm",setupFunc:Lme,kernelFunc:Bme};function Qr(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Vme={kernelName:tl,backendName:"wasm",kernelFunc:Qr},_S;function Ume(e){_S=e.wasm.cwrap(js,null,["number","array","number","number","array","number","number","number","number"])}function Gme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=Qr({inputs:{x:a},backend:r,attrs:{shape:x}}),C=Qr({inputs:{x:s},backend:r,attrs:{shape:b}}),S=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(C.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?C.shape[1]:C.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),O=r.dataIdMap.get(I.dataId).id,z=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return _S(S,z,v.shape.length,E,j,C.shape.length,i,o,O),r.disposeData(v.dataId),r.disposeData(C.dataId),I.shape=A,I}var jme={kernelName:js,backendName:"wasm",setupFunc:Ume,kernelFunc:Gme};function No(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=kf(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Hme(l,d[0],p,s,i);else if(c===3)qme(l,d[0],d[1],p,s,i);else if(c===4)Kme(l,d[0],d[1],d[2],p,s,i);else{let f=kf(l,s,i,t.shape,t.dtype);p.set(f)}return u}function Hme(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function qme(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Kme(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Xme={kernelName:il,backendName:"wasm",kernelFunc:No};function Zme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Qr({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Iu({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:d}}),g=No({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var Yme={kernelName:Fo,backendName:"wasm",kernelFunc:Zme};function _h(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var Jme={kernelName:Hs,backendName:"wasm",kernelFunc:_h},Qme=br(qs),zS;function e0e(e){zS=e.wasm.cwrap(qa,null,["number","number","number","number"])}function t0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o=r.dataIdMap.get(a.dataId).id,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(l.dataId).id;return zS(o,s,i,u),l}var r0e={kernelName:qa,backendName:"wasm",setupFunc:e0e,kernelFunc:t0e};function OS(e){let{inputs:t,backend:r}=e,n=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=N.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>w.sizeFromShape(c.shape)>0);if(s.length===1)return c0({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(w.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(N.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=w.sizeFromShape(x.shape.slice(n));return Qr({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),f=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=N.computeOutShape(c.map(x=>x.shape),1);let m=c[0].shape[0]===1,g=Rx(f,a,t[0].dtype,m),y=N.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=N.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let f=w.sizeFromShape(c.shape.slice(n));return u+=f,f}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let f=c*u;for(let m=0;m<h.length;m++){let g=d[m],y=c*g,A=h[m].subarray(y,y+g);p.set(A,f),f+=g}}return i}var n0e={kernelName:$o,backendName:"wasm",kernelFunc:OS},DS;function a0e(e){DS=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function s0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,C=f.dilationWidth,S=f.strideHeight,E=f.strideWidth,R=f.inChannels,_=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let I=n.makeOutput(f.outShape,"float32"),O=n.dataIdMap.get(I.dataId).id;return DS(i,a.shape[0],a.shape[1],a.shape[2],o,m,g,y,A,x,b,M,v,C,S,E,R,_,O),I}var i0e={kernelName:Ks,backendName:"wasm",setupFunc:a0e,kernelFunc:s0e},LS;function o0e(e){LS=e.wasm.cwrap(Xs,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 l0e(e){let{backend:t,inputs:r,attrs:n}=e,{dy:a,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=n,h=1,p=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:v,outWidth:C,strideHeight:S,strideWidth:E}=c,R=m-1-c.padInfo.top,_=g-1-c.padInfo.left,M=c.dataFormat==="channelsLast",I=w.computeStrides(c.inShape),O=w.computeStrides(a.shape),[z,j,X]=w.computeStrides(s.shape),D=I[0],Q=M?I[1]:I[2],V=M?I[2]:1,ee=M?1:I[1],Y=O[0],se=M?O[1]:O[2],Z=M?O[2]:1,ae=M?1:O[1],de=t.makeOutput(c.inShape,"float32"),Ae=t.dataIdMap.get(de.dataId).id,be=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return LS(be,Ee,f,m,g,A,x,y,v,C,b,S,E,R,_,z,j,X,D,Q,V,ee,Y,se,Z,ae,Ae),de}var u0e={kernelName:Xs,backendName:"wasm",setupFunc:o0e,kernelFunc:l0e},d0e=br(Zs),p0e=br(Ys),BS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(BS||{}),WS;function h0e(e){WS=e.wasm.cwrap(_o,null,["number","number","number","number","array","number","number","number","number","number"])}function c0e(e){let{backend:t,inputs:r,attrs:n}=e,{method:a,extrapolationValue:s,cropSize:i}=n,{image:o,boxes:l,boxInd:u}=r,d=l.shape[0],[h,p]=i,c=[d,h,p,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=_h({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(o.shape).buffer);return WS(g,y,A,d,v,h,p,BS[a],s,b),m!=null&&t.disposeData(m.dataId),x}var f0e={kernelName:_o,backendName:"wasm",setupFunc:h0e,kernelFunc:c0e},VS;function m0e(e){VS=e.wasm.cwrap(Po,null,["number","number","number","number","number","number"])}function g0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;w.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),d=a;u!==null&&(d=Iu({inputs:{x:a},attrs:{perm:u},backend:r}));let h=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],f=r.dataIdMap.get(d.dataId).id,m=r.dataIdMap.get(p.dataId).id;VS(f,i?1:0,o?1:0,c,m,Ut[a.dtype]);let g=p;if(u!==null){let y=N.getUndoAxesPermutation(u);g=Iu({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var y0e={kernelName:Po,backendName:"wasm",setupFunc:m0e,kernelFunc:g0e},US;function A0e(e){US=e.wasm.cwrap(zo,null,["number","number","number","array","number","array","array","number","number"])}function x0e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return US(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,f.length,b),m}var b0e={kernelName:zo,backendName:"wasm",setupFunc:A0e,kernelFunc:x0e},GS;function v0e(e){GS=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function w0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return GS(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,A,x,_,b,v,C,S,E,R,I),M}var k0e={kernelName:Js,backendName:"wasm",setupFunc:v0e,kernelFunc:w0e},I0e=br(ei),S0e=!1,C0e=jr(Oo,S0e,"bool"),T0e=br(ti,"float32");function Wy(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Qr({inputs:{x:a},backend:n,attrs:{shape:o}})}var N0e={kernelName:Do,backendName:"wasm",kernelFunc:Wy};function jS(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var E0e={kernelName:Du,backendName:"wasm",kernelFunc:jS},HS;function R0e(e){HS=e.wasm.cwrap(Bo,null,["number","number","number","number","number","number"])}function M0e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return HS(s,o,l,u,d,i),a}var F0e={kernelName:Bo,backendName:"wasm",kernelFunc:M0e,setupFunc:R0e},$0e=br(ri),P0e=!1,_0e=jr(ni,P0e),qS;function z0e(e){qS=e.wasm.cwrap(ai,null,["number","number","number","number","number","number","number"])}function O0e(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return qS(d,h,p,c,f,a,g),m}var D0e={kernelName:ai,backendName:"wasm",setupFunc:z0e,kernelFunc:O0e},KS;function L0e(e){KS=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function B0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=h0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.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 (${x})`);b=Z.id}let v=m.filterHeight,C=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),Y=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return KS(y,D,Q,V,A,v,C,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,Y),ee}var W0e={kernelName:Ns,backendName:"wasm",setupFunc:L0e,kernelFunc:B0e},XS;function V0e(e){XS=e.wasm.cwrap(Es,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 U0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=h0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.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 (${x})`);b=Z.id}let v=m.filterHeight,C=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),Y=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return XS(y,D,Q,V,A,v,C,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,Y),ee}var G0e={kernelName:Es,backendName:"wasm",setupFunc:V0e,kernelFunc:U0e},ZS;function j0e(e){ZS=e.wasm.cwrap(Vo,null,["number","number","number","number","number","number","array","number"])}function H0e(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=n2.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return ZS(p,Ut[n.dtype],c,i,h,o,f,m),u}var q0e={kernelName:Vo,backendName:"wasm",setupFunc:j0e,kernelFunc:H0e},YS;function K0e(e){YS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function X0e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let S=0;S<u.length;++S){let E=u[S];w.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=Qr({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=w.sizeFromShape(s.shape),f=Qr({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),m=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(m,a.dtype);if(w.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(f.dataId).id,b=t.dataIdMap.get(g.dataId).id,v=new Uint8Array(new Int32Array(w.computeStrides(p.shape)).buffer),C=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return YS(A,Ut[a.dtype],v,y,x,h.batchSize,C,b),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=h.outputShape,g}var Z0e={kernelName:Wo,backendName:"wasm",setupFunc:K0e,kernelFunc:X0e},Y0e=!1,J0e=jr(Uo,Y0e,"bool"),Q0e=!1,ege=jr(si,Q0e,"bool"),JS;function tge(e){JS=e.wasm.cwrap(oi,null,["number","number","number","number"])}function rge(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;JS(a,Ut[t.dtype],r,i)}return s}var nge={kernelName:oi,backendName:"wasm",setupFunc:tge,kernelFunc:rge},age=!1,sge=jr(Go,age,"bool"),ige=!1,oge=jr(jo,ige,"bool"),lge=br(li),uge=!1,dge=jr(Ho,uge,"bool"),QS;function pge(e){QS=e.wasm.cwrap(ui,null,["number","number","number","number"])}function hge(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("max",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;QS(o,Ut[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var cge={kernelName:ui,backendName:"wasm",setupFunc:pge,kernelFunc:hge},fge=!1,mge=jr(di,fge),eC;function gge(e){eC=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yge(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;w.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,v=d.inChannels,C=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let S=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(S.dataId).id;return eC(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,b,v,C,E),S}var Age={kernelName:pi,backendName:"wasm",setupFunc:gge,kernelFunc:yge},tC;function xge(e){tC=e.wasm.cwrap(hi,null,["number, number, number"])}function bge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t),f=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=_h({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(m,"float32");if(w.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;tC(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var vge={kernelName:hi,backendName:"wasm",setupFunc:xge,kernelFunc:bge},rC;function wge(e){rC=e.wasm.cwrap(ci,null,["number","number","number","number"])}function kge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let f=u.shape.length;N.assertAxesAreInnerMostDims("min",h,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,h),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;rC(l,Ut[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Ige={kernelName:ci,backendName:"wasm",setupFunc:wge,kernelFunc:kge},Sge=!1,Cge=jr(fi,Sge),nC=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(nC||{}),aC;function Tge(e){aC=e.wasm.cwrap(mi,null,["number","array","number","number","array","array","number","number"])}function Nge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return aC(i,u,t.shape.length,Ut[t.dtype],p,c,nC[a],l),o}var Ege={kernelName:mi,backendName:"wasm",kernelFunc:Nge,setupFunc:Tge},Rge=!0,Mge=jr(gi,Rge),Fge=br(qo);function ib(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var sC;function $ge(e){sC=e.wasm.cwrap(Xo,"number",["number","number","number","number","number"])}function Pge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=sC(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=ib(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var _ge={kernelName:Xo,backendName:"wasm",setupFunc:$ge,kernelFunc:Pge},iC;function zge(e){iC=e.wasm.cwrap(ju,"number",["number","number","number","number","number","bool"])}function Oge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=iC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ib(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var Dge={kernelName:ju,backendName:"wasm",setupFunc:zge,kernelFunc:Oge},oC;function Lge(e){oC=e.wasm.cwrap(Zo,"number",["number","number","number","number","number","number"])}function Bge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=oC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ib(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var Wge={kernelName:Zo,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge},Vge=!1,Uge=jr(Ko,Vge,"bool"),lC;function Gge(e){lC=e.wasm.cwrap(Jo,null,["number","number","number","number","number"])}function jge(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return lC(d,s,i,o,u),l}var Hge={kernelName:Jo,backendName:"wasm",setupFunc:Gge,kernelFunc:jge};function qge(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var Kge={kernelName:Yo,backendName:"wasm",kernelFunc:qge};function Xge(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Wy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Wy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=OS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var Zge={kernelName:Qo,backendName:"wasm",kernelFunc:Xge},uC;function Yge(e){uC=e.wasm.cwrap(yi,null,["number","array","number","number","array","array","number","number"])}function Jge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return jS({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return uC(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var dC={kernelName:yi,backendName:"wasm",kernelFunc:Jge,setupFunc:Yge},Qge=!1,e1e=jr(Ai,Qge),pC;function t1e(e){pC=e.wasm.cwrap(xi,null,["number","number","number"])}function r1e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=_h({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return pC(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var n1e={kernelName:xi,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},hC;function a1e(e){hC=e.wasm.cwrap(el,null,["number","number","number","number"])}function s1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;hC(l,y,Ut[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var i1e={kernelName:el,backendName:"wasm",setupFunc:a1e,kernelFunc:s1e},o1e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=$x(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},l1e={kernelName:Hu,backendName:"wasm",kernelFunc:o1e},u1e=!0,d1e=jr(Qs,u1e),p1e=br(bi),h1e=br(wi),cC;function c1e(e){cC=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number"])}function f1e(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=_h({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(w.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return cC(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var m1e={kernelName:vi,backendName:"wasm",setupFunc:c1e,kernelFunc:f1e},fC;function g1e(e){fC=e.wasm.cwrap(rl,null,["number","array","number","array","number","number"])}function y1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=w.parseAxisParam(s,a.shape);if(a.shape.length===0)return c0({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);fC(l,d,i.length,h,a.shape.length,u);let p=Qr({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var A1e={kernelName:rl,backendName:"wasm",kernelFunc:y1e,setupFunc:g1e},mC;function x1e(e){mC=e.wasm.cwrap(gl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function b1e(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,f]=a.shape,[m,g]=N.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return mC(u,h,p,c,f,s,m,g,b,x.length,d),l}var v1e={kernelName:gl,backendName:"wasm",kernelFunc:b1e,setupFunc:x1e},w1e=br(nl),k1e=br(ki),gC;function I1e(e){gC=e.wasm.cwrap(al,null,["number","number","number","number","number","number","array","number","number"])}function S1e(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=a2.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return gC(c,f,Ut[s.dtype],l,u,d,m,p,g),o}var C1e={kernelName:al,backendName:"wasm",setupFunc:I1e,kernelFunc:S1e},yC;function T1e(e){yC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function N1e(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:w.sizeFromShape(a.shape.slice(1));return yC(i,o,l,c,d),u}var E1e={kernelName:sl,backendName:"wasm",kernelFunc:N1e,setupFunc:T1e},AC;function R1e(e){AC=e.wasm.cwrap(Si,null,["number","number"])}function M1e(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return w.sizeFromShape(a.shape)===0||AC(n,s),a}var F1e={kernelName:"Sigmoid",backendName:"wasm",setupFunc:R1e,kernelFunc:M1e},$1e=br(Ii),xC;function P1e(e){xC=e.wasm.cwrap(Ni,null,["number","number","number","number"])}function _1e(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=w.sizeFromShape(r.shape)/o;return w.sizeFromShape(s.shape)===0||xC(a,i,o,l),s}var z1e={kernelName:Ni,backendName:"wasm",setupFunc:P1e,kernelFunc:_1e};function O1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=dC.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=Qr({inputs:{x:u},backend:r,attrs:{shape:d}}),f=Iu({inputs:{x:c},backend:r,attrs:{perm:h}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(f.dataId),m}var D1e={kernelName:ll,backendName:"wasm",kernelFunc:O1e},bC;function L1e(e){bC=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function B1e(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),v=t.dataIdMap.get(b.dataId).id,C=t.makeOutput([4],"int32"),S=t.dataIdMap.get(C.dataId).id,E=bC(h,p,Ut[a.dtype],o,u,l,c,m,y,x,v,S),R=t.readSync(C.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{_=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:_=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:_=""}if(t.disposeData(C.dataId),_)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(_);let M=f,I=g;return E!==d[0]&&(M=No({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),I=No({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[M,I,A,b]}var W1e={kernelName:Kp,backendName:"wasm",setupFunc:L1e,kernelFunc:B1e},vC;function V1e(e){vC=e.wasm.cwrap(Ju,null,["number","number","number","number","number","number","number"])}function U1e(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(n.dataId).id,o=t.dataIdMap.get(a.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=n.shape[0],d=w.sizeFromShape(s.shape),h=t.makeOutput([u,d],n.dtype),p=t.dataIdMap.get(h.dataId).id,c=t.makeOutput([d],s.dtype),f=t.dataIdMap.get(c.dataId).id,m=t.makeOutput([3],"int32"),g=t.dataIdMap.get(m.dataId).id;vC(i,o,l,u,p,f,g);let y=t.readSync(m.dataId),A;switch(y[0]){case 0:{A=N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{A=N.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:A=N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let x=Array.from(t.readSync(a.dataId)),b=Array.from(t.readSync(c.dataId));A=N.getSparseReshapeInputOutputMultipleErrorMessage(x,b);break}case 4:{let x=Array.from(t.readSync(a.dataId)),b=Array.from(t.readSync(c.dataId));A=N.getSparseReshapeInputOutputMismatchErrorMessage(x,b);break}default:A=""}if(t.disposeData(m.dataId),A)throw t.disposeData(h.dataId),t.disposeData(c.dataId),new Error(A);return[h,c]}var G1e={kernelName:Ju,backendName:"wasm",setupFunc:V1e,kernelFunc:U1e},wC;function kC(e){wC=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function IC(e,t){let{backend:r,inputs:n}=e,{data:a,indices:s,segmentIds:i}=n,o=s.shape[0],l=r.readSync(i.dataId,o-1,o)[0],u=o>0?l+1:0;if(u<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=a.shape.slice();d[0]=u;let h=r.dataIdMap.get(a.dataId).id,p=r.dataIdMap.get(s.dataId).id,c=r.dataIdMap.get(i.dataId).id,f=r.makeOutput(d,a.dtype),m=r.dataIdMap.get(f.dataId).id,g=r.makeOutput([4],"int32"),y=r.dataIdMap.get(g.dataId).id;wC(h,Ut[a.dtype],a.shape[0],p,c,m,y,t,0);let A=r.readSync(g.dataId),x;switch(A[0]){case 0:{x=N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(A[1],A[2]);break;case 3:x=N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(A[1],A[2],A[3]);break;default:x=""}if(r.disposeData(g.dataId),x)throw r.disposeData(f.dataId),new Error(x);return f}function j1e(e){return IC(e,!0)}var H1e={kernelName:Xp,backendName:"wasm",setupFunc:kC,kernelFunc:j1e};function q1e(e){return IC(e,!1)}var K1e={kernelName:Zp,backendName:"wasm",setupFunc:kC,kernelFunc:q1e};function X1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),d=a.shape.slice();return l.map(h=>{let p=[...d];p[o]=h;let c=No({inputs:{x:a},attrs:{begin:u,size:p},backend:n});return u[o]+=h,c})}var Z1e={kernelName:ul,backendName:"wasm",kernelFunc:X1e},Y1e=br(Ci),J1e=br(Qu),Q1e=!0,eye=jr(Ei,Q1e),SC;function tye(e){SC=e.wasm.cwrap($i,null,["number","number","number","number"])}function rye(e){let{backend:t,inputs:r,attrs:n}=e,{alpha:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return SC(i,a,Ut[s.dtype],l),o}var nye={kernelName:$i,backendName:"wasm",setupFunc:tye,kernelFunc:rye},CC;function aye(e){CC=e.wasm.cwrap(dl,null,["number","array","number","array","array","array","array","array","number","number"])}function sye(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=Qr({inputs:{x:a},backend:t,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let C=_t.computeOutShape(A,x,b),S=No({inputs:{x:a},backend:t,attrs:{begin:A,size:C}});v=Qr({inputs:{x:S},backend:t,attrs:{shape:f}}),t.disposeData(S.dataId)}else{let C=t.makeOutput(c,"float32"),S=t.dataIdMap.get(a.dataId).id,E=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),R=new Uint8Array(new Int32Array(A).buffer),_=new Uint8Array(new Int32Array(x).buffer),M=new Uint8Array(new Int32Array(b).buffer),I=new Uint8Array(new Int32Array(c).buffer),O=new Uint8Array(new Int32Array(w.computeStrides(c)).buffer),z=t.dataIdMap.get(C.dataId).id;CC(S,E,a.shape.length,R,_,M,I,O,c.length,z),v=Qr({inputs:{x:C},backend:t,attrs:{shape:f}}),t.disposeData(C.dataId)}return v}var iye={kernelName:dl,backendName:"wasm",setupFunc:aye,kernelFunc:sye},oye=!0,lye=jr(Ri,oye),TC;function uye(e){TC=e.wasm.cwrap(Ti,null,["number","number","number","number"])}function dye(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;TC(l,y,Ut[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var pye={kernelName:Ti,backendName:"wasm",setupFunc:uye,kernelFunc:dye},hye=br(pl),cye=br(Mi),NC;function fye(e){NC=e.wasm.cwrap(Ka,null,["number","array","number","array","number","number"])}function mye(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,s=r.dataIdMap.get(a.dataId).id,{reps:i}=n,o=new Array(a.shape.length);for(let p=0;p<o.length;p++)o[p]=a.shape[p]*i[p];let l=new Uint8Array(new Int32Array(a.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),d=r.makeOutput(o,a.dtype),h=r.dataIdMap.get(d.dataId).id;return NC(s,l,a.shape.length,u,o.length,Ut[d.dtype],h),d}var gye={kernelName:Ka,backendName:"wasm",setupFunc:fye,kernelFunc:mye},EC;function yye(e){EC=e.wasm.cwrap(hl,null,["number","array","number","number","number","bool","number","number"])}var Aye=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{k:a,sorted:s}=r,i=t.dataIdMap.get(n.dataId).id,o=new Uint8Array(new Int32Array(n.shape).buffer),l=n.shape.slice();l[l.length-1]=a;let u=t.makeOutput(l,n.dtype),d=t.dataIdMap.get(u.dataId).id,h=t.makeOutput(l,"int32"),p=t.dataIdMap.get(h.dataId).id;return EC(i,o,n.shape.length,Ut[n.dtype],a,s,d,p),[u,h]},xye={kernelName:hl,backendName:"wasm",setupFunc:yye,kernelFunc:Aye},RC;function bye(e){RC=e.wasm.cwrap(cl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function vye(e){let{backend:t,inputs:r,attrs:n}=e,{image:a,transforms:s}=r,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=t.makeOutput(g,a.dtype),x=t.dataIdMap.get(A.dataId).id,b=t.dataIdMap.get(a.dataId).id,v=t.dataIdMap.get(s.dataId).id,C=i==="nearest"?1:2,S;switch(o){case"constant":S=1;break;case"reflect":S=2;break;case"wrap":S=3;break;case"nearest":S=4;break;default:S=1;break}return RC(b,v,s.shape[0]>1,d,f,m,c,p,h,y,a.shape.length-1,C,S,l,x),A}var wye={kernelName:cl,backendName:"wasm",setupFunc:bye,kernelFunc:vye};function kye(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a.shape[s],o=a.shape.length,l=new Array(o-1),u=0;for(let c=0;c<o;c++)c!==s&&(l[u++]=a.shape[c]);let d=new Array(i),h=new Array(o).fill(0),p=a.shape.slice();p[s]=1;for(let c=0;c<d.length;c++)h[s]=c,d[c]=No({inputs:{x:a},attrs:{begin:h,size:p},backend:r});return d.map(({dataId:c,dtype:f})=>({dataId:c,dtype:f,shape:l}))}var Iye={kernelName:fl,backendName:"wasm",kernelFunc:kye};function Sye(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(0),n}var Cye={kernelName:ml,backendName:"wasm",kernelFunc:Sye},Tye=[Ame,xme,vme,Ime,Fme,_me,Dme,Wme,jme,Yme,Jme,Qme,r0e,n0e,i0e,u0e,d0e,p0e,f0e,y0e,b0e,k0e,I0e,C0e,T0e,N0e,E0e,F0e,$0e,_0e,D0e,W0e,G0e,q0e,Z0e,J0e,ege,Sme,nge,sge,oge,lge,dge,cge,mge,Age,vge,Ige,Cge,Ege,Mge,Fge,_ge,Dge,Wge,Uge,Hge,Kge,Zge,dC,e1e,n1e,i1e,l1e,d1e,p1e,h1e,Vme,m1e,A1e,v1e,w1e,k1e,C1e,E1e,F1e,$1e,Xme,z1e,D1e,W1e,G1e,H1e,K1e,Z1e,Y1e,J1e,eye,nye,iye,lye,pye,hye,cye,gye,xye,wye,Eme,Iye,Cye];for(let e of Tye)Wn(e);var Vy=J();Vy.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])));Vy.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Vy.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 Wv=Eo(jE()),Nye=`"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
");return}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;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(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})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}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);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`,Eye=Eo(HE()),MC=class extends Su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(FC),Uy=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new zp(this,kr())}write(e,t,r){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,r,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}move(e,t,r,n,a){let s=this.dataIdNextNumber++;if(n==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:r,dtype:n,memoryOffset:null,refCount:a});return}let i=w.sizeFromShape(r),o=i*w.bytesPerElement(n),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:r,dtype:n,refCount:a}),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,t,r){let{memoryOffset:n,dtype:a,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(a==="string")return(t==null||t===0)&&(r==null||r>=i.length)?i:i.slice(t,r);t=t||0,r=r||w.sizeFromShape(s);let o=w.bytesPerElement(a),l=this.wasm.HEAPU8.slice(n+t*o,n+r*o);return Fye(l.buffer,a)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let r=this.dataIdMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;this.wasm._free(r.memoryOffset),this.wasm.tfjs.disposeData(r.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,r){let n;if(r==null)n=this.write(null,e,t);else{let a=this.dataIdNextNumber++;n={id:a},this.dataIdMap.set(n,{id:a,memoryOffset:r,shape:e,dtype:t,refCount:1});let s=w.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,r)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:r}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(r),s=w.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,a,s);case"int32":return new Int32Array(n,a,s);case"bool":return new Uint8Array(n,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Rye(e){return(t,r)=>(w.fetch(e,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${e}'`),n.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{r(s.instance,s.module)})})}),{})}function Vv(e,t,r){if(Ef!=null)return Ef;let n="tfjs-backend-wasm.wasm";return e&&t?n="tfjs-backend-wasm-threaded-simd.wasm":e&&(n="tfjs-backend-wasm-simd.wasm"),bp!=null&&bp[n]!=null?bp[n]:r+n}async function Mye(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((r,n)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=Nye,d=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(d)}return o.endsWith(".wasm")?Vv(e,t,gp!=null?gp:l):l+o},ob&&(a.instantiateWasm=Rye(Vv(e,t,gp!=null?gp:"")));let s=!1;a.onAbort=()=>{s||vp||(vp=!0,n({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&&Ef==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Wv.default.toString()],{type:"text/javascript"}),i=(0,Wv.default)(a)):i=(0,Eye.default)(a),i.then(o=>{s=!0,vp=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),initWithThreadsCount:o.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:o.cwrap("get_threads_count","number",[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},r({wasm:o})})})}function Fye(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 $ye=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Ef=null,gp=null,bp={},vp=!1,ob=!1;function Pye(e,t=!1){if(p2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),vp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Ef=e,ob=t}function lb(e,t=!1){if(vp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")gp=e;else{bp=e;let r=$ye.filter(n=>bp[n]==null);if(r.length>0)throw new Error(`There were no entries found for the following binaries: ${r.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.`)}ob=t}var FC=-1,Uy=-1;function _ye(e){FC=e}function zye(){if(Uy===-1)throw new Error("WASM backend not initialized.");return Uy}var Oye="0.0.0",Dye=2;Al("wasm",async()=>{let{wasm:e}=await Mye();return new MC(e)},Dye);var ys="3.14.0-20220307",zh={tfjs:ys,"tfjs-core":ys,"tfjs-data":ys,"tfjs-layers":ys,"tfjs-converter":ys,"tfjs-backend-cpu":ys,"tfjs-backend-webgl":ys,"tfjs-backend-wasm":ys};var $C=`
  precision highp float;
  attribute vec2 pos;
  attribute vec2 uv;
  varying vec2 vUv;
  uniform float flipY;
  void main(void) {
    vUv = uv;
    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
  }
`;var PC=`
  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];
  }
`,_C=`
  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;
  }
`,zC=`
  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);
  }
`,OC=`
  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;
  }
`,DC=`
  precision highp float;
  varying vec2 vUv;
  uniform sampler2D texture;
  uniform vec2 px;
  uniform float m[9];
  void main(void) {
    vec4 c11 = texture2D(texture, vUv - px); // top left
    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
    vec4 c22 = texture2D(texture, vUv); // mid center
    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
    gl_FragColor = 
    c11 * m[0] + c12 * m[1] + c22 * m[2] +
    c21 * m[3] + c22 * m[4] + c23 * m[5] +
    c31 * m[6] + c32 * m[7] + c33 * m[8];
    gl_FragColor.a = c22.a;
  }
`;var ub=(e,t,r)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(a,s)=>(r[s]=0,a))},LC=class{constructor(t,r,n){fe(this,"uniform",{});fe(this,"attribute",{});fe(this,"gl");fe(this,"id");fe(this,"compile",(t,r)=>{let n=this.gl.createShader(r);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(ie(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(ie("filter: could not create shader"),null)});this.gl=t;let a=this.compile(r,this.gl.VERTEX_SHADER),s=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!a||!s)){if(!this.id){ie("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,a),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){ie(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),ub(r,"attribute",this.attribute);for(let i in this.attribute)this.attribute[i]=this.gl.getAttribLocation(this.id,i);ub(r,"uniform",this.uniform),ub(n,"uniform",this.uniform);for(let i in this.uniform)this.uniform[i]=this.gl.getUniformLocation(this.id,i)}}};function BC(){let e=0,t=null,r=!1,n=-1,a=[null,null],s=[],i=null,o=null,l=Hr(100,100),u={},d={INTERMEDIATE:1},h=l.getContext("webgl");if(this.gl=h,!h){ie("filter: cannot get webgl context");return}function p(A,x){if(!(A===l.width&&x===l.height)){if(l.width=A,l.height=x,!i){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);i=h.createBuffer(),h.bindBuffer(h.ARRAY_BUFFER,i),h.bufferData(h.ARRAY_BUFFER,b,h.STATIC_DRAW),h.pixelStorei(h.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}h.viewport(0,0,l.width,l.height),a=[null,null]}}function c(A,x){let b=h.createFramebuffer();h.bindFramebuffer(h.FRAMEBUFFER,b);let v=h.createRenderbuffer();h.bindRenderbuffer(h.RENDERBUFFER,v);let C=h.createTexture();return h.bindTexture(h.TEXTURE_2D,C),h.texImage2D(h.TEXTURE_2D,0,h.RGBA,A,x,0,h.RGBA,h.UNSIGNED_BYTE,null),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_MAG_FILTER,h.LINEAR),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_MIN_FILTER,h.LINEAR),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_WRAP_S,h.CLAMP_TO_EDGE),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_WRAP_T,h.CLAMP_TO_EDGE),h.framebufferTexture2D(h.FRAMEBUFFER,h.COLOR_ATTACHMENT0,h.TEXTURE_2D,C,0),h.bindTexture(h.TEXTURE_2D,null),h.bindFramebuffer(h.FRAMEBUFFER,null),{fbo:b,texture:C}}function f(A){return a[A]=a[A]||c(l.width,l.height),a[A]}function m(A=0){if(!o)return;let x=null,b=null,v=!1;e===0?x=t:x=f(n).texture||null,e++,r&&!(A&d.INTERMEDIATE)?(b=null,v=e%2===0):(n=(n+1)%2,b=f(n).fbo||null),h.bindTexture(h.TEXTURE_2D,x),h.bindFramebuffer(h.FRAMEBUFFER,b),h.uniform1f(o.uniform.flipY,v?-1:1),h.drawArrays(h.TRIANGLES,0,6)}function g(A){if(u[A])return o=u[A],h.useProgram((o?o.id:null)||null),o;if(o=new LC(h,$C,A),!o)return ie("filter: could not get webgl program"),null;let x=Float32Array.BYTES_PER_ELEMENT,b=4*x;return h.enableVertexAttribArray(o.attribute.pos),h.vertexAttribPointer(o.attribute.pos,2,h.FLOAT,!1,b,0*x),h.enableVertexAttribArray(o.attribute.uv),h.vertexAttribPointer(o.attribute.uv,2,h.FLOAT,!1,b,2*x),u[A]=o,o}let y={colorMatrix:A=>{let x=new Float32Array(A);x[4]/=255,x[9]/=255,x[14]/=255,x[19]/=255;let b=x[18]===1&&x[3]===0&&x[8]===0&&x[13]===0&&x[15]===0&&x[16]===0&&x[17]===0&&x[19]===0?_C:PC,v=g(b);!v||(h.uniform1fv(v.uniform.m,x),m())},brightness:A=>{let x=(A||0)+1;y.colorMatrix([x,0,0,0,0,0,x,0,0,0,0,0,x,0,0,0,0,0,1,0])},saturation:A=>{let x=(A||0)*2/3+1,b=(x-1)*-.5;y.colorMatrix([x,b,b,0,0,b,x,b,0,0,b,b,x,0,0,0,0,0,1,0])},desaturate:()=>{y.saturation(-1)},contrast:A=>{let x=(A||0)+1,b=-128*(x-1);y.colorMatrix([x,0,0,0,b,0,x,0,0,b,0,0,x,0,b,0,0,0,1,0])},negative:()=>{y.contrast(-2)},hue:A=>{A=(A||0)/180*Math.PI;let x=Math.cos(A),b=Math.sin(A),v=.213,C=.715,S=.072;y.colorMatrix([v+x*(1-v)+b*-v,C+x*-C+b*-C,S+x*-S+b*(1-S),0,0,v+x*-v+b*.143,C+x*(1-C)+b*.14,S+x*-S+b*-.283,0,0,v+x*-v+b*-(1-v),C+x*-C+b*C,S+x*(1-S)+b*S,0,0,0,0,0,1,0])},desaturateLuminance:()=>{y.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{y.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{y.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{y.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{y.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{y.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{y.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{y.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:A=>{let x=new Float32Array(A),b=1/l.width,v=1/l.height,C=g(DC);!C||(h.uniform1fv(C.uniform.m,x),h.uniform2f(C.uniform.px,b,v),m())},detectEdges:()=>{y.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{y.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{y.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:A=>{let x=A||1;y.convolution.call(this,[0,-1*x,0,-1*x,1+4*x,-1*x,0,-1*x,0])},emboss:A=>{let x=A||1;y.convolution.call(this,[-2*x,-1*x,0,-1*x,1,1*x,0,1*x,2*x])},blur:A=>{let x=A/7/l.width,b=A/7/l.height,v=g(OC);!v||(h.uniform2f(v.uniform.px,0,b),m(d.INTERMEDIATE),h.uniform2f(v.uniform.px,x,0),m())},pixelate:A=>{let x=A/l.width,b=A/l.height,v=g(zC);!v||(h.uniform2f(v.uniform.size,x,b),m())}};this.add=function(A){let x=Array.prototype.slice.call(arguments,1),b=y[A];s.push({func:b,args:x})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(A){p(A.width,A.height),e=0,t||(t=h.createTexture()),h.bindTexture(h.TEXTURE_2D,t),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_WRAP_S,h.CLAMP_TO_EDGE),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_WRAP_T,h.CLAMP_TO_EDGE),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_MIN_FILTER,h.NEAREST),h.texParameteri(h.TEXTURE_2D,h.TEXTURE_MAG_FILTER,h.NEAREST),h.texImage2D(h.TEXTURE_2D,0,h.RGBA,h.RGBA,h.UNSIGNED_BYTE,A);for(let x=0;x<s.length;x++){r=x===s.length-1;let b=s[x];b.func.apply(this,b.args||[])}return l},this.draw=function(A){return this.add("brightness",0),this.apply(A)}}async function f0(e){let t=e.shape.length===4?et(e):e,r=Kt(t,3,2),n=[$s(r[0]),$s(r[1]),$s(r[2])],a=[fr(r[0]),fr(r[1]),fr(r[2])],s=await Promise.all(a.map(c=>c.data())),i=.99*Math.max(s[0][0],s[1][0],s[2][0]),o=[he(r[0],n[0]),he(r[1],n[1]),he(r[2],n[2])],l=[he(a[0],n[0]),he(a[1],n[1]),he(a[2],n[2])],u=[pe(i,l[0]),pe(i,l[1]),pe(i,l[2])],d=[L(o[0],u[0]),L(o[1],u[1]),L(o[2],u[2])],h=sr([d[0],d[1],d[2]],2),p=G(h,[1,t.shape[0],t.shape[1],3]);return re([...r,...n,...a,...o,...l,...u,...d,h,t]),p}var m0=2048,ut=null,or=null,kd=null,Tt,ts={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function Hr(e,t){let r;if(ce.browser)if(ce.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");r=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");r=document.createElement("canvas"),r.width=e,r.height=t}else typeof ce.Canvas!="undefined"?r=new ce.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(r=new globalThis.Canvas(e,t));return r}function db(e,t){let r=t||Hr(e.width,e.height);return r.getContext("2d").drawImage(e,0,0),r}async function Id(e,t,r=!0){if(!e)return t.debug&&ie("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof rt)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ce.Canvas!="undefined"&&e instanceof ce.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type is not recognized");if(e instanceof rt){let n=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)n=Ht(e,0);else if(e.shape[2]===4){let a=vl(e,[0,0,0],[-1,-1,3]);n=Ht(a,0),re(a)}}else e.shape.length===4&&(e.shape[3]===3?n=Or(e):e.shape[3]===4&&(n=wo(e,[0,0,0,0],[-1,-1,-1,3])));if(n==null||n.shape.length!==4||n.shape[0]!==1||n.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape}`);if(n.dtype==="int32"){let a=me(n,"float32");re(n),n=a}return{tensor:n,canvas:t.filter.return?or:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&ie("input stream is not ready"),{tensor:null,canvas:ut};let n=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!n||!a)return t.debug&&ie("cannot determine input dimensions"),{tensor:null,canvas:ut};let s=n,i=a;if(s>m0&&(s=m0,i=Math.trunc(s*a/n)),i>m0&&(i=m0,s=Math.trunc(i*n/a)),(t.filter.width||0)>0?s=t.filter.width:(t.filter.height||0)>0&&(s=n*((t.filter.height||0)/a)),(t.filter.height||0)>0?i=t.filter.height:(t.filter.width||0)>0&&(i=a*((t.filter.width||0)/n)),!s||!i)throw new Error("input error: cannot determine dimension");(!ut||(ut==null?void 0:ut.width)!==s||(ut==null?void 0:ut.height)!==i)&&(ut=Hr(s,i));let o=ut.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?o.putImageData(e,0,0):t.filter.flip&&typeof o.translate!="undefined"?(o.translate(n,0),o.scale(-1,1),o.drawImage(e,0,0,n,a,0,0,ut==null?void 0:ut.width,ut==null?void 0:ut.height),o.setTransform(1,0,0,1,0,0)):o.drawImage(e,0,0,n,a,0,0,ut==null?void 0:ut.width,ut==null?void 0:ut.height),(!or||ut.width!==or.width||(ut==null?void 0:ut.height)!==(or==null?void 0:or.height))&&(or=Hr(ut.width,ut.height)),t.filter.enabled&&ce.webgl.supported){if(Tt||(Tt=ce.browser?new BC:null),ce.filter=!!Tt,!Tt||!Tt.add)return t.debug&&ie("input process error: cannot initialize filters"),{tensor:null,canvas:ut};Tt.reset(),t.filter.brightness!==0&&Tt.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Tt.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Tt.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Tt.add("blur",t.filter.blur),t.filter.saturation!==0&&Tt.add("saturation",t.filter.saturation),t.filter.hue!==0&&Tt.add("hue",t.filter.hue),t.filter.negative&&Tt.add("negative"),t.filter.sepia&&Tt.add("sepia"),t.filter.vintage&&Tt.add("brownie"),t.filter.sepia&&Tt.add("sepia"),t.filter.kodachrome&&Tt.add("kodachrome"),t.filter.technicolor&&Tt.add("technicolor"),t.filter.polaroid&&Tt.add("polaroid"),t.filter.pixelate!==0&&Tt.add("pixelate",t.filter.pixelate),Tt.get()>0?or=Tt.apply(ut):or=Tt.draw(ut)}else db(ut,or),Tt&&(Tt=null),ce.filter=!!Tt;if(!r)return{tensor:null,canvas:or};if(!or)throw new Error("canvas error: cannot create output");let l,u=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ce.browser&&$n)l=$n?$n.fromPixels(e):null;else{u=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=ct(p,[e.height,e.width,u],"int32")}else if((!kd||or.width!==kd.width||or.height!==kd.height)&&(kd=Hr(or.width,or.height)),$n&&ce.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=$n.fromPixels(or):(kd=db(or),l=$n.fromPixels(kd));else{let f=db(or).getContext("2d").getImageData(0,0,s,i);u=f.data.length/s/i;let m=new Uint8Array(f.data.buffer);l=ct(m,[s,i,u])}if(u===4){let p=vl(l,[0,0,0],[-1,-1,3]);re(l),l=p}if(!l)throw new Error("input error: cannot create tensor");let d=me(l,"float32"),h=t.filter.equalization?await f0(d):Ht(d,0);return re([l,d]),{tensor:h,canvas:t.filter.return?or:null}}}async function WC(e,t){let r=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return r;if(!ts.inputTensor)ts.inputTensor=Or(t);else if(ts.inputTensor.shape[1]!==t.shape[1]||ts.inputTensor.shape[2]!==t.shape[2])re(ts.inputTensor),ts.inputTensor=Or(t);else{let n={};n.diff=he(t,ts.inputTensor),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;re([ts.inputTensor,n.diff,n.squared,n.sum]),ts.inputTensor=Or(t),r=s<=(e.cacheSensitivity||0)}return r}async function VC(e,t,r){let n={};if(!t||!r||t.shape.length!==4||t.shape.length!==r.shape.length)return e.debug||ie("invalid input tensor or tensor shapes do not match:",t.shape,r.shape),0;if(t.shape[0]!==1||r.shape[0]!==1||t.shape[3]!==3||r.shape[3]!==3)return e.debug||ie("input tensors must be of shape [1, height, width, 3]:",t.shape,r.shape),0;n.input1=Or(t),n.input2=t.shape[1]!==r.shape[1]||t.shape[2]!==r.shape[2]?Ie.resizeBilinear(r,[t.shape[1],t.shape[2]]):Or(r),n.diff=he(n.input1,n.input2),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return re([n.input1,n.input2,n.diff,n.squared,n.sum]),s}var UC=class{constructor(){fe(this,"browser");fe(this,"node");fe(this,"worker");fe(this,"platform","");fe(this,"agent","");fe(this,"backends",[]);fe(this,"initial");fe(this,"filter");fe(this,"tfjs");fe(this,"offscreen");fe(this,"perfadd",!1);fe(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});fe(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});fe(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});fe(this,"cpu",{model:void 0,flags:[]});fe(this,"kernels",[]);fe(this,"Canvas");fe(this,"Image");fe(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:zh["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let r=t[0].match(/\(([^()]+)\)/g);this.platform=r&&r[0]?r[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(kr().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&pn()==="wasm"&&(this.wasm.simd=await J().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Hr(100,100),r=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof r!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(pn()==="webgl"||pn()==="humangl")){let n=ga().gpgpu!=="undefined"?await ga().getGPGPUContext().gl:null;n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.renderer=n.getParameter(n.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name)}catch(n){this.webgpu.supported=!1}try{this.kernels=Na(pn()).map(n=>n.kernelName.toLowerCase())}catch(n){}}async updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},ce=new UC;var rs={cacheModels:!1,verbose:!0,debug:!1,modelBasePath:""};async function Vye(e,t){return rs.debug&&ie("load model fetch:",e,t),fetch(e,t)}function GC(e){rs.cacheModels=e.cacheModels,rs.verbose=e.debug,rs.modelBasePath=e.modelBasePath}async function je(e){let t=o3(rs.modelBasePath,e||""),r=t.split("/"),n="indexeddb://"+r[r.length-1].replace(".json",""),a=await Ir.listModels(),s=rs.cacheModels&&Object.keys(a).includes(n),i=typeof fetch=="undefined"?{}:{fetchFunc:(l,u)=>Vye(l,u)},o=new t0(s?n:t,i);try{o.findIOHandler(),rs.debug&&ie("model load handler:",o.handler);let l=await o.handler.load();o.loadSync(l),rs.verbose&&ie("load model:",o.modelUrl)}catch(l){ie("error loading model:",t,l)}if(rs.cacheModels&&!s)try{let l=await o.save(n);ie("model saved:",n,l)}catch(l){ie("error saving model:",t,l)}return o}var pb="2.6.4";var Hn,hb=[],jye=["white","black","asian","indian","other"],Hye=[15,23,28,35.5,45.5,55.5,65],jC=0,HC=0,cb=Number.MAX_SAFE_INTEGER;async function qC(e){return ce.initial&&(Hn=null),Hn?e.debug&&ie("cached model:",Hn.modelUrl):Hn=await je(e.face.gear),Hn}async function fb(e,t,r,n){var i,o;if(!Hn)return{age:0,gender:"unknown",genderScore:0,race:[]};let a=cb<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>oe()-HC;return t.skipAllowed&&s&&a&&jC===n&&hb[r]?(cb++,hb[r]):(cb=0,new Promise(async l=>{var y,A;if(!(Hn!=null&&Hn.inputs[0].shape))return;let u={},d=[[0,.1,.9,.9]];u.resize=Ie.cropAndResize(e,d,[0],[Hn.inputs[0].shape[2],Hn.inputs[0].shape[1]]);let h={age:0,gender:"unknown",genderScore:0,race:[]};(y=t.face.gear)!=null&&y.enabled&&([u.age,u.gender,u.race]=Hn.execute(u.resize,["age_output","gender_output","race_output"]));let p=await u.gender.data();h.gender=p[0]>p[1]?"male":"female",h.genderScore=Math.round(100*(p[0]>p[1]?p[0]:p[1]))/100;let c=await u.race.data();for(let x=0;x<c.length;x++)c[x]>(((A=t.face.gear)==null?void 0:A.minConfidence)||.2)&&h.race.push({score:Math.round(100*c[x])/100,race:jye[x]});h.race.sort((x,b)=>b.score-x.score);let m=Array.from(await u.age.data()).map((x,b)=>[Hye[b],x]).sort((x,b)=>b[1]-x[1]),g=m[0][0];for(let x=1;x<m.length;x++)g+=m[x][1]*(m[x][0]-g);h.age=Math.round(10*g)/10,Object.keys(u).forEach(x=>re(u[x])),hb[r]=h,jC=n,HC=oe(),l(h)}))}var Qe={tf255:255,tf1:1,tf2:2,tf05:.5,tf127:127.5,rgb:[.2989,.587,.114]};function XC(){Qe.tf255=Se(255,"float32"),Qe.tf1=Se(1,"float32"),Qe.tf2=Se(2,"float32"),Qe.tf05=Se(.5,"float32"),Qe.tf127=Se(127.5,"float32"),Qe.rgb=St([.2989,.587,.114],"float32")}var cn,g0=[],ZC=0,YC=0,mb=Number.MAX_SAFE_INTEGER;async function JC(e){return ce.initial&&(cn=null),cn?e.debug&&ie("cached model:",cn.modelUrl):cn=await je(e.face.ssrnet.modelPathAge),cn}async function gb(e,t,r,n){var i,o,l,u;if(!cn)return{age:0};let a=mb<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-YC;return t.skipAllowed&&a&&s&&ZC===n&&((l=g0[r])==null?void 0:l.age)&&((u=g0[r])==null?void 0:u.age)>0?(mb++,g0[r]):(mb=0,new Promise(async d=>{if(!(cn!=null&&cn.inputs)||!cn.inputs[0]||!cn.inputs[0].shape)return;let h={};h.resize=Ie.resizeBilinear(e,[cn.inputs[0].shape[2],cn.inputs[0].shape[1]],!1),h.enhance=L(h.resize,Qe.tf255);let p={age:0};if(t.face.ssrnet.enabled&&(h.age=cn.execute(h.enhance)),h.age){let c=await h.age.data();p.age=Math.trunc(10*c[0])/10}Object.keys(h).forEach(c=>re(h[c])),g0[r]=p,ZC=n,YC=oe(),d(p)}))}var qn,y0=[],eT=0,tT=0,yb=Number.MAX_SAFE_INTEGER,Ab=[.2989,.587,.114];async function rT(e){return ce.initial&&(qn=null),qn?e.debug&&ie("cached model:",qn.modelUrl):qn=await je(e.face.ssrnet.modelPathGender),qn}async function xb(e,t,r,n){var i,o,l,u;if(!qn)return{gender:"unknown",genderScore:0};let a=yb<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-tT;return t.skipAllowed&&a&&s&&eT===n&&((l=y0[r])==null?void 0:l.gender)&&((u=y0[r])==null?void 0:u.genderScore)>0?(yb++,y0[r]):(yb=0,new Promise(async d=>{if(!(qn!=null&&qn.inputs[0].shape))return;let h={};h.resize=Ie.resizeBilinear(e,[qn.inputs[0].shape[2],qn.inputs[0].shape[1]],!1),h.enhance=K(()=>{let[f,m,g]=Kt(h.resize,3,3),y=L(f,Ab[0]),A=L(m,Ab[1]),x=L(g,Ab[2]),b=im([y,A,x]);return L(he(b,Qe.tf05),2)});let p={gender:"unknown",genderScore:0};t.face.ssrnet.enabled&&(h.gender=qn.execute(h.enhance));let c=await h.gender.data();p.gender=c[0]>c[1]?"female":"male",p.genderScore=c[0]>c[1]?Math.trunc(100*c[0])/100:Math.trunc(100*c[1])/100,Object.keys(h).forEach(f=>re(h[f])),y0[r]=p,eT=n,tT=oe(),d(p)}))}var Tr,A0=[],bb=Number.MAX_SAFE_INTEGER,aT=0,sT=0;async function iT(e){var t;return ce.initial&&(Tr=null),Tr?e.debug&&ie("cached model:",Tr.modelUrl):Tr=await je((t=e.face.antispoof)==null?void 0:t.modelPath),Tr}async function vb(e,t,r,n){var i,o;if(!Tr)return 0;let a=(((i=t.face.antispoof)==null?void 0:i.skipTime)||0)>oe()-sT,s=bb<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&aT===n&&A0[r]?(bb++,A0[r]):(bb=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Tr!=null&&Tr.inputs[0].shape?Tr.inputs[0].shape[2]:0,Tr!=null&&Tr.inputs[0].shape?Tr.inputs[0].shape[1]:0],!1),d=Tr==null?void 0:Tr.execute(u),h=(await d.data())[0];A0[r]=Math.round(100*h)/100,aT=n,sT=oe(),re([u,d]),l(A0[r])}))}var Kn={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]},wb={count:468,mouth:13,symmetryLine:[13,Kn.midwayBetweenEyes[0]]},Dh={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},kb=[{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]}],Lh=[[.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]],El=[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 Kye=[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],Xye=[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],Zye=[33,133,362,263,1,78,308],VAe=Kye.map(e=>Lh[e]),UAe=Xye.map(e=>Lh[e]),GAe=Zye.map(e=>Lh[e]);var Sd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],x0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Tb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Nb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],dT=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},Sb=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return re(i),o},b0=(e,t)=>{let r=x0(e),n=Sd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},v0=e=>{let t=x0(e),r=Sd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},pT=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Cb=[[1,0,0],[0,1,0],[0,0,1]],Yye=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Jye=(e,t)=>Yye(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var lT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Rl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},Qye=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},uT=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Rl(e[a],Qye(t,s)))}return r},hT=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=lT(t[0],t[1]),i=uT(s,a),o=lT(-t[0],-t[1]);return uT(i,o)},e2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Rl(t[0],r),-Rl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},t2e=(e,t)=>[Rl(e,t[0]),Rl(e,t[1])];function cT(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function fT(e,t,r,n,a){let s=Sd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?hT(r,[0,0]):Cb,u=o?i.map(c=>[...t2e(c,l),c[2]]):i,d=o?e2e(n):Cb,h=x0(t),p=[Rl(h,d[0]),Rl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function mT(e,t,r,n){let a=t.landmarks.length>=wb.count?wb.symmetryLine:Dh.symmetryLine,s=0,i=Cb,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=Jye(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=x0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=hT(-s,u),o=Sb(t,h,[n,n]),re(h)}else o=Sb(t,r,[n,n]);else o=Sb(t,r,[n,n]);return[s,i,o]}var r2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},gT=(e,t)=>{let r=r2e(e),n=Sd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var yT=6,n2e=1.2,_a,AT=null,Bi=0,Bh=null,w0=()=>Bi;async function xT(e){var t;return ce.initial&&(_a=null),_a?e.debug&&ie("cached model:",_a.modelUrl):_a=await je((t=e.face.detector)==null?void 0:t.modelPath),Bi=_a.inputs[0].shape?_a.inputs[0].shape[2]:0,Bh=Se(Bi,"int32"),AT=oa(cT(Bi)),_a}function a2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,AT),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Bh),t.centersNormalized=pe(t.centers,Bh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Bh),t.endNormalized=L(t.ends,Bh);let r=td([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function bT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Bi,Bi]),r.div=pe(r.resized,Qe.tf127),r.normalized=he(r.div,Qe.tf05);let n=_a==null?void 0:_a.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=a2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Sr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],yT-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[yT,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=dT(m,[(e.shape[2]||0)/Bi,(e.shape[1]||0)/Bi]),y=b0(g,t.face.scale||n2e),A=v0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var k0={};Qd(k0,{connected:()=>Mb,kpt:()=>Rb});var Rb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Mb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var wT=224,s2e,i2e=5,I0=[8,16,32,32,32];async function kT(){let e=[],t=0;for(;t<i2e;){let r=0,n=t;for(;n<I0.length&&I0[n]===I0[t];)r+=2,n++;let a=I0[t],s=Math.ceil(wT/a),i=Math.ceil(wT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}s2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function ns(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function IT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function S0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var TT={initial:!0},fn={detector:null,landmarks:null},Cd={detector:[224,224],landmarks:[256,256]},Fb=Number.MAX_SAFE_INTEGER,l2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},T0=null,Wh,Wi=[[0,0],[0,0],[0,0],[0,0]],ST=0,CT=e=>1-1/(1+Math.exp(e));async function NT(e){if(TT.initial&&(fn.detector=null),!fn.detector&&e.body.detector&&e.body.detector.modelPath){fn.detector=await je(e.body.detector.modelPath);let t=Object.values(fn.detector.modelSignature.inputs);Cd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&fn.detector&&ie("cached model:",fn.detector.modelUrl);return await kT(),fn.detector}async function ET(e){if(TT.initial&&(fn.landmarks=null),fn.landmarks)e.debug&&ie("cached model:",fn.landmarks.modelUrl);else{fn.landmarks=await je(e.body.modelPath);let t=Object.values(fn.landmarks.modelSignature.inputs);Cd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return fn.landmarks}async function u2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Wh&&(r.cropped=Ie.cropAndResize(e,[Wh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Wi=[[0,0],a,s,[0,0]],r.pad=Vn(r.cropped||e,Wi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function d2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Wi[2][0]+Wi[2][1])/t[0]-Wi[2][0]),Math.trunc(r.position[1]*(t[1]+Wi[1][0]+Wi[1][1])/t[1]-Wi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Wh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Wh[1],r.positionRaw[1]+Wh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function p2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function h2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=fn.landmarks)==null?void 0:f.execute(e,l2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=CT(s[l*m+3]),y=CT(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Cd.landmarks[0],s[l*m+1]/Cd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Rb[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;p2e(o);let u=d2e(o,r),d=u.map(m=>m.position),h=ns(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Mb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function $b(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-ST,a=Fb<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&T0!==null)Fb++;else{let s={};s.landmarks=await u2e(e,256),T0=await h2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),ST=oe(),Fb=0}return T0?[T0]:[]}var Td=[{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 as,Ml=0,Pb=[],MT=0,_b=Number.MAX_SAFE_INTEGER;async function FT(e){if(ce.initial&&(as=null),as)e.debug&&ie("cached model:",as.modelUrl);else{as=await je(e.object.modelPath);let t=Object.values(as.modelSignature.inputs);Ml=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return as}async function c2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Kt(n.squeeze,6,1);n.stack=sr([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Td[h].label,[c,f]=[s[0][u][0]/Ml,s[0][u][1]/Ml],m=[c,f,s[0][u][2]/Ml-c,s[0][u][3]/Ml-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function zb(e,t){let r=(t.object.skipTime||0)>oe()-MT,n=_b<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&Pb.length>0?(_b++,Pb):(_b=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ml,Ml]),o=t.object.enabled?as==null?void 0:as.execute(i,["tower_0/detections"]):null;MT=oe(),re(i);let l=await c2e(o,s,t);Pb=l,a(l)}))}var N0={};Qd(N0,{connected:()=>Db,kpt:()=>Ob});var Ob=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Db={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Nr,PT=0,qr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Lb=Number.MAX_SAFE_INTEGER;async function _T(e){return ce.initial&&(Nr=null),Nr?e.debug&&ie("cached model:",Nr.modelUrl):Nr=await je(e.body.modelPath),Nr}async function f2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=fr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Cn(a,0),l=ad(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function Bb(e,t){let r=(t.body.skipTime||0)>oe()-PT,n=Lb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(qr.keypoints).length>0?(Lb++,[qr]):(Lb=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Nr!=null&&Nr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Nr.inputs[0].shape[2],Nr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return he(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Nr==null?void 0:Nr.execute(s)),PT=oe(),re(s),i){qr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await f2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&qr.keypoints.push({score:Math.round(100*y)/100,part:Ob[f],positionRaw:[m/Nr.inputs[0].shape[2],g/Nr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Nr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Nr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}qr.score=qr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=qr.keypoints.map(p=>p.position[0]),l=qr.keypoints.map(p=>p.position[1]);qr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=qr.keypoints.map(p=>p.positionRaw[0]),d=qr.keypoints.map(p=>p.positionRaw[1]);qr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(Db)){let f=[];for(let m=0;m<c.length-1;m++){let g=qr.keypoints.find(A=>A.part===c[m]),y=qr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}qr.annotations[p]=f}a([qr])}))}var m2e=["angry","disgust","fear","happy","sad","surprise","neutral"],_n,E0=[],OT=0,DT=0,Wb=Number.MAX_SAFE_INTEGER;async function LT(e){var t;return ce.initial&&(_n=null),_n?e.debug&&ie("cached model:",_n.modelUrl):_n=await je((t=e.face.emotion)==null?void 0:t.modelPath),_n}async function Vb(e,t,r,n){var i,o;if(!_n)return[];let a=Wb<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-DT;return t.skipAllowed&&s&&a&&OT===n&&E0[r]&&E0[r].length>0?(Wb++,E0[r]):(Wb=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=_n!=null&&_n.inputs[0].shape?_n.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=_n==null?void 0:_n.execute(p.grayscaleMul),DT=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:m2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}E0[r]=u,OT=n,l(u)}))}var mn,Ub=[],WT=0,VT=0,UT=Number.MAX_SAFE_INTEGER;async function GT(e){return ce.initial&&(mn=null),mn?e.debug&&ie("cached model:",mn.modelUrl):mn=await je(e.face.mobilefacenet.modelPath),mn}async function Gb(e,t,r,n){var i,o;if(!mn)return[];let a=UT<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-VT;return t.skipAllowed&&s&&a&&WT===n&&Ub[r]?(UT++,Ub[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(mn==null?void 0:mn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[mn.inputs[0].shape[2],mn.inputs[0].shape[1]],!1),h.data=mn==null?void 0:mn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}Ub[r]=u,WT=n,VT=oe(),l(u)})}var ss,Vi=0,g2e=2.3,jb=Kn.leftEyeLower0,Hb=Kn.rightEyeLower0,Nd={leftBounds:[jb[0],jb[jb.length-1]],rightBounds:[Hb[0],Hb[Hb.length-1]]},Ed={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function XT(e){var t;return ce.initial&&(ss=null),ss?e.debug&&ie("cached model:",ss.modelUrl):ss=await je((t=e.face.iris)==null?void 0:t.modelPath),Vi=ss.inputs[0].shape?ss.inputs[0].shape[2]:0,Vi===-1&&(Vi=64),ss}function R0(e,t,r,n){for(let a=0;a<kb.length;a++){let{key:s,indices:i}=kb[a],o=Kn[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var y2e=e=>{let t=e[Nd.leftBounds[0]][2],r=e[Nd.rightBounds[0]][2];return t-r},HT=(e,t,r,n,a,s=!1)=>{let i=v0(b0(pT([e[r],e[n]]),g2e)),o=Sd(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Vi,Vi]);if(s&&ce.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},qT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Ed.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Vi:i/Vi)*r[0]+t.startPoint[0],o/Vi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Ed.index)}},KT=(e,t,r)=>{let n=e[Kn[`${r}EyeUpper0`][Ed.upperCenter]][2],a=e[Kn[`${r}EyeLower0`][Ed.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function ZT(e,t,r,n){if(!ss)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=HT(e,t,Nd.leftBounds[0],Nd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=HT(e,t,Nd.rightBounds[0],Nd.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=ss.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Ed.numCoordinates*3),{rawCoords:f,iris:m}=qT(c,a,s,!0),g=p.slice(Ed.numCoordinates*3),{rawCoords:y,iris:A}=qT(g,o,l),x=y2e(e);Math.abs(x)<30?(R0(e,f,"left",null),R0(e,y,"right",null)):x<1?R0(e,f,"left",["EyeUpper0","EyeLower0"]):R0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=KT(e,m,"left"),v=KT(e,A,"right");return e.concat(b).concat(v)}var za={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},is=null,Rd=0;async function JT(e,t){var o,l,u,d,h,p,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-za.timestamp,n=za.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||za.boxes.length===0?(za.boxes=await bT(e,t),za.timestamp=oe(),za.skipped=0):za.skipped++;let a=[],s=[],i=0;for(let g=0;g<za.boxes.length;g++){let y=za.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=mT((u=t.face.detector)==null?void 0:u.rotation,y,e,(d=t.face.mesh)!=null&&d.enabled?Rd:w0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let v=await f0(b.tensor);re(b.tensor),b.tensor=v}if(b.boxScore=Math.round(100*y.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!is)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let[v,C,S]=is.execute(b.tensor),E=await C.data();b.faceScore=Math.round(100*E[0])/100;let R=G(S,[-1,3]),_=await R.array();if(re([S,R,C,v]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{(f=t.face.iris)!=null&&f.enabled&&(_=await ZT(_,b.tensor,t,Rd)),b.mesh=fT(_,y,A,x,Rd),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Rd]);for(let I of Object.keys(Kn))b.annotations[I]=Kn[I].map(O=>b.mesh[O]);b.score=b.faceScore;let M={...gT(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=Tb(M,e),b.boxRaw=Nb(M,e),s.push(M)}}else{b.box=Tb(y,e),b.boxRaw=Nb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(v=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*v[0]/w0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*v[1]/w0()]),b.meshRaw=b.mesh.map(v=>[v[0]/(e.shape[2]||0),v[1]/(e.shape[1]||0),(v[2]||0)/Rd]);for(let v of Object.keys(Dh))b.annotations[v]=[b.mesh[Dh[v]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?a.push(b):re(b.tensor)}return za.boxes=s,a}async function QT(e){var t;return ce.initial&&(is=null),is?e.debug&&ie("cached model:",is.modelUrl):is=await je((t=e.face.mesh)==null?void 0:t.modelPath),Rd=is.inputs[0].shape?is.inputs[0].shape[2]:0,is}var e9=El,t9=Lh;var gn,M0=[],r9=0,n9=0,Kb=Number.MAX_SAFE_INTEGER;async function a9(e){var t;return ce.initial&&(gn=null),gn?e.debug&&ie("cached model:",gn.modelUrl):gn=await je((t=e.face.description)==null?void 0:t.modelPath),gn}function Xb(e){let t=e.image||e.tensor||e;if(!(gn!=null&&gn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[gn.inputs[0].shape[2],gn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function Zb(e,t,r,n){var i,o,l,u;if(!gn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=Kb<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-r9;return t.skipAllowed&&a&&s&&n9===n&&((l=M0[r])==null?void 0:l.age)&&((u=M0[r])==null?void 0:u.age)>0?(Kb++,M0[r]):(Kb=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=Xb(e),m=gn==null?void 0:gn.execute(f);r9=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Cn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let C=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(C[b-1]>C[b+1]?10*b-100*C[b-1]:10*b+100*C[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}M0[r]=h,n9=n,d(h)}))}function F0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Vh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function o9(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function l9(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function $0(e,t=1.5){let r=Vh(e),n=F0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function P0(e){let t=Vh(e),r=F0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function A2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function u9(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return A2e(r)}var s9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ui(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function x2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function i9(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ui(e[a],x2e(t,s)))}return r}function Jb(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=s9(t[0],t[1]),i=i9(s,a),o=s9(-t[0],-t[1]);return i9(i,o)}function d9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ui(t[0],r),-Ui(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function Qb(e,t){return[Ui(e,t[0]),Ui(e,t[1])]}var h9=[{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 e5=class{constructor(t){fe(this,"model");fe(this,"anchors");fe(this,"anchorsTensor");fe(this,"inputSize");fe(this,"inputSizeTensor");fe(this,"doubleInputSizeTensor");this.model=t,this.anchors=h9.map(r=>[r.x,r.y]),this.anchorsTensor=oa(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=St([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=St([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let r={};r.boxOffsets=Pe(t,[0,0],[-1,2]),r.boxSizes=Pe(t,[0,2],[-1,2]),r.div=pe(r.boxOffsets,this.inputSizeTensor),r.boxCenterPoints=le(r.div,this.anchorsTensor),r.halfBoxSizes=pe(r.boxSizes,this.doubleInputSizeTensor),r.sub=he(r.boxCenterPoints,r.halfBoxSizes),r.startPoints=L(r.sub,this.inputSizeTensor),r.add=le(r.boxCenterPoints,r.halfBoxSizes),r.endPoints=L(r.add,this.inputSizeTensor);let n=td([r.startPoints,r.endPoints],1);return Object.keys(r).forEach(a=>re(r[a])),n}normalizeLandmarks(t,r){let n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=he(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Sr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=l9(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var w2e=5,c9=1.65,f9=[0,5,9,13,17,1,2],k2e=0,I2e=2,m9=0,t5=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>Qb([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return $0(P0(a),w2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=$0(P0(r),c9);n.palmLandmarks=[];for(let a=0;a<f9.length;a++)n.palmLandmarks.push(t[f9[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=F0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Jb(n,[0,0]),u=o.map(c=>[...Qb(c,l),c[2]]),d=d9(a),h=[...Vh(r),1],p=[Ui(h,d[0]),Ui(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-m9,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?u9(u.palmLandmarks[k2e],u.palmLandmarks[I2e]):0,h=Vh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=Jb(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=o9(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);m9=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),C=await v.array();re(x),re(v);let S=this.transformRawCoords(C,m,d,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=$0(P0(u),c9),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Kr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Kr.nameMapping[e],getPoints:e=>Kr.pointsMapping[e]},Gi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Gi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Fl=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:Aa,index:os,middle:ls,ring:$l,pinky:Pl}=Kr,{none:xa,half:C2e,full:ba}=Gi,{verticalUp:Md,verticalDown:$xe,horizontalLeft:r5,horizontalRight:T2e,diagonalUpRight:N2e,diagonalUpLeft:Fd,diagonalDownRight:Pxe,diagonalDownLeft:_xe}=Dt,ji=new Fl("thumbs up");ji.curl(Aa,xa,1);ji.direction(Aa,Md,1);ji.direction(Aa,Fd,.25);ji.direction(Aa,N2e,.25);for(let e of[Kr.index,Kr.middle,Kr.ring,Kr.pinky])ji.curl(e,ba,1),ji.direction(e,r5,1),ji.direction(e,T2e,1);var Yt=new Fl("victory");Yt.curl(Aa,C2e,.5);Yt.curl(Aa,xa,.5);Yt.direction(Aa,Md,1);Yt.direction(Aa,Fd,1);Yt.curl(os,xa,1);Yt.direction(os,Md,.75);Yt.direction(os,Fd,1);Yt.curl(ls,xa,1);Yt.direction(ls,Md,1);Yt.direction(ls,Fd,.75);Yt.curl($l,ba,1);Yt.direction($l,Md,.2);Yt.direction($l,Fd,1);Yt.direction($l,r5,.2);Yt.curl(Pl,ba,1);Yt.direction(Pl,Md,.2);Yt.direction(Pl,Fd,1);Yt.direction(Pl,r5,.2);Yt.weight(os,2);Yt.weight(ls,2);var Hi=new Fl("point");Hi.curl(Aa,ba,1);Hi.curl(os,xa,.5);Hi.curl(ls,ba,.5);Hi.curl($l,ba,.5);Hi.curl(Pl,ba,.5);Hi.weight(os,2);Hi.weight(ls,2);var qi=new Fl("middle finger");qi.curl(Aa,xa,1);qi.curl(os,ba,.5);qi.curl(ls,ba,.5);qi.curl($l,ba,.5);qi.curl(Pl,ba,.5);qi.weight(os,2);qi.weight(ls,2);var $d=new Fl("open palm");$d.curl(Aa,xa,.75);$d.curl(os,xa,.75);$d.curl(ls,xa,.75);$d.curl($l,xa,.75);$d.curl(Pl,xa,.75);var g9=[ji,Yt,Hi,qi,$d];var E2e=.7,_l={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function y9(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function x9(e,t){if(!e||!t)return[0,0];let r=y9(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=y9(e[1],e[2],t[1],t[2]);return[r,n]}function A9(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function R2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>_l.NO_CURL_START_LIMIT?y=Gi.none:g>_l.HALF_CURL_START_LIMIT?y=Gi.half:y=Gi.full,y}function b9(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function v9(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function M2e(e,t,r,n,a,s,i,o){let l,u=v9(e,t,r,n),d=b9(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function F2e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=_l.DISTANCE_VOTE_POWER:m>.66?c+=_l.DISTANCE_VOTE_POWER:f+=_l.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],C=r[0],S=r[1];x===g?(C=r[0],S=r[1]):x===A&&(b=t[0],v=t[1]);let _=x9([b,v],[C,S]),M=A9(_,_l.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let O of n){let z=A9(O,_l.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],f+=z[2]}let I;return p===Math.max(p,c,f)?I=v9(l,o,u,h):f===Math.max(c,f)?I=b9(s,a,i,d):I=M2e(l,o,u,h,s,a,i,d),I}function w9(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Kr.all){let i=Kr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=x9(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Kr.all){let i=s===Kr.thumb?1:0,o=Kr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=R2e(l,u,d),p=F2e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function _0(e){if(!e||e.length===0)return null;let t=w9(e),r={};for(let n of Kr.all)r[Kr.getName(n)]={curl:Gi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function k9(e){let t=[];if(!e||e.length===0)return t;let r=w9(e);for(let n of g9){let a=n.matchAgainst(r.curls,r.directions);a>=E2e&&t.push({name:n.name,confidence:a})}return t}var I9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Pd,_d,S9;async function a5(e,t){let r=await S9.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(I9))s[d]=I9[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[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=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=_0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function s5(e){var r,n;ce.initial&&(Pd=null,_d=null),!Pd||!_d?[Pd,_d]=await Promise.all([e.hand.enabled?je((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?je((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",Pd.modelUrl),e.debug&&ie("cached model:",_d.modelUrl));let t=new e5(Pd);return S9=new t5(t,_d),[Pd,_d]}var lr=[null,null],$2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ki=[[0,0],[0,0]],P2e=["hand","fist","pinch","point","face","tip","pinchtip"],T9=4,N9=1.6,_2e=512,z2e=1.4,z0=Number.MAX_SAFE_INTEGER,i5=0,us=[0,0],Gt={boxes:[],hands:[]},E9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function R9(e){var t;if(ce.initial&&(lr[0]=null),lr[0])e.debug&&ie("cached model:",lr[0].modelUrl);else{O0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),lr[0]=await je((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(lr[0].modelSignature.inputs);Ki[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[0]}async function M9(e){var t;if(ce.initial&&(lr[1]=null),lr[1])e.debug&&ie("cached model:",lr[1].modelUrl);else{lr[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(lr[1].modelSignature.inputs);Ki[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[1]}async function O2e(e,t){let r=[];if(!e||!lr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,_2e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await lr[0].executeAsync(n.cast,$2e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=en(n.scores,1);re(o[T9]),o.splice(T9,1),n.filtered=sr(o,1),re(o),n.max=fr(n.filtered,1),n.argmax=Cn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=S0(m,z2e),y=[Math.trunc(m[0]*us[0]),Math.trunc(m[1]*us[1]),Math.trunc(m[2]*us[0]),Math.trunc(m[3]*us[1])],A=d[p],x=P2e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function o5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&lr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[Ki[1][0],Ki[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=lr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/Ki[1][1],h[1]/Ki[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[us[0]*(h[0]+t.boxRaw[0]),us[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=_0(n.keypoints);for(let h of Object.keys(E9))n.annotations[h]=E9[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function l5(e,t){var a,s;if(!lr[0]||!lr[1]||!((a=lr[0])!=null&&a.inputs[0].shape)||!((s=lr[1])!=null&&s.inputs[0].shape))return[];us=[e.shape[2]||0,e.shape[1]||0],z0++;let r=(t.hand.skipTime||0)>oe()-i5,n=z0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Gt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-i5,l=z0<3*(t.hand.skipFrames||0);t.skipAllowed&&Gt.hands.length===t.hand.maxDetected?Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))):t.skipAllowed&&o&&l&&Gt.hands.length>0?Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))):(Gt.boxes=await O2e(e,t),i5=oe(),Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))),z0=0);let u=[...Gt.boxes];if(Gt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Gt.hands.length;d++){let h=IT(Gt.hands[d].keypoints,us);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Gt.hands[d].fingerScore&&Gt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=S0(h.box,N9),c=S0(h.boxRaw,N9);Gt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Gt.hands.length;d++){let h=ns(Gt.hands[d].keypoints,us);Gt.hands[d].box=h.box,Gt.hands[d].boxRaw=h.boxRaw}i(Gt.hands)})}var Er,D0=[],u5=Number.MAX_SAFE_INTEGER,$9=0,P9=0;async function _9(e){var t;return ce.initial&&(Er=null),Er?e.debug&&ie("cached model:",Er.modelUrl):Er=await je((t=e.face.liveness)==null?void 0:t.modelPath),Er}async function d5(e,t,r,n){var i,o;if(!Er)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-P9,s=u5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&$9===n&&D0[r]?(u5++,D0[r]):(u5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Er!=null&&Er.inputs[0].shape?Er.inputs[0].shape[2]:0,Er!=null&&Er.inputs[0].shape?Er.inputs[0].shape[1]:0],!1),d=Er==null?void 0:Er.execute(u),h=(await d.data())[0];D0[r]=Math.round(100*h)/100,$9=n,P9=oe(),re([u,d]),l(D0[r])}))}var Uh={};Qd(Uh,{connected:()=>B0,horizontal:()=>p5,kpt:()=>L0,relative:()=>c5,vertical:()=>h5});var L0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],p5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],h5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],c5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],B0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var O9=.005,yn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function f5(e){for(let t of p5){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of h5){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of c5){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function D9(e){for(let t=0;t<e.length;t++)if(e[t]&&yn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-yn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-yn.keypoints[t].positionRaw[1])];r[0]<O9&&r[1]<O9?e[t]=yn.keypoints[t]:yn.keypoints[t]=e[t]}else yn.keypoints[t]=e[t];return e}function L9(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;yn.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Vn(e,yn.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function B9(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+yn.padding[2][0]+yn.padding[2][1])/t[0]-yn.padding[2][0],n.position[1]*(t[1]+yn.padding[1][0]+yn.padding[1][1])/t[1]-yn.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=ns(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var An,W0=0,m5=Number.MAX_SAFE_INTEGER,zl={boxes:[],bodies:[],last:0};async function W9(e){return ce.initial&&(An=null),An?e.debug&&ie("cached model:",An.modelUrl):(O0(["size"],e),An=await je(e.body.modelPath)),W0=An.inputs[0].shape?An.inputs[0].shape[2]:0,W0<64&&(W0=256),An}async function L2e(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:L0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ns(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(B0)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return f5(u),i.push(u),i}async function B2e(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:L0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ns(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(B0)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};f5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function g5(e,t){if(!An||!(An!=null&&An.inputs[0].shape))return[];t.skipAllowed||(zl.boxes.length=0),m5++;let r=(t.body.skipTime||0)>oe()-zl.last,n=m5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?zl.bodies:new Promise(async a=>{let s={};m5=0,s.input=L9(e,W0),s.res=An==null?void 0:An.execute(s.input),zl.last=oe();let i=await s.res.array();zl.bodies=s.res.shape[2]===17?await L2e(i,t,e):await B2e(i,t,e);for(let o of zl.bodies)B9(o,[e.shape[2]||1,e.shape[1]||1]),D9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(zl.bodies)})}var zd,V0=[],U9=0,y5=Number.MAX_SAFE_INTEGER,G0=0,U0=2.5;async function G9(e){if(!zd||ce.initial){zd=await je(e.object.modelPath);let t=Object.values(zd.modelSignature.inputs);G0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",zd.modelUrl);return zd}async function W2e(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Td.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Td.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/G0)),[v,C]=[A-U0/l*b[0],x-U0/l*b[1]],[S,E]=[A+U0/l*b[2]-v,x+U0/l*b[3]-C],R=[v,C,S,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Td[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function A5(e,t){let r=(t.object.skipTime||0)>oe()-U9,n=y5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&V0.length>0?(y5++,V0):(y5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?V0:new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[G0,G0],!1),o=pe(i,Qe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let u;t.object.enabled&&(u=zd.execute(l)),U9=oe(),re(l);let d=await W2e(u,s,t);V0=d,a(d)}))}var jh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],V2e=jh.length,Gh=jh.reduce((e,t,r)=>(e[t]=r,e),{}),U2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],sbe=U2e.map(([e,t])=>[Gh[e],Gh[t]]),H9=[["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 q9(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,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 K9(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],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:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var x5=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=r}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 r=2*t;if(r<this.numberOfElements&&this.less(r,r+1)&&r++,!this.less(t,r))break;this.exchange(t,r),t=r}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,r){return this.getValueAt(t)<this.getValueAt(r)}exchange(t,r){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[r],this.priorityQueue[r]=n}};function b5(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+V2e)}}function v5(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=b5(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function w5(e,t,r){return e<t?t:e>r?r:e}function X9(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function k5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var va,j2e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],j0=1,Od=16,H2e=50**2;function Z9(e,t,r,n,a,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:w5(Math.round(y.y/Od),0,A-1),x:w5(Math.round(y.x/Od),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),f=k5(t.position,p);for(let y=0;y<i;y++){let A=l(f,u,d),x=b5(A.y,A.x,r,a);f=k5({x:A.x*Od,y:A.y*Od},{x:x.x,y:x.y})}let m=l(f,u,d),g=n.get(m.y,m.x,r);return{position:f,part:jh[r],score:g}}function q2e(e,t,r,n,a){let s=H9.map(([p,c])=>[Gh[p],Gh[c]]),i=s.map(([,p])=>p),o=s.map(([p])=>p),l=t.shape[2],u=i.length,d=new Array(l),h=v5(e.part,Od,r);d[e.part.id]={score:e.score,part:jh[e.part.id],position:h};for(let p=u-1;p>=0;--p){let c=i[p],f=o[p];d[c]&&!d[f]&&(d[f]=Z9(p,d[c],f,t,r,a))}for(let p=0;p<u;++p){let c=o[p],f=i[p];d[c]&&!d[f]&&(d[f]=Z9(p,d[c],f,t,r,n))}return d}function K2e(e,t,r,n,a){let[s,i]=a.shape,o=!0,l=Math.max(r-j0,0),u=Math.min(r+j0+1,s);for(let d=l;d<u;++d){let h=Math.max(n-j0,0),p=Math.min(n+j0+1,i);for(let c=h;c<p;++c)if(a.get(d,c,e)>t){o=!1;break}if(!o)break}return o}function X2e(e,t){let[r,n,a]=t.shape,s=new x5(r*n*a,({score:i})=>i);for(let i=0;i<r;++i)for(let o=0;o<n;++o)for(let l=0;l<a;++l){let u=t.get(i,o,l);u<e||K2e(l,u,i,o,t)&&s.enqueue({score:u,part:{heatmapY:i,heatmapX:o,id:l}})}return s}function Y9(e,{x:t,y:r},n){return e.some(({keypoints:a})=>{var i;let s=(i=a[n])==null?void 0:i.position;return s?X9(r,t,s.y,s.x)<=H2e:!1})}function Z2e(e,t){return t.reduce((n,{position:a,score:s},i)=>(Y9(e,a,i)||(n+=s),n),0)/t.length}function Y2e(e,t,r,n,a,s){let i=[],o=X2e(s,t);for(;i.length<a&&!o.empty();){let l=o.dequeue(),u=v5(l.part,Od,e);if(Y9(i,u,l.part.id))continue;let d=q2e(l,t,e,r,n);d=d.filter(c=>c.score>s);let h=Z2e(i,d),p=q9(d);h>s&&i.push({keypoints:d,box:p,score:Math.round(100*h)/100})}return i}async function I5(e,t){let r=K(()=>{if(!va.inputs[0].shape)return[];let i=Ie.resizeBilinear(e,[va.inputs[0].shape[2],va.inputs[0].shape[1]]),o=he(pe(me(i,"float32"),127.5),1),u=va.execute(o,j2e).map(d=>et(d,[0]));return u[1]=Sr(u[1]),u}),n=await Promise.all(r.map(i=>i.buffer()));for(let i of r)re(i);let a=await Y2e(n[0],n[1],n[2],n[3],t.body.maxDetected,t.body.minConfidence);return va.inputs[0].shape?K9(a,[e.shape[1],e.shape[2]],[va.inputs[0].shape[2],va.inputs[0].shape[1]]):[]}async function J9(e){return!va||ce.initial?va=await je(e.body.modelPath):e.debug&&ie("cached model:",va.modelUrl),va}var Oa,S5=!1;async function C5(e){return!Oa||ce.initial?Oa=await je(e.segmentation.modelPath):e.debug&&ie("cached model:",Oa.modelUrl),Oa}async function eN(e,t,r){var m,g;if(S5)return{data:[],canvas:null,alpha:null};S5=!0,Oa||await C5(r);let n=await Id(e,r),a=((m=n.tensor)==null?void 0:m.shape[2])||0,s=((g=n.tensor)==null?void 0:g.shape[1])||0;if(!n.tensor)return{data:[],canvas:null,alpha:null};let i={};i.resize=Ie.resizeBilinear(n.tensor,[Oa.inputs[0].shape?Oa.inputs[0].shape[1]:0,Oa.inputs[0].shape?Oa.inputs[0].shape[2]:0],!1),re(n.tensor),i.norm=pe(i.resize,Qe.tf255),i.res=Oa.execute(i.norm),i.squeeze=et(i.res,0),i.squeeze.shape[2]===2?(i.softmax=id(i.squeeze),[i.bg,i.fg]=en(i.softmax,2),i.expand=Ht(i.fg,2),i.pad=Ht(i.expand,0),i.crop=Ie.cropAndResize(i.pad,[[0,0,.5,.5]],[0],[a,s]),i.data=et(i.crop,0)):i.data=Ie.resizeBilinear(i.squeeze,[s,a]);let o=Array.from(await i.data.data());if(ce.node&&!ce.Canvas&&typeof ImageData=="undefined")return r.debug&&ie("canvas support missing"),Object.keys(i).forEach(y=>re(i[y])),{data:o,canvas:null,alpha:null};let l=Hr(a,s);$n&&await $n.toPixels(i.data,l);let u=l.getContext("2d");r.segmentation.blur&&r.segmentation.blur>0&&(u.filter=`blur(${r.segmentation.blur}px)`);let d=u.getImageData(0,0,a,s),h=Hr(a,s),p=h.getContext("2d");n.canvas&&p.drawImage(n.canvas,0,0),p.globalCompositeOperation="darken",r.segmentation.blur&&r.segmentation.blur>0&&(p.filter=`blur(${r.segmentation.blur}px)`),p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none";let c=p.getImageData(0,0,a,s);for(let y=0;y<a*s;y++)c.data[4*y+3]=d.data[4*y+0];p.putImageData(c,0,0);let f=null;if(t&&h){f=Hr(a,s);let y=await Id(t,r);re(y.tensor);let A=f.getContext("2d");A.drawImage(y.canvas,0,0,f.width,f.height),A.drawImage(h,0,0)}return Object.keys(i).forEach(y=>re(i[y])),S5=!1,{data:o,canvas:h,alpha:l}}var T5=class{constructor(){fe(this,"ssrnetage",null);fe(this,"gear",null);fe(this,"blazeposedetect",null);fe(this,"blazepose",null);fe(this,"centernet",null);fe(this,"efficientpose",null);fe(this,"mobilefacenet",null);fe(this,"emotion",null);fe(this,"facedetect",null);fe(this,"faceiris",null);fe(this,"facemesh",null);fe(this,"faceres",null);fe(this,"ssrnetgender",null);fe(this,"handpose",null);fe(this,"handskeleton",null);fe(this,"handtrack",null);fe(this,"liveness",null);fe(this,"movenet",null);fe(this,"nanodet",null);fe(this,"posenet",null);fe(this,"segmentation",null);fe(this,"antispoof",null)}};function N5(e){for(let t of Object.keys(e.models))e.models[t]=null}async function rN(e){var t,r,n,a,s,i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,C,S,E,R,_,M,I,O,z;ce.initial&&N5(e),e.config.hand.enabled&&(!e.models.handpose&&((r=(t=e.config.hand.detector)==null?void 0:t.modelPath)==null?void 0:r.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await s5(e.config)),!e.models.handskeleton&&e.config.hand.landmarks&&((a=(n=e.config.hand.detector)==null?void 0:n.modelPath)==null?void 0:a.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await s5(e.config))),e.config.body.enabled&&!e.models.blazepose&&((i=(s=e.config.body)==null?void 0:s.modelPath)==null?void 0:i.includes("blazepose"))&&(e.models.blazepose=ET(e.config)),e.config.body.enabled&&!e.models.blazeposedetect&&e.config.body.detector&&e.config.body.detector.modelPath&&(e.models.blazeposedetect=NT(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((l=(o=e.config.body)==null?void 0:o.modelPath)==null?void 0:l.includes("efficientpose"))&&(e.models.efficientpose=_T(e.config)),e.config.body.enabled&&!e.models.movenet&&((d=(u=e.config.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(e.models.movenet=W9(e.config)),e.config.body.enabled&&!e.models.posenet&&((p=(h=e.config.body)==null?void 0:h.modelPath)==null?void 0:p.includes("posenet"))&&(e.models.posenet=J9(e.config)),e.config.face.enabled&&!e.models.facedetect&&(e.models.facedetect=xT(e.config)),e.config.face.enabled&&((c=e.config.face.antispoof)==null?void 0:c.enabled)&&!e.models.antispoof&&(e.models.antispoof=iT(e.config)),e.config.face.enabled&&((f=e.config.face.liveness)==null?void 0:f.enabled)&&!e.models.liveness&&(e.models.liveness=_9(e.config)),e.config.face.enabled&&((m=e.config.face.description)==null?void 0:m.enabled)&&!e.models.faceres&&(e.models.faceres=a9(e.config)),e.config.face.enabled&&((g=e.config.face.emotion)==null?void 0:g.enabled)&&!e.models.emotion&&(e.models.emotion=LT(e.config)),e.config.face.enabled&&((y=e.config.face.iris)==null?void 0:y.enabled)&&!e.models.faceiris&&(e.models.faceiris=XT(e.config)),e.config.face.enabled&&((A=e.config.face.mesh)==null?void 0:A.enabled)&&!e.models.facemesh&&(e.models.facemesh=QT(e.config)),e.config.face.enabled&&((x=e.config.face.gear)==null?void 0:x.enabled)&&!e.models.gear&&(e.models.gear=qC(e.config)),e.config.face.enabled&&((b=e.config.face.ssrnet)==null?void 0:b.enabled)&&!e.models.ssrnetage&&(e.models.ssrnetage=JC(e.config)),e.config.face.enabled&&((v=e.config.face.ssrnet)==null?void 0:v.enabled)&&!e.models.ssrnetgender&&(e.models.ssrnetgender=rT(e.config)),e.config.face.enabled&&((C=e.config.face.mobilefacenet)==null?void 0:C.enabled)&&!e.models.mobilefacenet&&(e.models.mobilefacenet=GT(e.config)),e.config.hand.enabled&&!e.models.handtrack&&((E=(S=e.config.hand.detector)==null?void 0:S.modelPath)==null?void 0:E.includes("handtrack"))&&(e.models.handtrack=R9(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((_=(R=e.config.hand.detector)==null?void 0:R.modelPath)==null?void 0:_.includes("handtrack"))&&(e.models.handskeleton=M9(e.config)),e.config.object.enabled&&!e.models.centernet&&((I=(M=e.config.object)==null?void 0:M.modelPath)==null?void 0:I.includes("centernet"))&&(e.models.centernet=FT(e.config)),e.config.object.enabled&&!e.models.nanodet&&((z=(O=e.config.object)==null?void 0:O.modelPath)==null?void 0:z.includes("nanodet"))&&(e.models.nanodet=G9(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=C5(e.config));for await(let j of Object.keys(e.models))e.models[j]&&typeof e.models[j]!="undefined"&&(e.models[j]=await e.models[j])}async function nN(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let r of Object.keys(e.models)){let n=e.models[r];if(!n)continue;let a=[],s=n==null?void 0:n.executor;if(s&&s.graph.nodes)for(let o of Object.values(s.graph.nodes)){let l=o.op.toLowerCase();a.includes(l)||a.push(l)}else!s&&e.config.debug&&ie("model signature not determined:",r);let i=[];for(let o of a)!t.includes(o)&&!e.env.kernels.includes(o)&&!e.env.kernels.includes(o.replace("_",""))&&!e.env.kernels.includes(o.replace("native",""))&&!e.env.kernels.includes(o.replace("v2",""))&&i.push(o);e.config.debug&&i.length>0&&ie("model validation failed:",r,i)}}var Nt={name:"humangl",priority:999,canvas:null,gl:null,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function J2e(){let e=Nt.gl;!e||(Nt.extensions=e.getSupportedExtensions())}async function sN(e){var t;if(e.config.backend==="humangl"&&(Nt.name in kr().registry&&(!Nt.gl||!Nt.gl.getParameter(Nt.gl.VERSION))&&(ie("error: humangl backend invalid context"),N5(e)),!c2(Nt.name))){try{Nt.canvas=await Hr(100,100)}catch(n){ie("error: cannot create canvas:",n);return}try{if(Nt.gl=(t=Nt.canvas)==null?void 0:t.getContext("webgl2",Nt.webGLattr),!Nt.gl.getParameter(Nt.gl.VERSION).includes("2.0")){ie("override: using fallback webgl backend as webgl 2.0 is not detected"),e.config.backend="webgl";return}Nt.canvas&&(Nt.canvas.addEventListener("webglcontextlost",async a=>{throw ie("error: humangl:",a.type),ie("possible browser memory leak using webgl or conflict with multiple backend registrations"),e.emit("error"),new Error("backend error: webgl context lost")}),Nt.canvas.addEventListener("webglcontextrestored",a=>{ie("error: humangl context restored:",a)}),Nt.canvas.addEventListener("webglcontextcreationerror",a=>{ie("error: humangl context create:",a)}))}catch(n){ie("error: cannot get WebGL context:",n);return}try{a0(2,Nt.gl)}catch(n){ie("error: cannot set WebGL context:",n);return}try{let n=new du(Nt.gl);Al(Nt.name,()=>new Th(n),Nt.priority)}catch(n){ie("error: cannot register WebGL backend:",n);return}try{Na("webgl").forEach(a=>{let s={...a,backendName:Nt.name};Wn(s)})}catch(n){ie("error: cannot update WebGL backend registration:",n);return}let r=ga().getGPGPUContext?ga().getGPGPUContext().gl:null;if(r)ie(`humangl webgl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`);else{ie("error: no current gl context:",r,Nt.gl);return}try{ma.set("WEBGL_VERSION",2)}catch(n){ie("error: cannot set WebGL backend flags:",n);return}J2e(),ie("backend registered:",Nt.name)}}function Q2e(){if(!ce.kernels.includes("mod")){let e={kernelName:"Mod",backendName:pn(),kernelFunc:t=>K(()=>he(t.inputs.a,L(pe(t.inputs.a,t.inputs.b),t.inputs.b)))};Wn(e),ce.kernels.push("mod")}if(!ce.kernels.includes("floormod")){let e={kernelName:"FloorMod",backendName:pn(),kernelFunc:t=>K(()=>sh(t.inputs.a/t.inputs.b)*t.inputs.b+ad(t.inputs.a,t.inputs.b))};Wn(e),ce.kernels.push("floormod")}}async function H0(e,t=!1){if(e.state="backend",t||ce.initial||e.config.backend&&e.config.backend.length>0&&pn()!==e.config.backend){let r=oe();if(e.config.backend&&e.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&e.config.debug&&e.config.debug&&ie("running inside web worker"),ce.browser&&e.config.backend==="tensorflow"&&(e.config.debug&&ie("override: backend set to tensorflow while running in browser"),e.config.backend="humangl"),ce.node&&(e.config.backend==="webgl"||e.config.backend==="humangl")&&(e.config.debug&&ie(`override: backend set to ${e.config.backend} while running in nodejs`),e.config.backend="tensorflow"),ce.browser&&e.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")ie("override: backend set to webgpu but browser does not support webgpu"),e.config.backend="humangl";else{let a=await navigator.gpu.requestAdapter();e.config.debug&&ie("enumerated webgpu adapter:",a)}e.config.backend==="humangl"&&await sN(e);let n=Object.keys(kr().registryFactory);if(e.config.debug&&ie("available backends:",n),n.includes(e.config.backend)||(ie(`error: backend ${e.config.backend} not found in registry`),e.config.backend=ce.node?"tensorflow":"webgl",e.config.debug&&ie(`override: setting backend ${e.config.backend}`)),e.config.debug&&ie("setting backend:",e.config.backend),e.config.backend==="wasm"){if(e.config.debug&&ie("wasm path:",e.config.wasmPath),typeof(Ue==null?void 0:Ue.setWasmPaths)!="undefined")await lb(e.config.wasmPath,e.config.wasmPlatformFetch);else throw new Error("backend error: attempting to use wasm backend but wasm path is not set");let a=await J().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&ie(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),e.config.debug&&!a&&ie("warning: wasm simd support is not enabled")}try{await h2(e.config.backend),await ed(),XC()}catch(a){return ie("error: cannot set backend:",e.config.backend,a),!1}}if(pn()==="humangl"&&(ma.set("CHECK_COMPUTATION_FOR_ERRORS",!1),ma.set("WEBGL_CPU_FORWARD",!0),ma.set("WEBGL_USE_SHAPES_UNIFORMS",!0),ma.set("CPU_HANDOFF_SIZE_THRESHOLD",256),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(ie("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),ma.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),ga().getGPGPUContext)){let n=await ga().getGPGPUContext().gl;e.config.debug&&ie(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}pn(),d2(),await ed(),e.performance.initBackend=Math.trunc(oe()-r),e.config.backend=pn(),await ce.updateBackend(),Q2e()}return!0}function O0(e,t){for(let r of e){let n={kernelName:r,backendName:t.backend,kernelFunc:()=>{t.debug&&ie("kernelFunc",r,t.backend)}};Wn(n)}ce.kernels=Na(pn()).map(r=>r.kernelName.toLowerCase())}var ds={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1},E5=0,Ol=e=>{if(!e)ie("draw error: invalid canvas");else if(!e.getContext)ie("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)ie("draw error: cannot get canvas context");else return t}return null},Dd=e=>Math.round(e*180/Math.PI);function R5(e,t,r,n,a){n=n||0,e.fillStyle=a.useDepth&&n?`rgba(${127.5+2*n}, ${127.5-2*n}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,r,a.pointSize,0,2*Math.PI),e.fill()}function Hh(e,t,r,n,a,s){if(e.beginPath(),e.lineWidth=s.lineWidth,s.useCurves){let i=(t+t+n)/2,o=(r+r+a)/2;e.ellipse(i,o,n/2,a/2,0,0,2*Math.PI)}else e.moveTo(t+s.roundRect,r),e.lineTo(t+n-s.roundRect,r),e.quadraticCurveTo(t+n,r,t+n,r+s.roundRect),e.lineTo(t+n,r+a-s.roundRect),e.quadraticCurveTo(t+n,r+a,t+n-s.roundRect,r+a),e.lineTo(t+s.roundRect,r+a),e.quadraticCurveTo(t,r+a,t,r+a-s.roundRect),e.lineTo(t,r+s.roundRect),e.quadraticCurveTo(t,r,t+s.roundRect,r),e.closePath();e.stroke()}function lN(e,t,r){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t){let a=n[2]||0;e.strokeStyle=r.useDepth&&a!==0?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.fillStyle=r.useDepth&&a!==0?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.lineTo(n[0],Math.round(n[1]))}e.stroke(),r.fillPolygons&&(e.closePath(),e.fill())}}function tAe(e,t,r){if(!(t.length<2)){if(e.lineWidth=r.lineWidth,!r.useCurves||t.length<=2){lN(e,t,r);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let a=(t[n][0]+t[n+1][0])/2,s=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),r.fillPolygons&&(e.closePath(),e.fill())}}function oN(e,t,r,n=5){let a,s,i;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(r[0],r[1]),a=Math.atan2(r[1]-t[1],r[0]-t[0]),s=n*Math.cos(a)+r[0],i=n*Math.sin(a)+r[1],e.moveTo(s,i),a+=1/3*(2*Math.PI),s=n*Math.cos(a)+r[0],i=n*Math.sin(a)+r[1],e.lineTo(s,i),a+=1/3*(2*Math.PI),s=n*Math.cos(a)+r[0],i=n*Math.sin(a)+r[1],e.lineTo(s,i),e.closePath(),e.stroke(),e.fill()}async function M5(e,t,r){let n=vr(ds,r);if(!(!t||!e)&&n.drawGestures){let a=Ol(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}async function F5(e,t,r){var s,i,o,l,u;let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a)for(let d of t){if(a.font=n.font,a.strokeStyle=n.color,a.fillStyle=n.color,n.drawBoxes&&Hh(a,d.box[0],d.box[1],d.box[2],d.box[3],n),n.drawLabels){let h=[];if(h.push(`face: ${Math.trunc(100*d.score)}%`),d.genderScore&&h.push(`${d.gender||""} ${Math.trunc(100*d.genderScore)}%`),d.age&&h.push(`age: ${d.age||""}`),d.iris&&h.push(`distance: ${d.iris}`),d.real&&h.push(`real: ${Math.trunc(100*d.real)}%`),d.live&&h.push(`live: ${Math.trunc(100*d.live)}%`),d.emotion&&d.emotion.length>0){let p=d.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),h.push(p.join(" "))}d.rotation&&d.rotation.angle&&d.rotation.gaze&&(d.rotation.angle.roll&&h.push(`roll: ${Dd(d.rotation.angle.roll)}\xB0 yaw:${Dd(d.rotation.angle.yaw)}\xB0 pitch:${Dd(d.rotation.angle.pitch)}\xB0`),d.rotation.gaze.bearing&&h.push(`gaze: ${Dd(d.rotation.gaze.bearing)}\xB0`)),h.length===0&&h.push("face"),a.fillStyle=n.color;for(let p=h.length-1;p>=0;p--){let c=Math.max(d.box[0],0),f=p*n.lineHeight+d.box[1];n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(h[p],c+5,f+16)),a.fillStyle=n.labelColor,a.fillText(h[p],c+4,f+15)}}if(a.lineWidth=2,d.mesh&&d.mesh.length>0){if(n.drawPoints)for(let h of d.mesh)R5(a,h[0],h[1],h[2],n);if(n.drawPolygons){if(d.mesh.length>450)for(let h=0;h<El.length/3;h++){let p=[El[h*3+0],El[h*3+1],El[h*3+2]].map(c=>d.mesh[c]);lN(a,p,n)}if(d.annotations&&d.annotations.leftEyeIris&&d.annotations.leftEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.leftEyeIris[3][0]-d.annotations.leftEyeIris[1][0])/2,p=Math.abs(d.annotations.leftEyeIris[4][1]-d.annotations.leftEyeIris[2][1])/2;a.ellipse(d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(d.annotations&&d.annotations.rightEyeIris&&d.annotations.rightEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.rightEyeIris[3][0]-d.annotations.rightEyeIris[1][0])/2,p=Math.abs(d.annotations.rightEyeIris[4][1]-d.annotations.rightEyeIris[2][1])/2;a.ellipse(d.annotations.rightEyeIris[0][0],d.annotations.rightEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(n.drawGaze&&((s=d.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){a.strokeStyle="pink";let h=d.box[0]+d.box[2]/2-d.box[3]*Dd(d.rotation.angle.yaw)/90,p=d.box[1]+d.box[3]/2+d.box[2]*Dd(d.rotation.angle.pitch)/90,c=new Path2D(`
            M ${d.box[0]+d.box[2]/2} ${d.box[1]}
            C
              ${h} ${d.box[1]},
              ${h} ${d.box[1]+d.box[3]},
              ${d.box[0]+d.box[2]/2} ${d.box[1]+d.box[3]}
          `),f=new Path2D(`
            M ${d.box[0]} ${d.box[1]+d.box[3]/2}
            C 
              ${d.box[0]} ${p},
              ${d.box[0]+d.box[2]} ${p},
              ${d.box[0]+d.box[2]} ${d.box[1]+d.box[3]/2}
          `);a.stroke(f),a.stroke(c)}if(n.drawGaze&&((o=(i=d.rotation)==null?void 0:i.gaze)==null?void 0:o.strength)&&((u=(l=d.rotation)==null?void 0:l.gaze)==null?void 0:u.bearing)&&d.annotations.leftEyeIris&&d.annotations.rightEyeIris&&d.annotations.leftEyeIris[0]&&d.annotations.rightEyeIris[0]){a.strokeStyle="pink",a.fillStyle="pink";let h=[d.annotations.leftEyeIris[0][0]+Math.sin(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[3],d.annotations.leftEyeIris[0][1]+Math.cos(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[2]];oN(a,[d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1]],[h[0],h[1]],4);let p=[d.annotations.rightEyeIris[0][0]+Math.sin(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[3],d.annotations.rightEyeIris[0][1]+Math.cos(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[2]];oN(a,[d.annotations.rightEyeIris[0][0],d.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function $5(e,t,r){var s;let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Hh(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:n.color,R5(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=n.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:n.color,a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)tAe(a,l,n)}}}async function P5(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Hh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=n.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:n.color,R5(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||0;a.fillStyle=n.useDepth?`rgba(${127.5+2*u}, ${127.5-2*u}, 255, 0.5)`:n.color,a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=n.useDepth?`rgba(${127.5+l*u}, ${127.5-l*u}, 255, 0.5)`:n.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function _5(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Hh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function uN(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Hh(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),a.fillStyle=n.labelColor,a.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}a.stroke()}}}async function dN(e,t){if(!e||!t)return;let r=Ol(t);!r||r.drawImage(e,0,0)}async function pN(e,t,r){if(!t||!t.performance||!t||!e)return null;let n=oe(),a=vr(ds,r),s=Promise.all([F5(e,t.face,a),$5(e,t.body,a),P5(e,t.hand,a),_5(e,t.object,a),M5(e,t.gesture,a)]);return E5=ce.perfadd?E5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=E5,s}var Ld=.1,z5=.5;function nAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function hN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Kn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Ld&&Ld>0&&(a=a.map(i=>({x:i.x>.5?i.x+Ld:i.x-Ld,y:i.y>.5?i.y+Ld:i.y-Ld})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)nAe(i/t,o/t,a)||(n.set(z5*n.get(0,o,i,0),0,o,i,0),n.set(z5*n.get(0,o,i,1),0,o,i,1),n.set(z5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var sAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(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=a?[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]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);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}},cN=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,C,S,E]=m,R,_,M;return x<1?x>-1?(M=Math.asin(x),_=Math.atan2(-C,g),R=Math.atan2(-v,b)):(M=-Math.PI/2,_=-Math.atan2(S,E),R=0):(M=Math.PI/2,_=Math.atan2(S,E),R=0),isNaN(R)&&(R=0),isNaN(_)&&(_=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-_,roll:2*-M}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(m=>[m[0]*t[0]/o,m[1]*t[1]/o,m[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),f=i.length===478?sAe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:f}};var O5=async(e,t)=>{var c,f,m,g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X,D,Q;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await JT(t,e.config);if(e.performance.face=ce.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){ie("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await hN(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?cN(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(f=e.config.face.emotion)!=null&&f.enabled?Vb(p[V].tensor||ct([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(m=e.config.face.emotion)!=null&&m.enabled?await Vb(p[V].tensor||ct([]),e.config,V,p.length):[],e.performance.emotion=ce.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?vb(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await vb(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.antispoof=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?d5(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await d5(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.liveness=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?fb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(v=e.config.face.gear)!=null&&v.enabled?await fb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(C=e.config.face.ssrnet)!=null&&C.enabled?gb(p[V].tensor||ct([]),e.config,V,p.length):null,s=(S=e.config.face.ssrnet)!=null&&S.enabled?xb(p[V].tensor||ct([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await gb(p[V].tensor||ct([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await xb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(_=e.config.face.mobilefacenet)!=null&&_.enabled?Gb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=(M=e.config.face.mobilefacenet)!=null&&M.enabled?await Gb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?Zb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(O=e.config.face.description)!=null&&O.enabled?await Zb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Y=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),Y&&Y!==0&&(Z.iris=Math.trunc(500/Y/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),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),h};var fN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},mN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},gN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},yN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=k9(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},D5=0;function AN(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ne.canvas=e.canvas),e.error&&(Ne.error=e.error),!Ne.body||e.body.length!==Ne.body.length)Ne.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ne.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ne.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ne.body[D].keypoints[ae]?((a-1)*(((de=Ne.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 0:be[0],Ne.body[D].keypoints[ae]?((a-1)*(((Ee=Ne.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=Z.distance)==null?void 0:Me[1])||0))/a:(De=Z.distance)==null?void 0:De[1],Ne.body[D].keypoints[ae]?((a-1)*(((Be=Ne.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Z.distance)==null?void 0:Ze[2])||0))/a:(ot=Z.distance)==null?void 0:ot[2]]}}),Y={},se={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?se=N0:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?se=k0:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(se=Uh);for(let[Z,ae]of Object.entries(se.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find(Me=>Me.part===ae[Ae]),Ee=ee.find(Me=>Me.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}Y[Z]=de}Ne.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:ee,annotations:Y}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((se,Z)=>((a-1)*Ne.hand[D].box[Z]+se)/a),V=e.hand[D].boxRaw.map((se,Z)=>((a-1)*Ne.hand[D].boxRaw[Z]+se)/a);Ne.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ne.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((se,Z)=>se.map((ae,de)=>((a-1)*(Ne.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],Y={};if(Object.keys(Ne.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ne.hand[D].annotations=e.hand[D].annotations,Y=Ne.hand[D].annotations;else if(e.hand[D].annotations)for(let se of Object.keys(e.hand[D].annotations))Y[se]=e.hand[D].annotations[se]&&e.hand[D].annotations[se][0]?e.hand[D].annotations[se].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ne.hand[D].annotations[se][ae][Ae]+de)/a)):null;Ne.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:Y}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((ee,Y)=>((a-1)*Ne.face[D].box[Y]+ee)/a),V=e.face[D].boxRaw.map((ee,Y)=>((a-1)*Ne.face[D].boxRaw[Y]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((f=(c=Ne.face[D].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[D].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ne.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((C=(v=Ne.face[D].rotation)==null?void 0:v.angle)==null?void 0:C.pitch)||0)+(((E=(S=e.face[D].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((_=(R=Ne.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((I=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((z=(O=Ne.face[D].rotation)==null?void 0:O.gaze)==null?void 0:z.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ne.face[D]={...e.face[D],rotation:ee,box:Q,boxRaw:V}}Ne.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((ee,Y)=>((a-1)*Ne.object[D].box[Y]+ee)/a),V=e.object[D].boxRaw.map((ee,Y)=>((a-1)*Ne.object[D].boxRaw[Y]+ee)/a);Ne.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ne.persons||D.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ne.persons[Q].box=D[Q].box.map((V,ee)=>((a-1)*Ne.persons[Q].box[ee]+V)/a)}e.gesture&&(Ne.gesture=e.gesture);let s=oe();return D5=ce.perfadd?D5+Math.round(s-r):Math.round(s-r),e.performance&&(Ne.performance={...e.performance,interpolate:D5}),Ne}function q0(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var xN=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function bN(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=q0(e,t,r);return xN(n,r.order||2,r.min||0,r.max||1)}function vN(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=q0(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let s=xN(n,r.order||2,r.min||0,r.max||1);return{index:a,distance:n,similarity:s}}function wN(e,t,r,n,a){var o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,C;let s=0,i=[];for(let S of e){let E={id:s++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)S.box[0]>z.box[0]&&S.box[0]<z.box[0]+z.box[2]&&S.box[1]+S.box[3]>z.box[1]&&S.box[1]+S.box[3]<z.box[1]+z.box[3]&&(E.body=z);if(E.body)for(let z of r)z.box[0]+z.box[2]>E.body.box[0]&&z.box[0]+z.box[2]<E.body.box[0]+E.body.box[2]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=z),z.box[0]<E.body.box[0]+E.body.box[2]&&z.box[0]>E.body.box[0]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=z);for(let z of n)z.face!==void 0&&z.face===S.id?(o=E.gestures)==null||o.push(z):z.iris!==void 0&&z.iris===S.id?(l=E.gestures)==null||l.push(z):z.body!==void 0&&z.body===((u=E.body)==null?void 0:u.id)?(d=E.gestures)==null||d.push(z):z.hand!==void 0&&z.hand===((p=(h=E.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=E.gestures)==null||c.push(z):z.hand!==void 0&&z.hand===((m=(f=E.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=E.gestures)==null||g.push(z));let R=[],_=[],M=z=>{z&&z.length===4&&(R.push(z[0],z[0]+z[2]),_.push(z[1],z[1]+z[3]))};M((y=E.face)==null?void 0:y.box),M((A=E.body)==null?void 0:A.box),M((b=(x=E.hands)==null?void 0:x.left)==null?void 0:b.box),M((C=(v=E.hands)==null?void 0:v.right)==null?void 0:C.box);let I=Math.min(...R),O=Math.min(..._);E.box=[I,O,Math.max(...R)-I,Math.max(..._)-O],a&&a[1]&&a[2]&&(E.boxRaw=[E.box[0]/a[2],E.box[1]/a[1],E.box[2]/a[2],E.box[3]/a[1]]),i.push(E)}return i}var K0=`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==`,X0=`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`;async function hAe(e){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),r,n;switch(e.config.warmup){case"face":r=await t(K0);break;case"body":case"full":r=await t(X0);break;default:r=null}if(r){let a=await createImageBitmap(r);n=await e.detect(a,e.config),a.close()}return n}async function cAe(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+K0;break;case"full":case"body":r="data:image/jpeg;base64,"+X0;break;default:r=null}let n;if(typeof Image!="undefined")n=new Image;else if(ce.Image)n=new ce.Image;else return;n.onload=async()=>{let a=Hr(n.naturalWidth,n.naturalHeight);if(!a)ie("Warmup: Canvas not found"),t(void 0);else{let s=a.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(a),o=await e.detect(i.tensor,e.config);t(o)}},r?n.src=r:t(void 0)})}async function fAe(e){let t=a=>Buffer.from(a,"base64"),r;e.config.warmup==="face"?r=t(K0):r=t(X0);let n;if("node"in Ue){let a=(void 0).decodeJpeg(r),s=a.expandDims(0);e.tf.dispose(a),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&ie("Warmup tfjs-node not loaded");return n}async function kN(e,t){let r=oe();if(e.state="warmup",t&&(e.config=vr(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null};let n;return new Promise(async a=>{typeof createImageBitmap=="function"?n=await hAe(e):typeof Image!="undefined"||ce.Canvas!==void 0?n=await cAe(e):n=await fAe(e);let s=oe();e.config.debug&&ie("Warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),a(n)})}var Bd,qh,Kh,Z0,IN=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");tp(this,Bd,void 0);tp(this,qh,void 0);tp(this,Kh,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!ep(this,qh))return;let r=this.tf.engine().state.numTensors,n=ep(this,Bd);rp(this,Bd,r);let a=r-n;a!==0&&ie(...t,a)});tp(this,Z0,t=>{if(!ep(this,Kh))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof rt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",bN);fe(this,"distance",q0);fe(this,"match",vN);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=ce,gs.wasmPath=zh["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${u2}/dist/`,gs.modelBasePath=ce.browser?"../models/":"file://models/",gs.backend=ce.browser?"humangl":"tensorflow",this.version=pb,Object.defineProperty(this,"version",{value:pb}),this.config=JSON.parse(JSON.stringify(gs)),Object.seal(this.config),t&&(this.config=vr(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",GC(this.config),this.tf=Ue,this.state="idle",rp(this,Bd,0),rp(this,qh,!1),rp(this,Kh,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new T5,this.draw={options:ds,canvas:(r,n)=>dN(r,n),face:(r,n,a)=>F5(r,n,a),body:(r,n,a)=>$5(r,n,a),hand:(r,n,a)=>P5(r,n,a),gesture:(r,n,a)=>M5(r,n,a),object:(r,n,a)=>_5(r,n,a),person:(r,n,a)=>uN(r,n,a),all:(r,n,a)=>pN(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=e9,this.faceUVMap=t9,this.gl=Nt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(gs)),this.config.backend=t}validate(t){return T1(gs,t||this.config)}now(){return oe()}image(t,r=!0){return Id(t,this.config,r)}async segmentation(t,r){return eN(t,r,this.config)}enhance(t){return Xb(t)}compare(t,r){return VC(this.config,t,r)}async init(){await H0(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=vr(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await H0(this)||ie("error: backend check failed"),await ed(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await rN(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await nN(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return AN(t,this.config)}async warmup(t){let r=oe(),n=await kN(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X,D,Q,V,ee,Y;this.state="config";let a;this.config=vr(this.config,r),this.state="check";let s=ep(this,Z0).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await H0(this),await this.load(),a=oe(),this.state="image";let o=await Id(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await WC(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?O5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await O5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?vr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?I5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?$b(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?Bb(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?g5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await I5(o.tensor,p):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?u=this.config.body.enabled?await $b(o.tensor,p):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?u=this.config.body.enabled?await Bb(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("movenet")&&(u=this.config.body.enabled?await g5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?vr(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?a5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?l5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(O=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?await a5(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await l5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?A5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?zb(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await A5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await zb(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...mN(l),...fN(u),...yN(d),...gN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((Y=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Y.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return wN(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Bd=new WeakMap,qh=new WeakMap,Kh=new WeakMap,Z0=new WeakMap;return IE(gAe);})();
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use backend file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * Human main module
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */